Search results for: Junichi Murata

Items from 1 to 20 out of 36 results

chapter

Design of incentive-based demand response programs using inverse optimization

Masaru Murakami, Ryohei Funaki, Junichi Murata

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) > 2754 - 2759

2017 IEEE International Conference on Systems, Man and Cybernetics (SMC)

An incentive design method is proposed for incentive-based demand response programs targeting residential consumers. Consumers are modelled as decision-makers and their models represent, unlike existing models, dynamical nature of power consumption behaviors. The design is done based on inverse optimization. The degree of freedom that exists in the solution can be effectively utilized to make the...

article

Spectrum-Diverse Neuroevolution With Unified Neural Models

Danilo Vasconcellos Vargas, Junichi Murata

IEEE Transactions on Neural Networks and Learning Systems > 2017 > 28 > 8 > 1759 - 1773

Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However, by increasing the representation complexity, a second problem arises. The search space becomes huge, and therefore, an associated scalable and efficient...

article

Fossil fuel cost saving maximization: Optimal allocation and sizing of Renewable-Energy Distributed Generation units considering uncertainty via Clonal Differential Evolution

Madihah Md Rasid, Junichi Murata, Hirotaka Takano

Applied Thermal Engineering > 2017 > 114 > C > 1424-1432

Renewable-Energy Distributed Generation Units (REDGs) are attractive alternative power sources to solve economic, environmental and fuel depletion problems. Maximizing their benefits, however, requires a proper size and location planning considering various aspects of the planning problem. This paper proposes a problem formulation of REDG location and size planning that considers various constraints,...

chapter

A study on effective demand response program in electric power system based on results of demonstrative field test

Kiyonori Kawamura, Tsutomu Doki, Yuuji Oono, Hirotaka Takano, more

2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) > 264 - 269

2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

Since the Great East Japan Earthquake in 2011, development and utilization of Demand Response (DR) has been getting more active in Japan, which can, as one of the demand control methods, contribute to efficient power supply. Necessity of such DR has also been pointed out recently that does not put a burden on consumers. Peak Time Rebate (PTR) type DR does not impose too much a burden on consumers...

article

Day‐Ahead Global Solar Radiation Forecasting Using Binary Trees and Factor Analysis

FUMITOSHI NOMIYAMA, TAKUMA MURAKAMI, JOJI ASAI, HIROTAKA TAKANO, more

Electrical Engineering in Japan > 196 > 3 > 13 - 21

A method is proposed for forecasting global solar radiation. The method is based on weather information using binary trees and factor analysis. The feature of this method is that it is possible to use a simple linear forecasting equation. The method has been tested on meteorological and global solar radiation data obtained at several observation sites, and the results show that it is a promising means...

chapter

Tree structure based differential evolution for optimization of trees and interactive evolutionary computation

Ryohei Funaki, Hirotaka Takano, Junichi Murata

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) > 331 - 336

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

A new Differential Evolution (DE) technique called Tree Structure Based Differential Evolution (TSDE) is proposed for an Interactive Evolutionary Computation (IEC). In IEC, repetitious evaluations by the user is used for optimization, therefore user's fatigue is a practical issue. IEC needs to discover a solution with smaller population size and fewer search generations. DE turns out to be suited...

chapter

Novelty-Organizing Team of Classifiers in noisy and dynamic environments

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

2015 IEEE Congress on Evolutionary Computation (CEC) > 2937 - 2944

2015 IEEE Congress on Evolutionary Computation (CEC)

In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is applied to the continuous action mountain car as well as two variations of it: a noisy mountain car and an unstable weather mountain car. These problems take...

article

A Determination Method for the Optimal Operation of Controllable Generators in Micro Grids That Copes with Unstable Outputs of Renewable Energy Generation

Hirotaka Takano, Peng Zhang, Junichi Murata, Takuhei Hashiguchi, more

Electrical Engineering in Japan > 190 > 4 > 56 - 65

Micro grids are expected to be one of the most realistic energy systems for efficient use of renewable energy sources with few adverse effects on the main electric power grids. However, it is difficult to maintain the supply‐and‐demand balance because distributed renewable energy generation units (DREGs), such as photovoltaic generation systems and wind turbine generation systems, generate a significant...

