A new identification method for single-input and single-output linear systems with colored input and output noises is proposed. Variances and autocovariances of the input noise are assumed to be known and those of the output noise are assumed to be unknown. The proposed method uses the eigenvector method and the noise gain functions which are derived in this paper. By using the noise gain functions, the variances and autocovariances of the output noise generated by the input noise can be obtained from those of the input noise. This algorithm uses an iterative calculation and it consists of two parts. One part is the identification of the system parameters using eigenvector. The other part is the estimation of the variance and autocovariace of output noises using the noise gain function. Some results of the simulation show the effectiveness of the proposed method.
In this paper, we propose a NLOS (Non-Line-Of-Sight) signal rejection method to improve the positioning accuracy of integrated GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) system for a vehicle in dense urban environments. NLOS signals caused by reflection and diffraction always have positive measurement errors in pseudo-ranges, and they should be excluded in the Kalman filter of GNSS/INS, because the filter assumes that the measurement errors are zero-mean. In the proposed method, the positive errors in pseudo-ranges are geometrically estimated by simplifying the environments around a vehicle, and the signal that is supposed to be a NLOS signal based on the estimated errors is excluded from the measurements of the Kalman filter. We apply the proposed method to the measurement data obtained by actual driving in dense urban environments, and demonstrate that the positioning accuracy is improved by the proposed method.
Management of evacuation centers and flexible support of supplies are required when a largescale disaster occurs. It is necessary to predict demand in order to transport relief goods quickly and appropriately to evacuation centers. Therefore, it is effective to predict the number of refugees in evacuation centers. Generally, the supply and demand trends vary not only due to the scale of the earthquake disaster but also depending on the occurrence situation, power outages and other conditions. In this study, we propose a dynamic model for predicting the number of evacuees. The effectiveness of the proposed model is illustrated by numerical example about case study of the Kumamoto earthquake.
In recent years, the development of optimization methods in multi-agent systems has been remarkable. The scheduling problems belong to NP-hard and are not easily solved in a large-scale system. The distributed scheduling method is expected as one of the methods for large-scale systems. In this paper, as the first step of the research, we propose to apply the alternating direction method of multipliers for the consensus problem to the distributed scheduling problem and show that the job shop scheduling problem (JSP) can be formulated by the proposed method. In distributed optimization, the optimization of sub-problems is repeated until the convergence condition is satisfied, but since the scheduling problem is a nonconvex optimization problem, the convergence by the proposed method is not guaranteed. In some cases, a large number of iterations may be required to satisfy the convergence condition. In this paper, we propose two schemes to obtain a feasible solution within a smaller number of iterations. Then, the method is evaluated by computer experiments using benchmark instances of JSP.