Recently, a flexible control is required to the control such as robots. In the impedance control proposed so far, the resolution encoder for position detection, the motor current sensor, and torque sensor for power detection to control a motor are required. Therefore the control system causes making to high price. Then, we proposed the simple impedance control system which does not use any expensive sensors of these, and showed the validity.

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Characteristic of a new path control method with vector decomposition and its application is presented in this paper. A motion equation in the orthogonal coordinates is decomposed into gain component and phase component in the rotational coordinates. An effective path control is realized by controlling properly in both components.

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We have proposed a new schedule revision policy under a reactive scheduling strategy, which considers timing of schedule revision based on the concept of a control limit policy. Under the proposed policy, schedule revision is carried out based on cumulative job/task delay which can be a measure to determine suitable timing of schedule revision. This paper extends our previous work to single-machine dynamic scheduling with sequence-dependent setup. Computational experiments demonstrate the effectiveness of our delay-directed schedule revision policy by comparing it with event-driven rescheduling policy.

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Recently, extensive researches on scheduling have been reported from the theoretical as well as the practical view points. Here, as for the optimality criteria of schedules, regular ones have been considered in most of the reserches. There are, however, many applications in which non-regular objective functions are appropriate. This paper deals with identical parallel machine scheduling problems with a non-regular objective function of the minimax type based on the earliness and the tardiness penalties. First, a hybrid method adopting the meta-heuristics and the list scheduling techniques is reformulated. Then, through several computational experiments, the effectiveness and the potential of the hybrid method are investigated.

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This study deals with robust scheduling approach, in which additional dummy jobs are inserted into the schedule in order to absorb impacts of machine breakdowns,urgent jobs and other uncertain events. In this paper, effects of those events are previously measured by the deviations of job starting and completion times on the actual schedule from those of the predictive schedule. We consider a suitable amount of dummy jobs to be inserted in order to minimize the deviation. Through computational experiments, the effectiveness of dummy jobs insertion is demonstrated by applying our method to single machine scheduling problems with due date and unexpected delays of job processing due to machine breakdowns.

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A simulation-based production scheduling method including functions for interruptions of large lots' jobs and insertions of small lots' jobs is proposed, aiming at the elimination of tardy jobs. The proposed method consists of two steps' operations. First operation is that an active schedule is generated by Genetic Algorithms without the interruptions and the insertion, second is that the interruptions of jobs and the insertion are executed by using parameters. In this paper, the performance of the proposed method is evaluated by comparing it with the different methods on the flow shop model.

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In this paper, we deal with a class of scheduling problems in manufacturing systems of the flexible flow shop type, and consider a situation in which the system includes a type of delays due to disruption such as machine breakdowns. To cope with this situation, we compose a reactive scheduling approach in which a schedule revision method for acquiring a feasible and efficient schedule in real time is applied based on the multi-stage decision-making model. We demonstrate the effectiveness and the potential of the proposed method through some computational experiments.

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In the real world, the supply chain is too complex to analyze mathematically. Therefore, the concurrent and autonomous simulator by object-oriented technology is a reasonable solution for the system analysis and the decision making. We propose to embed policy and capacity in participants of the SCM simulator. For the first step, we have developed prototype system for the case that the buyer-seller relation is fixed.

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This paper describes e-Marketplace that is an area of SCM simulator where players trade autonomously. The requirements for e-Market place are as follows: (1) each player can join the place independently, (2) each player trades concurrently, and (3) the status of each transaction can be monitored and recorded for analysis. To satisfy the requirements, this paper presents (a) agent-type interface, (b) negotiation-type trading protocol, and (c) data load sharing.

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In many production factories, a production for make-to-order is adopted because of various customer's needs and short lifetime of products. Moreover, jobs for make-to-order are generally given to be short due-date so that lead time of these jobs needs be reduced to conform to the due-date. In order to reduce lead-time of orders, some factories have introduced a function which makes jobs for estimated production to be assigned jobs which are ordered with short due-date. In this paper, a production system including the function is proposed and the performance of the functions is evaluated with regard to reduction of lead-time of receipt order.

