Two wheeled inverted pendulum robot which has just two driving wheels is attracted as an interesting mobile robot for several applications over the last decade. In this study, we show a framework for the mobile robot constructed by the inverted pendulum mobile robot for a project “KoRo” in our university. It is necessary to be composed by several sensors, namely sensor fusion techniques, such as a laser scanner, an USB camera, a gyro and so on. The framework of the mobile robot composed by RT middleware component with some sensors were mainly investigated.

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This paper describes a method for the localization of wireless mobile clients in multistory buildings using a public wireless LAN system. The global positioning system (GPS) is used for the outdoor localization of a mobile client carried by humans or mobile robots;however, it is difficult to estimate the global position of the mobile client in multistory buildings since the GPS is not suitable for indoor localization. The proposed method uses public wireless LAN access points which are settled three-dimensionally in a building. The application of the method involves the assumption that the humans or robots carrying the mobile client move horizontally on each floor in the building. The method simultaneously estimates the position of the mobile client and its floor number. Experimental results indicate that the proposed method is feasible.

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This paper presents a new localization method using differential evolution (DE) algorithm in a mobile robot localization problem. Mobile robot localization is one of important problems in many mobile robot systems. Some stochastic approaches (e.g. Kalman Filter, Particle Filter, etc.) are the dominant methods on this research community. Applications of evolutionary computations (ECs) for the mobile robot localization are very rare. Hence, this paper considers the new localization method using DE for mobile robot’s position tracking. In some fundamental experiments using raw data, the efficacy of the proposed localization method is shown.

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This paper proposes a localization method of mobile robots with omnidirectional camera. An omnidirectional camera captures images of 360^◦, hence the view is invariant with respect to the direction of the robot’s sensor if the image is processed into correlation. The localization algorithm consists of two steps: regression from the correlation image into the position, and applying Kalman filter by incorporating the dynamic model of the mobile robot. The experiment shows the effectiveness of the proposed approach.

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In this paper, an identification method of the state-space model is proposed. Proposed method assumes that some entries of system matrix A is unknown and identify these entries from input and output data. The key idea of the proposed method is the use of pseudomeasurement which is fictitiously constructed data as if it were made on the unknown entries. Augmenting this pseudomeasurement with original observation data, unknown entries (and state-vector) are identified by the extended Kalman filter.

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This paper describes a method for estimating sensor biases by using a low-dimensional Unscented Kalman Filter (UKF) to maintain the positional estimation accuracy of an autonomous vehicle (AV). It is difficult to estimate attitude accurately in a blind situation (such as with no GPS satellites and no landmarks), because of sensor bias. We developed a dead reckoning system for an embedded system using the UKF. The UKF has high computational effort, so, we decreased the number of dimensions in the UKF by excluding sensor biases term. On the presumption that AV drives steadily, we derived equations for the relationship between the averages of angular acceleration and gyro bias, and corrected the sensor output. Instead of using high-dimensional UKF, we corrected sensor biases by using these equations. This method quickly and accurately estimated attitude.

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In this paper, we propose a prefiltering method for closed-loop MIMO system identification in the framework of joint input-output approach. The purpose of proposed prefiltering method is to eliminate the noise process from the joint input-output process more precisely, and can be achieved by using the orthogonal decomposition of the joint input-output process based on Wold’s decomposition. In our method, LQ decomposition and causal FIR model are used. Especially, the latter plays a key role to eliminate the noise process more precisely. In numerical experiments, the efficacy of the proposed method is verified by comparing some identification methods.

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In this paper, we study stochasticity of nonlinear systems of Van der Pol-Mathieu type. At first, a sine circle map to the system is derived. Secondly, an approximate piecewise continuous map to the sine circle map is derived. Finally, we study stochasticity of the system based on Lyapunov exponents to mapped points of approximate piecewise continuous map and to oscillation of the original nonlinear systems. We show that if the oscillation of approximate piecewise continuous map is stochastic, that is Lyapunov exponent to the map is positive, then the oscillation of original system of Van der Pol-Mathieu type exhibits stochastic motion.

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In this paper, some nonlinear systems with the invariant density is newly demonstrated. Permitting the existence of more than one interval whose image is identical, the variation of the nonlinear function with the invariant density increases. The validity of the proposed scheme is demonstrated by numerical experiments.

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A method of on-line residual life prediction is proposed for condition based maintenance of industrial equipment. With on-line monitoring of condition of the industrial equipment, the residual life is evaluated by using on-line prediction of the equipment deterioration. The deterioration prediction is based on the on-line identification of the mathematical model of deterioration process. To improve the accuracy of the deterioration prediction, outlier elimination technique is introduced. The proposed method was applied to actual data of rotating equipment in a thermal power plant and the results verified the effectiveness of the proposed method.

