Accurate estimation of urban travel times is a key to a successful implementation of intelligent traffic and travel information systems. Traditional survey-based travel time estimations is both expensive and inefficient and in practice is limited to off-line traffic analysis. With the development and installation of Traffic Control Systems collecting real-time traffic measurements, it has become possible to carry out travel time estimations in real-time. Most of the systems, due to economical reasons, utilise single-loop detectors. It is known, that single loop detectors are incapable of accurate spot speeds estimation due to their physical limitations. Therefore, much effort has been put into investigation and development of methodologies of travel time estimation that rely only on occupancy measurements. Unfortunately, most of the methods have been developed for highway traffic and strongly rely on some assumption, which make them inapplicable in urban traffic situation. In this paper, we develop a novel methodology, which is free from the disadvantages of treeway traffic estimation methods. The method has been shown to be successful in the application to urban links travel time estimation problem.

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This paper describes how to characterize convergence properties of the synchronization system which consists of two identical nonlinear dynamical systems linked by a common noisy input only. We consider two types of input: the harmonic and random (HR) forcing and the narrow-band random (NR) forcing. Statistical linearization approach enables us to characterize the convergence properties while Liapunov exponents, which is a well-known identifier to provide a necessary condition of occurrence of the synchronization, fail to characterize them. The result shows that it is possible to detect the condition of slow convergence as multi-valued solutions of moment equations of the target system.

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Various system noises exist in machine systems. Generally, cutting systems receive a bad influence from noise. This study describes the cutting system with system noise. We superpose Gaussian noises on the cutting speed parameter in Grabec's orthogonal cutting model. In present study, we consider dynamical aspect of the solution process using the method of pullback. In previous study, it was confirmed that the vibration displacement of workpiece decreases under the influence of a noise which is added to cutting speed. In this paper, we show that there is not dependence on an initial value in the vibration behavior of workpiece. Moreover, it was found from the extraction result of dynamical structures in random process by the method of pullback that there is a possibility of dynamical structures including irregularity vibration.

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When negative financial shocks attack the economy, firms and household feel financial anxieties and they prefer high liquidity asset, cash and deposit, that is, precautionary demand increases. Financial anxieties were formulated by Kimura et al as an asymmetric variance of financial shocks. We improved their result by using the growth rate model of EGARCH type. In both researches, negative financial shocks are related to precautionary demand, because positive financial shock does not cause financial anxieties. However, positive financial shocks in a typical case of bubble economy may decrease precautionary demand. In this paper, we consider two types of EGARCH models; the first one is with asymmetric variance and the other with symmetric one. Discriminating effects between negative and positive financial shocks, we propose a precautionary demand function that increases (decreases) in deflationary (inflationary) economy.

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A joint method is proposed for estimating the water quality in terms of BOD and DO and identifying both unknown magnitude and discharged location of the pollution load from the noisy measurements on the BOD and DO. In order to detect the discharged load, the innovation process for the water quality states plays an important role, and the idea of pseudo-measurements is introduced to identify the magnitude of the pollutive load for both cases of the point-source and nonpoint-source loads. The joint estimation and identification method is tested by simulation studies.

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Self-organization phenomena are often observed in various fields of engineering including chemical and biological engineering. The self-organization can generate complex spatio-temporal patterns. The analysis of the spatio-temporal patterns created by the self-organization is one of major nonlinear problems in engineering. For example, the analysis of the spatio-temporal patterns in phase transitions of polymeric materials is essential to develop new materials. In this paper, we analyze chemotactic bacterial colony patterns in the semi-solid media as the patterns created by the self-organization. Focusing on bacterial species Escherichia coli (E. coli) among the chemotactic bacteria, we study the influence of the disturbances such as impurities in the semi-solid media on the colony formation by numerical simulations.

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In our presentation, we propose a bank loan pricing model for non-listed companies. We present a pricing formula for a principal-equal-repayment loan and derive the corresponding formula of relevant loan interest rate, which is sufficiently tractable. Indeed, the pricing model is specified by the three factors, the distribution of recovery rate estimated from Balance Sheet(B/S), the term structure of default probability and the default-risk-premium structure that each bank must choose individually. Discussing how to adjust the asset on B/S, we compute the parameter called B/S-adjusted asset-debt coverage ratio that specifies the distribution of recovery rate. Moreover we present some numerical results based on real accounting data of non-listed companies.

