In this paper, a new design method of data-driven control systems is proposed, this scheme enables us to reduce the computational cost. According to the DD method, some local models are automatically generated by using imput/output data pairs of the control object stored in the plural databases. Especially, it is well known that the DD method works suitably on nonlinear systems. Therefore, even if the nonlinearities are contained in the controlled object, accuracy identification can be performed by the proposed method. Concretely, the nonlinear system to be controlled is divided into plural subsystems, and plural databases are first constructed corresponding to the subsystems. The effectiveness of the proposed control scheme is shown by simulation example.

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PID control has been widely used in industrial processes. PID gains tuning schemes have been proposed in many ways because these gains have great influence on the control performance. Moreover, lots of PID gains tuning schemes depend on system parameters which are estimated by the system identification. As part of this trend, the model-free controller design schemes which can determine PID gains without employing the system identification have atracted attention. These methods can reduce the calculation cost because it use only closed-loop data. As one of them, authors have proposed a design scheme of GMVC(Generalized Minimum Variance Control) based PID controllers(GMV-PID) which directly computes PID gains using the closed-loop data.
In this paper, the GMV-PID controller design is extended to nonlinear systems. Concretely, in order to cope with nonlinear components, the data-driven approach is introduced. The effectiveness of the proposed scheme is numerically evaluated on a simulation example.

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In this paper we present a new method for stability analysis of a class of nonlinear systems. We assume that the nonlinear system can be considered as a linear system with multiple uncertain parameters, of which system matrix is symmetric. Our proposed method enables one to derive complete intervals of an uncertain parameter which stabilize the considered system. The proposed method is based on the generalized stability feeler and the extreme point result for symmetric matrices. The generalized stability feeler is a tool for system matrices with single uncertain parameter. By using the generalized stability feeler, we can derive stability intervals of system matrices with single uncertain parameter. In order to derive stability intervals of system matrices with multiple uncertain parameters, we use not only the generalized stability feeler, but also the extreme point result for symmetric matrices. By using them, a new theorem is derived, which can be used to derive the stability intervals of the considered system.

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This paper proposes a new design method for a multirate control system, in which a continuous-time plant is controlled by using a discrete-time controller, in which the sampling interval of the plant output is restricted to an integer multiple of the hold interval of the control input. In such a multirate system, the intersample response might be deteriorated even if ideal sample response is obtained. In a conventional method, the steady-state intersample response can be improved independently of the sample response. However, this method can be useful only when neither a modeling error nor a disturbance exists. In this study, using an integral compensation, this problem is resolved. In a control system designed using an integral compensation, because the stability margin is degraded because of the integral compensation, a two-degree-of-freedom system is designed in this study. In the two-degree-of-freedom system, the integral compensation is revealed only when there is a modeling error or a disturbance. Therefore, the stability margin is not deteriorated by the integral compensation if neither a modeling error nor a disturbance exists. Finally, numerical examples demonstrate the effectiveness of the proposed method.

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This paper deals with a design problem of parallel feedforward compensator (PFC) via FRIT approach with T-S fuzzy like model data for discrete-time adaptive control systems. In order to obtain a desired PFC which realizes an ASPR augmented controlled system, PFC parameters are adjusted by the fictitious reference iterative tuning (FRIT) method so as to exist a feedback gain such that the output of the augmented closed loop system with the PFC track the output of the given SPR model by optimizing the performance function that consists of input-output data by T-S fuzzy like model.

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Our group has already studied a position control system for flexible link robot arms. The control law of this method is natural extension from the computed torque method for a rigid link robot arm. It is not limited within operating range which can be regarded as a linear system. In addition, it does not require a switching like an input used in a sliding mode control. However, we need to consider the modeling error because this method uses the model based control. In this paper, we propose a new position control of 2DOF flexible link robot arms based on computed torque method. In addition, we theoretically investigate the stability of the proposed method. Moreover, we propose a simple adaptive identification method in order to decrease the modeling error. The validity of the proposed scheme is illustrated by the theoretical analysis and experimental results.

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The well composition dependence of threshold current density (J_th) for a GRIN SCH AlGaInAs/InP 1.5% compressive strained single quantum well (SQW) laser operating at 1.55μm has been studied. High electron confinement energy of 0.25eV was obtained at a lower well composition of 0.09 with 7.1nm thick well. The threshold current density J_th has been calculated as a function of temperature. A minimum of J_th of 121Acm^-2 was obtained for the devices with Al_0.09Ga_0.17In_0.74As well, which is the lowest value ever reported at this wavelength. The characteristic temperature (T₀) has been estimated to be 112 K from the J_th as a function of temperature.

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This paper discusses the primary signal detection technique for sharing the spectrum of an ultra-wideband radio system with other radio systems. A detect and avoid technique is required to not affect the interference frequency band allowed under the regulations of some countries. In many interference avoidance techniques, it is necessary to detect the emission of the primary signals and their occupied bands. In this paper, the received primary signal is correlated with the sub-band pulses, averaged and compared with the threshold value to detect the emission of the primary signal. To obtain better detection performance, the sub-band pulse width is derived. The interfering frequency band is also estimated from the ratio of correlation values that are obtained from two consecutive sub-band pulses. The detection error rate, false alarm rate and root mean square error of frequency are evaluated to show the effectiveness of the proposed primary signal detection technique.

