Generalized reliability-oriented energy management for real-time embedded applications DVFS remains an important energy management technique for embedded systems. However, its negative impact on transient fault rates has been recently shown. In this paper, we propose the Generalized Shared Recovery (GSHR) technique to optimally use the DVFS technique in order to achieve a given reliability goal for real-time embedded applications. Our technique determines the optimal number of recoveries to deploy as well as task-level processing frequencies to minimize the energy consumption while achieving the reliability goal and meeting the timing constraints. The recoveries may be shared among tasks, improving the prospects of DVFS compared to existing reliability-aware power management frameworks. The experimental evaluation points to the close-to-optimal energy savings of our proposed technique. Portable embedded systems often have to operate under severe power and timing constraints. Consequently, research on low-power real-time embedded systems has been very active in the last decade. In that regard, Dynamic Voltage and Frequency Scaling (DVFS) has been one of the most commonly used techniques to manage energy. With DVFS, the processor frequency and supply voltage are lowered at run-time to reduce energy consumption. Since the tasks take longer to execute at lower frequencies, several research studies investigated the problem of guaranteeing the timing constraints in embedded applications while saving energy through DVFS [2, 5, 8, 11, 17] .. However, with reduced feature sizes and aggressive lowpower design techniques, soft errors that result from transient faults become more frequent and also an increasing concern for system designers and engineers [1] . More importantly, recent research [7, 16] has illustrated significantly increased (often by several orders of magnitude [16] ) transient fault rates when operating at low voltage/frequency levels with DVFS.. As a result, recently a number of research studies promoted the so-called Reliability-Aware Power Management (RA-PM) framework, where DVFS is applied for energy management only in a controlled manner while keeping an eye on reliability [6, 9, 15, 16] . Specifically, the RA-PM schemes reserve some CPU time (slack) for recovery tasks that may be invoked at run-time upon the detection of faults at the end of task executions. However, existing RA-PM studies share certain fundamental characteristics and limitations. First, the necessary and sufficient objective is casted as to preserve the task set's original reliability, which is defined as the reliability when all tasks run at the maximum frequency, and without any recovery task. Second, to achieve this aim a separate recovery task is scheduled for every task that runs at a low-frequency.. The main objective of this paper is to lay the foundations of a more general RA-PM framework by addressing these two limitations. Our motivation to address the first point is very natural: a more comprehensive framework is highly desirable in order to achieve any reliability goal, which can be set by the designer to be lower or higher than the application's original reliability. This is because, some modest reliability degradation might be acceptable in energy-scarce settings. In contrast, it may be necessary to achieve very high reliability levels for safety-critical systems (e.g. electronicshostile settings encountered in space applications). Clearly, merely maintaining task-level reliabilities would be too conservative or insufficient, respectively, in these two scenarios.. The second motivation is to further reduce energy consumption: by scheduling a separate recovery for every scaled task, the existing RA-PM solutions inherently reduce the amount of available slack for DVFS and thus the extent of energy savings. A preliminary solution to this problem was recently proposed: the technique, called shared recovery (SHR), reserves only one shared recovery task that can be used by any single faulty task at runtime [14] . Hence, SHR can save more energy as larger slack is made available for slow-down through DVFS. Upon the detection of a fault, the shared recovery task is invoked and the remaining tasks are forced to run at the maximum frequency until the end of the current period to preserve the system's original reliability [14] .. In this paper, we propose the Generalized Shared Recovery (GSHR) technique to potentially use any number of recoveries, and using this as a leverage, we tackle the generalized Energy-Optimal Reliability Configuration (EORC) problem: determine the number of recoveries and task-level frequency assignments to minimize the system level energy consumption while satisfying given target reliability and deadline constraints. Note that each additional (potential) recovery task, while improving the reliability, reduces the available slack for DVFS and limits the opportunities for scaling down frequencies. In other words, the problem mandates an efficient technique in three-dimensional timing, energy, and reliability space to satisfy all the constraints. For this problem we develop a fast algorithm with comparable performance to that of an optimal, but computationally expensive exhaustive-search based solution.. Motivational Example. To illustrate the potential gains that can be obtained by the new GSHR technique, let us consider an application with five tasks and a common period/deadline of 80 ms. The worst-case execution times of T 1 and T2 are given as 2 ms each, while those of T3 and T5 are 6 ms and that of T4 is 5 ms. As can be seen in Figure 1(a) , originally there is 80 − 21 = 59 ms slack when all tasks execute at the maximum frequency 1 GHz (No Power Management case). In that case, by using the fault rate model from [15] , the original probability of failure (PoF) is computed as ρ0 = 2.1 × 10 −7 . Here the P oF is defined as 1 − reliability, where the reliability is the probability that all tasks will complete without encountering soft errors caused by transient faults. If we consider the traditional greedy RA-PM scheme [15] , five separate recovery tasks (denoted by {Bi}) will be statically allocated to five tasks, yielding the RA-PM schedule depicted in Figure 1(b) . In that schedule, the first four tasks are executed at the energy-efficient frequency [8] fee = 0.29 GHz and the fifth task runs at the frequency 0.78 GHz. Applying the power model from [15] indicates that the energy consumption of the RA-PM solution is 7.88 and its actual PoF is 9 × 10 −10 . Now, if the objective is to merely preserve the original reliability ρ0, alternatively, we can use the GSHR technique with one shared recovery task of size 6 ms (denoted by SRT1) as shown in Figure 1(c) . Then, all the tasks can be executed at the frequency 0.31 GHz. Simple calculation shows that the total energy consumption is 5.4, giving a 32% reduction with respect to RA-PM. Figure 1(d) presents the GSHR solution with two recoveries that yield the exact reliability obtained from traditional RA-PM scheme (Figure 1(b) ). Thus, one can obtain the reliability achieved by RA-PM through GSHR with 15% less energy (6.68), by using the frequency 0.46 GHz. Notice that with two shared recoveries, the system can also potentially recover from two faults affecting the same task (unlike RA-PM that statically allocates one recovery to each task), which enables achieving the target (e) GSHR with three recoveries Figure 1 : The motivational example reliability yielded by RA-PM (PoF = 9 × 10 −10 ) with more energy savings. Further, through GSHR one can achieve reliability levels even higher than that achieved by RA-PM, for example P oF = 5 × 10 −10 , by deploying three shared recoveries and still consuming less energy than RA-PM (7.47) at the uniform frequency level of 0.51 GHz. In all these examples, we illustrated the potential gains of GSHR in terms of energy and reliability by using a simple uniform frequency approach -as we demonstrate later in this paper, these gains can be further improved by optimizing the frequency selection.. The remainder of this paper is organized as follows. Section 2 introduces the system models used in this paper and presents our assumptions. The generalized shared recovery technique (GSHR) and the energy-optimal reliability configuration problem are introduced Section 3. Our specific solution to the problem, the IRCS algorithm, is presented in Section 4. Section 5 presents the simulation results and Section 6 concludes the paper. In this paper, we addressed the energy-optimal reliability configuration (EORC) problem for real-time embedded applications with the goal of minimizing system energy consumption while satisfying an arbitrary target reliability and deadline constraints. To this aim, we developed the Generalized Shared Recovery (GSHR) technique through which a small number of recovery tasks are shared by all the tasks. Our experimental evaluation indicates that for a broad range of reliability objective goals, our proposed solution IRCS delivers close-to-optimal energy savings.

