Multiprocessor Speed Scaling for Jobs with Arbitrary Sizes and Deadlines In this paper we study energy efficient deadline scheduling on multiprocessors in which the processors consumes power at a rate of sα when running at speed s, where α ≥ 2. The problem is to dispatch jobs to processors and determine the speed and jobs to run for each processor so as to complete all jobs by their deadlines using the minimum energy. The problem has been well studied for the single processor case. For the multiprocessor setting, constant competitive online algorithms for special cases of unit size jobs or arbitrary size jobs with agreeable deadlines have been proposed [4]. A randomized algorithm has been proposed for jobs of arbitrary sizes and arbitrary deadlines [13]. We propose a deterministic online algorithm for the general setting and show that it is O(logα P)- competitive, where P is the ratio of the maximum and minimum job size. Energy consumption has become an important concern in the design of modern processors, not only for battery-operated mobile devices with single processors but also for server farms or laptops with multi-core processors. A popular technology to reduce energy usage is dynamic speed scaling (see e.g., [1, 2, 3, 6] ) where the processor can vary its speed dynamically. The power consumption is modelled by s α when the processor runs at speed s, where α is typically 3 [4] . Running a job slower saves energy, yet it takes longer to finish the job. The study of speed scaling was initiated by Yao et al. [6] . They studied deadline scheduling on a single processor in which jobs with arbitrary sizes and deadlines arrive online and the aim is to finish all jobs by their deadlines using the minimum amount of energy. They gave a simple online algorithm AVR which is 2 α α α -competitive and they also proposed an online algorithm OA which was later shown by Bansal et al. to be α α -competitive [3] . Algorithms with better competitive ratios were later proposed in [3, 2] .. Albers et al. [1] have extended the study to the multiprocessor setting in the special cases of unit-size jobs or jobs with agreeable deadlines (jobs arriving earlier have earlier deadlines), and presented constant competitive algorithms for both cases. In the multiprocessor setting, in addition to determining processor speed, a job dispatching algorithm is required to assign jobs to processors. In this paper we study the generalized problems where jobs have arbitrary sizes and arbitrary deadlines. We first observe that the classified round robin algorithm (CRR) [1] does not scale well when jobs have arbitrary sizes and deadlines; the competitive ratio is at least m α−1 where m is the number of processors. We take one step forward to propose a non-migratory job dispatching algorithm, called DCRR, and show that DCRR coupled with AVR is O(log α P )-competitive, where P is the ratio between the maximum and minimum job size; note that the competitive ratio is independent of m and holds even against an optimal migratory offline algorithm.

O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Test signal generation for service diagnosis based on local structural properties The paper presents a new approach to the generation of test signals used in service diagnosis. The tests make it possible to isolate faults, which are isolable only if the system is brought into specific operating points. The basis for the test signal selection is a structure graph that represents the couplings among the external and internal signals of the system and the fault signals. Graph-theoretic methods are used to identify edges that disappear under certain operating conditions and prevent a fault from changing the system behavior at this operating point. These operating conditions are identified by validuals, which are indicators obtained during the graph-theoretic analysis. The test generation method is illustrated by a process engineering example. Service diagnosis is carried out in repair shops in order to identify a faulty component to be replaced. It differs from process diagnosis in the sense that the primary system objectives do not have to be met during the diagnostic process. Thus, specific signals can be applied to facilitate the isolation of specific faults. Increasing system complexity entails severe problems for this task. Strong interactions among components, nonlinear dynamics and a large number of possible fault scenarios impede the selection of dedicated tests for fault detection and isolation. Different faults may cause similar behavior of the faulty plant and, therefore, are difficult to be distinguished by a model-based diagnostic method. This paper proposes a method for test signal generation that is based on determining operating points, in which the influence of specific faults on the input-output (I/O) behavior of the system under consideration is reduced. The method uses a structural model of the system that represents the relation among the external and internal signals and the faults. The structural representation is used to find particular operating regions, in which some of the interactions among these signals are not active and, hence, prevent a specific fault from influencing the I/Obehavior, whereas other faults remain in the cause effect chains from the inputs towards the outputs. If the system is brought into such an operating region, specific fault candidates can be excluded or validated.. In the literature, there are only a few publications that deal with the selection of dedicated test signals for service diagnosis of dynamical systems (e.g., Clever and Isermann, 2008) . In a broader view, service diagnosis can be interpreted as a kind of active diagnosis in which the diagnostic unit may influence the input signal u(t) via the input generator by some signal v(t) (Fig. 1 ).. Active diagnosis has been investigated, for example, by Niemann and Poulsen (2005) , and with the goal of maintaining system operability by Zhang (1989) as well as Campbell and Nikoukhah (2004) . In contrast to these publications, the present paper is based on a structural representation of faulty dynamical systems, which results from a generalization of the structural modeling method described by Blanke et al. (2006) . Like in the work of Krysander et al. (2008) , it combines structural analysis with the goal of fault isolation in order to find permanent single faults.. The combination of active diagnosis and structural methods was introduced by Laursen et al. (2008) by investigating the isolability properties of hybrid systems in all their discrete operation modes. This method utilized the individual system structures that correspond to the different discrete modes of the hybrid system. Bayoudh et al. (2008; 2009) propose to abstract the continuous dynamics of hybrid systems to obtain purely discrete-event models and to apply discrete-event techniques for diagnosis. Finding inputs that allow refining the diagnostic result is reached by formulating the active diagnosis problem as a conditional planning problem.. The present paper deals with nonlinear continuous systems, which do not have a priori defined operation modes. A method for identifying a set of operating points, in which the continuous system has a particular system structure, is developed together with a method to steer the potentially faulty system into these operating points. The specific dynamical properties of the system that are associated with these operating points are then used to distinguish otherwise indistinguishable faults.. In an earlier paper of the authors (Ungermann et al., 2010a) , the more specific idea of excluding the influence of the fault variables from the structural model was used for the diagnosis of automotive systems. The present paper details the approach presented by Ungermann et al. (2010b) , which is much more general in the sense that the focus is not on edges adjacent to fault variables, but on arbitrary edges in the structure graph. This corresponds to finding operating regions, in which the whole incidence matrix M = [M X M K M F ] of the graph defined by Ungermann et al. (2010a) changes.. The structure of the paper is as follows. Section 2 explains the analytical and structural models of dynamical systems used. Section 3 briefly reviews the idea of consistency-based diagnosis and structural analysis for fault diagnosis. Section 4 describes the main idea of searching for structural changes of the system and identifying the corresponding operating points. The method is summarized in Section 5 in two algorithms and applied in Section 6 to a process engineering example. This paper describes a method for test generation based on local system structures. It is shown that particular input signals may steer a system in an operating region where some of the edges in the structure graph disappear and, hence, faults can be distinguished, which otherwise cannot. In this way structural analysis makes it possible to identify operating regions together with specific local residual generators. The main result of this work is a method for determining signals from known variables guaranteeing the presence of a particular local structure. This was achieved by transforming the original problem into that of finding MSOs and solving it with well known methods (Krysander et al., 2008) .. The presented approach offers a solution of the problem observed by Laursen et al. (2008) that the diagnostic result can be wrong when steering in the corresponding operating region fails.. A dynamical system may have a large number of different local structures, some of which entail different local structural isolability properties. Future research will therefore concentrate on the problem of identifying local system structures that allow the isolation of faults which are not isolable by using an arbitrary local structure.