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In order to deal with the modelling error between the system to be controlled and its T-S fuzzy model, this paper proposes a discrete-time adaptive controller, in which unavailable functions involved in the system are approximated by fuzzy approximators. The proposed controller consists of two parts: one is obtained by solving certain LMIs (fixed part) and the other is acquired using fuzzy approximator where related parameters are tuned by adaptive law (variable part). The proposed controller can guarantee the closed-loop control system uniformly asymptotically stable. Also, computer simulations are provided to illustrate the effectiveness of the proposed controller.

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The authors have developed a technology to produce a 3D surface model by detecting the 3D edges on the stereo-images of a digital camera. Then we arrived to register the 3D data obtained from the stereo-images and the 3D edge data detected on the 3D point-cloud of a Terrestrial Laser Scanner (TLS), and thus succeeded to develop the new technology to fuse the 3D data of the Camera and TLS. The basic idea is to take stereo-pictures by a digital camera around the areas where the scanner cannot, because of the occlusion. The camera, with the digital photogrammetry, can acquire the data of complicates and hidden areas instantly, thus shutting out the possibility of noises in a blink. The data of the camera are then integrated into the data of scanner to produce automatically the model of great perfection. In this paper, the authors show (1) how to detect the 3D edges on the photo images and to detect from the scanner's point cloud, (2) how to register the data of both 3D edges to produce the unified model, (3) how to assess the accuracy and the speed of analyzing process, which turned out to be quite satisfactory.

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In this paper, an equivalency of the SSCF (Square Sum of Correlation Function) adaptive algorithm to the noise free LMS (Least Mean Square) adaptive algorithm is considered. It is known that the SSCF adaptive algorithm estimating the inverse transfer function of the unknown system is robust for the sound noise added at the input. It is shown that the SSCF adaptive algorithm is equivalent to the noise free LMS adaptive algorithm if the input signal is white and the number of the square sum of correlation function satisfies the certain condition. The numerical verifications of the theoretical consideration are also presented.

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At signalized intersections, a driver is forced to decide whether to stop or pass when the signal turns yellow from green. It has been pointed out that this dilemma sometimes leads to accidents at intersections. This paper proposes two methods based on vehicle behavior for predicting whether a driver approaching a signalized intersection decides to stop or pass through the intersection when the signal turns yellow for the purpose to support the drivers' decision-making to prevent accidents at and around intersections. The first method predicts whether a driver decides to stop when the signal turns yellow, based on the idea that whether a driver decides to stop should depend on the situation and the vehicle behavior at the moment the signal turns yellow. The second method estimates whether a driver has decided to stop, in other words, whether the driver has the intent to stop, based on the idea that the driver's intent will appear in the vehicle behavior when the driver has decided to stop at the intersection; this method can detect driver's overlook or neglect of traffic signals on the way to the intersection. The effectiveness of these two methods is demonstrated with experiments using real-world data.

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This paper proposes an optimal power distribution method on decentralized energy network with battery degradation control. Because the battery degradation is unavoidable phenomenon for the battery utilization, we solve the optimal power distribution problem by MIP formulation to keep the battery degradation minimum.

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Flocking algorithms for a multi-agent system are distributed algorithms that only have simple rules for each agent but generate complex formational movement. These algorithms are known as swarm intelligence and are robust and disaster tolerant for most cases. We consider that flocking algorithms that have these characteristics are the way to generate homeostasis in a system. We expect that by making use of this algorithm the system can tune its self parameters and thus maintain a high performance. First, to apply a flocking algorithm to a system, we extended the flocking algorithm to form an arbitrary lattice for further flexibility. We then applied extended flocking algorithm to position tracking camera system as an example.

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Development of high quality digital audio watermarking with robustness to data compression and watermark attacks such as clopping is an important research topic. Recently, an effective watermarking method using vector quantization index modulation (VQIM) was proposed to improve the watermark performance. In this paper, a new auditory band MDCT based VQIM-type watermarking using audio fingerprint (AFP) is proposed to advance the performance. The parameter values of VQIM are essential for audio quality and robustness that are determined as individual codebook parameters. The parameters are estimated using AFP in watermark detection process. In the simulations, effectiveness of the method is examined to show robustness to watermark attacks. The audio quality is evaluated based on PEAQ.

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Efficient interactive foreground extraction from color images is of great practical importance in computer vision. In recent yaers, an approach, based on optimization using graph-cuts has been widely used. However, an interactive foreground extraction approach, which is intuitive and efficient for users, is required for practical applications. In this paper, we propose a novel interactive foreground extraction method based on color saliency. In our method, the user provides only some foreground pixels as the initial reference. To achieve extraction of multi target objects, likelihoods of the foreground and background are defined by a gaussian mixture model and a color saliency map based on the provided reference pixels. These likelihoods are added to the cost of the proposed graph. Finally, image segmentation is performed by optimizing the proposed graph using the graph-cut algorithm.

