Faculty Profiles - SHIGEI Noritaka

Papers - SHIGEI Noritaka

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Li Shijia, Noritaka Shigei . A Preliminary Study on Calibration Method of Low-Cost MOX-type CO2 Sensors for Indoor Occupancy Estimation . Proceedings of the Fuzzy System Symposium 44 - 47 2025.9

Tsubasa HIDAKA, Kanta KUBO, Kan TANABE, Rara DEGUCHI, Haruka ETO, Kenji BABA, Naoki KUROSHIMA, Masumi WADA, Mashiho MUKAIDA, Takao OHTSUKA, Satoshi ONO, Noritaka SHIGEI . A Data Augmentation Method for Temporal Action Segmentation in Laparoscopic Surgery Videos . Proceedings of the Fuzzy System Symposium 472 - 475 2025.9

Miyajima H., Shigei N., Miyajima H., Shiratori N. . BP Methods for Securely Distributed System with Communication Failures . Lecture Notes in Networks and Systems1553 326 - 340 2025.9

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima and Norio Shiratori . Neural Gas method using autonomous and secure distributed processing with decomposed data . Nonlinear Theory and Its Applications, IEICE16 ( 3 ) 377 - 389 2025.7Reviewed

Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

Much research on secure and safe AI analysis methods for users has been conducted. They are 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, 3) federated learning (FL), and so on. It is known that both utility and confidentiality are essential for machine learning using confidential data. However, there is a trade-off between them. In the previous paper, we proposed a learning method for secure distributed processing using decomposition data and parameters. The characteristic feature of this method is to achieve high confidentiality through distributed processing with decomposed data and parameters. On the other hand, the realization of machine learning through the distribution and integration of decomposition data leads to an increase in computational complexity and a deterioration of computational accuracy. In particular, while increasing the number of servers improves confidentiality, this problem becomes more pronounced. In this paper, we propose scalability improvement of simplified, secure distributed processing methods of BP and NG methods to suppress the computational complexity associated with an increase in the number of servers and demonstrate its effectiveness through a numerical simulation.

DOI： 10.1587/nolta.16.377

Miyajima H., Shigei N., Miyajima H., Shiratori N. . Toward the development of learning methods with distributed processing using securely divided data . Computers and Electrical Engineering123 2025.4Reviewed International journal

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Computers and Electrical Engineering

To pave the way to a super-smart society, artificial intelligence (AI) methods are being developed to discover and analyze necessary information instantly from cyberspace and utilize it in physical space. However, privacy protection is necessary for AI to process big data in cyberspace. From the viewpoint of developing safe and secure machine learning methods, research on (1) homomorphic cryptography, (2) differential privacy, (3) secure multiparty computation, and (4) federated learning is underway. The goal of these studies is to develop useful learning methods while maintaining data privacy. We propose a method to address the trade-off between security and usability in machine learning. This method balances usability and data confidentiality by using decomposed data to achieve secure distributed processing. However, such methods using distributed processing increase computational and communication overhead as the number of servers increases. To address this problem, we propose a method to control the computational complexity as the number of servers increases. On the basis of these studies, this study first systematically addresses the construction of secure distributed processing methods with decomposed data. A comprehensive approach is essential to advance the field and allow these methods to be effectively applied to different domains. On the basis of these methods, we propose back-propagation and neural gas learning methods with reduced computational and communication requirements. We then apply the proposed methods to numerical simulations of class classification and clustering problems and show that accuracy comparable to that of conventional models can be achieved with 1/Q computational and communication complexity for distributed models with Q servers.