O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A

Bayesian

method

for

comparing

and

combining

binary

classifiers

in

the

absence

of

a

gold

standard

Background:

Many

problems

in

bioinformatics

involve

classification

based

on

features

such

as

sequence,

structure

or

morphology.

Given

multiple

classifiers,

two

crucial

questions

arise:

how

does

their

performance

compare,

and

how

can

they

best

be

combined

to

produce

a

better

classifier?

A

classifier

can

be

evaluated

in

terms

of

sensitivity

and

specificity

using

benchmark,

or

gold

standard,

data,

that

is,

data

for

which

the

true

classification

is

known.

However,

a

gold

standard

is

not

always

available.

Here

we

demonstrate

that

a

Bayesian

model

for

comparing

medical

diagnostics

without

a

gold

standard

can

be

successfully

applied

in

the

bioinformatics

domain,

to

genomic

scale

data

sets.

We

present

a

new

implementation,

which

unlike

previous

implementations

is

applicable

to

any

number

of

classifiers.

We

apply

this

model,

for

the

first

time,

to

the

problem

of

finding

the

globally

optimal

logical

combination

of

classifiers.

Results:

We

compared

three

classifiers

of

protein

subcellular

localisation,

and

evaluated

our

estimates

of

sensitivity

and

specificity

against

estimates

obtained

using

a

gold

standard.

The

method

overestimated

sensitivity

and

specificity

with

only

a

small

discrepancy,

and

correctly

ranked

the

classifiers.

Diagnostic

tests

for

swine

flu

were

then

compared

on

a

small

data

set.

Lastly,

classifiers

for

a

genome-wide

association

study

of

macular

degeneration

with

SNPs

were

analysed.

In

all

cases,

run

times

were

feasible,

and

results

precise.

The

optimal

logical

combination

of

classifiers

was

also

determined

for

all

three

data

sets.

Code

and

data

are

available

from

downloads/.The

examples

demonstrate

the

methods

are

suitable

for

both

small

and

large

data

sets,

applicable

to

the

wide

range

of

bioinformatics

classification

problems,

and

robust

to

dependence

between

classifiers.

In

all

three

test

cases,

the

globally

optimal

logical

combination

of

the

classifiers

was

found

to

be

their

union,

according

to

three

out

of

four

ranking

criteria.

We

propose

as

a

general

rule

of

thumb

that

the

union

of

classifiers

will

be

close

to

optimal.

The

method

presented

in

this

paper

addresses

two

significant

problems

with

ubiquitous

applications

in

bioinformatics:

comparing

binary

classifiers

in

the

absence

of

a

gold

standard,

and

identifying

the

optimal

logical

combination

of

such

classifiers.

Using

Bayesian

models

developed

for

evaluating

medical

diagnostic

tests,

we

present

the

first

applications

of

these

models

in

the

bioinformatics

domain

and

demonstrate

their

feasibility

and

utility

for

comparing

classifiers

on

genomic

scale

data

sets.

A

new,

concise

and

highly

efficient

implementation

of

these

models

was

developed

in

WinBUGS,

and

is

the

first

freely

available

implementation

applicable

to

an

arbitrary

number

of

classifiers.

To

identify

the

optimal

logical

combination

of

classifiers,

we

developed

an

entirely

new

algorithm

and

again

demonstrated

its

feasibility

for

genomic

scale

data

sets.

The

algorithm

is

the

first

to

employ

the

above-mentioned

Bayesian

models

to

evaluate

logical

combinations

of

classifiers

and

indeed

apparently

the

first

to

systematically

evaluate

all

logical

combinations.

It

is

implemented

in

R

and

is

freely

available.

The

algorithm

is

K

in

the

number

of

classifiers

K,

and

thus

further

research

is

required

for

large

K.

The

methods

were

evaluated

on

a

protein

subcellular

localisation

data

set

for

which

a

gold-standard

data

set

was

available

for

the

purpose

of

comparison.

Some

discrepancy

in

the

estimates

of

sensitivity

and

specificity

was

expected

because

a

key

assumption

of

the

model

-conditional

independence

of

the

classifiers

-is

often

violated

in

practice.

However,

we

found

that

the

discrepancy

was

in

most

cases

small

and

more

importantly,

that

the

method

was

able

to

correctly

rank

the

classifiers..

In

all

of

our

examples,

a

simple

union

of

the

classifiers

was

found

to

be

optimal

according

to

three

out

of

four

alternative

ranking

criteria

(and

in

some

cases

also

by

the

fourth).

While

this

finding

is

unlikely

to

be

general,

we

propose

as

a

rule

of

thumb

that

the

union

of

classifiers

is

likely

to

be

close

to

the

optimal

logical

combination.

A

common

problem

arising

in

bioinformatics

is

to

classify

experimental

results

into

two

categories,

according

to

the

presence

or

absence

of

some

property

of

interest.

Such

classification

problems

are

widespread

and

diverse.

For

example,

in

genome-wide

association

studies

(GWAS),

genotype

data

is

collected

at

SNP

or

other

marker

loci

across

the

entire

genome

for

a

large

number

of

cases

and

controls

(as

in

and

the

marker

loci

are

classified

according

to

whether

or

not

they

are

associated

with

the

quantify

the

accuracy

of

the

classifier,

in

order

to

assess

the

level

of

confidence

one

should

place

in

the

predictions,

and

so

that

alternative

classifiers

can

be

compared

and

ranked.

Classifiers

can

be

assessed

in

terms

of

their

sensitivity

and

specificity.

The

sensitivity

of

a

binary

classifier

is

the

proportion

of

positive

individuals

that

are

correctly

identified

as

such.

Similarly,

the

specificity

is

the

proportion

of

negative

individuals

that

are

correctly

identified..

Ideally,

there

will

be

a

gold

standard

data

set

available

for

evaluating

classifiers,

comprised

of

data

for

which

the

true

classification

of

each

individual

is

known

with

certainty,

or

at

least

for

which

there

is

an

accepted

best

available

classification.

When

a

gold

standard

is

available,

it

is

straightforward

to

estimate

the

sensitivity

and

specificity

as

proportions

of

the

gold

standard

positives

and

negatives

respectively.

However,

it

can

happen

that

there

is

no

gold

standard.

For

example,

if

the

classifier

is

a

medical

diagnostic

test

such

as

a

swab

for

swine

flu,

there

may

not

be

any

more

accurate

means

of

diagnosing

the

disease,

or

that

is

affordable

for

a

large

study.

Because

such

diagnostics

are

experimental

in

nature,

it

is

not

even

possible

to

simulate

data

against

which

to

benchmark,

and

a

gold

standard

for

evaluating

them

may

therefore

be

difficult

or

impossible

to

obtain.

In

bioinformatics

the

absence

of

a

gold

standard

can

occur

because

data

that

can

be

classified

with

perfect

accuracy

is

either

non-existent

or

too

limited

for

reliable

estimation.

It

may

also

be

that

any

gold

standard

data

that

was

available

has

already

been

used

to

train

one

or

more

of

the

classifiers,

rendering

the

data

unsuitable

for

comparing

the

classifiers.

A

concrete

example

is

the

analysis

of

genomewide

association

studies,

where

the

data

set

can

include

millions

of

individual

SNPs,

a

negligible

proportion

of

which

are

known

to

be

associated

with

the

disease.

Simulated

data

can

sometimes

be

generated,

but

it

may

not

be

clear

that

it

is

sufficiently

realistic.