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Two problems on the blind detection of phase shift keying (PSK) signals are studied. One is the detection of the existence of PSK signals and the other is the detection of the transmitted PSK symbols. From the probability density function of the absolute values of the received signals, a likelihood ratio test is derived for the detection of the existence. Then, when PSK signals are present, a method to determine the decision regions to detect PSK symbols is developed. It is shown that the estimation can be approximated as a least squares problem that can be numerically solved. Simulation results show that our detector for the existence of QPSK signals slightly outperforms the energy detector. The bit error rates of the detection of QPSK symbols using the estimated decision regions are also provided to demonstrate the performance of our estimator.

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This paper describes an optimal state estimator for a class of discrete-time linear stochastic systems subject to both colored observation noise and unknown inputs. The authors have already proposed an optimal filter in our previous paper for such systems based on the idea of Chen and Patton's ODDO (Optimal Disturbance Decoupling Observer). On the other hand, we have proposed a modification of the ODDO by showing a correction to the estimation error covariance matrix. In this paper, we combine those results and propose a new optimal state estimator for the system with colored observation noise and the unknown inputs.

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An optimal filter that separates a tone of particular pitch highness played by particular instrument applying the statistical least square criterion is discussed. The filter is based on a priori autocorrelations of and cross correlations among tones. It firstly identifies current amplitude factors of tones; then, it evaluates cross correlation between current input signal and each tone. Finally it obtains the estimate of seeking tone from the input signal which is mixture of sounds played by instruments and corrupted by white noise. Some numerical examples with violin, trumpet and flute tones are also introduced.

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In this paper, we propose a synthetic approach to improve the spatial resolution of Brillouin optical time-domain reflectometry (BOTDR). Due to the uncertainty relation between position and frequency, the spatial resolution of a conventional BOTDR system has been limited to about one meter. It is shown that this limit could be overcome by the synthetic approach. In the synthetic approach, a Brillouin spectrum is constructed by combining several spectrums obtained by BOTDR measurements with different pump lights and low-pass filters. The pump lights and low-pass filters are composed of short and long elements with phase differences. By using the synthetic Brillouin spectrum, it is possible to estimate the Brillouin frequency shift with an arbitrary spatial resolution. This property is verified by a numerical simulation.

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In this paper, a numerical study using dummy data to reproduce the empirical results of [4] without contradicting the theoretical result in [3] will be presented. We have generated dummy spot rate data by using dummy volatility matrices, dummy drift vector and Gaussian pseudo i.i.d. random vectors as driven noises.

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To construct a no-arbitrage defaultable bond market, we work on the state price density framework. Using the heat kernel approach (HKA, shortly) with the killing of a Markov process, we construct a single defaultable bond market that enables explicit expressions of the defaultable bonds and the credit spreads under quadratic Gaussian settings. Some simulation results show that the model is not only tractable but realistic.

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In this paper, we test jumps through high-frequency data by using three methods, Barndorff-Nielsen and Shephard [6], Jiang and Oomen [9], and Lee and Mykland [10], for Nikkei225 and Japanese individual stocks in 2008 year. As we well know, we had the financial crisis in 2008 year. Then from the testing results, we find that there exist jumps in the Japanese markets. Next through Lee and Mykland test, we study jump-size and jump-frequency of Nikkei225 and individual stocks. In consequence, we have significant difference of jump-size distributions of Nikkei225 between 2008 and 2009, but surprisingly no significant difference of frequency distribution.

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In this paper, we estimate some volatilities and analyze each value through high frequency data under financial crisis 2008. Precisely speaking, we study the asymmetric diversity of volatility fluctuation, the distribution mixture hypothesis proposed by Clark[4], prediction power of volatility and existence of jump on volatility process. As the result, 1. We observe the asymmetric diversity of volatility fluctuation. 2. Fluctuation of returns is occurred by not only volatility but also other factor. 3. When we forecast volatility, it is useful to add implied volatility as new exogenous variable in time series model. 4. We found it is necessary to formulate volatility estimation considering jump.

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In this paper, presented are some numerical computations of the hedging portfolio of plain, look-back and average options respectively, using a discrete version of Clark-Ocone formula proposed by the authors themselves in [1].

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Risk-sensitive portfolio optimization problem is studied with a specific setting: a market model with a two-dimensional linear-factor is considered, where the factor consisits of an Ornshtein-Uhlenbeck process. A sharp solvability condition is obtained in risk-seeking case. Further, an application of a CPPI technique is mentioned to treat a problem with floor-constraint. And as its application, we give the sample numerical simulation results with CPPI approach.