DOI： 10.1016/j.compeleceng.2025.110160

Scopus

Other Link： https://www.sciencedirect.com/science/article/pii/S004579062500103X

Nagatani K., Shigei N., Morita C., Ishizuka Y., Miyajima H. . CNN AND GAN BASED WEATHERING STEEL APPEARANCE EVALUATION IMPROVED BY IMAGE SIZE AND LOSS FUNCTION . International Journal of Innovative Computing, Information and Control21 ( 2 ) 433 - 445 2025.4Reviewed International journal

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control

Weathering steel is a steel material that can maintain its performance outdoors for a long period of time by forming protective rust. Periodic inspection of the rust condition is necessary to maintain this steel in good condition, and CNN-based methods for evaluating the appearance of rust have been studied. One of the problems in improving the classification accuracy of the CNN is how to prepare the rust images for training. The objectives of this paper are 1) to consider rust patch image size and 2) to improve the diversity of the generated images in rust image generation using GAN. From 1), we show that the accuracy can be improved by using large cropping images and shrinking the image when training the model. From 2), we propose SSIM-target loss, which is an improvement of the conventional SSIM loss, and show that it improves the CNN Score. Using methods 1) and 2), we achieved an F1 score of 90.3% for the three-class classification of the rust images, compared to the baseline method’s F1 score of 86.8%.

DOI： 10.24507/ijicic.21.02.433

Scopus

Other Link： http://www.ijicic.org/ijicic-210211.pdf

Ryuo Kawabata, Kotaro Nagata, Yukari Nakamaru, Kentaro Yasui, Chihiro Morita, and Noritaka Shigei . Detection of Concrete Cracks Using YOLOv8 and Estimation of Crack Width . Proceedings of International Symposium on Collaborative Informatics T2-4-1 - T2-4-8 2025.3Reviewed

Tsubasa HIDAKA, Kanta KUBO, Kan TANABE, Rara DEGUCHI, Kenji BABA, Naoki KUROSHIMA, Masumi WADA, Mashiho MUKAIDA, Takao OHTSUKA, Satoshi ONO, Noritaka SHIGEI . Proposal of Ensemble Method for Action Segmentation in Laparoscopic Surgery Videos . Journal of Japan Society for Fuzzy Theory and Intelligent Informatics37 ( 1 ) 571 - 576 2025.2Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . BP Learning Method using Secure Distributed Processing with Decomposition Data and Parameters Divided into Updated and Non-updated Portions . Proc. of International Symposium on Artificial Life and Robotics 625 - 630 2025.1Reviewed

Shuhei Hamashima, Noritaka Shigei, Hiromi Miyajima, Masanobu Miyazaki, Yoichi Ishizuka, Shinichi Abe, Tomoya Nishino . Construction of Regression Model of eGFR by Data Partitioning Based on Trend of Time Series Data . Proc. of International Symposium on Artificial Life and Robotics 563 - 568 2025.1Reviewed

Rara Deguchi, Kan Tanabe, Tsubasa Hidaka, Kenji Baba, Naoki Kuroshima, Masumi Wada, Mashiho Mukaida, Takao Ohtsuka, Noritaka Shigei, Satoshi Ono . A Preliminary Study on Surgical Skill Classification from Laparoscopic Surgery Videos Using Multi-Instance Learning . Proc. of International Symposium on Artificial Life and Robotics 274 - 277 2025.1Reviewed

Nagatani K., Shigei N., Morita C., Ishizuka Y., Miyajima H. . Improvement of GAN-based Training Image Generation for CNN-based rust Evaluation of Weathering Steel . ACM International Conference Proceeding Series 40 - 47 2024.12Reviewed International journal

Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：ACM International Conference Proceeding Series

Weathering steel is a steel material that produces a protective rust layer on the surface of the steel that suppresses the propagation of corrosion. Periodic inspection of the rust condition is necessary to maintain this steel in good condition, and CNN-based methods for evaluating the appearance of rust have been studied. One of the problems in improving the classification accuracy of the CNN is how to prepare the rust images for training. Since it is difficult to collect rust images in the real world, it has been studied to train CNN with rust images generated by GANs. Conventional GANs have the problem that when a large number of images are generated, the generated images are biased toward similar ones. The scope of this paper is to improve the accuracy of CNN using GAN-generated images in classification tasks of rust conditions of weathering steel. The specific objectives of this study are to improve the diversity of images generated by GANs and thereby improve the accuracy of CNNs. To improve image diversity in GAN-based image generation, we propose to use Wasserstein GAN with Gradient Penalty (WGAN-GP), in addition to Conditional GAN (CGAN), and to introduce SSIM (Structural Similarity) and LBP (Local Binary Pattern) as loss functions, in addition to L1 and L2 losses. Then, the images generated by the proposed five types of GANs are applied to the training of CNNs. Comparison between the proposed GANs and the conventional CGAN shows that 1) the proposed GANs can reduce the SSIM similarity of the generated images by up to 1/4, and 2) in the classification of three classes of rust images, the use of the generated images of the proposed GANs can improve the F1 score by up to about 4%pt. Overall analysis shows that WGAN-GP-based is better than CGAN-based and that the newly introduced SSIM, LBP, L1 and L2 losses improve both similarity and classification accuracy.

