Multisensor Data fusion uses observations from networked multiple sensors achieves the same common air picture which has wide surveillance area. In ideal environment, a fundamental assumption is that registration errors are zero. However, in real environment, the registration errors are not zero. The plural targets are seen in the common air picture, even if the all sensors observe the same one target. In this paper, we propose new registration error estimation method which is composed of two registration error estimator. One is a coarse registration error estimatior which is based on corresponding point-to-point calibration for moving target, the other is a fine registration error estimatior which is based on kalman filter. As a simulation result, we confirmed that the proposed method achieves 82 point reduction of registration error estimation error than conventional method.

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Optimization task of the control parameters of industrial positioning systems is a daily occurrence. It is necessary to explore unknown response landscape of the system by performing plural sampling of “control parameters and output combination” if the request specification is severe. Skilled operators have been conducting such tasks based on their experience and knowledge. The challenge is “The Optimization of Unknown Objective Function”. In their study, the authors have proposed their original optimization algorithm as a solution. This paper reports applicability study of their algorithm. Experimental results discovered that the algorithm found the optimal control parameter in 100 combination data set for seven times trial with some required specifications.

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This paper presents a measuring-position-determination method of Unmanned Aerial Vehicles (UAVs) for automatic visual inspection system. In the proposed method, it is assumed that 3D model of the target infrastructure, the specification of measurement and UAV's features are given. Under those assumptions, the measuring positions are calculated considering the accuracy of data. In this paper, the usefulness of the proposed method is examined by a computer simulation assuming visual inspections for several bridges. In the simulation, measuring positions are determined assuming several types of UAVs for each bridge.

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In the conventional system identification problems, it is commonly assumed that the output signal of a dynamical system is measured at regular time intervals. This paper addresses a system identification problem under the Lebesgue sampling, which is a type of irregular sampling methods and samples the output signal only when it crosses specific thresholds. In the proposed method, not only information in output samples but also that in inter-samples are utilized for the parameter estimation to efficiently improve the estimation accuracy. The asymptotic variance of the estimated parameter is also analyzed. Effectiveness of the proposed method is examined through numerical examples. In the numerical examples, systems driven by a Gaussian white signal is identified. We illustrate that the variance of the estimates by the Lebesgue sampled data is smaller than that by the Riemann sampled data.

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The enhancement of blood flow is effective to improve performance of exercise and reduce fatigue after it. This paper proposed a wearable thermal device that enhances the blood flow by alternative hot and cold stimuli at a local point of the ankle. The previous studies showed that thermal stimuli using waters is effective to enhance the blood flow, and our compact device achieved it by simple method using a Peltier device. At first, we evaluated whether the thermal stimuli for a local point on ankle could enhance the blood flow or not comparing with a case of mechanical pressure stimuli and actual exercise. Then, we investigated that how the previous thermal stimulus influences the performance of exercise taking pedaling motion. The results of experiments showed that the proposed thermal device can enhance the blood flow enough so that it improved persistent of exercise and reduced subjective hardness during pedaling motion.

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Our previous study proposed a positive quadratic system representation for molecular interaction in a cell, including a signal transduction pathway and a gene regulatory network, and also presented a method for estimating a positive invariant set depending on the initial state. As an extension towards wider applications of this approach, this paper proposes a system representation called here a singularly perturbed positive quadratic system, and shows that every positive rational system, which is used as a mathematical model expressing biological behavior, can be approximately represented by a quasi-steady state system of a singularly perturbed positive quadratic system. In addition, we prove that the singularly perturbed positive quadratic system preserves stability at an equilibrium point of the positive rational system.

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