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A production system including a function to reassign in-process products to receipt order is developed in order to supply a customer with products with short lead time in previous paper. Then, effect of the reassignment on reduction of lead time of receipt order is investigated by utilizing the developed system. One of the problems that scheduling personnel have to resolve is that they show precise due-date of order to customer. However, it is difficult to estimate appropriate due-date of receipt order, because the exact number of orders received in the future is impossible to be predicted and schedule is continuously changed by addition of new receipt orders. In this paper, a procedure to decide due-date of receipt order with scheduling and reassigning is proposed. Then, the performance of the proposed procedure is evaluated on simple shop model.

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One of the major research motivations in social science is the maximisation of the total public welfare in the society. Interactions amongst decision makers with rational behaviour in the society result in the social equilibrium with robustness. In this paper we try to take a metaphor of the social science aiming at the methodology to develop intelligent artificial systems with adaptive behaviour.

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Support Vector Machines (SVM) are a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare the performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperform other classification methods. Furthermore, the forecasting performance can be improved by integrating SVM with other classification methods.

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Nurse Scheduling Problem (NSP) is a kind of the optimal combination problem. The creation by hand is difficult and the needs for automation are high. Cooperative GA (CGA) is proposed as a solution method effective until now. However, various children are hard to be generated in order that CGA may cross by one parent pair. In this paper, we propose improvement in search efficiency method of CGA. The proposal method generates various children, by crossing by two or more parent pairs, and aims at improvement in search efficiency.

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We propose a solution algorithm for a Nurse scheduling Problem (NSP) using Genetic Algorithms (GA). The primary difference between our method and other existing GA-based algorithms is that our method reduces infeasible solutions by coding and decoding algorithms. Conducting an experiment on the computer, we evaluate this solution algorithm.

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We consider a class of scheduling problems to minimize total tardiness on uniform parallel machines with human resource constraints, in which each machine requires an operator to process a job. We conduct a branch and bound algorithm for this problem and examine the efficiency of it by numerical examples.

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In this paper we consider the static elevator operation problem with a destination hall call registration system , i.e., the elevator operation problem under the assumption that all information of the passengers is given apriori. We construct three types of algorithms based on Genetic Algorithms for this problem, and compare by numerical examples the obtained operations with selective collective operation and dynamically optimized operation.

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A portfolio has been calculated a rate of expected earnings and a risk from change of a stock price. However, only performed past analyses and no consideration of corporate performances which must be factors of stock price change are a big problem for determination of a future equity-investment. This paper describes a new decision support system helping actual investments. First, equity-investment brand selection is performed using SOM (Self-Organizing Maps). Second, an asset allocation to nonrisk / risk assets is optimized using the management indexes of the selected brands and fuzzy reasonings. Third, we apply this technology to real stock price data and show the better results than those by TOPIX. Finally, we discuss the robustness of the proposed system to the steep fall in stock prices such as IT Bubble in 2000.

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This paper examines the effect of mating restriction on the search ability of EMO algorithms. First we propose a simple but flexible mating restriction scheme where a pair of similar (or dissimilar) individuals is selected as parents. Next we examine the effect of mating restriction on the search ability of EMO algorithms through computational experiments on small and large test problems using the SPEA and the NSGA-II. Empirical results reported in this paper suggest that the proposed mating restriction scheme can improve the performance of EMO algorithms for many test problems while its effect is problem-dependent and algorithm-dependent.

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This paper is concerned with a stochastic modeling of order-disorder transitions. Using the stochastic model proposed here, the effect of thermal fluctuation and disturbance such as impurities in substances on order-disorder transitions is analyzed through simulation experiments in one and two dimensional spatial regions.

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A nonlinear index in harmonic analysis is proposed. The index is defined as the ratio of the amplitude of the output fundamental harmonics to that of all harmonics when a sinusoidal input is fed to a nonlinear element. It could serve as a back-up tool to guess the precision of describing function approach. As an illustrative example, correspondence is examined between accuracy and the index in the analysis of relay feedback systems by describing function method.

