New systems approaches, which include multidisciplinary and comprehensive concepts, are required to realise “Society 5.0 (Super Smart Society)” proposed in the 5th Science and Technology Basic Plan in Japan. It must be important to elaborate the SoS (System of Systems) approach towards the value co-creation in the whole society with integrating the several kind of systems, such as in nature, society, biology, and humanity. This paper describes the several concepts or methodologies discussed currently in the systems science research community related to advanced systems approach for the realization of Super Smart Society.

The Air-Conditioning Controlling System Managing both Power Consumption and Comfortableness has been developed. It chooses the best control schedule which is expected to consume the lowest energy in an acceptable level of comfortableness by a heat load simulation. It also can perceive an amount of energy reduction in a certain period of time under demand response situation.

The renewal of conventional energy systems is important countermeasures against global warming effects and natural hazards. A self-sustainable decentralized energy system is one of the promising solutions for future sustainable and resilient societies. In this paper, a mathematical programming model is formulated and design and utilization of the overall energy network is optimized based on the model, where stationary batteries are equipped. Through some numerical simulation results, the effectiveness and the potential, e.g. for clarifying the effect of the batteries, of the proposed model are investigated.

Due to the prevalence of Li-ion battery, the importance of the battery management system (BMS) is increasing. BMS is the system for managing the operation of batteries and monitoring their state of health.

By using the observer containing parameter estimator for analyzing the equivalent circuit model of batteries, the online diagnosis of the batteries becomes possible. However, the Li-ion battery has complicated dynamics derived from the Warburg impedance which described the diffusion phenomena of the Li-ion.

Applying fractional calculus to the expression of the system, the dynamics of Warburg impedance can be described simply. By designing the adaptive observer which can treat the system described as fractional order system, it is expected to become possible to identify equivalent circuit containing Warburg element online without approximation.

In this paper, we designed the Kreisselmeier type adaptive observer for the linear fractional order system. Then, we applied the proposed observer to the half-cell of the lithium-ion battery modeled by Randles circuit under semi-infinite linear diffusion, and confirm the effectiveness of proposed adapitve observer by numerical simulation.

The liberalization of electricity retail sales of Japan for all customers including households was begun on April, 2016. In this paper, the retail power supplier operational planning against the FIT imbalance special system in the electricity retail market after the liberalization of electricity retail sales is investigated. This paper formulates the retail power supplier operational planning problem whose objective is the maximization of the profit expected value with considering the imbalance between planned electric energy supply and actual electric energy supply, when the retail power supplier has a battery. Then, a solution method for the formulated problem is proposed. The effectiveness of the proposed method is confirmed through numerical experiments with wind data by Japan Meteorological Agency and transaction data of Japan Electric Power Exchange.

This paper proposes a method to predict net power demand of a few hours ahead using fixed information at the time of prediction, result value of net power demand, air temperature, and solar radiation. Because the result value is obtained easily, the proposed method updates the prediction value at short intervals using latest result value. We evaluate the performance of the proposed method using the demand data of an actual power company. The prediction error of the proposed method is 0.97% when we predict net power demand of 90 minutes ahead.

Recently, a large amount of Photovoltaic Generations (PV) are installed into a power system in large quantities. Therefore electric power suppliers must forecast the net demand profile that excluded quantity of PV generation from demand load. In this paper, a new technique of net demand forecasting is proposed. At first, we propose using Taguchi's T method (T method) that is one of the multivariate analysis techniques for net demand forecasting. Moreover, we propose data-select T method as improvement of T method for using net demand forecasting with less data. Furthermore, we examine the net demand forecasting technique, which is based on data in the last month on a forecasting day, by T method and data-select T method. In addition, the effectiveness of the proposed method by comparing the multiple regression analysis is discussed.

Electrical power distribution networks, whose component is distribution substations, distribution feeders, and sectionalizers, are operated to maintain the power supply reliability and the power quality using imprecise power demand measurements in each load section. The recent growth in penetration of photovoltaic generation systems (PVs) has brought new difficulties in the distribution network operations. This is because distribution operators cannot monitor or control status of PVs; nevertheless, the solar-irradiation-dependent PV output intensifies uncertainty in the demand measurements. If the PV installation keeps growing steadily, it will become more difficult to operate the distribution networks appropriately. In this paper, the authors focus on service restoration in the distribution networks, and propose a problem framework and its solution to determine the optimal restoration configuration in consideration of the extensive PV installation. There are two distinctive features in this study, 1) the reconfiguration problem of distribution networks is reformulated taking into account of the imprecise measurements of the electricity consumption and the PV output, and 2) an enumeration-based rigorous solution is applied to the reformulated problem. The validity of the authors' proposal is verified through numerical simulations and discussions on their results.

In this study, thawing of biological tissue was tried monitoring using three dimensional electrical impedance tomography (EIT). To measure three dimensional throwing process, a printed circuit board was developed. It contained a circular measurement hole with 5mm in diameter and included four layers of eight electrodes arrays individually. A frozen columned beef was set at the center of the measurement hole and thawing process could be observed real-timely. The EIT measured from bottom layer showed the fastest throwing of frozen columned beef where had highest thermal conductivity. And other EITs from upper layers showed slower thrawing. Results showed feasibility of three dimensional monitoring of thawing using EIT.

