bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fuzzy CDS-based Scheduling Algorithm for More Than Two Machine Centers
Authors: Hong Tzung-Pei ,   Department of Information Management, I-Shou University Kaohsiung, 84008, Taiwan R.O.C., Wang Tzu-Ting , Wang Shyue-Liang ,   Institute of Information Engineering, I-Shou University Kaohsiung, 84008, Taiwan R.O.C.
Published: April 2016

In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow-shop problem. Flexible flow shops can thus be thought of as generalizations of simple flow shops. In Ref.14), we have demonstrated how fuzzy concepts can easily be used in the Sriskandarajak and Sethi’s algorithm for managing uncertain scheduling on two-machine centers. This paper extends application to fuzzy flexible flow-shops with more than two machine centers. A new fuzzy heuristic flexible flowshop scheduling algorithm is then designed since optimal solutions seem unnecessary for uncertain environments. We first use fuzzy LPT algorithm to allocate jobs into the groups and then use the fuzzy CDS algorithm to deal with sequencing the tasks. The proposed method thus provides a flexible way to more fit real world applications.
A HiLog Journey from a Classical Dictionary to Hypertext Structure (via the Grammar, of course)
Authors: Cubrilo Mirko ,   Faculty of Organization and Informatics, Zagreb University, Pavlinska 2, Varazdin, Croatia, Lovrencic Alen , Malekovic Mirko
Published: April 2016

The long evolution of everyday life brought to our heritage the "mountains" of relatively structured data (dictionaries, phone books, hospital data files, government data files, driving licence data, crime data, etc.)). New information "media", physical (such as CD-ROM or DVD-ROM) as well as logical (organizational), such as hypertext, simply crave for mapping these old-fashioned information sources into new shape. This article shows how to achieve that shape automatically using the paradigm of higher order logic programming and particularly the HiLog language.
Human Versus Robotic Shoulder Motion
Authors: Klopw Nives ,   "Jozzef Stefan" Institute, Department of Automatics, Biocybernetics and Robotics, Jamova 39, 1000 Ljubljana, Slovenia, Lenarcic Jadran
Published: April 2016

In this paper, we studied the maximum inclination angles in the human shoulder girdle and compared them with the motion abilities of the humanoid robotic shoulder proposed in 1,2) We found that the extreme inclination angles of the human shoulder girdle are different when the arm is constantly stretched downward or when the arm executes an inclination co-planar to the girdle's motion. The humanoid robotic shoulder fulfills the range of the maximum inclination angles in the second type of motion but its workspace is obviously too small for the first type of motion. We also measured the working cone of the human arm and found that central axis of the human arm working cone is about 60° inclined in the horizontal plane.
Intelligent Hybrid System: A Reliability-Based Failure Management Application
Authors: Aguilar Jose ,   Universidad de Los Andes. Facultad de Ingenieria. Departamento de Computacion. CEMISID Avenida Tulio Febres Cordero, Merida 5101, Venezuela, Cerrad Mariela , Morillo Katiuska ,   Universidad de Los Andes. Facultad de Ingenieria. Departamento de Sistemas de Control. CEMISID Avenida Tulio Febres Cordero, Merida 5101, Venezuela,   Universidad de Los Andes. Facultad de Ingenieria. Departamento de Sistemas de Control. Postgrado en Ingeniera de Control y Automatizacion. Edif. FACES. Nucleo La Hechicera, 3er. Piso, Merida 5101, Venezuela
Published: April 2016

The integration of different intelligent techniques (such as Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, etc.) into a hybrid architecture allows to overcome their individual limitations. In industrial environments, these intelligent techniques can be combined to reach more effective solutions to complex problems. On the other hand, failure management in processes, equipment or plants, acquires more importance in modern industry every day, in order to minimize unexpected faults and guaranties a greater reliability, safety, disposition and productivity in the industry. In this paper, an intelligent system is designed for failure management based on Reliability Centered Maintenance methodology, Fuzzy Logic and Neural Networks. The system proposes the maintenance tasks according to the historical data of the equipment.
Tracking Control of Variable Structure System Using Variable Boundary Layer
Authors: Lee Heejin ,   Dept. of Control and Instrumentation Engr., Hankyong National University 67 Seokjeong-Dong, Ansung, Kyonggi-Do, 456-749 Korea
Published: April 2016

In this paper, a new scheme is presented for the accurate tracking control of the second-order variable structure systems using the variable boundary layer. Up to now, variable structure controller(VSC) applying the variable boundary layer did not remove chattering from an arbitrary initial state of the system trajectory because VSC has used the fixed sliding surface. But, by using the linear time-varying sliding surfaces, the scheme has the robustness against chattering from all states. The suggested method can be applied to the second-order nonlinear systems with parameter uncertainty and extraneous disturbances, and have better tracking performance than the conventional method.To demonstrate the advantages of the proposed algorithm, it is applied to a two-link manipulator.
Modeling Photon Counting Experiments using Fuzzy Logic Controller
Authors: Nádai László ,   Computer and Automation Research Institute, Hungarian Academy of Science, Kende u. 13-17, Budapest, H-1111 Hungary
Published: April 2016