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It is known that many actual optimization problems can be formulated as combinatorial optimization problems. Meta-heuristics, which is a category of approximation algorithm as an effective method in complex and large scale combinatorial optimization problems, attracts expectation in recent years. This study focuses on Proximate Optimality Principle (POP). POP is the principle that good solutions posses some similar structure and experientially known that it holds for many combinatorial optimization problems. In this study, POP is quantitatively evaluated from the view points of parts and distance, and can be applied to search of optimization method. The proposed combinatorial optimization method based on quantitatively evaluated POP has higher optimality and lower computational complexity than conventional neighborhood search methods.

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Recent studies on evolutionary negotiation systems generally focus on bilateral negotiations (i.e., two parties including a buyer and a seller). Multilateral negotiations, where many buyers and sellers participate in the negotiations are usually applied to auctions, for example, Continuous Double Auction (CDA). We find that the negotiation process of auctions like CDA is different from the pure multilateral negotiation, because in CDA, the offers and counter-offers of the buyers and sellers are open to all. As a result, the privacy of the negotiators is lost. Whereas, in this study, a multilateral negotiation system is proposed, where the negotiations are done pair wise between many buyers and sellers, therefore, the privacy of negotiators is reserved strictly. In CDA, competition between buyers and sellers is hard to realize, because the negotiation is done in public, therefore, the buyers and sellers can follow what the other buyers and sellers proposed in the previous rounds. In this study, competitive attitude of agents is realized in the negotiation, which produced the competitive results also. The advantage of this system is that when compared to a bilateral negotiation, the buyer and seller can choose a potential partner from many participants. To validate the proposed approach, the results are compared with the conventional negotiation systems.

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In this paper, we propose a so-called n-state ant colony algorithm for efficiently solving the crossbar switching problem. In the proposed algorithm, n kinds of pheromone and n kinds of heuristic information are introduced to reinforce the search ability. The conception of the n-state ant colony algorithm provides a novel searching mechanism. In order to evaluate the n-state ant colony algorithm for solving the crossbar switching problem, a large number of simulations are performed, and some other algorithms are used for comparison. The simulation results show that the proposed n-state ant colony algorithm performs remarkably well and outperforms its competitors.

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In this paper, we propose a robust control system based on a cerebellar perceptron improved model. Using function of cerebellum, human can unconsciously and properly control the movement of the body to realize certain purposes. It is considered that such an ability of the cerebellum is due to the stored memory of movement skill. In other words, human can behave smoothly due to recall the memory, thus it can do things better. So, we can say the cerebellum is a superior control system. We consider use of cerebellar improved model without conventional look up table, and apply it to a control system. Here the proposed system is called “Cerebellar Perceptron-based Robust Control System (CPRCS)”. Through the computer simulation for controlling an inverted pendulum system, we show the effectiveness of the proposed method.

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TCP, a current de facto standard transport-layer protocol of the Internet, cannot fully utilize the available bandwidth. Fairness between TCP flows is another important measure of TCP performance. We proposed a method for predicting the optimal size of the congestion window to avoid network congestion by using a machine learning approach. In this paper, based on the machine learning approach, we further improve the congestion algorithm with respect to utilization of the available bandwidth and fairness between TCP flows. The improvement includes bringing a size of the congestion windows closer to the optimum value, realizing fairness against congestion algorithms that aggressively use bandwidth, and adapting to the network where the available bandwidth abruptly changes. The proposed method is evaluated with respect to utilization of bandwidth and fairness between TCP flows including flows aggressively using bandwidth by simulation using NS-2.

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In this study, we are aimed to the Route Search which satisfied the preferences of individual drivers. Preferences here are those primarily involved in the Route Search, for example there are various elements of preference, such as “a lot of straight roads”, “a way with wide roads”. The preference is fuzzy due to human subjectivity and intuition. When considering the multiple elements of preference, it is difficult to capture in the Route Search for the fuzziness and the interaction between the elements. Therefore, the authors proposed a method to reflect preferences to the Route Search cost by the Fuzzy-AHP. From the experimental results, it can be seen that the preferences are integrated into the route search, and be expressed more flexibility. And from the discussion of the processing time, it can be also seen the merit than the method which after multiple routes search then the driver to select, so we think the proposed method is effective.

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The effect of an alerting odorant on the secretion of salivary cortisol during sleep and after awakening was investigated. Salivary cortisol was assessed because this glucocorticoid represents activation of the hypothalamus-pituitary-adrenal (HPA) system, which is a dominant stress reaction pathway in the body. Subjects were exposed to an alerting odorant, Thesaron^®, during a 6h sleep period but not after awakening. Surprisingly, cortisol secretion after awakening was found to be suppressed, as well as during sleep. Exposure of this alerting aroma during sleep may have an impact on cortisol secretion after awakening by suppressing HPA activity after awakening.

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