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We consider the dynamics of forward rate process which is modeled by the parabolic type infinite-dimensional factor model. The parameters included in this parabolic model are estimated by using the yield curve as the observation data. In this paper, we propose the filtering and identification method for the parabolic type factor model by using the particle filter algorithm.

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In this study, a new multi-layered Group Method of Data Handling (GMDH)-type neural network self-selecting optimum neural network architecture is proposed. We call this algorithm as revised GMDH-type neural network algorithm self-selecting optimum neural network architecture. Revised GMDH-type neural network algorithm has an ability of self-selecting optimum neural network architecture from three neural network architectures such as sigmoid function neural network, radial basis function (RBF) neural network and polynomial neural network. Revised GMDH-type neural network also have abilities of self-selecting the number of layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to medical image recognition and it is shown that this algorithm is useful for medical image recognition and is very easy to apply practical complex problem because optimum neural network architecture is automatically organized.

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Nonlinear systems described by the simple mathematical model often exhibit extremely complicated behavior called chaos. In this paper, chaotic behavior, exhibited by the one-dimensional difference equation, is discussed. A class of nonlinear discrete system with the invariant density, which is piece-wise uniform, is newly introduced.

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In this paper, the authors investigate a class of fuzzy random vectors, where they are considered as vague perceptions of random phenomena.First, based on the result previously proposed by the author[1, 2, 3], fuzzy random vectors are defined from the viewpoint of the multivalued logic, where for the convenience of numerical feasibility, the set representation of fuzzy sets is approximated by the stepwise membership levels. Secondly, the expectation and the variance of fuzzy random vectors obtained by the multivalued logic are reviewed, and the variance by Aréchef approach is introduced. The estimates of the expectation and two types of variances are also considered. Finally, the proposed fuzzy random vectors are tried to apply the analysis of statistical properties of the data obtained from the questionnaire concerned with bowling ball design.

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In this paper, we propose a so called Quasi-ARMA model, for financial time series prediction which is to model the history data of an existing series, and forecast the future unknown values. The Quasi-ARMA model contains two parts: the first is a macro-part, which is a linear interface with linear ARMA like structure and embeds the complexity into the coefficient; The second part is a kernel-part, which is a nonlinear model using a neurofuzzy networks, which is used to parameterize the coefficients. The model we proposed shows better performance on prediction accuracy, and gives more consistent and reliable forecasting results than conventional neural networks (NNs) in modeling high noisy financial time series. Computer simulations are carried out and confirms the effectiveness of the proposed model.

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In this paper, an ensemble classifier constructed by AdaBoost boosting of weak SVM with Gaussian RBF kernel is studied. Each SVM classifier is trained by small part of given training data and boosted by AdaBoost technique. By using on small data set for training, it is able to reduce computational burden. The classification accuracy is improved by boosting based ensemble. We propose such weak SVM ensemble method using AdaBoost and show availability of the proposed method by some numerical simulation results. Though setting of the kernel parameter is in general done by trial and error approaches or based on some kind of prior knowledge, it is also shown that the proposed method is able to give an ensemble SVM classifier that has low sensitivity to the kernel parameter setting.

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We have developed a real-time speech visualization system called “KanNon”[1, 2] which supports speech communication of hearing-impaired people. The KanNon system presents informations of the speech such as loudness, pitch, sound spectrogram, and characters by speech recognition system in real-time. In the present system, we are adapting word unit speech recongniton system using large scale dictionary. However the KanNon system requires quick and simple display of speech contents for smooth communication. For this purpose, we apply the methods based on neural network and statistical classfication for Japanese 5 vowels(/a/, /i/, /u/, /e/, /o/). Finally, we show results of phonemic recognition using real speech data.

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A new framework is presented for identifying objects in multi-viewpoint imagery. As an invariant feature, chromatic complexity of random texture is extracted and adapted for multi-viewpoint association. The feasibility of the framework is investigated through experimental studies.

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Until now, we introduced the statistical pattern recognition method based on AR (autoregressive) model in order to discriminate the EEG (electroencephalogram) signals recorded during right and left hand motor imagery, and we confirmed the possibility of discrimination of EEG waves under such situation. Therefore, in this paper, we try to discriminate the EEG signals recorded during right hand, left hand and right foot motor imagery by using the same method. The learning and feature of AR parameter in pattern recognition are discussed in experimental studies.