The concept of point fractal is applied to analyze the time-scale variability of point processes resulting from photon counting experiments. A new algorithm – using a fuzzy logic controller – for estimating the fractal dimension is proposed to eliminate systematic and stochastic errors. It is shown that scale invariance exists in time and clustering will decrease in accordance with the increase of the threshold that corresponds to the inverse sensitivity of the detector. Under the variation of the threshold, it is verified that the maximum values of the homogenous scale invariant interval are the same. In addition, taking the probability scale law on different levels of threshold value, a relation is established between the saturation scale (return period) and the threshold (design experiment variable).
Kinematic Robustness of Manipulating Systems
Authors: Galhano Alexandra M.S.F. ,   Institute of Engineering of the Polytechnique Inst. of Porto, Department of Electrical Engineering, Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto, Portugal, Machado J. A. Tenreiro
Published: April 2016

Present day mechanical manipulators have poor performances when compared with the human arm. In fact, joint-driven manipulators are not efficient due to the high actuator requirements imposed by the transients of the operational tasks. Muscle-actuated arms are superior because the anatomic levers adapt the manipulating exigencies to the driving linear actuators. The kinematic analysis of these systems highlights its main properties and constitutes a step towards the design of new mechanical biological-like. robotic structures adopting linear actuators replacing the standard rotational joint-driving motor systems.
A Method for Recognizing and Separating Trademark Image Outer Frames
Authors: Abe Koji ,   Institute for Image Data Research, University of Northumbria at Newcastle Newcastle upon Tyne NEI 8ST, UK, Kimura Haruhiko , Nagashima Hideo , Kanda Taki ,   Faculty of Engineering, Kanazawa University,   Dept. of Computer Science and Communication Engineering, Kogakuin University,   Dept. of Service Management, Bunri University of Hospitarity
Published: April 2016

We present a method for recognizing the existence of outer frames in binary trademark images and segmenting a trademark that contains an outer frame into the frame and its inner figure, even if both touch. This focuses on the development of content-based image retrieval (CBIR) for trademark registration. Using our proposed method, CBIR systems examine the similarity between images using only main image components. This includes a study for describing image components. We detail criteria of trademark image outer frames and propose an algorithm for recognizing and segmenting outer frames based on the criteria. Experimental results using 1843 registered trademark images and experimental evaluation by 13 participants showed that 98.4% of recognitions agreed with human perception.
Query Expansion Using Conceptual Fuzzy Sets for Search Engines
Authors: Tajima Masanori ,   e-Solutions Company Toshiba Corporation, 1-1-1, Shibaura, Minato-ku, Tokyo, 105-8001 Japan, Kawabata Takayuki , Tomiyama Tomoe , Takagi Tomohiro ,   Department of Computer Science, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki, Kanagawa, 214-8571 Japan
Published: April 2016

We propose a search engine which conceptually matches input keywords and text datas. The conceptual matching is realized by context-dependent keyword expansion using conceptual fuzzy sets. First, we show the necessity and also the problems of applying fuzzy sets to information retrieval. Next, we introduce the usefulness of conceptual fuzzy sets in overcoming those problems, and propose the realization of conceptual fuzzy sets using Hopfield Networks. We also propose the architecture of the search engine which can execute conceptual matching dealing with context-dependent word ambiguity. Finally, we evaluate our proposed method through a simulation of retrieving large number of article datas, and compare the proposed method with the ordinary TF-IDF method. We show that our method can correlate seemingly unrelated input keywords and produce matching text datas, whereas the TF-IDF method cannot.
Acquisition of Knowledge for Gymnastic Bar Action by Active Learning Method
Authors: Sakurai Yoshitaka ,   Department of systems Engineering, University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585 Japan, Honda Nakaji , Nishino Junji
Published: April 2016

In this paper, we aim at engineering realization of human active learning function by top-down approach noticing the macro functions of the brain. Concretely, we propose the Active Learning Method, the method to acquire the control knowledge actively by the method of trial and error. In this method, the input-output information is collected for the control object by the method of trial and error, and the controller is constructed based on the information. The active learning is the learning form in which the information is acquired from the behavior which the learner himself takes. In the Active Learning Method, the output is decided actively and the action result is evaluated, and the data with high evaluation are modeled. This modeled pattern information becomes the behavior policy optimized based on the evaluation. For this modeling, the method called Ink Drop Spread method (IDS) is used. In this system, the object system is modeled functionally from the data by the fuzzy-like processing. It is not the linguistic approach like fuzzy inference but represents the knowledge by the pattern-like approach. By using the model of bar gymnast, the learning simulation is done for the behavior policy, and we examine the validity of this method.
Fuzzy Three-valued Switching Functions Using Choquet Integral
Authors: Takahagi Eiichiro ,   School of Commerse, Senshu University, 2-1-1 Higashi-Mita, Tama-ku, Kawasaki, 214-8580 Japan
Published: April 2016