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We consider the adaptive mean-variance hedging problem for pricing bond options . The model considered contains infinite-dimensional noise sources with the stochastically-varying risk premium. Hence our model becomes incomplete. After constructing the adaptive estimation algorithm for risk premium and systems parameters, we study the adaptive mean-variance hedging problem under the real world measure and obtain an explicit form of the optimal hedging strategy.

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In this paper, we are concerned with the optimization of observations in stationary LQG stochastic control systems which employ the stationary Kalman filter. The performance of the LQG stochastic control system is dependent on the gain matrix in the linear observation. From the view point of the performance of the LQG regulator, it is better to take the dimension and the value of this gain matrix as large as possible. However, it is usually the case that we cannot take these values so large but there exist certain physical restrictions. By taking a performance criterion for the selection of the gain matrix as a quadratic function on the estimation error and the gain matrix and by introducing the eigenvalues-eigenvectors representation of a nonnegative definite symmetric matrix, the condition of optimality is derived under weaker assumptions than already known. Also, numerical calculations are easily carried out by introducing an n-dimensional polar coordinates system.

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We discuss the effectiveness of monetary policy in the prolonged Japan's recession. Many researchers considered this topic, and a majority of previous papers denies the effectiveness of monetary policy in the deflation. Although the Bank of Japan always insists that monetary policy does not work well especially in the severe deflation, the role of money should not be ignored when we consider M. Friedman 's word ”Both inflation and deflation are monetary phenomenon.” We analyze Japan 's economy over the period from 1980q1 through 2009q4, and examine whether or not there exists a long-run equilibrium relationship between the monetary base and economic activity, paying a close attention to the precautionary money demand increased by the financial crisis in the recession. The survey data is used to estimate the precautionary demand. The result shows that the cointegration property among monetary base and economic activity still holds even after Japan 's economy fallen into the deflation in 1997, when the precautionary demand is taken into account. We also check the existence of the liquidity trap by the same model. The result denies the existence of the trap. Thus, we conclude that monetary policy is still effective and the BOJ' role is crucially important to combat against the prolonged recession.

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In this paper we study the H_∞ state estimation problems for a class of linear continuous-time Markovian jump systems. We adopt a game theoretic approach and stochastic variational calculus method to derive estimates and forms of dynamic estimators on the fixed time interval. The necessary and sufficient conditions for the solvability of the H_∞ state estimation problems are given by the coupled Riccati differential equations with initial conditions.

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In this paper we study the H_∞ state estimation problems for a class of linear discrete-time Markovian jump systems. We adopt a game theoretic approach and stochastic variational calculus method to derive estimates and forms of dynamic estimators on the fixed time interval. The necessary and sufficient conditions for the solvability of the H_∞ state estimation problems are given by the coupled Riccati difference equations with initial conditions.

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In this paper we study the H_∞ tracking problems with preview for a class of linear continuous-time Markovian jump systems. The systems are described by the linear continuous-time switching systems with Markovian mode transition. The necessary and sufficient conditions for the solvability of the H_∞ tracking problem are given by coupled Riccati differential equations with terminal conditions. Correspondingly feedforward compensators introducing future information are given by coupled differential equations with terminal conditions. In this paper we consider the case that the measured output is only partially observed and present the design theory by output feedback.

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A food chain describes predator-prey interaction between species. Since behaviors of some species in the food chain have an effect on the whole ecosystem, it is very important to consider temporal-spatio change of the population of species from the view point of maintenance, management, protection of the ecosystem. In this paper, we consider the food chain consisting of three populations, fish, zooplankton and phytoplankton, and study the modeling of such a food chain and analyze their behaviors by numerical simulations. In the food chain, environmental changes cause some kinds of random fluctuations in the growth and the death rates of species. In order to study the impact of these uncertainties on the food chain, we need the stochastic predator-prey model. Thus, we propose the stochastic plankton-fish model. In the numerical analyses for the stochastic plankton-fish systems, we show that the size, number and motion of fish school have a great influence on appearance of a spiral wave of plankton density and such a spiral pattern is robust for disturbance.

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In this paper, we present statistical models and ML (maximum likelihood) positioning algorithms using received signal powers in sensor networks. The purpose of this study is to develop the indoor positioning system with utilizing the IEEE Std 802.15.4 [1] based wireless sensor network.The distance between nodes can be presumed by using the RSSI (received signal strength indicator) of the wireless data communication in the sensor network. Therefore, it becomes possible to presume the position of the sensor node by using the RSSI from a certain sending source, namely from the base station with already measured position.The variation of the RSSI is large because of the influence by the measurement environment. Therefore, it is necessary to acquire a lot of the RSSI data to improve the position estimation. In this paper, we present the models of RSSI of radio signal propagation applying the Rayleigh distribution and gamma distribution. We propose a positioning algorithm based on the ML method from the probability density functions of the signal powers.