DOI： 10.1145/3688268.3688275

Scopus

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Neural Gas Method using Autonomous and Secure Distributed Processing with Decomposed Data . Proc. of International Symposium on Nonlinear Theory and Its Applications 137 - 140 2024.12Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Autonomous and Securely Distributed Processing of BP Method Using Decomposed Data . International Journal of Biomedical Soft Computing and Human Sciences29 ( 2 ) 37 - 44 2024.10Reviewed

日高翼, 久保莞太, 田辺寛, 出口楽々, 馬場研二, 黒島直樹, 和田真澄, 向田眞志保, 大塚隆生, 小野智司, 重井徳貴 . 腹腔鏡下手術映像におけるアクションセグメンテーションの分類モデルの最適化とアンサンブル手法の検討 . 日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集40 ( 0 ) 648 - 653 2024.9

Miwa Keigo, Shigei Noritaka, Sugimoto Satoshi, Ishizuka Yoichi, Miyajima Hiromi . Estimation of Sediment Disaster Prone Areas from Map Images Using Mask R-CNN . ICIC Express Letters, Part B: Applications15 ( 06 ) 575 - 584 2024.6Reviewed International journal

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC International

In Japan, where sediment disasters such as debris flow, landslides, and slope failures frequently occur, for disaster prevention purposes, the government publishes areas at risk of sediment disasters as Sediment Disaster Prone Areas. Since this requires time and effort for experts to conduct field surveys, attempts are being made to estimate the collapse hazard areas from elevation maps using deep learning. However, the conventional method of estimating collapse hazard areas pixel by pixel has the problem that it is difficult to identify the area clearly. To solve these problems, we propose using instance segmentation and object detection in computer vision techniques. This study investigates effective methods for estimating areas at risk of sediment disasters using a Mask R-CNN model that simultaneously processes segmentation and object detection. Specifically, we develop 1) a method for generating mask images, 2) a learning method combining Red Relief Image Map images, aerial photographs, standard maps, and land use maps as input images, and 3) a method for overlapping prediction. Numerical experiments conducted under multiple conditions evaluate the accuracy and the predicted images and demonstrate the effectiveness of the developed methods.

DOI： 10.24507/icicelb.15.06.575

Scopus

Other Link： http://www.icicelb.org/ellb/contents/2024/6/elb-15-06-04.pdf

Yamaguchi Yuki, Shigei Noritaka, Miyazaki Masanobu, Ishizuka Yoichi, Abe Shinichi, Nishino Tomoya, Miyajima Hiromi . Development of Assistance System to Diagnose Changes in Kidney Function Using Machine Learning . ICIC Express Letters18 ( 06 ) 615 2024.6Reviewed

MIYAJIMA Hirofumi, SHIGEI Noritaka, MIYAJIMA Hiromi, SHIRATORI Norio . Neural Gas and K-Means Methods Improving the Computational Complexity for Secure Distributed Processing . International Journal of Biomedical Soft Computing and Human Sciences: the official journal of the Biomedical Fuzzy Systems Association28 ( 1 ) 15 - 22 2023.11Reviewed