Fuzzy three-valued switching functions have true, false and unknown inputs and outputs values. The functions are calculated by Choquet integral whose fuzzy measure values are assigned from normal three-valued switching functions. The functions are extension of normal three-valued functions and satisfy the monotonicity with respect to vagueness.
Adaptive Fuzzy Control for a SISO Nonlinear System
Authors: Han Hugang ,   School of Business, Hiroshima Prefectural University, Shobara, Hiroshima 727-0023, Japan, Murakami Shuta ,   Faculty of Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka 804-8550, Japan
Published: April 2016

When using the Lyapunov synthesis approach to construct an adaptive fuzzy control system, one important way is to regard the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled. Concerning the unknownness, generally there are two cases: a completely unknown case, and a partly unknown case. However, most of the schemes presented so far have only focused on the former. Clearly, if an unknown function belongs to the latter, the knowledge available about the function should be utilized as much as possible in the development of the control system. In this paper, our goal is to design an adaptive fuzzy controller for a class of nonlinear systems with uncertainty, which can correspond to the either case. Also, we propose a unique way to deal with the uncertainty, i.e., adopt a switching function with an alterable coefficient, which is tuned by adaptive law based on the tracking error.
A Study on a Foraging Behavior of Interacting Simple Robots
Authors: Sugawara Ken ,   PRESTO, Japan Science and Technology Corporation (JST), Sano Masaki , Watanabe Toshinori ,   Graduate School of Information Systems, University of Electro-Communications, Chofugaoka, Chofu, Tokyo, 182-8585, Japan,   Department of Physics, University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Published: April 2016

Considerable research is currently being conducted in the area of multi-robot systems. The most remarkable characteristic of these types of systems is that the robots are able to work cooperatively to complete a task that a single robot cannot accomplish by itself. This characteristic is essential in the investigation of the effect of the number of robots in a given system. Out of the various possible multi-robot tasks, a foraging task was chosen for these experiments. The robots used in the experiments referenced by this paper had a simple interaction method with a light signal. The robots’ behavior in a one feeding point field was first discussed. This behavior was analyzed by both a robot simulation and a mathematical model. In the next experiment, numerous feeding points, equidistant from the home location, were arranged in the foraging field. The performance of the robots in this arrangement was then discussed. This report highlights the ordered behavior of the robot group, which greatly depends upon the number of robots and the strength of their interaction.
Psychological and Social Effects of Robot-assisted Activity in the Elderly Robot-assisted at Health Service Facilities
Authors: Wada Kazuyoshi ,   Intelligent Systems Institute, AIST, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japan, Shibata Takanori , Saito Tomoko , Tanie Kazuo ,   PRESTO, JST,   Institute of Engineering Mechanics, University of Tsukuba
Published: April 2016

We have been developing cat and seal robots – called "mental commit robots" – to help people psychologically, physiologically, and socially. The actual animal seal robot was developed for use in therapy. We used seal robots to assist the elderly at a health service facility for the aged. To determine psychological and social effects of seal robots in such a setting, we evaluated moods using a face scale, which expresses moods by illustrating facial expressions, and Profile of Mood States, which evaluates moods through a questionnaire. Seal robots used at the facility for three weeks. The moods of elderly people were improved by interaction with them.
Fuzzy Information Granules: a Compact, Transparent and Efficient Representation
Authors: Castellano Giovanna ,   Department of Computer Science, University of Bari, Bari, 71026, Italy, Fanelli Anna Maria , Mencar Corrado
Published: April 2016

This paper presents a method to construct information granules that provide a relevant description of experimental observations and, at the same time, are represented in a compact and semantically sound form. The method works by first granulating data through a fuzzy clustering algorithm, and then representing granules in form of fuzzy sets. Specifically, an optimal Gaussian functional form for the membership functions is derived by solving a constrained optimization problem on the membership values of the partition matrix returned by the clustering algorithm. The granules represented with Gaussian functional forms can be used to build a fuzzy inference system that performs inferences on the working environment. To illustrate the behavior of the proposed method a real-world information granulation problem has been used. Simulation results show that compact and robust fuzzy granules are attained, with the appreciable feature of being represented in a short functional form. In addition to the information granulation problem, a descriptive fuzzy model for a prediction benchmark has been developed to verify how much fuzzy granules identified form data through the proposed method are useful in providing good mapping properties. The obtained results are reported, supported by comparison with other works.
Synergetic Stereo Matching Algorithm for Occlusion and Reversal Position
Authors: Irie Tohru ,   Dept. of Computer Engineering, Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata, Kitakyushu, 804-8550, Japan, Maeda Hiroshi , Ikoma Norikazu
Published: April 2016