In

such

cases,

it

is

still

possible

in

principle

to

evaluate

and

compare

competing

classifiers,

provided

that

multiple

classifiers

are

available.

In

medical

diagnostics,

this

is

typically

in

the

order

of

classifiers..

The

intuition

here

is

that

the

extent

to

which

competing

classifiers

agree

or

disagree

provides

information

about

the

reliability

of

each

classifier.

In

the

absence

of

a

gold

standard,

all

that

is

known

is

the

imperfect

binary

classifications,

which

can

be

organised

into

a

matrix

such

as

shown

in

Table

for

K

classifiers

and

N

individuals.

Note

that

the

true

classification

is

not

known

for

any

individual.

However,

in

broad

terms,

where

two

classifiers

tend

to

agree

(i.e.

have

similar

columns)

our

confidence

in

both

of

them

increases,

whereas

where

two

classifiers

tend

to

disagree

(i.e.

have

dissimilar

columns),

we

cannot

have

high

confidence

in

both.

This

intuition

is

given

a

mathematical

expression

in

the

Bayesian

models

discussed

below..

A

variety

of

techniques

have

been

proposed

in

the

medical

statistics

literature

for

comparing

diagnostic

tests

in

the

absence

of

a

gold

standard.

A

spectrum

of

approaches

has

been

developed

to

suit

specific

variations

of

the

problem;

for

example,

some

approaches

assume

log-linear

models

of

errors,

as

compared

to

error

models

that

assume

normally

distributed

errors.

Approaches

also

differ

in

methodology,

for

example,

maximum

likelihood

approaches

compared

to

Bayesian

approaches.

Not

only

do

the

approaches

vary,

but

the

assumptions

also

vary,

with

some

approaches

requiring

data

from

two

or

more

populations

with

different

prevalences

of

the

disease

(for

example

and

others

considering

tests

re-administered

to

the

same

individuals

at

two

or

more

time-points

(for

example

A

review

of

the

diverse

approaches

and

methods

is

provided

by

We

focus

on

the

simplest

and

most

common

setting

in

practice,

in

which

K

binary

classifiers

are

applied

to

N

individuals

randomly

selected

from

a

single

population,

each

at

a

single

time-point.

In

particular,

we

consider

the

Bayesian

model

of

and

its

potential

for

application

to

data

sets

in

the

bioinformatics

domain.

This

model

is

described

in

detail

in

the

first

sub-section

of

the

Methods.

To

the

best

of

our

knowledge,

the

model

has

not

previously

been

applied

in

this

domain,

despite

the

numerous

potential

applications.

We

provide

a

new,

concise

and

highly

efficient

implementation

in

Win-BUGS.

Our

implementation

is

freely

available,

applicable

to

any

number

of

classifiers,

and

as

we

demonstrate

below,

is

able

to

handle

genomic-scale

data

sets..

Once

classifiers

have

been

compared,

the

question

naturally

arises

how

to

combine

them

to

form

a

new

classifier

that

is

better

than

any

of

the

constituents.

A

simple

method

is

to

take

a

consensus,

that

is,

to

classify

an

individual

as

positive

if

most

of

the

component

classifiers

'vote'

for

a

positive

classification,

and

classify

an

individual

as

negative

otherwise.

A

weighted

consensus,

in

which

the

vote

of

some

classifiers

counts

more

than

others,

is

also

possible.

But

what

is

the

optimal

way

to

combine

classifiers?

This

problem

has

been

extensively

studied

(see

for

an

introduction

and

for

an

extended

treatment).

However,

the

problem

of

how

to

combine

classifiers

in

the

absence

of

gold

standard

data

does

not

appear

to

have

been

studied,

and

in

particular

the

potential

for

the

model

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


3D Topology Optimization Using an Immune Algorithm Purpose -The paper introduces an evolutionary algorithm based on the artificial immune systems paradigm for topology optimization in 3D.Design/methodology/approach -The 3D topology optimization algorithm is described, and experimentally validated on an electromagnetic design problem.Findings -The proposed method is capable of finding an optimal configuration for the validation problem used.Research limitations/implications -More tests are needed in order to fully assert the capabilities of the algorithm. Moreover, further improvements are needed in order to obtain smoother topologies at the end of the optimization procedure.Practical implications -The paper presents a novel tool for the design of electromagnetic devices, using a topology optimization approach.Originality/value -So far most of the topology optimization algorithms did not tackle true 3D problems, and the ones capable of 3D optimization do it at high computational costs. The algorithm presented here is capable of true 3D design at a reasonable computational budget. Shape optimization of engineering devices usually means maximizing a given performance measure by varying the dimensions of a basic shape set by the designer, in a process sometimes called parametric optimization. While this strategy can lead to very good results, it is fundamentally dependent on the basic configuration provided, i.e., the optimization algorithm is not able to introduce fundamental changes in the topology of the device being designed. In other words, the algorithms lack the ability to develop innovative solutions.. A different approach to the geometrical design of such devices is the Topology Optimization (TO) paradigm. Instead of being limited to predefined shapes and variables, in TO the material properties at every point of the design space are considered in the design processes. By allowing the free variation of the material properties at any point of the design space, TO algorithms are able to return novel or unexpected solutions, independently from any initial design (Campelo, 2006) .. The computational cost associated with TO methods, however, remains as an obstacle to a broader use of this kind of technique. This has somehow limited the use of TO algorithms to 2D problems or problems with axial symmetry or planar structure, where the design space can be reduced to two dimensions. Indeed, there are very few works in the literature dealing with 3D design of electromagnetic devices using TO (Yoo, 2000; Yoo, 2002; Okamoto, 2006) . Among the methods used for two-dimensional TO, the Clonal Selection Algorithm for Topology Optimization (TopCSA) (Campelo, 2006) presents some interesting features. This technique is an evolutionary algorithm that presents local and global search characteristics at a reasonably low computational cost (Campelo, 2006; Watanabe, 2006) , valuable features when designing electromagnetic devices.. In this work we extend the TopCSA to deal with real 3D problems. The operators of the algorithm are adapted, and the technique is tested on the design of a magnetic device.. The paper is organized as follows: Section II presents a brief overview on methods used for topology optimization; the 3D version of the TopCSA is presented in Section III; in Section IV the multigrid method, used by the TopCSA as the numerical solver, is described; the results obtained by the proposed technique for the design of a perpendicular magnetic recording head are presented in Section V; final remarks, conclusions and ideas for future work close the paper in Section VI. In this paper, we have introduced a 3D formulation for a topology optimization algorithm. The TopCSA is an evolutionary approach that combines local exploitation and global exploration of the search space as a way to accelerate the convergence on computationally expensive problems, commonly found in many areas of engineering where the evaluation of numerical models is required.. The proposed approach was applied to the design of a perpendicular magnetic recording head, such as the ones found in magnetic hard disks. The solution found for the problem was able to satisfy the design requirements, that is, the maximization of the magnetic field in a certain region of the space while keeping a low value in the neighboring region.. The development of better ways to generate smoother solutions, more interesting from an implementation point of view, is a natural next step on this research. Also, the use of the TopCSA for the solution of multiobjective problems, or of problems where more than two materials are involved, are possibilities to be explored in the future.