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


High performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management Modern cyber-physical systems assume a complex and dynamic interaction between the real world and the computing system in real-time. In this context, changes in the physical environment trigger changes in the computational load to execute. On the other hand, task migration services offered by networked control systems require also management of dynamic real-time computing load in nodes. In such systems it would be difficult, if not impossible, to analyse off-line all the possible combinations of processor loads. For this reason, it is worthwhile attempting to define new flexible architectures that enable computing systems to adapt to potential changes in the environment.We assume a system composed by three main components: the first one is responsible of the management of the requests arisen when new tasks require to be executed. This management component asks to the second component about the resources available to accept the new tasks. The second component performs a feasibility analysis to determine if the new tasks can be accepted coping with its real-time constraints. A new processor speed is also computed. A third component monitors the execution of tasks applying a fixed priority scheduling policy and additionally controlling the frequency of the processor. This paper focus on the second component providing a "correct" (a task never is accepted if it is not schedulable) and "near-exact" (a task is rarely rejected if it is schedulable) algorithm that can be applicable in practice because its low/medium and predictable computational cost. The algorithm analyses task admission in terms of processor frequency scaling. The paper presents the details of a novel algorithm to analyse tasks admission and processor frequency assignment. Additionally, we perform several simulations to evaluate the comparative performance of the proposed approach. This evaluation is made in terms of energy consumption, task rejection ratios, and real computing costs. The results of simulations show that from the cost, execution predictability, and task acceptance points of view, the proposed algorithm mostly outperforms other constant voltage scaling algorithms. Modern cyber-physical systems (CPS) assume a complex and dynamic interaction between the real world and the computing system in real-time (Wolf, 2009) . In this scenario, computing systems are commonly formed of many embedded units that are heterogeneously networked. One of the challenges facing future systems is to address the separation of modal control design from the deployment of executable code. In such systems, it would be difficult, if not impossible, to analyze off-line all the possible combinations of processor loads. Because of the large range of control process domains it is difficult to address control modality only through the parameterization of pre-loaded local controllers. For this reason, it is worthwhile attempting to define new flexible architectures that enable computing systems to optimally adapt to potential changes in the environment in a dynamic manner.. From the point of view of embedded control systems, it is well known that the integration of controller design and real-time scheduling is necessary (Cervin, 2003) . However, this approach is not enough in dynamic environments with limited computing resources. Therefore, additional mechanisms must be included in the design to optimize the use of resources and provide adaptation to the changing environmental conditions. Likewise, specific quality of services (QoS) levels must be guaranteed to ensure correct system operation. These QoS levels maintain a suitable control performance and a temporal predictability in the control system.. In this context, changes in the system operation mode, or in the environment, may force the disabling of some controllers and the enabling of others. In addition, controller switching mechanisms must be added in both local and distributed forms, resulting in workload changes for the embedded and networked units (Emberson and Bate, 2007; Real and Crespo, 2004) . This can have a significant impact on the overall performance and real-time constraints of the system. For this reason, task and component migration approaches (Briao et al., 2007 ) may contribute to online redistribution and reallocation of workload -according to each situation.. An example of this kind of architecture is proposed in Simarro et al. (2008) , which is developed in the context of a distributed real-time control system and from the perspective of control kernel middleware Albertos et al., 2006) . The proposal includes features such as control basis, controller switching, and code delegation. From the point of view of CPS, these approaches partially bridge the abstraction gap (Lee, 2006) .. In summary, cyber-physical systems should be able to deliver new levels of performance and efficiency thanks to sophisticated control computing co-design. Control systems mean that we must change our understanding of computing systems and accept that cyber-physical systems actively engage with the real world and real-time restrictions -and so expend real energy.. This paper explores methods that can be used in task migration when combined with techniques of energy management. These methods can maximize overall energy savings; facilitate thermal chip management by moving tasks away from the hot processing unit; balance the workload or concentration of parallel processing elements; decrease communication among those tasks that are particularly energy-efficient on wireless sensor networks (WSN) systems (Yi et al., 2009; Yuan and Wang, 2008; Kumar and Manimaran, 2007) ; and help guarantee the fulfillment of real-time system constraints.. Several power-aware techniques have been widely addressed in real-time literature (Piao et al., 2009; Tavares et al., 2008; Aydin et al., 2006; Pillai and Shin, 2001 ). These papers have discussed dynamic voltage scaling (DVS) of the processor, also known as dynamic voltage and frequency scaling. This approach exploits the convex, and normally quadratic relationship between CPU energy consumption and voltage. Additionally, these techniques have been extended to reduce the energy consumed during memory cycles (Cho and Chang, 2006; Liang et al., 2008 ) and network energy consumption (Yi et al., 2009; Kumar et al., 2008 ).. Let's consider a dynamic environment where an embedded and networked system operates with the support of task migration and processor frequency scaling. Assuming that the system knows where and when it must allocate tasks (Briao et al., 2007; Emberson and Bate, 2007) , we must perform feasibility analyses when each task arrives and departs. This guarantees that the temporal requirements of the system will be accomplished during the task allocation phase. Additionally, a new processor speed (frequency scaling) should be also computed to enable the system to adapt itself to the new computational workload and so reduce energy consumption. Although some authors have carried out these two phases (feasibility analysis and frequency scaling computation) separately (AlEnawy and Aydin, 2005) , these analyses are strongly related and in some cases can be performed together.. The main motivation to use fixed priorities approaches is to accomplish the standard ARINC-653 (AASS Interface, 2003) , which is used mainly in Avionics and currently also in Aerospace environment (Windsor and Hjortnaes, 2009 ). This standard defines a Time and Space partitioning system and each partition should be executed on fixed priority scheduling approaches. An example is the RTEMS O.S. on XtratuM hypervisor, which we are currently working (Galizzi et al., 2011 (Galizzi et al., , 2012 . However, we use the EDF-based scheduler to compare with the proposed approach by suggestion of a reviewer. As is well known, the EDF algorithm has a higher performance than fixed priority based scheduling, but we have design constraints.. This work mainly focuses on a proposal for a new algorithm to be used on-line during the task allocation and processor speed assignment phases. The algorithm uses fixed priority scheduling schemes with deadlines less than, or equal to, the period of the tasks when the dynamic processor workloads are being