We propose a stereo matching algorithm based on a pattern recognition algorithm using synergetics. Matching between left and right images is represented by the differential equations with order parameter and attention parameter. This algorithm gives us a free hand designing the two parameters. This paper presents parameter design and experiment results confirming that the proposed algorithm has high matching precision compared to the conventional template matching and DP matching.
Interactive 3-D Segmentation of the Frontal Lobe in 3.0T IR-FSPGR MR Images Using Fuzzy Rule-Based ACM
Authors: Fujiki Yuji ,   Graduate School of Engineering, Himeji Institute of Technology, 2167, Shosha, Himeji, Hyogo, 671-2201, Japan, Kobashi Syoji , Matsui Mieko , Inoue Noriko , Kondo Katsuya , Hata Yutaka , Sawada Tohru ,   BF Research Institute, Inc.
Published: April 2016

Volumetry and surface rendering of the cerebral lobes are effective for evaluating lobar atrophy. This paper proposes a novel computer-aided system for segmenting the frontal lobe from 3-D human brain IR-FSPGR MR images with the fuzzy rule-based active contour model (ACM). The proposed system uses the criterial curves consisting of the central sulcus and the Sylvian fissure, which are given by users with the proposed graphical user interface. The user-given criterial curves are optimized by the fuzzy rule-based ACM. The fuzzy rule-based ACM can represent physicians’ knowledge with fuzzy if-then rules. With these optimized curves and the anterior and posterior commissures, the frontal lobe is segmented automatically. The experimental results on three healthy volunteers operated by an expert user and three beginner users showed that our system could segment the frontal lobe with high repeatability by any users and to any subjects.
Properties of Interval Truth Values with Certainty Factor
Authors: Emoto Masashi ,   Department of Computer Science, Meiji University, 1-1-1, Higashi-mita, Tama-ku, Kawasaki-shi, Kanagawa, Japan, Mukaidono Masao
Published: April 2016

Until now some truth values in fuzzy logic were suggested such as numerical truth value, interval truth value, fuzzy truth value and etc. Fuzzy truth value is considered as the most generalized truth values in fuzzy logic. Truth values except fuzzy truth value were suggested to clarify the properties of fuzzy truth values. However it is not sufficient to clarify the properties of fuzzy truth values from the properties of other truth values, because fuzzy truth value may not be convex or normal. On the other hand, the other truth values do have to be convex and normal. In this paper, we consider subnormal truth value and propose interval truth value with certainty factor. Then, we clarify the properties of interval truth value with certainty factor.
Analysis of a Method Improving Reinforcement Learning Agents’ Policies
Authors: Kitakoshi Daisuke ,   Muroran Institute of Technology, Mizumoto 27-1, Muroran, 050-8585, Japan, Shioya Hiroyuki , Kurihara Masahito ,   Graduate School of Hokkaido University, Kita 13 Nishi 8, Kita-ku, Sapporo, 060-8628, Japan
Published: April 2016

Reinforcement learning (RL) is a kind of machine learning. It aims to optimize agents’ policies by adapting the agents to an environment according to rewards. In this paper, we propose a method for improving policies by using stochastic knowledge, in which reinforcement learning agents obtain. We use a Bayesian Network (BN), which is a stochastic model, as knowledge of an agent. Its structure is decided by minimum description length criterion using series of an agent’s input-output and rewards as sample data. A BN constructed in our study represents stochastic dependences between input-output and rewards. In our proposed method, policies are improved by supervised learning using the structure of BN (i.e. stochastic knowledge). The proposed improvement mechanism makes RL agents acquire more effective policies. We carry out simulations in the pursuit problem in order to show the effectiveness of our proposed method.
Real Time Adaptive Color Segmentation for Mars Landing Site Identification
Authors: Duong Tuan A. ,   Bio-Inspired Technologies and Systems Group, Avionic Systems and Technology Division, Duong Vu A.
Published: April 2016

