In this paper, the author investigates the asymptotic distributions of estimators concerned with the class of fuzzy random sets, which is considered as a model of the capricious vague perceptions of a crisp random phenomenon.

First, the class of fuzzy random sets, which has been proposed by author as a model of the capricious vague perception of a crisp random phenomenon, is refined from the practical point of view. Secondly, using the refined class of fuzzy random sets, the expectations of fuzzy random sets are also refined. Applying the standard strong law of large numbers for the random elements in a separable Banach space, the convergence properties of estimators for expectations of refined random fuzzy sets are examined. Finally, asymptotic distributions concerned with the estimators are also investigated, applying the central limit theorem for the random elements in a separable Banach space.

The theoretical aspect of this research for investing asymptotic properties of proposed estimators is mainly inspired by the papers of Krätschmer[1, 2, 3, 4, 5].

In this research, water level control of flash chamber in a spray flash desalination system is investigated. A water level model is constructed by introducing white Gaussian processes to water level and valve opening in an existing model. The valve opening is determined by PI controller with saturation. In order to check the influence of white Gaussian processes, some kinds of simulations were carried out.

Multi-agent based load balancing dispatch is considered for power systems with renewable energy. It is assumed that each agent generates uncertain amount of power due to renewable energy resources. A suitable communication gain of the distributed algorithm is derived for the desired consensus in mean square or with bounded error variance.

Stochastic resonance is a phenomenon in which the signal response of non-linear systems is enhanced by additive stochastic noise. Chaos also causes a stochastic resonance-like phenomenon called chaotic resonance. In this study, we proposed utilizing the negative strength of a reduced region of orbit (RRO) feedback signal to control chaotic resonance. This RRO feedback signal was applied to a discrete cubic map. The results confirmed that it has the effect of merging the separated attractor and inducing chaotic resonance. As an alternative approach to attractor merging, Gaussian white noise was applied to the cubic map. In this case, the results showed that the added noise induces stochastic resonance through attractor merging; however, its ability in this respect is less than that of chaotic resonance.

A Bayesian dynamic method for analysis of software debugging process data is handled. It is addressed to predict states of software reliability. In the Bayesian analysis, hierarchical prior models are structured with the Boltzmann machine, and empirical and expert knowledge priors are supposed. These priors play roles recognized as representations for complex situations. The empirical prior based on observed data is used for the representation of uncertainty corrections. The prior of success probability for the tests is assumed based on expert knowledge of engineers. The reliability is estimated based on the posterior mean of the failure states. The Bayesian inferences are derived based on the computational simulation methods, and the information criterion EIC is used to choose appropriate models.

This paper is concerned with an electromagnetic interrogation method for human muscle tissues using stochastic inversion methodology. The frequency dependence of relative permittivity of human muscle tissues is modeled by Debye formula with multiple dielectric relaxations. The associated signal response model is given by a complex reflectance at the interface between free space and the human body. The problem considered here is to identify posteriori distribution of the dielectric parameters from the measurements by a reflective interferometer. A computational experiments using Hamiltonian Monte Carlo sampling are summarized.

The simplest “long-run neutrality” proposition specifies that a permanent change of the money stock has no long-run consequences for the level of real output. King and Watson exhibited the basic framework of long-run neutrality. In this paper, we extend King & Watson’s method and succeed direct estimations of unknown parameters. We investigate the neutrality of (real GDP, M3) in Japan during (1980q1,2007q4), where M3 is a wider class of money than M2. We show that this interval rejects the neutrality to the positive direction.

In this paper, we present the single frequency PPP algorithms based on our GR models with applying CLAS(Centimeter Level Augmentation Service) data from Japan Quasi-Zenith-Satellite-System (QZSS), comparing with the positioning results applying the MADOCA (Multi- GNSS Advanced Demonstration tool for Orbit and Clock Analysis) Products which had been developed by JAXA (Japan Aerospace Exploration Agency) and are provided by GPAS (Global Positioning Augumentation Service Corporation), Throughout the numerical experiments the total 3DMMS error by applying CLAS is approximately 0.28 [m], in contrary with the total 3DMMS error by applying MADOCA Productsis being aproximately 0.52 [m].

A signal often includes missing parts and is often affected by noise when it is obtained; thus, all available data includes errors. In this study, we considered a method in which only highly reliable signals, which are close to the true value, are extracted for estimating the signals. When highly reliable signals (candidate points) are extracted from the acquired signals at an arbitrary ratio, the signals from the remaining parts constitute the missing parts. The signals with missing parts after the extraction of the candidate points were estimated by Fourier series approximation. Herein, we examined the optimal extraction ratio for the estimation, including the order used for approximation. First, we examined the required percentage of the candidate points extracted from the acquired signals for increasing the estimation accuracy. As a result, if at least about 80% candidate points are available, the estimation accuracy was high. Subsequently, we proposed three methods for selecting the candidate points; we compared the errors between the original and the estimated signals, and the estimation iteration times. Consequently, the following method had the highest estimation accuracy. Further, we performed the estimation once using all the obtained signals and then removed the signal with large error between the original and the estimated signals at an arbitrary ratio and performed the estimation again. In this method, we can extract signals close to the true value. This method has fewer iteration times compared with other methods. Furthermore, by using the third order, the error was insignificant and the number of calculations was also limited.

In this paper, for the GNSS (Global Navigation Satellite System) positioning, based on the method to efficiently distinguish non-line-of-sight (NLOS) signals from line-of-sight (LOS) signals [1] , and show further results of actual GNSS positioning in various circumstances.

Classical models of opinion dynamics utilize deterministic methods to describe how opinions evolve within a social network. More recent models are shifting towards randomized processes to emulate real-world interactions. These include gossip-based models that use pairwise interactions to represent the opinion formation process. In this paper, we propose to extend the gossip-based models for opinion dynamics by allowing group interactions with randomly selected participants.

In this work, we study the repeatability of game situations in different soccer matches. This analysis is aimed to evaluate the possibility of reusing these records as part of a game AI system. Due to a variety of team formations, an appropriate comparison of game situation pairs is a challenging task. Identification of similar situations in the game of soccer can be presented as an evaluation of geometrical similarity of players’ coordinates on the field. Team formations have semantic value, and we show that role-based analysis is essential for successful matching. Obtained results can be applied for the tasks of sports analytics and AI design.