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In this paper, we propose a method of identifying the talker based on modeling the voice for reading free sentences by AR(auto regressive) models. By remarking the similarity of the AR models and the no loss sound-tube models for vocal tracts, we assume the features of the talker voice are the higher reflection coefficients of AR models. In the simulation, there are 6 talkers' voice as speech examples from ”Japanese speech sound database of ATR.” As a result, we get that average recognition rate is 90.67 %.

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To realize a model-based 3D object recognition, we propose a feature extraction method and a shape descriptor using the geometric features. First, the feature extraction method based on a novel gaze modeling is proposed. In the modeling process, the surface model is independently estimated for a part of range data restricted by several gaze domains. Hence, since the features are independently extracted from each gaze domain, inconsistent or incorrect features may be obtained. Therefore a stochastic method that enables us to integrate such features by evaluating the reliability of each gaze model is introduced. Next a shape descriptor, curvature spin image, is proposed. The CSI is created based on the ratio of surface curvatures. The main contribution of this paper is experimental analysis of the use of CSIs with various tuning parameters.

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A new framework is presented for saliency based object detection in naturally complex scenes. In this framework, the random distribution of the scale and chromatic aspects of image complexity are exploited for the multi-fractal articulation of scene image into the ground-object structure and object images, respectively. Due to the intrinsic coherence of the scale and chromatic aspects, detected object images are well organized within the ground-object structure to indicate various types of maneuvering context arising in the scenes.

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This paper is concerned with stochastic inverse methodology arising in electromagnetic imaging. Nondestructive testing using guided microwave covers wide range of industrial applications including early detection of anomalies in supraconducting materials. Our focus in this paper is in the identification of electromagnetic material parameters and emphasis is on one spatial dimensional scattering problems on dielectric slabs.

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Human motion has been analyzed in the field of welfare, care worker, sports and so on. Motion capture system is one of the useful measurement equipments of human motion [1]. It measures refection of light from some markers which are attached on a body and calculates their positions based on a coordinate set beforehand. In this paper, we are going to measure motion of a rider on a unicycle by motion capture system and describe it by mathematical model. As numerous multivariable time series data for a motion is obtained by this system, processing multivariable time series data is needed to model the motion.

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This paper describes the high accurate Virtual Metrology (VM) technique we propose, which may be applied to a mass production process of multiple products, e.g. semiconductors.VM technology is necessary in a production process where sufficient amount of inspection data is not available. By applying VM technology, quality of the product and process error can be predicted based on the process data and relatively small amount of inspection data. Most common VM technology applied to these fields of manufacturing process for quality prediction uses a multivariate statistical model. However, the accuracy of prediction models using linear multiple regression drops when, during the generation of the model, a collinearity is found between terms in the process data that forms the explanatory variables. In addition, when updating the prediction model in order to increase its prediction accuracy, a collinearity among explanatory variables causes the regression coefficient to become unstable, resulting in a degradation of the prediction accuracy.The method proposed in this paper enables us to create a quality prediction model composed of mutually uncorrelated variables and to perform prediction while updating the coefficient of each variable in the prediction model formula.Our proposed technology will solve two problems: degradation of the quality prediction accuracy due to mutual interference between multiple process data and overfitting upon update of the metrology model.

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In this paper, we derive a filtering algorithm for a discrete time system with Gaussian sum distributed measurement noise by extending and modifying the Gaussian sum filter proposed by Alspach and Sorenson[1], and its applications to satellite positioning and navigation systems are proposed. Also simulation results of the proposed Gaussian sum filter for simple first order Markov process are shown.

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In the long baseline GPS (Global Positioning System)/GNSS (Global Navigation Satellite System) relative positioning the ionospheric and tropospheric delays are dominant factors for the positioning accuracy. In this paper, we present Real Time Kinematic (RTK) relative positioning algorithms for long baselines with simultaneously estimating ionospheric and tropospheric delays and their gradients. Also some dynamical models [1-3] of the rover station are reviewed for applying Kalman filters, and we show the experimental results of relative positioning for various baselines (short, medium, long) by using the Gps Earth Observation NETwork (GEONET) data provided by Geospatial Information Authority (GSI) of Japan.