handled. In this paper a new algorithm (A approach) for computing the processor frequency scaling factor under fixed priority assignment with D = T and D ≤ T is proposed. This algorithm is performed with a reduced computational cost and minimizes CPU energy consumption while guaranteeing no missed deadlines. Therefore, it can be used as on-line acceptance test in systems with dynamic workload. However, as is well known, if it is compared with dynamic priority approaches, such as EDF, they have a higher performance than fixed priority based scheduling, but we have design constraints and we have to use fixed priority algorithms in order to complaint the standard ARINC-653 (AASS Interface, 2003) . Using extensive simulations, we have evaluated the behaviour of several existing feasibility tests that have been adapted to compute a frequency scaling factor ˛ for the proposed approach A. The analysis has been presented from the point of views of energy consumption, task rejection ratio, and real computing costs.. Our simulation results show that the proposed algorithm outperforms other constant voltage scaling algorithms from the cost, predictability, and task acceptance points of view. However, some small deviations were observed in the energy saved. The proposed approach is compared with other approaches. The proposed algorithm enables not only the high precision verification of system feasibility and the computation of ˛ in an environment with a dynamic processor load. The algorithm can also be used in realtime operating systems because the computational cost represents a small system overload.. The main features of the described approach are presented in Table 2 .. As a future work, we plan to investigate the case where memory access and the system bus perform at different clock speeds -together with processor frequency scaling (Marvell, 2008) . In this case, the combination of these parameters will enable an even greater reduction in energy consumption for the whole computing system. Note that typical values of frequency scaling in XScale processors are 208, 156 and 104 MHz for system bus and 312, 208, 156 and 104 MHz for SRAM memory access. Such as was mentioned in the Section 5, not all execution cycles are scaled for the processor speed because some operations deal with memory or other I/O devices. Therefore, they can be handled according to the activities of the tasks, for instance, while a task does not interact extensively with I/O devices, the access frequency to these can be reduced.. On the other hand, top-level algorithms to reclaim additional idle time resulting from the early completions of tasks will be investigated, such as approaches based on feedback control scheduling methodology. It to be used together with the proposed approach A. Additionally, testing of the proposed method on real hardware will be addressed. Few works cover task migration in the context of embedded and networked systems. Moreover, most algorithms are aimed at multiprocessor systems with soft real-time constraints (Yazdi et al., 2008; Emberson and Bate, 2007; Anderson et al., 2005) . Some papers have addressed both task migration and energy consumption minimization (Briao et al., 2007; Chen, 2005) . Briao et al. (2007) analyze the impact of task migration and justify its use because it compensates for the performance and energy costs involved in the system. Several heuristics has been proposed for task allocation problems with deadlines equal to the periods (Mejia-Alvarez et al., 2004; Chen, 2007) when earliest-deadline-first (EDF) scheduling is in use. AlEnawy and Aydin (2005) use algorithms for allocating real-time tasks by applying rate monotonic (RM) scheduling. In their article, a comparison of some admission control algorithms is presented in function of complexity, feasibility, and energy consumption parameters. However, only tasks with deadlines that are equal to the period are analyzed. Moreover, aspects such as predictability of execution, behaviour related to the arrival of tasks, and the real computing cost of the algorithms are not taken into account.. At the CPU-level, DVS techniques can be classified as static or dynamic. Static algorithms for hard real-time systems use parameters such as period or minimum inter-arrival time, and assume that each task executes its worst-case execution time when selecting the processor frequency, and that this is statically decided before execution. Dynamic algorithms are based on the reclamation of additional slack resulting from the early completions of tasks. These are then used to further reduce the processor frequency and save more energy. These algorithms are applied at run-time.. Using static algorithms we can obtain a single processor frequency that never changes, or obtain variable frequencies that are statically decided before execution. An example of the former is Saewong and Rajkumar (2003) , in which an algorithm that chooses a single frequency for a fixed priority scheduling scheme is proposed. In Bini et al. (2005) the authors present a method for approximating any speed level with two given discrete values that are switched as a pulse width modulation signal in order to obtain the average value. For the latter, in Mejia-Alvarez et al. (2004) and Saewong and Rajkumar (2003) , the authors propose an approach whereby each task is assigned a different frequency. Several authors have published works that find the optimal voltage schedule for recurrent tasks -examples for periodic tasks include Liu and Mok (2003) and Yun and Kim (2003) and examples for periodic and aperiodic tasks include Zhong et al. (2007) and Scordino and Lipari (2006) . Furthermore, speed function and the characterization of the points in time at which speed changes occur has been presented in Liu and Mok (2003) and Gaujal and Navet (2007) . However, a drawback in the works with statically established variable frequencies can appear if a task activation is lost or delayed. The drawback is that the entire frequency assignment will be affected, and this leads to missed deadlines.. Some authors, such as Piao et al. (2009) , have proposed dynamic algorithms working in combination with static methods. These algorithms can be divided into inter-task and intra-task methods. In the inter-task algorithms, the processor frequency is determined task-by-task, whereas intra-task algorithms may adjust the frequency within the boundaries of a given task. In Kim et al. (2002) , a performance comparison of several dynamic techniques is presented. In Pillai and Shin (2001) , the authors propose the algorithms named Cycle Conserving DVS for both EDF and RM schedulers and Look-Ahead for EDF. Saewong and Rajkumar (2003) , Quan (2004), and Leung (2005) propose a simple dynamic scheme for fixed priorities. Aydin et al. (2004) presents a generic dynamic reclaiming algorithm, as well as an adaptive and speculative speed adjustment mechanism. Dynamic frequency changes for periodic and aperiodic tasks are discussed in Piao et al. (2009) .. The use of elastic scheduling to improve DVS management has been proposed in Marinoni and Buttazzo (2007) and in Zhu et al. (2004) . Xia et al. (2008) proposes energy management based on a feedback control scheduling methodology. The processor DVS analysis has been extended by other authors to reduce energy consumption in the memory (Cho and Chang, 2006; Liang et al., 2008) and network levels (Yi et al., 2009; Kumar et al., 2008) .. However, some of the works mentioned above are based on an analysis of the hyper period. This approach can result in hard computing and is unsuitable for execution in run-time during task migration. Some other works consider only deadlines equal to periods and although this can be considered in specific cases, it can result in hard simplifications being applied in embedded control systems. Additionally, some studies have applied heuristics (Jejurikar and Gupta, 2004; Mejia-Alvarez et al., 2002) to obtain solutions that are sometimes far from optimal.

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


On

middleware

for

emerging

health

services

Healthcare

concerns

have

become

diverse,

ranging

from

acute

and

chronic

conditions

to

lifestyle,

wellbeing

and

the

prevention

of

illness.

Increasingly,

individuals

are

taking

responsibility

for

monitoring

their

own

conditions.