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In this paper, we present a minimum cross entropy spectrum estimation method (MCEM) with the constraints of uncertain autocorrelations. Based on the MCE principle using a prior spectrum for speech data, we show the results of applying to the so-called Kan-Non system. MCEM has been proposed by Shore and Johnson, that is an extension of the maximum entropy spectral estimation method (MEM) by Burg. Subsequently the original MCEM has been extended to more practical and general directions. Here, we derive the computational algorithms for the continuous spectral estimation by MCEM with the constraints of uncertain autocorrelations. Further, we show the results of the numerical simulation of the proposed method.

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Recently, neurophysiology has made rapid progress and many studies on mechanism of our brains are being done. Some researches say that the emergence of bifurcation phenomena may elicit our intelligence in the brains. In this regard, it may give some insight into researches of our brains to realize bifurcation phenomena in artificial neural networks. Bifurcation phenomena occur when the qualitative properties of equilibrium points or limit cycles in the nonlinear systems change. In this paper, we propose a method of realizing bifurcation phenomena in neural networks by determining values of network parameters which satisfy bifurcation conditions.

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We set up a signal-driven scheme of associative neural networks with the coupling constants corresponding to certain information, and investigate the stimulus-response behaviors under the deterministic dynamics of chaotic neurons, comparing with the conventional case of Hopfield network with stochastic noise. It is shown that the chaotic neural network can enhance weak subthreshold signals and have higher coherence abilities between stimulus and response than those attained by the conventional stochastic model.

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In this paper, we propose a detection of confusion for blood samples based on SOM(Self-Organizing Maps). We apply the differentials of time-series CBC(Complete Blood Count) as blood test data, and it is assumed that a confusion is occurred between subjects. The SOM of our method classifies input data into two categories, namely confused data and non-confused ones. Experimental results show that our method achieves the high accuracy of detection especially when the input data, not to be employed during the learning, are applied.

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In recent years, products in each industry have shifted to knowledge-insentive from resource-insentive. In this research, we construct an artificial market based on the model of “ Economics of Complex Systems ” , and incorporate “ law of increasing returns ” in this market. We analyze various phenomena produced in this market.

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This paper introduces the design of a gaming-simulation in which several software agents try to find tactical rules with playing the game that aims at the second place. The agent has two neural network models for predicting behavior of other agents, including itself, and for achieving the target, respectively. In addition, the agent has a classifier model for tactical decision-making. This three-model structure enables us to monitor behavior of agents easily.

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A neural network is used as a function approximator of an action value function for reinforcement learning , in order to cope with a large number of discrete states. The learning of lambda return by the proposed network is based on a backward view of Sarsa(λ), which enables an on-line learning. The proposed method is applied to acquire heuristic strategy of a board game, which is known as Dots-and-Boxes. Computer experiments are executed for the learning by training matches competing with a mini-max method of the search depth 1.

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In this paper, an application of an autonomous decentralized model to parallel machine scheduling problems is studied.First, we formalize two kinds of models -- a physical model and an information model -- are introduced, where the physical model is used for simulating the process of manufacturing activities,while the information model is used for a decision making on scheduling. Then, four types of the implementations of the imformation model are reformulated for parallel machine scheduling problems and, to each model, a genetic solution is designed. Finally, through some computational examples, the effectiveness and the potential of the approach is investigated.

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Various types of genetic algorithms have been proposed in literature. One of the active topics is on the distribution of genetic algolithms. The distributed genetic algolithms generally fall into two categories: Coarse-grained genetic algorithms and fine-grained genetic algorithms. In this paper, we propose a hybrid method of the two distributed genetic algolithms. In computer simulations on function optimization problems, we examine the performance of the proposed method. Specifically, we examine the effect of the position of elites, the method of selection and the topology of subpopulations.

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We propose an automatic rule acquisition method for obtaining the optimal sized classifier systems by genetic algorithm considering the coexistence of heterogeneous populations. The genetic algorithm used in this method, has a new genetic operation called ``the movement of individual among heterogeneous populations" to generate suitable sized classifier systems automatically. Experimental results are also shown in order to demonstrate the effectiveness of the proposed method.

