Yoshikazu Fukuyama

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Yoshikazu verified their affiliation via an institutional email.

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• Ph.D.
• Professor (Full) at Meiji University

This paper proposes connection phase estimation of pole mounted distribution transformers by majority voting of high-quality solutions using sampled stages. The essential challenge of the connection phase estimation of pole mounted distribution transformers problem is that the correct connection phase combination and the combination with the best o...

This paper proposes a hierarchical optimization framework for automatic generation of maintenance worker schedules at rolling stock depots. Maintenance worker schedules at rolling stock depots are daily schedules assigned to maintenance worker teams such as cleaning rolling stock and performing inspection during turnaround operations of superior tr...

This paper proposes discrete spider monkey optimization with dynamic multiple populations for a vending machine column optimization problem. For beverage vending machines, there is a need to reduce the number of vending machine restocking trips in order to tackle challenges of increasing fuel costs and decreasing the number of drivers. Therefore, i...

Various practical problems including power system problems have been solved using least squares methods as basic techniques. However, the methods cannot be applied when outliers exist in data or errors are not normally distributed. In order to tackle the challenge, various methods have been developed. Recently, maximum correntropy criterion (MCC) h...

Maintenance worker scheduling generates a daily schedule for each worker group to perform inspection and maintenance work such as cleaning rolling stock when superior trains turn around at a terminal station. Since the schedule varies from day to day according to changes in daily train timetables and rolling stock operations, it is required to be c...

This paper proposes an estimation method of actual load and PV output values in distribution systems using a power flow measuring instrument and smart meters. For distribution system operations, the requirement for estimating actual load and PV output values separately has been expanding as introduction of PV systems increases. Therefore, in order...

This paper proposes a cooperation framework of the optimal operational planning of an energy plant and optimal production scheduling in factories using an improved integer form of adaptive population-based incremental learning (IIAPBIL) and a production simulator. Traditionally, the optimal operation planning of energy plants has been solved using...

Recently, many countries focus on achieving carbon neutral by 2050, and researches on optimal operation of energy plants in factories and large commercial buildings have been conducted for energy saving and reduction of CO2 emissions. Since the optimal operation problem of energy plants can be formulated as a mixed integer nonlinear optimization pr...

This paper proposes anomaly detection for hydroelectric generating units by Fast Robust Random Cut Forest with a fast feature selection method by considering characteristics of operating data and Random Cut Trees. Hydroelectric generating units are renewable electric generating sources for electricity supply. Therefore, it is crucial to accurately...

This paper proposes a dynamic state estimation method for distribution systems using measurement values from IT switches and smart meters. In future distribution systems, introduction of solar power generations and electric vehicles is expected to increase complexity of directions and changes in the power flow. The proposed method is able to observ...

This paper proposes a kernel principal component analysis (KPCA) based multivariate statistical process control (KPCA-MSPC) method for fault detection of refrigeration showcase systems using a feature selection method with maximal information coefficient (MIC). Refrigeration showcase system data include non-linear relationships among pairs of featu...

This paper proposes distribution state estimation (DSE) using multiple stages considering asynchronous measurement data by dependable parallel multi-population global-best brain storm optimization with differential evolution strategies. In actual distribution systems, measurement data are obtained asynchronously by polling in distribution automatio...

This paper proposes a kernel principal component analysis (KPCA) based multivariate statistical process control (KPCA-MSPC) method for fault detection of refrigeration showcase systems using a feature selection method with maximal information coefficient (MIC). Refrigeration showcase system data include non-linear relationships among pairs of featu...

This paper proposes daily peak load forecasting by a correntropy based Artificial Neural Network (ANN) using an adaptive kernel size method for reduction of engineering loads considering outliers. When outliers exist in the training data, estimation accuracy of daily peak load forecasting using a conventional least mean square (LMS) based ANN can b...

This study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies hav...

This paper proposes dependable parallel multi-population modified brain storm optimization for load adjustment distribution state estimation (DSE) considering outliers using just in time (JIT) modeling and correntropy. If the outliers are measured at the measurement points, estimation results at the measurement points only using correntropy are aff...

This study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies hav...

Refrigeration showcases are commonly utilized equipment in super markets and convenience stores to maintain the temperature and quality of products. Being also susceptible to fault events, the detection of symptoms of unusual operation is still difficult as only samples of normal behavior are usually available. This paper introduces a new use of au...

This paper proposes refrigerated showcase fault detection by a correntropy based Artificial Neural Network (ANN) using Fast Brain Storm Optimization (FBSO). Since there are approximately 50,000 convenience stores in Japan and it is difficult for experts to tune up all of showcase systems with different characteristics. Therefore, an automatic param...

The authors wish to make the following corrections to their paper. [...]

This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolut...