O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy SUMMARY The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics. Ant colony optimization (ACO) was adopted to solve different problems in both classic computer literature and real world challenges just after its introduction to the world [1] . Although the underlying theory of this evolutionary method is pretty simple and follows the philosophy similar to many other iterative evolutionary approaches, its multiagent potential provides a scalable and flexible structure to tackle different kinds of problems even better than some other evolutionary methods. Inspired by real foraging ants, ACO takes advantage of independent software agents simulating real foraging ants to find partial solutions. These artificial ants also make use of an indirect communication strategy which is called stigmergy. This strategy uses the environment as a shared medium for the exploring ants to inform each other about their experience [2] . The platform of stigmergy is the advantage of ACO over other schemes. This feature eliminates the need for centralized control and prepares an infrastructure to encompass both local and global information for the search process. In other words, artificial ants in ACO are not confined to a special area of the problem space; they can spread throughout the search space based on the predefined plans and/or heuristic actions to find better sub-solutions and accumulate their local/non-local experience onto artificial pheromone trails which can be further used by the whole colony. In almost any ACO algorithm, problem is modeled as a connected graph together with the required data structures for the process of stigmergy. Artificial ants traverse the graph edges to build a partial solution and store the related optimization data on nodes. This information is used by other ants to choose better alternatives to converge to the problem solution [1] .. Although the artificial ants are mostly reputed as software artificial agents, graph nodes are also important agents sometimes much more active than artificial ants [3] . Deeper conceptual study on the graph nodes reveals their capabilities and intelligence in terms of learning and decision making [4] . In ACO, graph nodes can be studied as learning automata (LA) in which action set for each automaton contains the goals towards which the artificial ants are triggered. For each automaton the environmental response is the goodness of the selected action evaluated through a problem dependent evaluation function. We will use the LA model to study the characteristics of ACO pheromone update and to describe the theoretical aspects of our proposal.. Our proposed algorithm deals with the pheromone update phase of ACO which impacts the efficiency of the algorithm. Finding better solutions for a specific problem with a huge or dynamic search space closely depends on how timely and extensively the algorithm covers the search space and how the learning process converges to the global optima. The novel strategy proposed in this paper tries to increase exploration towards potential areas of the problem space according to previously gathered optimization data. To analyze the efficiency of our proposed algorithm, we focused on two ACO static algorithms which are ACS which faces travelling salesman problem and Ant-Miner which is used for classification rule mining through data sets and equipped them with our new pheromone update strategy. As simulation results show, the proposed approach works well in both cases in almost all evaluation metrics. Our proposed strategy focuses on the concept of exploration which is one of the key points in swarm-based evolutionary algorithms particularly when the algorithm deals with huge search spaces. The way the algorithm controls exploration determines how it covers the problem search space. This issue is much more critical when the algorithm deals with an instance of NP-hard problems such as TSP and rule mining with a huge or dynamic search space. The proposed non-linear pheromone update strategy tries to increase exploration but in a controlled way to cover the potential areas of the problem space to find more feasible solutions. As simulation results show, our new idea improves both metrics of algorithms' runtimes and accuracy.

O O O O B I I O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Watermarking for HDR Image Robust to Tone Mapping Abstract-High Dynamic Range (HDR) images have been widely applied in daily applications. However, HDR image is special format, which need to pre-process known as tone mapping operators for display. Since the visual quality of HDR images are very sensitive to luminance value variations, conventional watermarking methods for low dynamic range (LDR) images are not suitable and may even cause catastrophic visible distortion. Currently, few methods for HDR image watermarking are discussed. In this paper, we propose a watermarking scheme for HDR image. Both of the subjective and objective qualities of watermarked image are improved. What's more, it also shows higher robustness against tone mapping operations. Protection of copyright attracts lot of attention in recent years, because digital contents become easily accessed on computer networks. To solve this problem, the so-called digital watermarking technology has been developed, which is the embedded information insensible to human visual system. In general, digital image watermarking technology must follow two properties. First, the perceptual quality of the original image should not be degraded by the embedded watermark. Moreover, the watermark is robust to image manipulations, such as lossy compression, cropping, collusion attacks, etc.. By adapting lights in any viewing condition, a wider range of radiance can be perceived by the human visual system than by commercially available sensors. In the last decade, to capture the high dynamic range of natural scene brightness, techniques have been proposed based on the multi-exposure image principle [4] . A method for merging multiple photographs shot with different exposures has been proposed by Mann et al. [4] , in which, high dynamic ranges have been realized. Devebec et al. [4] has also proposed a method to create High Dynamic Range (HDR) Images (e.g. contrast ratio of 10 10 : 1) and applied to high quality image based lighting.. Big mount of applications have been inspired by HDR imaging, such as high quality CG rendering, in-vehicle sensors, camera surveillance, digital negative developments, etc. Since the dynamic range of HDR images may be far beyond the one of display devices, it is difficult to display directly. "Tone mapping operations" have been proposed, in which the dynamic ranges of the HDR images are reduced to displayable ranges [4] - [7] . These operations aim at reducing the high dynamic range without loss of detail.. Nevertheless, to our knowledge, little contribution has been made on HDR image watermarking. Since the HDR image is very sensitive to luminance value variations, conventional watermarking methods for low dynamic range images are not suitable and may even cause catastrophic visible distortion. In this paper, a digital watermarking scheme targeting HDR images is proposed, and the HDR image is converted to the LDR image part and the residual part based on µ-law. The watermark is embedded in the LDR image part of the HDR image by conventional watermarking method. Due to an average of 15dB is increased in PSNR, which can be achieved by the proposed algorithm for the watermarked images with corresponding improvement in subjective quality, it is quite different from the way of conventional watermarking method for LDR image apply to the HDR image directly. It also shows higher robustness against tone mapping operations. The paper begins with description of previous research in Section 2. The proposed method is given in Section 3. Experimental results are shown in Section 4 followed by conclusions in Section 5.

B O B I O O B I O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I B O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Retinal

blood

vessels

extraction

using

probabilistic

modelling

The

analysis

of

retinal

blood

vessels

plays

an

important

role

in

detecting

and

treating

retinal

diseases.

In

this

review,

we

present

an

automated

method

to

segment

blood

vessels

of

fundus

retinal

image.

The

proposed

method

could

be

used

to

support

a

non-intrusive

diagnosis

in

modern

ophthalmology

for

early

detection

of

retinal

diseases,

treatment

evaluation

or

clinical

study.

This

study

combines

the

bias

correction

and

an

adaptive

histogram

equalisation

to

enhance

the

appearance

of

the

blood

vessels.

Then

the

blood

vessels

are

extracted

using

probabilistic

modelling

that

is

optimised

by

the

expectation

maximisation

algorithm.

The

method

is

evaluated

on

fundus

retinal

images

of

STARE

and

DRIVE

datasets.

The

experimental

results

are

compared

with

some

recently

published

methods

of

retinal

blood

vessels

segmentation.

The

experimental

results

show

that

our

method

achieved

the

best

overall

performance

and

it

is

comparable

to

the

performance

of

human

experts.

Automated

segmentation

of

retinal

structures

allows

ophthalmologist

and

eye

care

specialists

to

perform

large

population

vision

screening

exams

for

early

detection

of

retinal

diseases

and

treatment

evaluation.

This

non-intrusive

diagnosis

in

modern

ophthalmology

could

prevent

and

reduce

blindness

and

many

cardiovascular

diseases

around

the

world.

An

accurate

segmentation

of

retinal

blood

vessels

(vessel

diameter,

colour

and

tortuosity)

plays

an

important

role

in

detecting

and

treating

symptoms

of

both

the

retinal

abnormalities

and

diseases

that

affect

the

blood

circulation

and

the

brain

such

as

haemorrhages,

vein

occlusion,

neo-vascularisation.