Recently, automatic parking systems that operate under the assumption of a flat road surface have been studied. In real-life situations, issues such as road surface gradient, swollen road surfaces, and car stopper of the coin-operated parking lots are frequently encountered. In this paper, an automatic parking system that has robustness against these road disturbances is proposed. To verify the advantages of the proposed method, the proposed system is evaluated through simulations of a parallel parking scene with road surface gradient and a bump.

Recently, train traffic management operation is being complex because of congested train schedules, mutual line operations and various kinds of cars/classes. So it is necessary for decreasing operator's workload and raising his skill to help his work by computer systems. A research in train traffic simulation has been done by several approaches. But the approaches have focused on simulating only physical constraints such as running velocity of cars or signaling equipment and haven't dealt with atypical constraints such as speed regulation under bad weather condition. In this paper, we proposes a modeling method of the atypical constraints and an expandable simulation architecture for adding the constraints. Being evaluated using our prototype system, it is shown that our approach can assemble simulation logics for various kind of constrains handily and simulate effects of the constraints precisely keeping practical response.

In this paper we consider, from the point of view of probability theory, an effective search method for large scale combinatorial optimization problems. The fundamental ideas on which our method is based are the following: 1) Many different neighborhood operations, which consist of the iterations of unit neighborhood operations, are applied to solutions. 2) The probability distribution of the objective function values of the neighborhood solutions is estimated, from the data obtained in the search process. 3) The neighborhood operation, which maximizes the expected value of the amount of the improvement of the current solution, is selected to be applied. From these ideas and the fundamentals of probability theory, a new method for searching for solutions is derived. We have applied the local search method, the genetic algorithm, and the proposed method to traveling salseman problems and maximum satisfiability problems. The effectiveness of the proposed method is shown by the computational experiments.

Missing value estimation is an important task in data mining and analysis of data containing missing values. The purpose of this study is to improve the accuracy of missing value estimation using self-organizing maps (SOMs), which have been studied in recent years. We have focused on the ensemble learning algorithms based on bootstrap sampling that have been successfully used in recent years, in cluster ensembles and pattern recognition. In the present study, in order to improve the accuracy of missing values estimation, we applied the bagging and sub-bagging major ensemble learning algorithms to SOM. We tested the effectiveness of the proposed methods through computational experiments using bench mark data sets published in the UCI Machine Learning Repository. The reproducibility error with respect to the artificial missing values was evaluated. The experimental results show that our methods were better in estimation using conventional SOM and simple ensemble of SOMs, from the viewpoint of the accuracy of missing value estimation. Further, sub-bagging was confirmed to tend to have higher accuracy than bagging

The condition monitoring system based on sensor network becomes widely used among various fields. The sensor data is utilized to monitor the present condition, to predict future condition and to detect the state change point from the behavior of sensor data. The behavior of sensor data before reaching the abnormal state would interact with other behaviors of sensor data if the sensors monitor the same object. In this paper we focus on the relationship between sensor data represented by the graphs. The sequences of the relationship from the change detection time are stored in the database. We propose that the state change detection is determined by searching the similar sequence of the relationship.

In almost all Japanese railway companies, rolling stock management still relies on manual procedure. As density of trains and interconnectivity between lines are increased, a support system for the management gets to be important. This paper focuses on a scheduling task for vehicle units' operation and a support system for rescheduling vehicle units' operation in train service disruption. A schedule of vehicle units' operation is modified by a dispatcher to keep consistency between train timetable and vehicle unit assignment. When there is no solution, the dispatcher has to ask a train service manager to change train timetable. To support this task, we developed an algorithm of rescheduling vehicle unit operation, which included relaxation of constraints. Our algorithm represents constraints as a network model, and, if necessary, relaxes constraints by modifying the network model during search of a solution. This paper indicates the network model, the algorithm of searching for a solution as relaxing constraints, and numerical experiments using a real middle size data. The results of the experiments showed that our algorithm could calculate various solutions with relaxed constraints. Analyzing the solutions at the view points of train service and rolling stock operation, we showed the effectiveness of a system that proposes various solutions using our algorithm.

Communication using smartphone devices connected to the cellular network in a bullet train is increasing and there is currently a limited capacity in the train-unit entrance circuit. In the Sequentially Switched Antenna Array Receiver (SSAAR), the array antenna fixes the receiving point by sequentially switching the elements against the direction of travel. The authors propose a multiple antenna combining scheme that satisfies the requirements for the sequentially switching, and show its effectiveness by computer simulations using experimental data.

This paper proposes an extension method of generalized minimum variance control without coprime factorization. The proposed controller is obtained by defining new generalized output, instead of conventional one. Because of not using coprime factorization, the proposed method has the ability that constructs strong stability system preventing being higher order controller.