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This paper presents a method of collision detection and control for parallel-structured two-link flexible manipulators based on the unscented Kalman filter. The exact dynamics of parallel-structured two-link flexible manipulators are described by quite complex nonlinear partial and ordinary differential equations. In this paper, the proposed parallel-structured two-link flexible manipulators are approximately modeled by a two-link flexible manipulator consisting of a couple of flexible beams with the same boundary conditions. In order to find the time when the flexible manipulator collides with the unlooked-for obstacle, the innovation process of the unscented Kalman filter which is one of the nonlinear state estimators is introduced. As the controller for the manipulator, the sliding mode controller is employed. In the normal situation, the sliding mode controller generates the control torques so that the tip position follows the reference trajectory. When the collision between the flexible manipulator and an unlooked-for obstacle is detected, the objective of the controller is switched from the position control to the suspend control, which is achieved by changing the reference trajectories. The performance of the proposed collision detection algorithm and controller is demonstrated by several numerical simulations.

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A stochastic explanation is provided to investigate how human subjects maximize robustness of their balance control while exhibiting on-off intermittent behavior. To this end, the human balance control is modeled by an inverted pendulum with random delayed state feedback. Stochastic analysis based on Lyapunov exponents demonstrates that the on-off intermittency can arise under a neutrally stable condition. Furthermore, the frequency response of statistical moments is derived to show that the neutrally stable condition can be caused by a trade-off between maximal robustness and minimal phase-shift from the disturbance to the second moments.

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Electroencephalograph (EEG) recorded during left and right hand motor imagery can be used to move a cursor to a target on a computer screen (such systems are called a brain computer interface: BCI). A BCI has been studied for one of rehabilitation programs intensively. However, the spatial resolution of EEG is inferior to other acquisition methods. Focusing attention on new information of EEG is required. In this paper, we proposed the Pulse Complex Model (PCM) as a new pattern recognition model to extract features from EEG concerning with motor imagery. In applying to the model, the parameters were estimated by using a Genetic Algorithm (GA). Then a discrimination rule based on a Support Vector Machine (SVM) was constructed. From the results, the best preprocessing and the optimal order of the model were estimated. On the basis of these results, some types of discriminant analyses were conducted. According to the results, a relation of approximation errors to the feature of motor imagery was revealed.

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The feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed and is applied to the nonlinear system identification and the medical image analysis of liver cancer. In this feedback GMDH-type neural network algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as the sigmoid function type neural network, the radial basis function (RBF) type neural network and the polynomial type neural network. Furthermore, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and the relevant input variables are automatically selected so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). The identification results show that the feedback GMDH-type neural network algorithm is useful for the nonlinear system identification and the medical image analysis of liver cancer and is ideal for practical complex problems since the optimum neural network architecture is automatically organized.

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In the restoration problem of motion blurred images, many research activities have been done in the past. In this paper, we propose a resotration algorithm of rotational motion blurred images using inverse filters. In paticular, to overcome this problem, we focus attention on the two methods. First, we interpolate the pixels near the rotational paths by shift-invariant operation. Second, we reconstruct the input image on the another domain.

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In this paper, we investigate problems of the blurred image caused by linear motion. The distortion model caused by camera linear motion is presented by a impulse function with unknown length and direction parameters. To restore the image, its distorted version is inverse filtered with the estimated model. Therefore, the problem of restoring of the undistorted image consists in estimating model parameters. To estimate the parameters such as length and direction we examine the lean of distortion image applied Fourier transform. The experimental results support the correctness of our approach.

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Recently, many kinds of particle swarm optimization (PSO) algorithms have been proposed to improve the performance of the standard one. However, the systematic analysis of the behavior of such algorithms is not sufficient. This paper extends the previous analysis results for the standard PSO algorithm to two types of PSO algorithms. The one is a PSO algorithm with multiswarms, and its properties of decay rate and l² gain are analyzed. The decay rate and l² gain are closely related to the convergence speed and swarm diversity,respectively, of the algorithm, which also corresponds to exploitation and exploration abilities. The other one is the type of PSO algorithm with time-varying parameters. The characteristics of the parameter settings are investigated from the viewpoints of the convergence speed and swarm diversity.

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Stochastic approximation is a recursive procedure to seek for a solution of an unknown nonlinear equation based on random noise corrupted residuals. This paper presents an upper bound of the expected squared estimation error of stochastic approximation in finite samples of the measurements. The bound is given as an affine function of the squared error of the initial candidate of the solution with parameters. Once the parameters are specified, the necessary number of the measurements can readily be computed in advance of execution of the procedure, which establishes a rigorous stopping rule for stochastic approximation.

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