However,

the

intensity

inhomogeneity

and

the

poor

contrast

of

the

retinal

images

cause

a

significant

degradation

to

the

performance

of

automated

blood

vessels

segmentation

techniques.

The

intensity

inhomogeneity

of

the

fundus

retinal

image

is

generally

attributed

to

the

acquisition

of

the

image

under

different

conditions

of

illumination..

Previous

methods

of

blood

vessels

segmentation

can

be

classified

into

two

categories:

pixels

processing

based

tracking-based

or

vectorial

tracking

or

tracing

methods

Pixel

processing

based

methods

use

filtering

and

morphological

pre-processing

techniques

to

enhance

the

appearance

of

the

blood

vessels

in

the

image.

The

enhanced

image

is

then

processed

using

techniques

such

as

thinning

or

branching

to

classify

pixel

as

either

belonging

to

vessels

or

background.

Hoover

et

al.

proposed

a

framework

to

extract

blood

vessel

from

retinal

images

using

a

set

of

twelve

directional

kernels

to

enhance

the

vessels

before

applying

threshold-probing

technique

for

segmentation.

Mendoca

et

al.

presented

a

method

to

extract

a

vessel

centreline

then

filled

it

using

the

global

intensity

characteristics

of

the

image

and

the

local

vessel

width

information.

Maritiner-perez

et

al.

proposed

a

new

segmentation

method

of

blood

vessels

from

red-free

and

fluorescein

retinal

images.

This

method

is

based

on

multiscale

feature

extraction,

which

used

the

first

and

second

spatial

derivatives

of

the

image

intensity

that

provides

information

about

vessel

topology.

A

multiple

pass

region

growing

procedure

is

applied

to

segment

the

vessels

using

both

vessels

feature

information

and

spatial

information.

Zhang

et

al.

extracted

the

blood

vessel

tree

by

matched

filter

with

first-order

derivative

of

Gaussian.

The

method

detects

the

blood

vessels

by

thresholding

the

retinal

image's

response

to

the

matched

filter,

while

the

threshold

is

adjusted

by

the

response

of

the

image

to

the

first-order

derivative

of

Gaussian.

Cinsdikici

et

al.

proposed

a

method

of

retinal

blood

vessel

detection

using

a

combination

of

a

hybrid

model

of

matched

filter

and

ant

colony

algorithm..

The

drawback

of

these

techniques

is

that

some

proprieties

of

blood

vessels

can

only

be

applied

in

the

segmentation

process

after

a

low

level

preprocessing

and

they

generally

output

poor

segmentation

results

on

unhealthy

retinal

images

that

have

the

presence

of

lesions.

They

also

need

more

computational

power

when

the

size

of

an

image

increases,

thus

a

special

hardware

is

required

for

real

time

processing..

The

tracking

based

approaches

or

vectorial

tracking

or

tracing

included

semi

automated

tracing

and

automated

tracing.

In

the

semi

automated

tracing

methods,

the

user

manually

selects

the

initial

vessel

seed

point.

The

methods

are

generally

used

in

quantitative

coronary

angiography

analysis.

In

fully

automated

tracing,

the

algorithms

select

automatically

the

initial

vessel

points

and

most

of

them

use

Gaussian

functions

to

characterise

a

vessel

profile

model,

which

locates

a

vessel

points

for

the

vessel

tracing.

The

tracking

based

methods

are

seen

as

single

pass

operations,

which

perform

the

vessels'

structure

detection

and

recognition

simultaneously.

The

advances

of

using

these

techniques

are

that,

they

are

computational

efficient

and

much

faster

than

pixels

processing

methods

because

the

algorithms

avoid

the

processing

of

every

pixels

in

the

image

and

use

only

the

pixels

in

the

neighbourhood

of

the

vessels

structure..

Xu

et

al.

combined

the

adaptive

local

thresholding

method

and

the

tracking

growth

technique

to

segment

retinal

blood

vessels.

Staal

et

al.

proposed

a

retinal

blood

vessel

segmentation

in

two

dimensional

colour

retinal

images.

The

method

extracts

the

images

ridges,

which

are

used

to

compose

primitives

in

the

form

of

line

elements.

The

line

elements

are

used

to

partition

an

image

into

patches,

then

a

feature

vectors

are

computed

for

every

pixel

and

classified

a

kappa

NN-classifier

and

sequential

forward

feature

selection.

Chaudhuri

et

al.

presented

a

framework

to

segment

the

blood

vessels

in

the

retina.

The

method

is

based

on

the

optical

and

spatial

properties

of

the

vessels,

where

the

gray-level

profile

of

the

cross

section

of

a

blood

vessel

is

approximated

by

a

Gaussianshaped

curve

and

a

matched

filter

is

used

to

define

the

piecewise

linear

segments

of

blood

vessels.

Finally

twelve

different

templates

are

designed

to

search

for

vessel

segments

along

all

possible

directions.

Salazar

et

al.

used

an

adaptive

histogram

equalisation

and

the

distance

transform

algorithm

to

enhance

the

vessels

appearance,

then

applied

the

graph

cut

technique

to

segment

vessels.

In

Yin

et

al.

proposed

an

automated

tracking

approach

to

segment

blood

vessels

in

retinal

images.

The

technique

detects

vessel

edge

points

iteratively

using

a

Bayesian

model

based

on

statistical

sampling

approach

and

the

intensity

profile

of

the

vessel

are

approximated

by

a

Gaussian

model.

New

vessel

edge

points

are

defined

based

on

local

grey

level

statistics

and

expected

vessel

features..

The

limitations

of

the

tracking

based

approaches

are

that

the

multiple

branches

models

are

not

always

applicable

and

they

do

not

perform

well

on

disease

retinal.

The

semi

automated

tracking

methods

also

require

manual

input,

which

required

more

times.

Thus

they

are

not

suitable

for

real

time

retinal

image

analysis.

In

this

paper,

we

present

a

new

automated

method

to

extract

blood

vessels

in

retinal

fundus

images.

The

proposed

method

is

divided

into

two

main

stages:

the

preprocessing

and

the

probabilistic

modelling.

Our

approach

takes

as

first

step

the

correction

of

the

intensity

inhomogeneity

of

the

retinal

image

using

a

bias

correction

algorithm

then

the

appearance

of

blood

vessels

are

enhanced

with

an

adaptive

histogram

equalisation.

A

probabilistic

model

optimised

by

an

expectation

maximisation

(EM)

algorithm

is

used

to

extract

the

vascular

tree

from

the

processed

images.

Finally,

a

length

filter

is

applied

on

the

output

of

the

EM

algorithm

to

eliminate

all

the

non-vessels

pixels..

The

experimental

results

are

performed

on

two

publicly

available

datasets.

The

STARE

(STructured

Analysis

of

the

Retina)

images

were

provided

by

the

Shiley

Eye

Center

at

the

University

of

California,

San

Diego,

and

by

the

Veterans

Administration

Medical

Centre

in

San

Diego.