Language：English Publisher：Biomedical Fuzzy Systems Association

To realize a super-smart society, it is necessary to aim for advanced integration of cyberspace and physical space (real society). Artificial Intelligence (AI) analysis of big data will bring effective information that meets the needs of individuals and companies to real society more quickly. On the other hand, to build a safe and secure society, it is important to develop AI methods that protect the privacy of big data in cyberspace. However, there is a little-known method that satisfies both data confidentiality and utilization of the learning method. Therefore, the authors proposed a learning method for secure distributed processing using decomposition data. This method has higher confidentiality and utilization than the conventional method, but the increase in computational complexity due to distributed processing is a problem. In the previous paper, the authors proposed the Back Propagation (BP) method to solve this problem. In this paper, we apply this method to the Neural Gas (NG) and k-means methods for secure distributed processing, which are unsupervised learning, and show its effectiveness.

DOI： 10.24466/ijbschs.28.1_15

Motomatsu T., Koga T., Shigei N., Yamaguchi M., Itagaki A., Ishizuka Y. . A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning . IEEE Transactions on Magnetics59 ( 11 ) 1 - 5 2023.11Reviewed International journal

Miyajima H., Shigei N., Miyajima H., Shiratori N. . Neural Gas and k-means Methods with Reduced Communication Costs for Secure Distributed Processing . Lecture Notes in Engineering and Computer Science2245 19 - 24 2023.7Reviewed

Motomatsu T., Koga T., Shigei N., Yamaguchi M., Itagaki A., Ishizuka Y. . A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning . 2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings 1 - 2 2023.5

Language：Japanese Publisher：2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings

Optimization of magnetic components design, such as power inductors and transformers, is most needed to improve the performance of future power electronics. However, power electronics designers are faced with the problem of not having sufficient magnetic component models available for their designs. In this paper, we propose a method to construct a unique nonlinear magnetic component model using parametric circuit simulation and deep learning.

DOI： 10.1109/INTERMAGShortPapers58606.2023.10228481

Scopus

Miyajima Hirofumi, Shigei Noritaka, Miyajima Hiromi, Shiratori Norio . Scalability improvement of simplified, secure distributed processing with decomposition data . Nonlinear Theory and Its Applications, IEICE14 ( 2 ) 140 - 151 2023.4Reviewed

Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

DOI： 10.1587/nolta.14.140

YAMAGUCHI Yuki, SHIGEI Noritaka, MIYAZAKI Masanobu, ISHIZUKA Yoichi, ABE Shinichi, NISHINO Tomoya, MIYAJIMA Hiromi . Classification of Renal Function Trends in Time Series Data Using Machine Learning . Journal of Japan Society for Fuzzy Theory and Intelligent Informatics35 ( 1 ) 511 - 516 2023.2Reviewed

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

As a preventive measure for chronic kidney disease requiring dialysis, some municipal governments and medical associations make comments to family doctors and examinees based on the results of health checkups. However, the judgment and preparation of comments are mostly done manually by physicians and public health nurses and require much labor. In this study, we examine the trend determination of kidney function level using machine learning as part of the study to realize the automation of this task. We propose a preprocessing method for time-series data dealing with different numbers of checkups and different duration of checkups. As a machine learning model, we propose ensemble learning methods using gradient boosting decision trees. The effectiveness of the proposed methods is demonstrated in evaluation using about 3,000 cases of specific health checkup data.

DOI： 10.3156/jsoft.35.1_511

MIYAJIMA Hirofumi, SHIGEI Noritaka, MIYAJIMA Hiromi, SHIRATORI Norio . Computational Complexity Reduction of Secure BP Learning Algorithm with Divided Data . Journal of Japan Society for Fuzzy Theory and Intelligent Informatics35 ( 1 ) 506 - 510 2023.2Reviewed

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

Many studies have been conducted on how to perform learning while maintaining data security. One of them is the secure distributed processing with decomposition data. The feature of this method is that it achieves high confidentiality by decomposing data and parameters in machine learning and performing distributed processing. On the other hand, realization of machine learning by distributed and integrated processing of decomposed data and parameters leads to an increase in computational complexity and degradation of computational accuracy. In this paper, we propose an improved BP learning method that suppresses the increase in computational complexity with the increase in the number of servers, and demonstrate its effectiveness.