chapter

Novelty-organizing team of classifiers - A team-individual multi-objective approach to reinforcement learning

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

2014 Proceedings of the SICE Annual Conference (SICE) > 1785 - 1792

2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

In reinforcement learning, there are basically two spaces to search: value-function space and policy space. Consequently, there are two fitness functions each with their associated trade-offs. However, the problem is still perceived as a single-objective one. Here a multi-objective reinforcement learning algorithm is proposed with a structured novelty map population evolving feedforward neural models...

chapter

A study on abstract policy for acceleration of reinforcement learning

Ahmad Afif Mohd Faudzi, Hirotaka Takano, Junichi Murata

2014 Proceedings of the SICE Annual Conference (SICE) > 1793 - 1798

2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

Reinforcement learning (RL) is well known as one of the methods that can be applied to unknown problems. However, because optimization at every state requires trial-and-error, the learning time becomes large when environment has many states. If there exist solutions to similar problems and they are used during the exploration, some of trial-anderror can be spared and the learning can take a shorter...

article

Differential diagnosis of small cell glioblastoma and anaplastic oligodendroglioma: a case report of an elderly man

Kenta Takahashi, Masumi Tsuda, Hiromi Kanno, Junichi Murata, more

Brain Tumor Pathology > 2014 > 31 > 2 > 118-123

Small cell glioblastoma is a histological subtype of glioblastoma with characteristic features of highly proliferative, monotonous small glial cells with high nuclear cytoplasm ratio. Morphologically, malignant lymphoma or small cell metastatic carcinoma should be carefully discriminated. Some cases are difficult to differentiate from anaplastic oligodendroglioma. In this report, we present a case...

chapter

Continuous adaptive reinforcement learning with the evolution of Self Organizing Classifiers

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL) > 1 - 2

2013 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

Learning classifier systems have been solving reinforcement learning problems for some time. However, they face difficulties under multi-step continuous problems. Adaptation may also become harder with time since the convergence of the population decreases its diversity. This article demonstrate that the novel Self Organizing Classifiers method can cope with dynamical multi-step continuous problems...

chapter

Energy efficient demand control for air-conditioning systems

Yasushi Tomita, Kenichi Kuwabara, Nobuhisa Kobayashi, Junichi Murata, more

2013 IEEE Power & Energy Society General Meeting > 1 - 5

2013 IEEE Power & Energy Society General Meeting

We propose an energy efficient demand control method for air-conditioning systems for buildings installed with multi-split packaged air-conditioning systems (PAC). Energy efficiency is improved by controlling the operating point constantly to target power consumption while limiting PAC electricity power demand and recovering a normal condition without relying on PAC internal logic. The constant operating...

chapter

A study on visual abstraction for reinforcement learning problem using Learning Vector Quantization

Ahmad Afif Mohd Faudzi, Hirotaka Takano, Junichi Murata

The SICE Annual Conference 2013 > 1326 - 1331

SICE 2013 - 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan

When applying the learning systems to real-world problems, which have a lot of unknown or uncertain things, there are some issues that need to be solved. One of them is the abstraction ability. In reinforcement learning, to complete each task, the agent will learn to find the best policy. Nevertheless, if a different task is given, we cannot know for sure whether the acquired policy is still valid...

chapter

Contingency Training

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

The SICE Annual Conference 2013 > 1361 - 1366

SICE 2013 - 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan

When applied to high-dimensional datasets, feature selection algorithms might still leave dozens of irrelevant variables in the dataset. Therefore, even after feature selection has been applied, classifiers must be prepared to the presence of irrelevant variables. This paper investigates a new training method called Contingency Training which increases the accuracy as well as the robustness against...