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This paper describes an application to support for designing LEGO robot arms in the 2-dimensional horizontal plane by using genetic algorithms. A LEGO robot arm consists of blocks, gears and motors, and the torque of each motor can be controlled by the RCX. The action rule and the structure of the LEGO robot arm are encoded as chromosomes of genetic algorithms, respectively. The fitness of an individual is calculated based on the trajectory of the hand of the generated robot through computer simulation. As a result of computer experiments, an individual which drew a target figure almost exactly was obtained. Then, we created a real LEGO robot arm to check the behavior of the best individual, and investigated the validity of the behavior observed in the simulation.

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This paper considers the stochastic modeling and the analysis of the combustion processes under gravitational and micro-gravitational fields by the stochastic reaction diffusion equation. The effect of disturbance on the burning processes under two situations above are studied by numerical simulations.

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This paper describes a modeling of a smart flexible beam patched four piezoelectric materials due to detect the failure, for example, the damage of piezoelectric materials and the breaking wire. Model of the smart flexible beam in case of the failure is expressed by a linear parameter varying system. The output matrix of the object system is affected by damage of piezoelectric materials and breaking wire. Subspace state space identification is adopted in order to detect the failure. Numerical simulation indicates that the failure detection is possible by using proposed method.

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Recently, importance of constructing the simulation models of the human organs or the biological systems is increasing. As the biological systems have hierarchical structures and involve many phenomena, simulation systems should provide the function that realize such features. In this paper, we propose a simultion system for constructing biological simulation models based on object oriented systemdesign.

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In this decade, many medical simulators based on Virtual Reality have been developed. However, most simulators deal with only a single organ mostly because of computational limitation. Accurate deformation and haptic display in the simulation of open surgery and palpation training requires to model interaction caused by collisions of elastic objects. This paper presents a method to produce accurate deformation based on physical properties of related objects. Subjective evaluation using prototype rectum palpation simulator clears that the proposed method can express difference of stiffness of an elastic object hidden another elastic object perceivably for haptic sensation of human-being.

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This paper deals with a method of three dimensional reconstruction of coronary arteries by mapping CAGs on myocardial nuclear image.The bi-plane CAG images are mapped onto LV surface modelwhich is pre-determined on nuclear 3D image.By maximizing a matching degree between two mapped CAG images,registration between CAGs and nuclear myocardial image is performed automatically. By taking only true images from the mapped CAG images, we can obtain 3D reconstructed coronary image. This method has the great advantage thatit's not necessary to identify the correspondence of feature points.The obtained images give us clear understanding of the relation between anatomy of coronary artery and function of the myocardium.

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We investigated a registration method between preoperative CT image and intraoperative fluoroscopic images during stent graft placement. In this method, DRR(Digitally Reconstructed Radiograph) are generated by ray-casting of CTA after extracting an aorta region. Subsequently we calculate matching measures between DRR and fluoroscopic images iteratively by changing imaging parameters. The most similar DRR to matching measures is selected. In the clinical application, fluoroscopic image sequence was successfully registered with abdominal CTA.

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In connection with an aging society or stress society, those who hold the trouble about sleep are increasing in number remarkably.Therefore, the apparatus which supports the healthy management at the time of sleep simple by being home is called for. Then, in order that we might measure breathing under sleep, we developed the capacity type sensor and examined the way method for extracting a respiratory ingredient from a body surface side change ingredient.

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Sleep is absolutely essential for a healthy living. Many studies have been devoted to clarify the mechanism of sleep. Especially, the relations between good sleep and temperature in the bed have been a focus of attention. The purpose of this research was to control the temperature in the bed for improvement in the quality of sleep. We warmed up the inside of bed just before falling asleep. In the result, we succeeded in promote the falling asleep and increase SWS time.