DOI： 10.3156/jsoft.35.1_506

Yoshiki Higo, Noritaka Shigei, Hiromi Miyajima, Hirofumi Miyajima . Computation Time Reduction and Accuracy Improvement of Secure Multiparty Computation for Back Propagation by Introducing Communication Timeout . Proc. of International Symposium on Artificial Life and Robotics 479 - 484 2023.1Reviewed

Hirofumi Miyajima; Noritaka Shigei; Hiromi Miyajima; Norio Shiratori . Secure Distributed Processing of NG with Updatable Decomposition Data and Parameters . 2022 International Conference on Networking and Network Applications (NaNA) 354 - 360 2022.12Reviewed

Shimasaki Ryota, Shigei Noritaka, Miyajima Hirofumi, Nakamura Yoshihiro, Miyajima Hiromi . Improvement of Self-Driving Model Car for Indoor Patrol Task . International Journal of Innovative Computing, Information and Control18 ( 06 ) 1829 - 1839 2022.12Reviewed

Miyajima H., Shigei N., Miyajima H., Shiratori N. . Secure Distributed Processing of NG with Updatable Decomposition Data and Parameters . Proceedings - 2022 International Conference on Networking and Network Applications, NaNA 2022 354 - 360 2022.12Reviewed

Language：Japanese Publishing type：Research paper (international conference proceedings) Publisher：Proceedings - 2022 International Conference on Networking and Network Applications, NaNA 2022

Machine learning using distributed data, such as federative learning (FL) and secure multiparty computation (SMC), is demanded to achieve both utility and confidentiality when using confidential data. There is a trade-off between utility and confidentiality, and in general, SMC can offer better confidentiality than FL and better utility than homomorphic encryption. In machine learning with SMC, confidentiality is improved by decomposing individual data and parameters into multiple pieces, storing each piece on each server, and learning without restoring the data or parameters themselves. However, once the conventional methods randomly decompose data and parameters, the decomposition remains permanently fixed. The fixed decomposition is considered undesirable because it gives malicious attackers more opportunities to attack the data and model. In this paper, we propose a secure distributed processing of neural gas (NG), which is one of unsupervised machine learning. In addition to the decomposition of data, the proposed method can update the decomposition of parameters during learning. Each server can independently update the decomposition of both data and parameters in the proposed method, and the data and parameters are never restored during learning. Our simulation result shows that it can achieve the same level of learning accuracy as the conventional methods.

DOI： 10.1109/NaNA56854.2022.00067

Scopus

MIYAJIMA Hirofumi, SHIGEI Noritaka, MIYAJIMA Hiromi, SHIRATORI Norio . Reducing the Computational Complexity of Machine Learning Methods Through Secure Distributed Processing . Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association35 ( 0 ) I-1 2022.12

Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Biomedical Fuzzy Systems Association

To realize a super-smart society, it is necessary to aim for advanced integration of cyberspace and physical space (real society). AI analysis of big data will bring effective information that meets the needs of individuals and companies to the real society more quickly. On the other hand, to build a safe and secure society, it is important to develop AI analysis methods that protect the privacy of big data in cyberspace. However, there is no known method that satisfies both data confidentiality and usability of the learning method at a high level. Therefore, the authors have proposed a distributed AI method using secret decomposition data. This method has higher confidentiality and usability than conventional methods, but the increase in computational complexity due to distributed processing is a problem. In this paper, we propose a new learning method to solve this problem. In particular, we apply this method to the Neural-Gas(NG) algorithm, which is unsupervised learning, and show its effectiveness.

DOI： 10.24466/pacbfsa.35.0_i-1

Yamaguchi Yuki, Shigei Noritaka, Miyazaki Masanobu, Ishizuka Yoichi, Abe Shinichi, Nishino Tomoya, Miyajima Hiromi . Classification of Kidney Function Level from Time Series Data Using Machine Learning . Proceedings of the Fuzzy System Symposium38 ( 0 ) 741 - 745 2022.9

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

In this study, we aim to construct effective classification models of renal function level using machine learning, which can be used by physicians in the diagnosis of renal function. We propose a data preprocessing method for the constructed classification models to deal with different numbers and different periods of medical checkups. As machine learning models, we use gradient boosting decision tree models and propose several ensemble learning methods. The effectiveness of the proposed methods is demonstrated by simulation results with about 3,000 cases of medical checkup data.