The

images

of

the

DRIVE

were

obtained

from

a

diabetic

retinopathy-screening

program

in

The

Netherlands

and

the

screening

population

consisted

of

diabetic

subjects

between

years

of

age.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Using repair priorities to reduce stock investment in spare part networks In this paper, we examine the impact of repair priorities in spare part networks. Several heuristics for assigning priorities to items as well as optimising stock levels are developed, extending the well-known VARI-METRIC method. We model repair shops by multi-class, multi-server priority queues. A proper priority setting may lead to a significant reduction in the inventory investment required to attain a target system availability (usually 10–20%). The saving opportunities are particularly high if the utilisation of the repair shops is high and if the item types sharing the same repair shop have clearly different characteristics (price, repair time). For example, we find an investment reduction of 73% for a system with single server repair shops with an utilisation of 0.90 that handle five different item types. For advanced technical systems, such as engines, medical equipment, and airplanes, a sufficiently high system availability is enhanced by a distribution network for repairable spare parts. In this network, a failed item can follow two different routes through the system: either it enters the local repair facility, or it is forwarded to the next echelon upstream to be repaired there (e.g. the downstream echelon consists of frigates, the upstream echelon consists of a depot in the harbour, see Fig. 1 ). Usually items are sent to the higher echelon if local repair is technically impossible, i.e. if the local repair shop does not have appropriate equipment or skills. It is usually (and here) assumed that the decision whether to repair locally or not is based on such technical considerations only, and not by e.g. current repair shop workload. This is modelled by a fixed probability that the item can be repaired locally, independent of the system state. We will use the same assumption.. It is also assumed that items have multi-indenture structure, which means that every item (assembly) may consist of other items (subassemblies), see Fig. 1 . If an assembly fails, we assume that either the failure is caused by the failure of exactly one of its subassemblies (so the replacement of this subassembly is sufficient to repair the assembly), or there is no specific subassembly causing the assembly failure (hence the assembly as a whole has to be repaired). In the case of a subassembly failure the assembly is disassembled and the failed subassembly is sent further for subassembly repair.. Next to each repair facility there is a stock with new items and when a failed item arrives at the repair shop an order for a new item is issued. If a new item is available on stock, it is dispatched to replace the failed item. At the same time, the failed item either enters the local the repair shop or dispatched to external repair/stock facility (subassembly repair of higher echelon repair) and after repair it is added to stock (Fig. 2) .. A well-known problem is to choose optimal spare part stock levels in the network, such that target system availability is attained at minimal inventory investment. Inventory levels have to be selected for each spare part, being an item in a multi-indenture product structure, and for each location in a multi-echelon distribution network structure.. The class of VARI-METRIC models (cf. Slay, 1984; Sherbrooke, 1992) has been developed to tackle this problem. Their method aims to determine initial stock levels, assuming that all failed items are either repaired or replaced by new items if repair is impossible, so that the original amount of items remains circulating through the network. A deficit of the original VARI-METRIC method is the assumption that the repair shop capacities are infinite. As has been shown by Sleptchenko et al. (2002a) this may lead to significant errors in estimating system availability and determining spare part stock levels if the repair shop utilisation is high. Therefore, they model repair shops as multi-class, multi-server queuing systems to resolve this issue. Thus, they develop a variant of VARI-METRIC with finite repair capacities. In such models, we can introduce other degrees of freedom for efficiency improvement, such as capacities of the repair facilities and repair priorities. Sleptchenko et al. (2003) capacities of repair facilities and inventory stock levels. In their model all repair jobs have the same priority. Here we discuss opportunities for efficiency improvement by setting appropriate priorities to different repair jobs under finite capacity. We explain this below.. One of the key parameters determining stock levels of repairable items in spare part networks is the repair shop throughput time. It is clear that less inventory is needed to obtain a certain system availability if the throughput times can be reduced. If a repair shop handles a variety of item types and its capacity is limited, we can reduce the throughput times of expensive items by giving the corresponding repair jobs high priority. As a consequence, other item types having low priority will have to face longer throughput times. This means that priority setting leads to reduced stock levels for high priority items and increased stock levels for low priority items. This provides us an opportunity for inventory investment reduction while maintaining the same system availability: if we choose expensive slow movers as high priority items and cheap fast movers as low priority items, the reduction of inventory investment in high priority items will probably be more than the increase of investment in low priority items. Equivalently, we can increase the system availability with the same budget by introducing adequate priority rules.. In this paper, we examine the impact of using repair priorities in spare part distribution networks. We focus on static priorities, i.e. an a priori assignment of item types to priority classes for each repair shop, which is state-independent. We need such priorities, because we have to choose stock levels a priori as well. In fact, we focus on logistic decisions at a tactical level. At the operational level, one may consider using dynamic priorities additionally. That is, we may sequence repair jobs based on the specific system state at a specific point in time. For example, we may consider giving high priority to an item for which no stock is available, even if it has been classified as low priority originally. Such an operational finetuning of priorities can further improve the system performance, but it is out of the scope of our research. We will address the following research questions in the remainder of this paper:. (1) To what extent can we reduce inventory investment using a priori priority setting of items? (2) Which rule should we use to classify items as high priority or low priority? (3) Which impact does priority setting have on the stock levels of individual items?. To answer these questions, we need (1) an algorithm for assigning priorities to items sharing the same repair capacity and (2) an algorithm for spare part inventory optimisation based on these priorities. We will discuss both sequential optimisation and integral priority assignment/stock optimisation. In our approach, we model the repair shops as multi-class, multi-server queues with preemptive priorities. Our model contains two priority groups (high and low), each consisting of one or more subclasses. Hence priority assignment boils down to assigning either high or low priority to each item. To analyse these queuing models, we use the method as developed by Sleptchenko et al. (2002b) . They solve the steady state equations, assuming that the probabilities of states with a high number of items in the system can be neglected. The performance measures required for the extended VARI-METRIC model can be obtained from these state probabilities. Because we have to solve high dimensional matrix equations, we have to restrict the number of item types (classes) and the number of servers per repair shop. Note that our priority assignment heuristics do not lead to optimal solutions. Even the case without priorities for capacitated repair shops is already that complex that optimal stock levels cannot be guaranteed except for some special cases.. Before we discuss our algorithms, we first position our research within the related spare part management literature (Section 2). Next, we explore the potential of repair priorities using a variant of VARI-METRIC for given priorities in Section 3. In Section 4, we discuss an algorithm for integral priority assignment and stock optimisation and a sequential algorithm. We compare the various heuristics in a numerical experiment in Section 5. As performance criteria, we use run time and solution quality, that is, the inventory investment needed to achieve a target availability. Finally, we give our conclusions in Section 6.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O


Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models Many engineering design optimization problems contain multiple objective functions all of which are desired to be minimized, say. This paper proposes a method for identifying the Pareto Front and the Pareto Set of the objective functions when these functions are evaluated by expensive-to-evaluate deterministic computer simulators. The method replaces the expensive function evaluations by a rapidly computable approximator based on a Gaussian process (GP) interpolator. It sequentially selects new input sites guided by values of an ''improvement function'' given the current data. The method introduced in this paper provides two advances in the interpolator/improvement framework. First, it proposes an improvement function based on the ''modified maximin fitness function'' which is known to identify well-spaced non-dominated outputs when used in multiobjective evolutionary algorithms. Second, it uses a family of GP models that allows for dependence among output function values but which permits zero covariance should the data be consistent with this model. A closed-form expression is derived for the improvement function when there are two objective functions; simulation is used to evaluate it when there are three or more objectives. Examples from the multiobjective optimization literature are presented to show that the proposed procedure can improve substantially previously proposed statistical improvement criteria for the computationally intensive multiobjective optimization setting. This paper proposes an algorithm for sequentially designing a sequence of inputs at which to run a set of expensiveto-evaluate functions so as to identify the Pareto Front of function values and the Pareto Set of inputs. Throughout, let y(x) = (y 1 (x), . . . , y m (x)) denote the m functions of interest, d the number of inputs, and x = (x 1 , . . . , x d ) a generic input. The input space for x is denoted by X ⊂ R d and the function values y(x), x ∈ X, form the objective space. The goal of this paper is to find the complement of the set inputs x ∈ X that are dominated by one or more inputs in X. An input x 1 ∈ X weakly dominates x 2 ∈ X (x 1 ≽ x 2 ) if y i (x 1 ) ≤ y i (x 2 ) for all i = 1, . . . , m. If at least one inequality is strict, then x 1 is said to dominate x 2 (x 1 ≻ x 2 ). Equivalently, an input x 1 does not dominate x 2 (x 1 ̸ ≻ x 2 ) if there exists any i such that y i (x 1 ) > y i (x 2 ). Geometrically, x 1 ≻ x 2 if y(x 1 ) is strictly to the ''southwest'' of y(x 2 ).. In an analogous fashion, for y(x 1 ) and y(x 2 ) in the objective space, y(x 1 ) is said to weakly dominate y(x 2 ) (y(x 1 ) ≽ y(x 2 )) if y i (x 1 ) ≤ y i (x 2 ) for all i = 1, . . . , m. If at least one inequality is strict, then y(x 1 ) is said to dominate y(x 2 ) (y(x 1 ) ≻ y(x 2 )).. where θ = (θ 1 , . . . , θ d ) . The parameters (β, σ 2 , θ) are unknown and must be estimated to complete specification of the GP model. The GP provides the basis for interpolation of y(·) and uncertainty assessment of the predicted values. When  σ 2 , θ  is known, the best linear unbiased predictor (BLUP) of y(x 0 ) is Sacks et al. (1989) . Here R = (R ij ) is the n × n matrix with R ij = R  x i − x j | θ  , r(x 0 ) = (r i (x 0 )) is the n × 1 vector with r i (x 0 ) = R (x 0 − x i | θ), and . The quantity s 2 (x 0 ) is the mean square prediction error (MSPE) of  y(x 0 ) is thus used to quantify the uncertainty of this predictor.. In practice,  σ 2 , θ  is unknown. The frequentist approach to modifying (2) is to estimate  σ 2 , θ  by, say, maximum likelihood and apply (2) and (4) yielding an ''empirical'' BLUP,  y(x 0 ), and estimated MSPE, s 2 (x 0 ). An alternative approach to modifying (2) when. is unknown is the Bayesian paradigm in which priors are identified that embody knowledge about  σ 2 , θ . . While both approaches can be applied below, this paper will follow Schonlau (1997) and Jones et al. (1998) by using estimated parameters in the BLUP and MSPE formulas.. The EGO algorithm is based on a heuristically selected improvement function defined for each new potential input x. Schonlau (1997) . where y n min is the smallest element in y n and 1 E is 1 if E is true and is 0 if E is false (see also Jones et al., 1998; Huang et al., 2006) . Of course, I(y(x) ) is unknown but a probabilistic assessment can be made of its possible values by substituting Y (x) for y (x) . The expected improvement is defined to be conditional expectation of I(Y (x)) given y n , i.e., EI(x) = E {I(Y (x))|y n }.. This quantity can be shown to be approximately. where Φ(·) is the standard normal cumulative distribution function and φ(·) is the associated density function. The value of EI(x) will be large if either the predicted value y(x) is much smaller than y n min or s(x) is large (the latter means there is a large amount of uncertainty in the estimated y(x)). The steps of the EGO algorithm are 1. Evaluate y(·) at an initial space-filling design D n = {x 1 , . . . , x n }, such as a maximin Latin hypercube. 2. Estimate the stochastic process parameters β, σ 2 , and θ based on y n .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Adaptive evolving strategy for dextrous robotic manipulation Abstract A robot task can be represented as a set of trajectories conformed by a sequence of poses. In this way it is possible to teach a mobile robot to accomplish a manipulation task, and also to reproduce it. Nevertheless robot navigation may normally introduce inaccuracies in localization due to natural events as wheel-slides, causing a mismatch between the endeffector and the objects or tools the robot is supposed to interact with. We propose an algorithm for adapting manipulation trajectories for different locations. The adaptation is achieved by optimizing in position, orientation and energy consumption. The approach is built over the basis of Evolution Strategies, and only uses forward kinematics permitting to avoid all the inconveniences that inverse kinematics imply, as well as convergence problems in singular kinematic configurations. Manipulation paths generated with this algorithm can achieve optimal performance, sometimes even improving original path smoothness. Experimental results are presented to verify the algorithm. The fundamental purpose of robots is to help humans in a variety of difficult tasks, enabling people to increase their capabilities of strength, energy, speed, memory, César Arismendi, DavidÁlvarez, Santiago Garrido and Luis Moreno Department of Systems Engineering and Automation, Carlos III University of Madrid. Leganés, Madrid, Spain. e-mails:(carismen, dasanche, sgarrido, moreno)@ing.uc3m.es and to operate in hazardous environments through telerobotics. Service robots, more precisely mobile manipulators, incorporate one or two robotic manipulators and a mobile base, and must accomplish complex manipulations tasks interacting with tools or objects.. In order to accomplish robotic manipulation, inverse kinematics is required to determine the manipulator configurations given an endpoint position and orientation coordinates. Usually, approximated numerical methods are used to solve inverse kinematics due to the complexity and sometimes indeterminacy of analytical solutions. Many numerical methods have been proposed; the most common methods are based on the Newton-Raphson method (Goldenberg et al 1985; Gupta and Kazerounian 1985) and the damped least squares method (Mayorga et al 1992; Chiaverini et al 1991) , but these methods have convergence problems in singular kinematic configurations because of their dependence on the Jacobian Matrix. Another approach to solve this problem consist of formulating the inverse kinematics as a constraint optimization problem. This way all singularities are avoided by using forward kinematics instead. In order to solve this optimization problem, several methods have been proposed, from classic methods like conjugate gradient and the Cyclic Coordinate Descent (Wang and Chen 1991) , to artificial intelligence methods such as neural networks (Oyama et al 2001; Bao-Liang and Ito 1995; Morris and Mansor 1997; Guez and Ahmad 1988; Tejomurtula and Kak 1999; Thiang and Pangaldus 2009) , Fuzzy Logic (Kim et al 1993; Borboni 2001; Yang et al 2001; Kumbla and Jamshidi 1994; Shen et al 2006) and evolutionary algorithms (Tabandeh et al 2006; Parker et al 1989; Huapeng and Handroos 2000) . Sampling-based algorithms that rapidly generate solutions have also been widely used, in particular the Rapidly-exploring Random Tree (RRT) has been used in a broad range of motion planning scenarios. Bertram et al (2006) propose an algorithm for manipulation planning based on exploring a connected free component of the configuration space with a single RRT. An algorithm based on the same approach is presented in Vande Weghe et al (2007) , but rather than randomly extending the search tree out from the node closest to the workspace goal, it computes a goal directed action using the Jacobian Transpose. RRT approaches stand out because of its ability to manage high number of dimensions in a reasonably short time period, but as drawback, paths generated through these approaches lacks smoothness, which could lead to malfunctioning of the robot through abrupt movements.. The Evolution Strategies (ES) have proven to be robust and versatile by not depending on any continuity or derivability condition and have been used successfully in various disciplines. In (González et al 2009) Differential Evolution (DE) is used to find an optimal manipulation path. This algorithm presents graceful properties relative to the approaches mentioned before. First, it needs no determination of the inverse kinematics. Second, all poses are executed in forward kinematics, which implies that all poses are reachable and valid. Third, the generated path tends to be smooth due to the energy consumption optimization. However our