Real time identification of planetary landing sites is of significant importance in NASA’s precision landing program. As a lander descends towards a potential landing site, it is important to determine whether or not the potential site is free of debris to allow safe landing. This requires real time processing of images acquired by the lander during the approach, so that appropriate navigational corrections can be made to direct the lander to a safe landing zone. In this paper we discuss an adaptive color segmentation technique that can aid in identifying safe landing terrain, and terrain that may be rock covered, dusty, and unfavorable to land. A new learning architecture that allows real time adaptation in a dynamically changing environment as the lander approaches a landing site is evaluated. Results indicate that a real time adaptive color segmentation approach is sufficient to identify safe landing zones. The paper also discusses the time required for adaptation, a critical parameter during an actual descent. The simulation-based case study reported in this paper is a primary step toward developing a more realistic case for landing site identification.
Reordering Adaptive Directed Acyclic Graphs for Multiclass Support Vector Machines
Authors: Phetkaew Thimaporn ,   Department of Computer Engineering, Chulalongkorn University, Phayathai, Patumwan, Bangkok, 10330, Thailand, Rivepiboon Wanchai , Kijsirikul Boonserm
Published: April 2016

The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the Adaptive Directed Acyclic Graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm-Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, Reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We then compare our performance with previous methods including the ADAG and the Max Wins algorithm. Experimental results demonstrate that our method gives higher accuracy. Moreover it runs faster than Max Wins, especially when the number of classes and/or the number of dimensions are relatively large.
Hybrid Probabilistic Models of Fuzzy and Rough Events
Authors: Intan Rolly ,   Department of Computer Science, Meiji University, Kawasaki-shi, Kanagawa-ken, Japan, Mukaidono Masao , Nguyen Hung T. ,   Petra Christian University, Siwalankerto 121-131, Surabaya, Indonesia 60236,   New Mexico State University, Las Cruces, NM 88003-8001, USA
Published: April 2016

This paper discusses the relationship between probability and fuzziness based on the process of perception. As a generalization of the crisp set, the fuzzy set is used to model fuzzy events as proposed by Zadeh. Similarly, we may consider the rough set to represent a rough event in terms of probability measures. Special attention will be given to conditional probability of fuzzy events as well as the conditional probability of rough events. Several combinations of formulation and properties are examined. In the relation to evidence theory, the probability of a rough event may be considered as a connecting bridge between belief-plausibility measures and the probability measures. Moreover, a generalized fuzzy-rough event is introduced to generalize both fuzzy and rough events.
Intelligent Control of Nonlinear Dynamic Systems Using Immune Fuzzy Fusion
Authors: Kim Dong Hwa ,   Dept. of Instrumentation and Control Eng., Hanbat National University, 16-1 San Duckmyong-Dong Yuseong-Gu, Daejon City Seoul, 305-719 Korea
Published: April 2016

We propose nonlinear control using immune-algorithm-based fuzzy logic. Nonlinear dynamic systems include chemical and biomedical processes and mainsteam temperature control at thermal power plants. PID controllers are conventionally used to operate these systems, but using these requires experience, often based on trial and error to tune them. We use an inverted pendulum control problem to demonstrate the efficiency of the proposed method and define the relationship of state variables using immune fuzzy logic. Simulation results show the effectiveness of the tuning algorithm, but study experiments are needed before actual application.
A Novel Parallel Model for Self-Organizing Map and its Efficient Implementation on a Data-Driven Multiprocessor
Authors: Thawonmas Ruck ,   Department of Computer Science, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan, Iwata Makoto , Fukunaga Satoshi ,   Course of Information Systems Engineering, Kochi University of Technology, Tosayamada-cho, Kami-gun, Kochi, 782-8502, Japan,   FUJISOFT ABC Inc.
Published: April 2016

The self-organizing map (SOM), with its related extensions, is one of the most widely used artificial neural algorithms in unsupervised learning and a wide variety of applications. Dealing with very large data sets, however, the training time on a single processor is too high to be acceptable for time-critical application domains. To cope with this problem, we present a scheme consisting of a novel parallel model and its implementation on a dynamic data-driven multiprocessor. The parallel model ensures that no load imbalance will occur, while the dynamic data-driven multiprocessor yields high scalability. We demonstrate the effectiveness of the scheme by comparing the parallel model with an existing parallel model, and the proposed implementation with an implementation on another multiprocessor.
New Error Diffusion Using Fuzzy Threshold Control
Authors: Suetake Noriaki ,   Yamaguchi University, 1677-1 Yoshida, Yamaguchi, Yamaguchi, 753-8512 Japan, Hashiba Rumiko ,   TOSHIBA Co., 1-12-3 Shibaura, Minato-ku, Tokyo, 105-0023 Japan
Published: April 2016

We propose error diffusion based on threshold control using fuzzy inference. In the proposed method, the same black or white pixels along a periphery that lowers image quality, is decreased without loss of edge enhancement using threshold control. We applied the method to standard digital images and confirmed that the method is superior to the conventional.
Special Issue on Selected Papers from Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM'03)
Authors: Dadios Elmer P. ,   Full Professor - Department of Manufacturing Engineering and Management, De La Salle University - Manila, PhilippinesDirector - School of Engineering, De La Salle University - Canlubang, Philippines
Published: April 2016