Healthcare

technologies

are

increasingly

used

not

only

for

administration,

but

also

in

specialist

treatments

and

many

forms

of

monitoring,

including

when

a

person

is

mobile.

As

well

as

formal

interactions

with

professional

carers

and

results

from

specialist

procedures,

care

may

involve

ad

hoc

interactions

with

an

individual's

community.

Together,

these

yield

a

wealth

of

data

relevant

in

different

contexts.

Unfortunately,

many

existing

healthcare

systems

are

inflexible,

singlepurpose,

and

self-contained,

so

that

we

cannot

fully

realise

their

potential.

We

believe

that

a

framework

for

flexible

interoperability

of

healthcare-relevant

components

is

crucial,

in

a

time

of

increasing

need

from

an

ageing

population.

We

present

a

vision

of

pervasive,

preventative

and

personalised

healthcare.

To

achieve

this

we

believe

that

the

application

logic

embodied

in

components

should

be

separated

from

the

policy

that

specifies

where

and

how

they

should

be

used-which

may

be

in

ways

not

contemplated

by

their

original

designers.

Middleware

should

therefore

provide

a

framework

that

supports

not

only

traditional

communication

among

components

but

also

dynamic

reconfiguration

of

components

in

response

to

circumstances

that

arise,

with

the

management

and

enforcement

of

high-level

policy

integrated

with

the

middleware.

By

this

means,

functionality

for

patients,

carers

and

health

administrators

can

be

customised

and

provided

as,

when

and

where

required.

This

paper

explores

middleware

requirements

and

challenges

arising

from

technology-and

population-driven

developments

in

healthcare

provision.

We

describe

the

specific

requirements

that

middleware

must

address,

and

present

some

practical

steps

towards

addressing

these

from

the

initial

stages

of

a

middleware

(SBUS).

With

the

world's

ageing

population,

there

is

an

increased

burden

on

healthcare

resources

As

such,

there

is

a

global

push

to

improve

the

efficiencies

and

effectiveness

of

health

care

services.

The

goal

is

to

improve

patient

outcomes,

alleviate

chronic

conditions,

and

more

generally

improve

an

individual's

quality

of

life

This

in

turn

reduces

healthcare

expenditure,

as

there

is

less

reliance

on

more

costly

health

services

Traditionally,

healthcare

provision

focuses

on

acute

issues.

These

are

significant,

highly

symptomatic,

health

concerns

that

often

require

urgent

treatment,

such

as

a

heart

attack

or

stroke.

Such

incidents

are

often

serious,

greatly

impacting

a

patient's

life,

and

consume

significant

health

resources.

Health

services

are

evolving

to

take

a

more

on-going,

preventative

approach

to

care

This

involves

risk

management,

taking

active

steps

to

mitigate

potential

health

concerns.

These

steps

can

be

clinical

interventions,

such

as

medication

or

periodic

diagnostic

tests,

as

well

as

lifestyle

factors,

such

as

exercise,

diet,

stress-levels,

and

so

forth..

Information

is

the

key

to

improving

health

and

wellbeing

Clearly,

better

informed

practitioners

give

a

better

quality

of

care.

Technological

advances,

such

as

sensors

and

monitoring

technologies,

means

that

there

is

increasingly

more

data

available

than

ever

before.

The

more

data

that

medical

professionals

have

access

to,

for

example

on

a

patient's

physiology,

lifestyle,

other

medical

interventions,

the

more

effective

advice

and

treatment

regimes

can

be.

This

enables

care

to

be

better

customised

to

the

individual

and

their

circumstances,

and

also

contributes

to

improving

general

care

standards

by

providing

input

into

case-studies,

best

practices

and

medical

research..

Information

is

relevant

not

only

to

the

medical

professional.

An

explicit

goal

of

emerging

care

models

is

to

empower

patients

to

take

control

of

their

health,

wellbeing

and

any

conditions

that

they

may

have

Therefore,

as

more

data

becomes

available,

it

is

important

for

patients,

and

their

care

community

(e.g.

family

members,

social

carers)

to

be

better

informed,

since

more

understanding

of

their

conditions

and

risks

can

help

to

reduce

risk

and

lead

to

positive

change..

Information

technology

already

plays

an

important

role

in

healthcare,

though

the

focus

so

far

has

predominantly

been

to

support

the

acute-based

approach

to

care.

Thus,

IT

systems

tend

to

concern

clinical

aspects,

providing

health-enterprise

services,

such

as

management

tools,

patient

administration

and

health

records

(to

various

degrees),

or

a

particular

diagnostic

capability

a

Many

of

these

systems

are

bespoke;

only

for

specific

purposes,

and

operate

in

vertical

silos.

If

interoperable,

there

tends

only

to

be

integration

with

other

local

systems

(cf.

across

care

units)

or

the

major

systems-often

an

expensive

undertaking

and

therefore

driven

by

management

concerns,

rather

than

focusing

on

more

flexible

care

pathways..

The

way

forward

is

to

realise

the

vision

of

personalised,

preventative

care

This

involves

a

more

holistic

approach,

where

healthcare

not

only

occurs

within

the

traditional,

clinical

space,

but

also

informally

through

patients

and

their

care

community

in

an

on-going,

dayto-day

manner.

Technology

is

central

to

this

vision.

With

respect

to

data,

sensor

technologies

are

increasingly

providing

rich

streams

of

information

giving

valuable

insight

into

a

person's

daily

activities,

progress

and

well-being.

Advances

in

communication

technologies

enable

interactions

at

a

distance

(remote

care),

and

improvements

in

connectivity

footprints

provide

the

means

for

patients

to

be

'continually

online'

for

monitoring.

The

uptake

of

mobile

devices

by

both

patients

and

medical

professionals

promises

their

increasing

familiarity

with

technology

providing

a

highly

personal

mechanism

that

can

be

leveraged

to

communicate,

inform,

alert

and

monitor.

From

the

clinical

perspective,

this

will

lead

to

better

informed

care,

more

efficient

use

of

carer

time,

and

improvements

in

the

speed

and

quality

of

response.

Continuous

monitoring

of

environmental

and

physiological

state

provides

rich

datasets

to

aid

diagnosis

through

analysis

of

risks,

traits

and

trends.

Monitoring

can

be

extended

to

provide

alerts

of

detected

conditions,

for

reminders,

etc.

This

is

important

for

patients

and

their

care

communities,

to

give

them

insight,

feedback

support

and

reinforcement

of

their

care

goals.

As

well

as

providing

rich

data

for

clinical

use,

to

customise

care

and

diagnosis,

this

data

is

also

useful

for

medical

research..

There

are

systems

for

remote

care

(telecare)

and

selfmonitoring

b

However,

these

tend

to

be

bespoke,

closed

systems,

designed

for

specific

conditions

or

exclusive

concerns,

such

as

raising

particular

alerts,

or

diabetes

management.