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In order to apply ICA (Independent Component Analysis) to EEG (electroencephalogram) analysis, this paper clarifies that the separation performance of the ICA algorithms depend on the number of unknown source signals. From several experimental results, when the number of the source signals is equal to or less than that of the mixture signals, it is found that the original source signals can be recovered by using ICA algorithm and a scaling adjuster. It is also found that the ICA algorithms works well to extract feature of the sources even if the sources are larger in number than the mixtures.

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This paper proposes a feature extraction method based on ICA (Independent Component Analysis) for EEG (electroencephalogram) signals under visual recognition obtained by oddball task experiments. The proposed method estimates the number of characteristic signals. Some characteristic peaks appear in the separated signals by ICA using this estimated number.

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We consider discrete-time linear stochastic systems with unknown disturbances and study two types of smoothing problems, i.e., the fixed-interval smoothing and the fixed-lag smoothing for those systems. We derive smoothing algorithms from the fixed-point smoothing algorithm proposed in the previous paper. It is shown that these algorithms reduce to the well known optimal smoothers derived from the Kalman filter when unknown inputs disappear.

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We are concerned with a problem of the optimal selection of the gain matrix of a linear observation for the Kalman filter. The innovations process included in the Kalman filter has the same structure as the model of a set of parallel transmission channels with the optimal output feedback. In the linear coding problem for this set of channels, it is well-known that the optimal output feedback which minimizes the power of the encoded signal is given by the least-squares estimate of the linear term and that the channel output becomes the innovations process. By applying a solution of the optimal transmission problem for this model, we obtain a set of gains which maximizes the mutual information between the observation and the signal under a constraint on the power of the innovations process.

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This paper is concerned with a method to recover natural cracks from magnetic images obtained by ECT (eddy current testing). In the proposed method, a Database is built to store knowledge base of cracks with different sizes and various measurement parameters. In the proposed Inverse Analysis, ε-Greedy search algorithm invokes knowledge from pre-built database to reconstruct multiple cracks from ECT signals.

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In recent years digital terrestrial broadcasting systems have been developed where OFDM signals are used for data transmission with single frequency network (SFN). But in a SFN relay station the effect of the coupling wave from the transmitter to the receiving antenna is significant and needs to be cancelled. In this paper a simple adaptive filter method is applied to this problem. The stationary point of the conventional LMS algorithm is first derived and its local stability is examined by using the averaging method. It is found that this algorithm has a bias. Then a modified algorithm is proposed to remove this bias. Simulation results show the validity of the theoretical findings.

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For a star sensor to be autonomous, it must detect the attitude change rate of a spacecraft, i.e., the body rate, even when it is high. In this paper, we propose a body rate estimation method from multi-head star sensor images based on a maximum likelihood method in a frequency domain. In a previous work, we showed that body rate could be estimated from star sensor images in a frequency domain. That was, however, for a one head star sensor case, and since the field of view of the star sensor head is narrow, the estimation accuracy of the component of the body rate vector parallel to the head's optical axis was low. In this paper, we extend the method to a multi-head star sensor case and show that sufficient estimation accuracy is obtained from the proposed method.

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In this paper, first, a methodological principle trial for the quantitative evaluation of the correlative and/or cumulative effect on indoor high-technology pollution has been proposed under an absolutely inseparable relationship at the same time and in the same ring. Then, the effectiveness of the proposed method is partly confirmed through some principle experiment between sound (served for utility) and leaked magnetic field (served for risk) especially before and after attachment of Tecno AO (active bio-controller as some magnetic oscillator -CE mark, ISO sanction, the 1st and unique device in the world).

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Recently, interests in localization and map building with laser range sensor have been increasing in robotics community. One of the reasons for this is that the algorithms with laser range sensor are with lower computational cost and are more robust than using vision sensors. Another reason is the robustness against environment changes (e.g. lighting condition, etc.). We are developing a walking aid system for physically-handicapped persons. As the first step in this project, we consider the localization of a moving cart with laser range sensor and building environment map. We estimate the moving cart position and orientation by maximizing the number of matched point pairs between current scan data and reference scan data. Our matching algorithm is based on point-to-point matching algorithms. In this paper, we show some preliminary results using raw data and discuss the availability of our matching algorithm for the future applications. We also show the way to apply particle filters for this problem and examine this localization algorithm.