I am happy and honored to publish a special issue on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) in the JACIII. The demand for the application of HNICEM is rapidly growing. We have received many papers focused in the fields of Manufacturing, Academe, and Business during the first international conference on HNICEM. This special issue includes 11 papers carefully and evenly selected from these fields. I strongly believed that the topics in this issue encompass technologies that will play a significant role in the development of HNICEM. I am very grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, the Chief editors of JACIII for inviting me as Guest editor in this Journal. I would like to express my warmest thanks and gratitude to the distinguished authors that have contributed to this special issue, for their outstanding contributions and cooperation. I am hoping that the JACIII will continually provide papers regarding progress of new technologies particularly in the fields of HNICEM.
A New Way of Discovery of Belief, Desire and Intention in the BDI Agent-Based Software Modeling
Authors: Jo Chang-Hyun ,   Department of Computer Science, California State University, Fullerton, Fullerton, CA 92834-6870, USA
Published: April 2016

Agent-based programming has been emerged as a next generation programming paradigm. There are many different definitions and usage for agents. In our research, however, an agent is defined as an autonomous, concurrent and intelligent object. Furthermore, our agents are modeled by the belief-desire-intention (BDI) concept. An agent is embodied when it is assigned to its BDI. We call it a BDI agent. A software process defines a set of activities and associated artifacts that lead to the construction of a software system. We have developed a software process based on the BDI agent model that is useful for a systematic development of BDI agent-based software construction. We named our process as the BDI Agent-based Software Process (BDI ASP). This paper presents a new way of modeling technique in our BDI ASP. This work will convince us that the BDI ASP is very sound and practicable in agent software construction. We will provide a few examples as a case study with brief explanations of activities and artifacts in our process.
A Searching and Tracking Framework for Multi-Robot Observation of Multiple Moving Targets
Authors: Liu Zheng ,   Dept. of Electrical & Computer Engineering, Marcelo H. Ang Jr.  , Seah Winston Khoon Guan ,   Dept. of Mechanical Engineering, National University of Singapore,   Institute for Infocomm Research, Singapore
Published: April 2016

The "museum problem" is a typical research topic on multi-robot observation of multiple moving targets. The objective of museum problem is to optimize the distribution of robots, such that the maximal moving targets can be observed. In this paper, we present our memory based searching and artificial potential field based tracking framework for museum problem. For searching, a memory table, either local or shared, can help shorten the searching time for targets. For tracking, our artificial potential field based motion control provides real-time tracking of moving targets with collision avoidance. Qualitative simulations demonstrate the capability of our searching and tracking framework.
Dynamic Color Object Recognition Using Fuzzy Logic
Authors: Reyes Napoleon H. ,   College of Computer Studies 2401 Taft Avenue, De La Salle University, Manila, 1004 Philippines, Dadios Elmer P. ,   Department of Manufacturing Engineering and Management 2401 Taft Avenue, De La Salle University, Manila, 1004 Philippines
Published: April 2016

This paper presents a novel Logit-Logistic Fuzzy Color Constancy (LLFCC) algorithm and its variants for dynamic color object recognition. Contrary to existing color constancy algorithms, the proposed scheme focuses on manipulating a color locus depicting the colors of an object, and not stabilizing the whole image appearance per se. In this paper, a new set of adaptive contrast manipulation operators is introduced and utilized in conjunction with a fuzzy inference system. Moreover, a new perspective in extracting color descriptors of an object from the rg-chromaticity space is presented. Such color descriptors allow for the reduction of the effects of brightness/darkness and at the same time adhere to human perception of colors. The proposed scheme tremendously cuts processing time by simultaneously compensating for the effects of a multitude of factors that plague the scene of traversal, eliminating the need for image pre-processing steps. Experiment results attest to its robustness in scenes with multiple white light sources, spatially varying illumination intensities, varying object position, and presence of highlights.
An Optical Coordinate Measuring Machine for Nanoscale Dimensional Metrology
Authors: Kirkland Eric ,   Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA, Kurfess Thomas R. , Liang Steven Y.
Published: April 2016

Coordinate Measuring Machines (CMMs) are commonly used for geometry verification and replication on solid parts and work pieces on many scales. As the parts predicable continues to grow smaller and smaller, so grows the need to accurately measure the dimensional accuracy of these parts. This is partly because of how the critical part tolerances become as dimensions shrink and geometries become more complex. Research is currently underway at the Georgia Institute of Technology to develop a system for dimensional metrology at the nano-scale. The goal is to develop a versatile, non-contact, three-dimensional system capable of making accurate measurements on the nano level of form attributes of micro electro-mechanically manufactured parts. The Nano-CMM developed consists of a laser probe, a 6-axis positioning table, and a support frame. This paper describes the calibration of probe, processing of measurement signals, detection of solid edges, and software interface for system automation.
Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications
Authors: Motomura Yoichi ,   National Institute of Advanced Industrial Science and Technology, 2-41-6 Aomi, Koto-ku, Tokyo 135-0064, Japan
Published: April 2016

Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.
Robust Fuzzy Clustering Based on Similarity between Data
Authors: Inoue Kohei ,   Faculty of Design, Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka-shi 815-8540, Japan, Urahama Kiichi
Published: April 2016

We present a robust fuzzy clustering method that utilizes a sequential cluster extraction scheme. In contrast to heuristic sequential methods, our algorithm is derived from an optimization problem and is an iterative solution to it. Our method is non-parametric and includes no heuristic parameter, and can deal with asymmetric similarity data. The determination of the number of clusters is simple and is based on a monotonic property of extracted cluster volumes. Our method can extract arbitrarily shaped clusters by extending the measure of distance between data to a shortest path length. The performance of the method is demonstrated for clustering of an image database and the segmentation of images.
A View-Invariant Face Detection Method Based on Local PCA Cells
Authors: Hotta Kazuhiro ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
Published: April 2016

This paper presents a view-invariant face detection method based on local PCA cells. In order to extract the general features of faces at each view and position, Gabor filters and local PCA (Principal Component Analysis) are used. Local PCA cells specialized for each view and position are made by applying the Gaussian to the outputs of local PCA of Gabor features. By applying the Gaussian, only local PCA cells having a view similar to an input give large values. This decreases an adverse influence of the local PCA cells of other views. As a result, only one classifier can treat multi-view faces well by integrating the outputs of local PCA cells. The effectiveness of the proposed method is confirmed by comparison with Support Vector Machine using Gaussian kernel and Radial Basis Function network. It is also confirmed that generalization ability is improved by selecting local PCA cells using the reconstruction error of local PCA.
Stave Extraction for Printed Music Scores Using DP Matching
Authors: Miyao Hidetoshi ,   Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan, Okamoto Masayuki
Published: April 2016

This paper proposes extraction composed of the following four processes to accurately extract the position of a stave, including inclination, break, and flexion: (1) Find stave candidate points on a scanning line when an image is scanned vertically at equally spaced intervals. (2) Use DP matching to link stave candidate points. (3) Use labeling to separate them into a group for each stave. (4) Extract end points of a stave and correct their positions. When this extraction was applied to 104 printed music scores, it accurately extracted 99.52% of stave positions.
Planar Spline Trajectory Following for an Autonomous Helicopter
Authors: Harbick Kale ,   Robotic Embedded Systems Laboratory Center for Robotics and Embedded Systems, Department of Computer Science, University of Southern California, Los Angeles, CA 90089-0781, U.S.A., Montgomery James F. , Sukhatme Gaurav S. ,   Jet Propulsion Laboratory, California Institute of Technology
Published: April 2016

This paper proposes a technique for planar trajectory following for an autonomous aerial robot. A trajectory is modeled as a planar spline. A behavior-based control system which stabilizes the robot and enforces trajectory following, has been implemented and tested on an autonomous helicopter. Results from two flight experiments are presented. The trajectory tracking error is on the order of the size of the robot (1.8m). Given the inherent error in GPS positioning, and environmental disturbances (wind), this is quite reasonable.
A Biological Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot
Authors: Yang Simon X. ,   Advanced Robotics and Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Meng Max Q.-H. , Yuan Gavin X. , Liu Peter X. ,   Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, N.T., Hong Kong,   Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, K1S 5B6, Canada
Published: April 2016

In this paper, a novel biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment. The real-time collision-free robot trajectory is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation derived from Hodgkin and Huxley’s biological membrane equation. The dynamically changing environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject to the nonholonomic kinematic constraint of the mobile robot. The tracking velocities for the mobile robot are generated by a novel neural dynamics based controller, which is based on two shunting equations and the conventional backstepping technique. Unlike the conventional backstepping controllers suffer from sharp jumps, the proposed tracking controller can generate smooth and continuous commands. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.
Intelligent Control of a Miniature Climbing Robot
Authors: Xiao Jizhong ,   Dept. of Electrical Engineering, The City College, City University of New York, NY 10031, U.S.A., Xiao Jun , Xi Ning ,   School of Information Science and Engineering, Northeastern University, Shenyang, China 110004,   Dept. of Electrical and Computer Engineering, Michigan State University, MI 48824, U.S.A.
Published: April 2016