This paper proposes dependable Multi-population Improved Brain Storm Optimization with Differential Evolution (MP-IBSODE) for optimal operational planning of energy plants. The problem can be formulated as a mixed-integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO),...

The paper proposes total optimization of smart community by multi-population global-best brains storm optimization with differential evolution strategies considering uncertainty of renewable energies. This paper tries to minimize total energy costs, actual electric power loads at peak load hours (peak shifting), and CO2 emission for whole of a smar...

The task of online operational planning and control functions in a control center, such as contingency analysis, is computationally demanding and hundreds of simultaneous power flow (PF) calculations should be performed. As practical and cost-effective tools for speedup calculation, this paper presents the implementation of simultaneous parallel PF...

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Many countries have conducted smart city demonstration projects for reduction of total energies and CO2 emission. The energy and environmental problem of smart city can be formulated as a mixed inte...

This paper proposes dependable parallel multi-swarm canonical differential evolutionary particle swarm optimization with migration (DPMS-CDEEPSOw/M) for voltage and reactive power control (Volt/Var Control: VVC). The proposed DPMS-CDEEPSOw/M is a general evolutionary computation technique for dependable and fast optimization applications. So far, a...

This paper proposes improved brain storm optimization with differential evolution strategies (IBSODE) for load adjustment distribution state estimation (DSE) using correntropy. If failure of the sensors and communication systems occur, outliers may exist in the measured values. If the outliers exist in measurement values, correntropy is an effectiv...

This paper proposes optimal operational planning of energy plants considering (OPEP) renewable energy (RE)’s uncertainty. In recent years, global warming is exacerbated by the increase in the carbon dioxide’s emission which belongs to greenhouse gases. Utilization of renewable energies is necessary in order to reduce its emissions. RE can also be u...

This paper proposes an Artificial Neural Network (ANN) based daily peak load forecasting method by differential evolutionary particle swarm optimization (DEEPSO) considering outliers. When outliers exist in the training data, forecasting accuracy of daily peak load forecasting can be affected by the outliers. Therefore, engineers have removed the o...

This paper proposes total optimization of energy networks in a smart city (SC) by multi-swarm differential evolutionary particle swarm optimization (MS-DEEPSO). Efficient utilization of energy is necessary for reduction of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission, and SC demo...

This paper proposes differential evolutionary particle swarm optimization (DEEPSO) for load adjustment distribution state estimation (DSE) using correntropy. Practical equipment in distribution systems causes nonlinear characteristics in an objective function and evolutionary computation methods have been applied to DSE so far. This paper applies D...

This paper proposes a total optimization method of a smart city (SC) by Global-best brain storm optimization (GBSO). The SC model includes natural gas utilities, electric power utilities, drinking and waste water treatment plants, industries, buildings, residences, and railroads. The proposed method minimizes energy cost, shifts actual electric pow...

This paper proposes differential evolutionary particle swarm optimization (DEEPSO) for load adjustment distribution state estimation (DSE) using correntropy. Practical equipment in distribution systems causes nonlinear characteristics in an objective function and evolutionary computation methods have been applied to DSE so far. This paper applies D...

This paper presents parallel multipopulation differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evoluti...

This paper presents parallel multi-population differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evolut...

This paper proposes optimal operation planning of energy plants by modified brain storm optimization (MBSO) The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), and differential evolutionary PSO (DEEPSO)...

This paper proposes parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt Var Control: VVC). The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP) and various evolutionary computation techniques have been applied to the problem including PSO...

This paper proposes dependable parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt/Var Control: VVC). Since the problem can be formulated as a mixed integer nonlinear optimization problem (MINLP), various evolutionary computation techniques have been applied to the problem...

This paper proposes a total optimization method of a smart city (SC) by Modified brain storm optimization (MBSO) The method utilizes a SC model. The model includes natural gas utilities, electric power utilities, drinking and waste water treatment plants, industries, buildings, residences, and railroads. It minimizes energy cost, shifts actual elec...

This paper proposes integration of optimal operational planning of energy plants and optimal production planning for actual reduction of energy costs in factories. Conventionally, fixed loads of the various tertiary energies have been utilized for solving optimal operational planning of energy plants so far. On the contrary, in this paper, the load...

This paper proposes dependable multi-population differential evolutionary particle swarm optimization (DEEPSO) for distribution state estimation (DSE) using correntropy. Considering deregulation of power systems and high penetration of renewable energies, power flow can be changed suddenly. One of the solutions for the problem is applications of pa...

This paper proposes dependable parallel differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The planning can be formulated as a mixed integer nonlinear optimization problem (MINLP). When optimal operational planning of numbers of energy plants are calculated simultaneously in a d...

This paper proposes a total optimization method of a smart community (SC) by Differential evolutionary particle swarm optimization considering reduction of search space. Japanese experts have developed various sectors of SC models such as an electric utility model, an industry model, and a building model. This paper utilizes the models and tries to...