While

a

step

in

the

right

direction,

to

properly

realise

the

goal

of

preventative

care

more

integrated,

flexible

approaches

are

required.

The

systems

should

be

able

to

deal

with

the

idiosyncrasies

of

individual

patients,

their

care

needs,

their

support

networks

and

their

care

team,

and

capable

of

leveraging

a

range

of

different

health

systems,

services

and

data

as

appropriate..

The

vision

for

a

system

environment

should

therefore

be

pervasive

healthcare

rather

than

a

number

of

specific

systems

(integrated

to

various

degrees).

That

is,

a

number

of

systems-including

applications,

services,

sensors,

and

so

forth-should

come

together,

seamlessly,

when

and

where

relevant,

as

appropriate

for

each

individual.

Data

drives

health

and

well-being

processes,

enabling

tailored

interactions

across

clinical

and

more

informal

and/or

personal

systems.

Care

efficiencies

are

improved,

as

integrated

systems

and

improved

communications

help

the

movement

away

from

the

environment

of

manual,

human-initiated

actions..

Pervasive

healthcare

imposes

significant

challenges

for

supporting

infrastructure.

Firstly,

in

such

an

environment,

it

will

be

less

clear

which

systems

are

health

related;

heartrate

sensors

are

obviously

relevant

but,

for

some

people,

lack

of

phone

communication

could

indicate

depression.

The

availability

and

relevance

of

different

systems

will

vary

between

individuals.

Each

user,

clinical

or

otherwise,

is

different,

they

will

vary

in

how,

when

and

why

they

use

particular

systems.

Further,

security

and

privacy

concerns

pervade,

given

the

sensitivity

of

personal/health

information.

However,

such

sensitivity

may

depend

on

context..

Perhaps

most

importantly,

system

developers

will

not

be

able

to

envisage

all

the

circumstances

in

which

their

systems

may

be

used.

Therefore,

these

concerns

can

no

longer

all

be

encoded

in

application

logic.

Indeed,

this

has

led

to

today's

vertical

silos

and

interoperability

problems.

Useful

functionality

comes

not

from

single

systems,

but

by

coordinating

systems

in

various

ways..

Middleware

provides

a

layer

of

abstraction

that

mediates

between

applications

and

network

infrastructures

Often

described

as

systems-glue,

middleware

is

crucial

in

supporting

this

emerging

healthcare

because

middleware

operates

across

systems,

to

assist

communication

and

management.

The

challenges

of

pervasive

healthcare

means

that

middleware,

in

addition

to

its

traditional

role

of

enabling

interoperability,

must

be

more

active

in

controlling

systems

and

driving

interactions

between

them,

when

and

where

appropriate,

in

order

to

meet

userdefined

goals.

Policy,

representing

an

individual's

(patient

and/or

carer)

requirements,

is

crucial;

defined

outside

application-logic,

it

operates

to

orchestrate

and

reconfigure

system

components

to

meet

particular

goals.

The

purpose

is

to

allow

people

(and

their

carers)

to

customise

how

and

when

various

systems

are

used,

and

how

their

data

is

handled,

in

order

to

best

manage

patients'

health

and

well-being..

This

paper

explores

the

impact

of

this

emerging

healthcare

vision

and

the

requirements

this

model

imposes

on

supporting

infrastructure.