work additionally includes mobile base disturbance, self-adjusting parameters and an improved algorithm implementation, that takes far less computation time to execute. This work is an expansion from our previous work Arismendi et al (2012) . Now further experimentation and the real robot implementation is presented.. The objective of this work is to adapt or provide previously learned manipulation tasks to other locations in a short period of time. A system is said to be real-time if the total correctness of an operation depends not only upon its logical correctness, but also upon the time in which it is performed (Laplante 2004) . Tasks considered here include reaching tools and objects; we considered these tasks to have a five seconds tolerance term. With this definition, the following algorithm presented here can be categorised as real-time, as long as the tolerance time is not outperformed. Operations completed after the deadline are considered useless.. Given a manipulation task in configuration space described by an end effector path in Cartesian space with a specific position and orientation of the robot base, a new manipulation path is generated in realtime using a modified approach of the basic ES scheme, assuming different initial conditions from the learned path. The ES algorithm implemented here uses criterions as simple as possible, minimizing computational burden, and reaching fast optimal results that coherently describe a manipulation task.. After manipulation path for a defined task is obtained, the challenge is to replicate it. In mobile robotics, localization error is a recurring problem caused by imperfections present on the floor surfaces and in robot hardware, causing wheels to slide among other errors (Xu and Collins 2009) ; in consequence, robot bases would rarely ever be situated in the exact same position and orientation where tasks were learned. Nonetheless, it is possible to determine the robot's exact position through measure equipment such as lasers and 3D cameras. Once the new location is obtained, the ES algorithm is applied to adapt the learned path of the original accomplished task, to a new sequence of poses. This paper is organized as follows. In Section II, the manipulation path adaptation problem is explained. Section III describes in detail the used ES algorithm with the proposed modifications to the original scheme. Section IV presents the experimental results obtained by testing the algorithm in simulations as well as in the real robot. The mobile manipulator used here is MANFRED-2 (Fig. 1) , which is an experimental platform developed by our research group. This robot has some built-in sensors, which have been used in this work. Finally, conclusions are presented in Section V. A methodology built over ES has been presented. The adaptation of manipulation paths for mobile manipulators is possible in real-time, achieving optimal manipulation relative to position, orientation and energy consumption. Given a learned manipulation path a new one is calculated when robot base is in a different location from that of the learned path, minimal position and orientation end-effector errors are obtained within the evolutionary process. Calculus are simplified to reduce computational time. A forward kinematics function was implemented for reaching optimal computational time, it implied a significant time reduction for the execution of the algorithm. Granted that the algorithm needs no inverse kinematics, singularities are avoided and convergence is guaranteed.. The experimental results showed the ability to apply the algorithm in real-time for obtaining adapted manipulation paths, proving to be a feasible solution for mobile robots manipulation problems. A computational time improvement was obtained by first optimizing position until position error is minimized, and then orientation error is added to the objective function with a reduced mutation step length until termination criterion is fulfilled at the end of the process. In addition, self-adjusting step length parameter was shown to perform efficiently compared to that of the DE algorithm. It is advisable to prioritize minimizing factors according to tasks because there is a proportional relationship between time and optimization parameters.. We are presently working on a number of extensions to our current work. First, the generation of paths with the specification of only one goal configuration, this seems to be easily implementable. Second, the use of efficient collision detection to avoid obstacles, this is going to be another optimization parameter, which on getting closer to obstacles will increase the error value.. Exploring these implementations, and conducting further analysis forms the basis of our future work.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A musical instrument for facilitating musical expressions In this paper, we propose a new musical instrument that allows people to concentrate on controlling indiscrete elements so that they can directly create their musical expressions. We describe a prototype musical instrument and demonstrate two applications of the prototype to show its effectiveness.Keywords Conventional music consists of two kinds of elements, i.e., discrete elements and indiscrete elements. The pitch of each note is the discrete element, while tempo, rhythm, "agogik," "dynamik," and so on are the indiscrete elements. By integrating both types of elements, an entire piece of music is constructed. Therefore, a composer carefully considers both types of elements and records them as the score of the piece, while a performer controls both types of elements when performing on a musical instrument so that s/he can create her/his musical expression for the piece.. However, the discrete element has a different importance for the composer than for the performer. Namely, the composer has complete freedom of choice in the pitches of the notes in a composition, which allows the composer to fully exhibit his/her creativity. However, the performer basically does not have the freedom to select the pitches of the notes. While the performer has some degree of freedom in selecting the pitches of the notes along with the directed pitches in a score for popular music (ad-lib performance in Jazz or Rock music, in particular), s/he must completely obey the composer's directions on pitches for classical music. Though the performer must input the discrete type of elements concurrently, it is not essential for his/her expression. Hence, we can say that the performer can express his/her musical expression by using only the indiscrete type of elements. Accordingly, the performer is obliged to input the pitches simply because the conventional musical instruments require the pitch data of the notes. Accurate reproduction of the pitches of notes as directed by the composer forces the performer to make a strong but non-creative effort. The performer wastes a significant amount of his/her cognitive ability on this effort. On the other hand, for the novice, accurate reproduction of the sequence of pitches is a formidable initial barrier to performing the musical instrument. This barrier often leads the novice to giving up the idea of mastering the musical instrument.. Consequently, we propose a new musical instrument that releases people from the requirement of inputting the discrete element and that allows them to concentrate on control of the indiscrete elements. As a result, they can directly create their musical expressions. In this paper, we illustrate the prototype musical instrument named "Coloring-in Piano (CiP)" and demonstrate two applications of the CiP to show its effectiveness. Figure 1 shows the structure of CiP. We used a Yamaha silent grand piano C5 professional model that outputs MIDI (Musical Instrument Digital Interface) note-on/off and pedal control messages. The piano is connected to an SGI Indy workstation. Before performing, it is necessary to feed the sequence of MIDI note-numbers (corresponding to the pitches) of the piece into the Indy. While performing, the Indy replaces the fed note-numbers with the played notenumbers in order. Finally, the Indy outputs the replaced pitch numbers and inputs them into the sound generator. Accordingly, CiP always outputs the correct pitch sound by touching any key. On the other hand, note-on (key down) velocity, note-off (key up) velocity, onset/offset timing, and pedal messages are output as the performer plays. We proposed a new musical instrument that releases the performer from the non-creative effort of controlling the discrete musical elements, i.e., the pitches. Using this instrument, the performer can concentrate only on the control of the indiscrete musical elements that are essential for musical expression in musical performance. Through the experiments on using CiP, our prototype instrument, we confirmed that this new musical instrument allows people, not only professionals but also novices, to directly exhibit their rich expressions in musical performance. Various supporting systems for musical performance have been proposed, and some of them are now commercially available. "Two Finger Piano" [1] is a toy system that allows the user to coarsely handle tempo and dynamik for each "beat" (not for each note) by using two fingers. However, it is impossible to fully control musical expressions by the beat-level performance. "Lighted Keyboard" [2] is equipped with the "3-Step Teaching System" to support self-study of a piano. The first teaching step provides a quite similar function to CiP in outputting available pitches by hitting any key. However, this system always outputs the constant velocity value. Thus, such ordinary systems can only be applied for novices' amusement, while our new musical instrument aims at facilitating pure musical expression not only by novices but also by professionals.

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O