chapter

Re-labeling Differential Evolution for combinatorial optimization

Ryohei Funaki, Hirotaka Takano, Junichi Murata

The SICE Annual Conference 2013 > 1550 - 1555

SICE 2013 - 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan

In this paper, the authors propose a new differential evolution (DE) technique called Re-labeling Differential Evolution for combinatorial optimization. DE was proposed in 1995 by Stone and Price. DE is one of the techniques of evolutionary computation (EC), and efficiently solves optimization problems using difference vectors between individuals in continuous space. DE can efficiently solve problems...

article

Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Md. Abdus Samad Kamal, Masakazu Mukai, Junichi Murata, Taketoshi Kawabe

IEEE Transactions on Control Systems Technology > 2013 > 21 > 3 > 831 - 841

Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system...

article

Self organizing classifiers: first steps in structured evolutionary machine learning

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

Evolutionary Intelligence > 2013 > 6 > 2 > 57-72

Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed which are similar to LCSs but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However,...

article

IDH1 mutation as a potential novel biomarker for distinguishing pseudoprogression from true progression in patients with glioblastoma treated with temozolomide and radiotherapy

Hiroaki Motegi, Yuuta Kamoshima, Shunsuke Terasaka, Hiroyuki Kobayashi, more

Brain Tumor Pathology > 2013 > 30 > 2 > 67-72

The purpose of this study was to distinguish pseudoprogression (PP) from early true progression in patients with glioblastoma (GBM) based on the presence of a mutation in isocitrate dehydrogenase 1 (IDH1). We retrospectively surveyed 32 patients with GBM or GBM with oligodendroglioma component (GBMO) who underwent biopsy or maximal tumor resection followed by concurrent radiotherapy and temozolomide...

article

Expression of O⁶‐methylguanine DNA methyltransferase (MGMT) and immunohistochemical analysis of 12 pineal parenchymal tumors

Hiromi Kanno, Hiroshi Nishihara, Mitsuteru Oikawa, Yoshimaru Ozaki, more

Neuropathology > 32 > 6 > 647 - 653

Pineal parenchymal tumors (PPTs) are rare neoplasms which occupy less than 1% of primary CNS tumors. Because of their rare incidence, previous reports on PPTs are limited in number and the useful molecular markers for deciding histological grading and even selecting chemotherapy are undetermined. In this study, we conducted immunohistochemical analysis of 12 PPT specimens, especially for expression...

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INFONA - science communication portal

Search results for: Junichi Murata

Design of incentive-based demand response programs using inverse optimization

Spectrum-Diverse Neuroevolution With Unified Neural Models

Fossil fuel cost saving maximization: Optimal allocation and sizing of Renewable-Energy Distributed Generation units considering uncertainty via Clonal Differential Evolution

A study on effective demand response program in electric power system based on results of demonstrative field test

Day‐Ahead Global Solar Radiation Forecasting Using Binary Trees and Factor Analysis

Tree structure based differential evolution for optimization of trees and interactive evolutionary computation

Novelty-Organizing Team of Classifiers in noisy and dynamic environments

A Determination Method for the Optimal Operation of Controllable Generators in Micro Grids That Copes with Unstable Outputs of Renewable Energy Generation

Novelty-organizing team of classifiers - A team-individual multi-objective approach to reinforcement learning

A study on abstract policy for acceleration of reinforcement learning

Differential diagnosis of small cell glioblastoma and anaplastic oligodendroglioma: a case report of an elderly man

Continuous adaptive reinforcement learning with the evolution of Self Organizing Classifiers

Energy efficient demand control for air-conditioning systems

A study on visual abstraction for reinforcement learning problem using Learning Vector Quantization

Contingency Training

Re-labeling Differential Evolution for combinatorial optimization

Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Self organizing classifiers: first steps in structured evolutionary machine learning

IDH1 mutation as a potential novel biomarker for distinguishing pseudoprogression from true progression in patients with glioblastoma treated with temozolomide and radiotherapy

Expression of O⁶‐methylguanine DNA methyltransferase (MGMT) and immunohistochemical analysis of 12 pineal parenchymal tumors

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