We

begin

by

describing

the

evolution

of

healthcare

provision

and

the

resulting

systems

requirements

before

emphasising

the

importance

of

policy

for

enabling

dynamic

management

across

system

components

We

then

explore

areas

of

systems

research,

and

consider

their

fit

within

the

broader

healthcare

vision

The

SBUS

middleware

is

introduced

as

proof

of

concept

to

practically

illustrate

the

types

of

capabilities

required

by

emerging

health

infrastructure

Policy

enforcement

specifics

are

then

explored

and

we

conclude

with

a

summary

of

open

challenges

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


Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling. European Journal of OperationalMany real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first realworld problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques. Real-world optimisation problems often contain non-linearities, combinatorial relationships, and uncertainty factors that are too complex to be effectively modelled analytically. For these problems, simulation-based optimisation is a powerful technique for determining optimal system parameters (April et al., 2004) . Simulation-based optimisation is the process of finding the best parameter values for a system, in which the performance is evaluated on the basis of output from a simulated model of the system. While traditional, analytical optimisation methods have been unable to cope with the challenges imposed by many simulation-based optimisation problems in an efficient way, such as multimodality, single optimisation run since they maintain a population of solutions.. In many practical applications of EAs, one has not only to cope with multiple optimisation objectives, but also with stochastic noise as a consequence of uncontrollable variations, caused, for example, by human operators or worn-out machines (Jin and Branke, 2005; Bui et al., 2005; Branke et al., 2007) . Noise means that even if the initial conditions of the system and its input parameters are known, the output of the system cannot be predicted but will vary from time to time. These unpredictable variations in the simulation output are harmful to the optimisation process since the EA can be misdirected to propagate inferior solutions. To deal with noise in evolutionary optimisation, three basic approaches have been proposed: explicit averaging, implicit averaging, and selection modification (Jin and Branke, 2005) . In explicit averaging, the same solution is simulated a number of times and the objective values are averaged. Simulating a solution n times reduces the noise by a factor of n, but at the same time increases the computational effort by a factor of n (Jin and Branke, 2005) . In implicit averaging, the sample size is adjusted to the population size; the larger the population the smaller the sample size (Fitzpatric and Grefenstette, 1988; Miller and Goldberg, 1996) . The assumption of this approach is that there are many similar solutions in a large population, and that the influence of noise is compensated for as the algorithm revisits promising regions of the search space frequently. In selection modification, the ranking and/or selection procedure is modified to compensate for noise, such that a solution is only considered better than another solution if certain conditions are satisfied (Jin and Branke, 2005) . For example, the probability of dominance can be considered as proposed by Hughes (2001) , or the closeness of solutions can be considered as proposed by Babbar et al. (2003) .. In this paper we present a new noise handling technique for multi-objective optimisation that efficiently deals with noise in simulations. This technique varies the number of samples used per solution based on the amount of noise in combination with a user-defined confidence level, controlling the trade-off between search space exploration and number of solution samplings. With the technique, resampling of solutions is performed iteratively until the noise is sufficiently reduced. The proposed technique is integrated in an algorithm called "Multi-Objective Parallel Surrogate-Assisted EA". (MOPSA-EA) described in Syberfeldt et al. (2008) . MOPSA-EA is designed to reduce the large time consumption associated with many real-world problems approached by simulation. The algorithm supports a high degree of parallelism by implementing the asynchronous master-slave parallelisation model in combination with a steady-state design. For improved efficiency, the algorithm also uses a simulation surrogate to screen candidate solutions and identify the most promising ones. A simulation surrogate is a computationally cheap approximation of a time-consuming simulation that can be used to estimate the objective values of solutions.. The rest of the paper is organised as follows. The next section discusses the effects of noise in EAs and outlines different techniques that have been suggested to cope with this problem in multiobjective optimisation. In Section 3, the new noise handling technique proposed in this paper is described. In evaluating the new technique, it is applied to five benchmark problems and two real-world problems of manufacturing optimisation, which are described in Section 4. The technique is implemented in MOPSA-EA, which is presented in Section 5. The results of the evaluation are presented in Section 6, along with a comparison of other noise handling techniques and an analysis of the results.. Conclusions of the study and future work are presented in Section 7. The new technique to deal with noise uses an iterative resampling procedure that efficiently reduces the noise by varying the number of samples used per solution based on the amount of noise in the local area of the search space. This dynamic strategy avoids wastful samplings when additional sampling is of little benefit, and promotes additional samplings when this is beneficial. Contrary to several existing techniques for noise handling, the new technique does not reduce noise for all solutions in the population, only for those participating in the evolutionary selection. The motivation for this approach is that noise is not harmful to every element of an EA, only to those evolutionary processes that involve comparative selection. In other evolutionary operations, such as mating or mutation, noise is of minor importance.. Similar to other noise handling techniques, the proposed technique involves user-defined parameters. Although the technique works well with a standard setting of the parameters, ideally there should be no user-defined parameters at all. Investigating how to get rid of the user-defined parameters, thereby making the use of the technique simpler, is an important topic for future research. Another potential improvement that we will evaluate in the future is to let a surrogate (e.g. an ANN) represent the noise landscape based on the information received when resampling solutions. If it is possible to construct a surrogate of adequate quality, this can be used to estimate the noise of a solution and simulation evaluations can thereby potentially be saved. A further aspect that we will investigate in the future related to surrogates is the relationship between noisy evaluations and the training of surrogates. It is known that a small amount of noise in the training data often improves the surrogate's ability to generalise on new data (Holmström and Koistinen, 1992) . However, in real-world simulationoptimisation every data sample used to train the surrogate is subject to a large amount of arbitrary noise. Such high degree of unknown randomness in the data samples cause an unstable training process and a deterioration of the accuracy of the surrogate. For improved efficiency of the optimisation, reducing the noise is therefore not only important with respect to the EA, but also with respect to the training of the surrogate. As far as we know, the problem of noise upon surrogate training has not been previously addressed within the field of multi-objective evolutionary optimisation.. In the next stage of this work, we will also consider robustness. The concept of robustness differs from noise in that the variations are not in the simulation (that is, the objective function), but in the input parameters. Robustness is important since in real-world problems it cannot usually be guaranteed that the exact parameters of a solution are actually implemented, but rather a solution close to the original one. We will investigate if it is possible to utilise the resamplings performed in its noise handling technique to bias the evolutionary process towards more robust solutions without any additional resamplings. This could be done by introducing small changes in the input parameters of a solution being resampled. A non-robust solution will then achieve poor results and be consequently punished in the evolutionary selection. The benefits of a dynamic resampling strategy have previously been discussed by Di Pietro et al. (Pietro et al., 2004) . Di Pietro et al. suggest two dynamic strategies to reduce noise. In the first strategy, called standard error dynamic resampling, all solutions are resampled until the standard error of the mean for each solution is below a user-defined threshold (the same threshold is used for all solutions). In the second strategy, called m-level resampling, different thresholds are used for different noise intervals.. Although Di Pietro et al. was able to demonstrate the potential of a dynamic procedure, they also found that properly applying a dynamic resampling strategy requires a more sophisticated approach than simply forcing the standard error below some threshold. The CDR technique is a step in this direction;. instead of using standard error thresholds, statistical tests based on the concept of Welch confidence interval are being used. Furthermore, the resampling procedure used in the CDR technique is more efficient since noise is not reduced for all solutions in the population, but only for those participating in the evolutionary selection. The motivation of this approach is that noise is not harmful to every element of an EA, but only to those evolutionary processes that involve comparative selection. In other evolutionary operations, such as mating or mutation, noise is irrelevant.. The following section presents a description of the evaluation of the CDR technique on a number of optimisation problems. In this subsection, the results of the five noise handling techniques are discussed. The sensitivity of the user-defined parameters of the proposed CDR technique is also analysed.

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


Local Minima of Quadratic Functionals and Control of Hydro-electric Power Stations Abstract In this paper we consider a control problem for a cascade of hydro-electric power stations where some of the stations have reversible turbines. The objective of our work is to optimize the prot of power production satisfying restrictions on the water level in the reservoirs. From mathematical point of view this is a problem of minimization of an innite-dimensional quadratic functional subject to cone constraints. We obtain sucient conditions of optimality and illustrate them with some example. Water is becoming a scarce resource and this has impact on how the water is used to produce electric energy. The management of multireservoir systems has attracted the attention of many researchers in dierent contexts [1] . It is especially important if there is also a possibility of reusing the downstream water in a situation of drought. This may be implemented in modern reversible hydro-electric power stations, associated with reservoirs along a river basin with a cascade structure, where it is possible both to turbine water from upstream to produce electric power and to pump from downstream to rell an upstream reservoir. Here we consider a simplied model (based on [2, 3, 4, 5] ) for a cascade of hydro-electric power stations where some of the stations have reversible turbines. The water level in the reservoirs is subject to some constraints. The problem is considered in the framework of optimal control theory. Its abstract formulation leads us to consideration of local (non-isolated) minima of innitedimensional quadratic functionals subject to cone constraint. This subject is considered in section 3. Sucient conditions of optimality for local minimizers and also for directional minimizers are established. To our knowledge the treatment of sucient conditions for directional minimizers is knew. Quadratic forms play an important role in the Calculus of Variations [6, 7] . Usually principal attention is paid to Legendre condition. Here we consider quadratic forms depending on the trajectories only and not on their derivatives. However, we assume that the trajectories and the derivatives are subject to some geometric constraints. Second order sucient optimality conditions for optimal control problems have been studied by many other authors; see, for example, [8, 9, 10] and the references therein. In general these results are deduced under very strict hypothesis or are elaborate and dicult to apply to particular problems. Taking advantage of the particular structure of the problem we prove sucient conditions of optimality allowing one to show that a given solution is optimal in a local sense. Moreover, in the case of one power station we analytically show an improvement of the prot, due to the use of reversible turbines. In this work we analysed an optimal control problem modeling a cascade of hydro-electric power stations where some of the stations have reversible turbines and with objective the optimization of the prot of power production. This is a problem of minimization of an innite-dimensional quadratic functional subject to cone constraints. Results on this subject where deduced. Generally and for the considered problems the minimum point may be not isolated or even it can be only a directional minimizer point. In such cases sucient conditions of optimality are much more eective when compared to necessary conditions. Besides sucient conditions of optimality for the problem of minimizing the prot on a cascade of hydro-electric power stations, it was determined the structure of the solution for such problem when the price is periodic. This can be important in real situations. The particular case of one power station was studied in more detail. It was proved that the use of reversible turbines always improves the prot in that case.