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This paper considers an agent-based problem of analyzing the effect of capricious agents. They are the agents which show attitudes either pioneers (conservative agents) or followers (reformists) at random (capriciously). In order to consider the capricious agents, we define some types of capricious agents according to the degree of partialness (pioneers or followers). Simulation studies show that the capricious agents are affected by the action rules of agents (pioneers and followers) and that the capricious agents can be expressed by using probability.

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This paper presents a problem of preview tracking control for discrete-time stochastic linear systems via risk-sensitive stochastic optimal control theory. The risk-sensitive preview tracking controller is designed for an augmented system consisting of a tracking error system and a command generator which makes preview information of the reference signal. In the simulation studies, it has been shown that the derived controller has successfully achieved reducing the tracking error and increasing the robustness against strong random disturbance.

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An Acoustical Positioning System that consists of several signal transmitters encircling the object area and a receiver on a vehicle is proposed. The method of the system features no synchronizing connection from the transmitters to the receiver. Each transmitted signal is Phase Reversal Keying (PRK) of one of the Gold sequences in order that the receiver can distinguish from which transmitter originates it. The receiver includes a microphone and a signal processor which detects signals with the matched filter of complex absolute detection type being free from errors caused by unknown phase shift. The receiver, which cannot know the point in time of signal originated, estimates the position of the vehicle in the area with the differences of arrival moments among signals from the several transmitters. The estimation method is based on the Maximum A Posteriori (MAP) criterion applying to the equation of motion.

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In this paper, we derive new relative positioning algorithms based on new GR models (: GNSS Regression models). We have proposed GR models for GNSS navigation such that the precise point positiong algorithms were derived. The derived PPP algorithm achieved a positioning accuracy at the decimeter error level without any external information such as from WAAS. After introducing more precise GR models which contain the so-called receiver's and satellite's hardware delays, we extend our PPP algorithms to relative positioning occasion based on applying the GR equations such that we derive new simple recursive relative positioning algorithms. Finally we show the results of relative positioning for real GPS deta.

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In this paper, we present a new carrier-phase-based precise point positioning (PPP) algorithm based on a new GR model (: GNSS Regression model) by using multiple antennas. Previously we introduced GR equations such that a PPP algorithm was derived which achieved a positioning accuracy at the decimeter error level without any external information such as from WAAS. In this paper, after introducing more precise GR models which contain the so-called receiver's and satellite's hardware biases, we extend our PPP algorithm to the positioning occasion of using multiple antennas. Very precise point positioning (VPPP) algorithms using two or more PPP antennas with common clock errors and known receivers' distances are derived.

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This paper proposes an integer ambiguity estimation and validation method in carrier phase differential GPS/GNSS positioning in order to attain continuous precise positioning. Firstly the mathematical models of the GPS carrier phase measurement and the method of the existing positioning algorithm are briefly reviewed especially by focusing on the integer ambiguity estimation. For the estimation, in this paper, the Kalman filter and the LAMBDA (Leastsquares AMBiguity Decorrelation Adjustment) method are applied, and the candidates of the integer ambiguity are obtained. Then the sequential probability ratio test is applied to select, with a given probability, the most likely integer ambiguity. Also results of the experiment by using real receiver data are shown.

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This paper introduces a method to reduce the occurrence of transfer loops on the network for the routing algorithm based on the reinforcement learning scheme. In former study, we proposed the routing algorithm “DARLA” which fulfills the traffic shaping of the network. However, our algorithm couldn't suppress the traffic using the links toward the source of packets. This is due to the property of the characteristic of reinforcement learning. We show the method to estimate the lower bound of the probability for valid route for the destination, and this lower bound can be used to reject the routes which cause the transfer loop.

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In this paper, a deterioration prediction method is proposed for maintenance of rotating equipment. Status of the rotating equipment is inspected by vibration measurement. A mathematical model of deterioration is introduced in order to predict future status of the rotating equipment. Defects of the rotating equipment are estimated by using the prediction using the mathematical model. For the construction of the mathematical model, outliers such as measurement errors are eliminated in order to improve accuracy of the mathematical model. The effectiveness of the proposed deterioration prediction method is assured by actual collected data of rotating equipment in thermal power plants.

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