This paper presents an intelligent control system of a miniature climbing robot to achieve precise motion control, minimize power consumption, and produce versatile behaviors. The gravitational effects degrade the motion control performance and are difficult to compensate exactly, especially in the case of the climbing robot. A fuzzy logic based control scheme is developed which is capable of compensating the gravity at different robot configurations and task conditions. In order to reduce power consumption, an optimal control law is developed which minimizes the energy used to vacuum the suction foot. Primitive behaviors are implemented to provide the robot with basic competence. A finite state machine (FSM) is developed to synthesize signals from multiple sensors, keep track of robot motion status, and schedule motion sequences. FSM is an effective computational method in behavior-based control paradigm to implement robot behaviors. Experimental results prove the validity of the proposed methods.
Incorporating Motivation in a Hybrid Robot Architecture
Authors: Stoytchev Alexander ,   Mobile Robot Laboratory, College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332-0280, U.S.A., Arkin Ronald C.
Published: April 2016

This paper describes a hybrid mobile robot architecture that addresses three main challenges for robots living in human-inhabited environments: how to operate in dynamic and unpredictable environment, how to deal with high-level human commands, and how to engage human users. The architecture combines three components: deliberative planning, reactive control, and motivational drives. It has been proven useful for controlling mobile robots in man-made environments. Results are reported for a fax delivery mission in a normal office environment.
Intelligent Scaling Control for Internet-Based Teleoperation
Authors: Liu Peter Xiaoping ,   Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada K1S 5B6, Meng Max Q-H , Gu Jason J. ,   Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong,   Electrical and Computer Engineering, Dalhousie University, Halifax, Nova Scotia, Canada B3J 2X4
Published: April 2016

In this paper we present an intelligent scaling control algorithm, which is based on the hierarchical decomposition and dynamic assignment of tasks between the human operator and the on-board controller of the remote robot, for mobile robot teleoperation over the Internet. This control scheme relies on the real-time estimation of concurrent roundtrip delays in order to assign tasks between the user and the robot optimally. For this purpose, we employ a linear neural network for which most conventional learning algorithms are infeasible since their required computation is usually too intensive to be practical. To get over this dilemma, we introduce a novel learning algorithm based on the maximum entropy principle. Compared to traditional schemes, the computing cost of this algorithm is very low, which makes it possible for the proposed neural network to be implemented on-line in real time. The scaling control scheme with the neural-network-based delay prediction algorithm is successfully implemented and tested on the developed platform for Internet mobile robots.
Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning
Authors: Fung Wai-keung ,   Department of Automation and Computer Aided Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong, Liu Yun-hui
Published: April 2016

The paper addresses feature extraction of sensor data for robot behavior learning using factor analysis. Redundancies in sensor types and quantities are common in sensing competence of robots. The redundancies cause the high dimensionality of the perceptual space. It is impractical to incorporate all available sensor information in decision-making and learning of robots due to the huge memory and computational requirements. This paper proposes a new approach to extract important knowledge from sensor data based on the inter-correlation of sensor data using factor analysis and construct logical perceptual space for robot behavior learning. The logical perceptual space is constructed by hypothetical latent factors extracted using factor analysis. Since the latent factors extracted have fewer dimensions than raw sensor data, using the logical perceptual space in behavior learning would significantly simplify the learning process and architecture. Experiments have been conducted to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning.
Trajectory Planning of Mobile Robots Using DNA Computing
Authors: Kiguchi Kazuo ,   Dept. of Advanced Systems Control Engineering, Saga University, 1 Honjomachi, Saga-shi, Saga 840-8502, Japan, Watanabe Keigo , Fukuda Toshio ,   Center of Cooperative Research in Advanced Science and Technology, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Published: April 2016

DNA computers are attracting increasing attention as next-generation replacements for conventional electronic computers. Computation is realized using the chemical reaction of DNA. This paper presents optimal trajectory planning for mobile robots using DNA computing. The working area of a mobile robot is divided into many sections and the shortest trajectory avoiding obstacles in the work area is calculated by DNA computing. The location of obstacles is known in advance. In DNA computing, Watson-Crick pairing is used to find this trajectory. DNA sequences representing locations of obstacles are removed in this process. The shortest DNA molecule that begins with the start section and terminates with the goal section represents the shortest trajectory avoiding obstacles in the robot’s work area. The proposed algorithm is especially effective with a DNA molecular computer.
Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control
Authors: Sun Dong ,   Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, Su Y. X. , Mills James K. ,   Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario, Canada
Published: April 2016

A position control approach for direct-drive robot manipulators with permanent magnet AC (PMAC) motors is proposed. The conventional vector control architecture has been simplified by specifying the motor stator phase so that the rotating d-axis current is zero. The position control is designed to be an enhanced fuzzy PD controller, by incorporating two nonlinear tracking differentiators into a conventional fuzzy PD controller. The proposed control methodology is easy to implement, and exhibits better control performance than conventional control methods. Experiments conducted on a single-link manipulator directly driven by a PMAC motor demonstrate the validity of the proposed approach.