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Measuring the performance of underplatform dampers for turbine blades by rotating laser Doppler Vibrometer c Experimental analysis of the effect of underplatform dampers on bladed disk dynamics is presented. c An improved rotating test rig with non-contact excitation and measurement systems is used. c The rotating measurements are performed by using a Scanning Laser Doppler Vibrometer. c The non-linear relationship between the force and the vibration amplitude has been measured. c The effects of the number of engine order excitation have been systematically measured. Underplatform friction dampers are commonly used to control the vibration level of turbine blades in order to prevent high-cycle fatigue failures. Experimental validation of highly non-linear response predictions obtained from FEM bladed disk models incorporating underplatform dampers models has proved to be very difficult so as the assessment of the performance of a chosen design. In this paper, the effect of wedgeshaped underplatform dampers on the dynamics of a simple bladed disk under rotating conditions is measured and the effect of the excitation level on the UPDs performances is investigated at different number of the engine order excitation nearby resonance frequencies of the 1st blade bending modes of the system. The measurements are performed with an improved configuration of a rotating test rig, designed with a noncontact magnetic excitation and a non-contact rotating SLDV measurement system. High vibratory stresses of turbomachinery components can be major cause of high-cycle fatigue (HCF) and so it is very important to maintain vibration amplitudes within the design parameters. A common method for passively controlling the level of vibrations of the blades is to use friction dampers, which are positioned between the blades. Actually, from the literature, the underplatform dampers (UPDs) is a well-established design method for vibration reduction and is thoroughly studied by researchers. UPDs consist of small metal parts, located under the blade platform, which work by the centrifugal force acting on the damper itself during rotation and thus forcing them against blades' platforms. Over the years, a great amount of research work has been produced on UPDs to develop reliable numerical tools capable of predicting their dynamics and effects on the bladed disks responses. In the early works [1] [2] [3] the UPDs were modelled as bodies always in contact with the blade platforms and their kinematics deduced from the kinematics of the blades. In later works [4, 5] , the role played by damper rotation and the possible partial detachment of the UPDs from the blades have been recognized and these features included in the models. Also the bulk elasticity of the UPDs [5] [6] [7] [8] is modelled and the relevance of the static/dynamic coupling of the UPDs contact forces has been investigated [9, 10] . However, experimental validation of highly non-linear response predictions obtained from FEM bladed disk models incorporating UDPs models has proved to be very difficult so as the assessment of the performance of a chosen design. In the literature, most of the experimental results available on UPDs refer to static rigs [2, 3, 6, 11, 12] consisting of a set of blades clamped into a fixture, with a set of interposed UPDs. An external excitation is applied by means of an electromagnetic shaker and the centrifugal force on the dampers is simulated by a pulling force applied by using dead weights connected to the UPDs with steel wires. The presented experimental set-ups have the following shortcomings: (i) the effect of the excitation system on the system dynamics, (ii) the effect of the steel wires on the damper kinematics, (iii) the dynamics characterized by standing mode shapes, which are different from the rotating mode shapes typical of axi-symmetric structures like bladed disks under rotating excitation.. An important step has been made in [13] , where the damping of thin-walled underplatform dampers is measured experimentally by using a rotating rig, where the test structure included two pairs of turbine blades mounted symmetrically on the disk and the excitation was produced by using piezoelectric devices connected to the blades. In order to test the effect of underplatform dampers on a full 3601 bladed disk, a static rig [14] has been designed where the bladed disk is excited by a rotating travelling wave excitation generated by a set of non-contact electromagnets and measured using a Scanning Laser Doppler Vibrometer (SLDV). Non-contact transducers are suitable for this type of measurements because of their capability to measure the vibration remotely and thereby not affecting the dynamics of the bladed disks, which may be sensitive to mistuning. As introduced earlier, the centrifugal force is simulated by pulling the dampers attached to dead weights by steel wires. A significant improvement of the design of the test rigs developed for the experimental characterization of UPDs for bladed disks is presented in [15, 16] , where a rotating rig has been designed with a non-contact magnetic excitation and a non-contact rotating SLDV measurement system, based on a mechanical tracking method. This paper aims to present an improved configuration of the test rig developed in [15, 17] . In fact, the most recent design configuration of the test rig relies on the tracking methods developed in which simplified sensibly the mechanics of the tracking measurement method.. Most of the studies reported on tracking techniques are based on a single acquisition point and so, either a line or an area had to be scanned, addressing a number of predefined positions sequentially. Basically, it was developed on the concept of a conventional step mode similar to the one performed on non rotating targets. The use of tracking techniques was developed to measure vibrations of rotating bladed discs. Three techniques: (i) Point Tracking, (ii) LineScan Tracking and (iii) AreaScan Tracking, respectively, were devised to perform such measurements and all of them have been packaged as a software platform called Caiser Mymesis [17] . The success of tracking measurements using SLDV methods depends on some fundamental contributions: the scanner is embedded into the laser head and the synchronization of the laser spot with the rotating target by means of tachometers, which output the angular position in the form of electrical signals readable by an electronic card.. In this paper the self-indexing mechanical tracking setup was replaced by exploiting the X-Y scanning mirrors installed inside a laser head of a SLDV system, thus improving the capabilities of the measurement technique. The modified test rig has then been used to measure the effect of wedge-shaped underplatform dampers on the dynamics of a simple bladed disk under rotating conditions. The effect of the excitation level on the UPDs performances has been measured and the effect of the number of engine order is systematically investigated nearby some of the resonance frequencies of the 1st blade bending modes of the system. This paper presents an experimental study of the effect of underplatform dampers on the dynamics of bladed disks. The work has been produced using an experimental rotating test rig equipped with non-contact excitation and measurement systems, this being important for preventing additional and unpredictable mistuning cause by the transducers. The measurements were performed by using tracking techniques developed by using a Scanning Laser Doppler Vibrometer. This new technique overcame some limitations of a previously developed technique, which was based on the self-tracking mechanical method. The Point track method worked by moving the laser beam synchronously with the target. This was achieved thanks to a tachometer, installed on the shaft, controlling the scanning mirrors of the SLDV laser head. The use of tracking Scanning LDV methods for measuring vibrations of rotating structure such as bladed discs is of invaluable importance to succeed in such experimental studies.. The effects of the inherited geometrical mistuning of the blisk have been measured and reproduced with a simplified linear model. The effects of underplatform dampers on the first bending vibration mode of the blades have been investigated for different levels of the excitation force and for different engine order values. The non-linear relationship between the force and the vibration amplitude has been observed and measured experimentally so as to identify a range of excitation forces within which a robust design of the damper worked efficiently. The effects of the number of engine order excitation on the damper performances has been systematically measured. These showed that dampers are effective in reducing vibrations of blades in a range of excitation force levels, which increases as much as the number of nodal diameter associated to the excited mode shapes becomes larger. Rotating test rigs used to measure the vibration of axi-symmetric structures, such as bladed disks, must be designed such that any experiment can be carried out under controlled-known conditions. A key point in the design of these rigs is the use of non-contact excitation and measurement systems not to introduce in the system additional mistuning, which may affect the dynamic properties of the bladed disk, and additional damping.