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Blood glucose control systemds using a state-predictive controller have been developed to control patient's blood glucose level properly after surgical operations. However, the performance of them on the overshoot and the settling time was not enough in animal experiments. This might be because the time variability of response of blood glucose to insulin infusion did not appropriately considered. In order to cope with it, an auto-tuning function using an online developed system is evaluated by simulations and animal experiments.

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We have been developing a blood pressure control system to prevent a high blood glucose level after surgery.However, the performance of the system on the overshoot and the settling time were not enough in animal experiments.This might be because the model of the response of blood glucoseto insulin infusion cannot represent the characteristics of the real response appropriately.In this paper, we study the characteristics of the response and propose a new model.Furthermore, we confirm the effectiveness of the blood glucose control system using the proposed model by animal experiments.

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In ambulatory surgery, anesthetic drug must be administerd at an appropriate rate to prevent side effects after discharge from hospital. In this study, we have been developing a sedation control system using a model predictive controller. Propofol is used as the intravenous anesthetic drug, and Bispectral Index (BIS) is used as the index of sedation in our system. In this paper, we improve the pharmacodynamic model used for estimation of the effect-site concentration of propofol. The effectiveness of the modified system is evaluated by clinical application.

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Under general anesthesia during surgical operations, three elements of anesthesia, i.e. sedation, analgesia, and muscle relaxant of patients are needed to keep at the appropriate levels respectively. In this paper, control of muscle relaxant under total intravenous anesthesia, which all elements of anesthesia are maintained by intravenous anesthetic drugs, is studied, and a muscle relaxant control system using a state-predictive controller has been developed. Vecuronium is used as a muscle relaxant and twitch response is used as an available index of muscle relaxation. In order to cope with individual differences, our system has an on-line identification function. The performance of our system is evaluated by simulations.

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Semi-active vibration control for rolling stocks attracts attentions as an effective method of improving riding quality. We apply MRF (magneto-rheological fluid) damper, whose yield strength can be controlled by applied magnetic field, to semi-active lateral suspension system for rolling stocks. In experiments, we measure basic characteristics of the MRF damper, and ascertain the improvement of riding quality level by using the bogie stability test equipment. The results show that the MRF semi-active suspension system can achieve much the same performance level as the conventional semi-active systems, while initial and maintenance costs are reduced.

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In this study, we consider the modelling through whole life cycle about cost and environmental burden, which has been discussed in each stage of life cycle individually. Furthermore, we propose evaluation and prediction system for the recycle design incorporating modeling by group method of data handling. In proposal system, based on that users store necessary information to data base, it evaluates the recycle design by computer simulation.

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This study considers whether the psychological impression for annoyance of noise during a mental task can be reduced by use of masking effect. More specifically, the authors first discussed how the psychological impression for annoyance of noise and performance indicated by factor such as the percentage of correct answers and reaction time would change under the influence of meaningless noise or interested/uninterested meaningful noise. Next, an investigation was made into how the above items would change when meaningful noise was masked with meaningless noise.

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The dietitians are needed hard work by considering large number of patients, menu, variety of amount of food intake. Therefore, there are some demands such as automatic measuring for amount of food intake.

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A review on several kinds of time-frequency analyses including short-time Fourier transform and wavelet transform is given. Neural networks based on wavelets called wavelet networks or fuzzy wavenets are explained and an application of neural networks to a pre-processing design for multiwavelet filters is given. As a future problem, learning and adapting under nonstationary environment are discussed.

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When the environment is dynamically changed for agents, knowledge acquired from an environment might be useless in the future environments. Therefore, agents should not only acquire new knowledge but also modify or delete old knowledge. However, this modification and deletion are not always efficient in learning. Because the knowledge once acquired in the past can be useful again in the future when the same environment reappears. To learn efficiently in this situation, agents should have memory to store old knowledge. In this paper, we propose an agent architecture that consists of four modules: resource allocating network (RAN), long-term memory (LTM), association buffer (A-Buffer), and environmental change detector (ECD). To evaluate the adaptability in a class of dynamic environments, we apply this model to a simple problem that some target functions to be approximated are changed in turn.

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