O O O O B I 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 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 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 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 B I 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 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 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 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 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 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 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 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


High Order Asymptotic Preserving Nodal Discontinuous Galerkin IMEX Schemes for the BGK Equation In this paper, we develop high-order asymptotic preserving (AP) schemes for the BGK equation in a hyperbolic scaling, which leads to the macroscopic models such as the Euler and compressible Navier-Stokes equations in the asymptotic limit. Our approaches are based on the so-called micro-macro formulation of the kinetic equation which involves a natural decomposition of the problem to the equilibrium and the non-equilibrium parts. The proposed methods are formulated for the BGK equation with constant or spatially variant Knudsen number. The new ingredients for the proposed methods to achieve high order accuracy are the following: we introduce discontinuous Galerkin (DG) discretization of arbitrary order of accuracy with nodal Lagrangian basis functions in space; we employ a high order globally stiffly accurate implicit-explicit (IMEX) Runge-Kutta (RK) scheme as time discretization. Two versions of the schemes are proposed: Scheme I is a direct formulation based on the micro-macro decomposition of the BGK equation, while Scheme II, motivated by the asymptotic analysis for the continuous problem, utilizes certain properties of the projection operator. Compared with Scheme I, Scheme II not only has better computational efficiency (the computational cost is reduced by half roughly), but also allows the establishment of a formal asymptotic analysis. Specifically, it is demonstrated that when 0 < ε ≪ 1, Scheme II, up to O(ε 2 ), becomes a local DG discretization with an explicit RK method for the macroscopic compressible Navier-Stokes equations, a method in a similar spirit to the ones in [Bassi & Rabey 1997 , Cockburn & Shu 1998 ]. Numerical results are presented for a wide range of Knudsen number to illustrate the effectiveness and high order accuracy of the methods. (local) DG methods, on the other hand, can be considered as DG methods with the discrete spaces represented by Lagrangian nodal basis functions [21] . In the context of solving the BGK equation based on the micro-macro decomposition, nodal DG methods allow convenient implementations of a projection operator that is spatially dependent, as well as many kinetic and macroscopic quantities. In addition, it simplifies the treatment when ε = ε(x) is spatially dependent. For the time discretization, we treat stiff terms implicitly and non-stiff terms explicitly by adopting the high order globally stiffly accurate IMEX RK methods developed in [1, 28] . Our proposed methodology differs from the implicit-explicit strategy in [29, 30] by working with the macroscopic variables U given in (2.3) as well as the microscopic one g = (f −M U )/ε (see (2. 2)), instead of directly working with the probability distribution function f . Because of this, the necessity of performing the moments realignment as in [30] is avoided. Finally, we test the proposed schemes with a collection of smooth and non-smooth examples for a wide range of Knudsen number which can be spatially dependent. Expected high order accuracy and correct asymptotic behavior are validated. Superior performance, when compared with lower order schemes, is observed in terms of accuracy for smooth test cases as well as the solution resolution when there are shock structures.. The rest of the paper is organized as follows. In Section 2, we provide the BGK equation in a hyperbolic scaling and its micro-macro decomposition. In Section 3, high order AP schemes are formulated with a nodal DG spatial discretization and a globally stiffly accurate IMEX temporal discretization. A formal asymptotic analysis is performed for the proposed methods to capture the Euler and Navier-Stokes limits. In Section 4, numerical results are presented. Finally, conclusions are given in Section 5. The work in this paper is a continuation of our research effort in [22, 23] to develop and analyze high order asymptotic preserving schemes for kinetic equations in different scalings. We here propose high order DG-IMEX schemes for the BGK equation in a hyperbolic scaling. Specifically, we employ high order nodal DG spatial discretizations coupled with a high order globally stiffly accurate IMEX scheme in time for the equivalent micro-macro decomposition of the BGK equation. Two versions of the schemes are proposed. While the first one is more straightforward based on the micro-macro decomposition and can be formally shown to be asymptotically equivalent to a widely used RK DG schemes for the Euler equations in the limit of ε → 0; the second version is computationally more efficient, and more importantly it allows a formal asymptotic analysis that shows the equivalence, up to O(ε 2 ), with a local DG scheme to the compressible Navier-Stokes equations when 0 < ε ≪ 1. Extensive numerical examples are presented to demonstrate the effectiveness of the proposed methods. Extension to kinetic equations with more general collisional operators will be explored in the future.

O O B I O B I 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 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 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


GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures Background: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. Results: We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. Conclusions: GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.Despite the rising quality and abundance of genetic data, epidemiologists continue to struggle to explain the known heritability of these complex phenotypes with existing This study introduces GAMETES, a fast and reliable strategy for the generation of complex genetic models with random architectures. Specifically, we focus on the generation of pure, strict n-locus epistatic models (considered to be the most difficult to detect). The benefits of our strategy include (1) speed; deterministic calculation of models makes our approach much faster than an EA approach, (2) randomness; models are generated using a strategy which seeks to maximize the randomness of model architecture, (3) the ability to precisely specify genetic constraints, (4) the ability to generate a large population of models from which to choose, and (5) the potential to combine the GAMETES modeling strategy with any data simulation strategy. An obvious limitation of this approach is the difficulty it has finding models of higher heritability. However, GAMETES is proficient at finding models with heritabilities typically used in evaluating and comparing new bioinformatic strategies. While the probability is low that these types of 'extreme' epistatic interactions occur in biology by chance alone, we instead focus on the fact that they 'can' occur. With that in mind, our focus on pure strict epistasis is intended to promote the development of strategies that can accommodate even the most challenging relationships. In doing so, we make minimal assumptions about the true nature of biological interaction. Notably, the GAMETES strategy may be extended to produce impure epistatic models, as well as nested epistasic models, which are more likely to occur by chance. This extension will be a focus of future work.. The GAMETES software is open source and freely available for download. It offers an intuitive and flexible framework for the simulation of complex genetic models, and the option to generate simulated datasets from these models. This software offers both a graphical user interface, as well as command line accessibility to facilitate the quick generation of a large simulated dataset archive. The GAMETES software along with a detailed users guide is included as Additional files 2 and 3, respectively. In this section, we describe (1) relevant background in genetics and modeling, (2) the specific steps for generating n-locus epistatic models that are both strict and pure and (3) an example simulation study using GAMETES.

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