DOI： 10.14864/fss.38.0_741

Miyajima Hirofumi, Shigei Noritaka, Miyajima Hiromi, Shiratori Norio . The improved BP learning algorithm with divided data . Proceedings of the Fuzzy System Symposium38 ( 0 ) 281 - 286 2022.9

Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

Many studies for privacy preserving data mining have been done. Secure Multiparty Computation (SMC) has been introduced for privacy preserving data. SMC was applied to some statistical computation methods. In previous papers, we proposed synchronous Back Propagation (BP) learning for SMC. In this paper, we propose the improved BP learning method with small number of parameters, even if the number of data and weight divisions is large.

DOI： 10.14864/fss.38.0_281

Miyajima H., Shigei N., Miyajima H., Shiratori N. . Securely Distributed Computation with Divided Data and Parameters for Hybrid Particle SwarmOptimization . IAENG International Journal of Applied Mathematics52 ( 3 ) 2022.8Reviewed

Language：English Publishing type：Research paper (scientific journal) Publisher：IAENG International Journal of Applied Mathematics

Machine learning (ML) on cloud and edge systems is widely used to analyze big data and address complex problems. On the other hand, many privacy issues have arisen due to inadequate data security controls. Therefore, studies on secure machine learning on cloud and edge systems have attracted much attention. One of such studies is Federated Learning (FL), in which data is distributed on multiple servers to achieve machine learning through distributed processing. We have proposed a method to realize learning by distributed processing on multiple servers by dividing the learning data and parameters into multiple pieces in advance and distributing these pieces to each server. In previous papers, we have proposed a learning method using local search based on the Steepest Descent Method (SDM) such as Back Propagation (BP) and Neural Gas (NG). In this paper, we propose a learning method that combines a global and local search for solutions for secure distributed processing computation. In particular, we propose a secret distributed processing method for Hybrid Particle Swarm Optimization (HPSO) which is one of the global and local search methods. Its effectiveness is demonstrated by numerical simulations.

Scopus

Kazuya ARIMURA, Noritaka SHIGEI, Chihiro MORITA, Yoichi ISHIZUKA, Hiromi MIYAJIMA . Rust appearance evaluation methods for weathering steel by using bagging CNN classifier and multiple patch images . Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 2022.5Reviewed

Kohei OGURA, Noritaka SHIGEI, Hiromi MIYAJIMA . Robust person tracking by pan-tilt camera with low-cost single board computer . ICIC Express Letters16 ( 4 ) 2022.4Reviewed International journal

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Secure Learning Systems using Vertically Partitioned Data with IoT . IAENG International Journal of Computer Science48 2022.3Reviewed International journal

西結人, 重井徳貴 . Pix2pixを用いた急傾斜地崩壊危険箇所の推定 . 火の国情報シンポジウム2022 1 - 8 2022.3

Kenshiro Yamanaka, Noritaka Shigei, Hirofumi Miyajima, Hiromi Miyajima . Face image recognition resistant to posture fluctuations using intermediate template vectors and UMAP . 760 - 764 2022.1Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . The generalized BP method of secret distributed processing with divided data . 691 - 696 2022.1Reviewed

宮島洋文, 重井徳貴, 宮島廣美, 白鳥則郎 . 秘匿分解データを用いた新しい機械学習 . 研究報告マルチメディア通信と分散処理（DPS）2021DPS189 ( 11 ) 1 - 8 2021.12

千々和航, 重井徳貴 . サーモグラフィーと深層学習を用いた非接触入力装置における背景情報を用いた識別精度改善 . 日本知能情報ファジィ学会九州支部学術講演会予稿集 64 - 68 2021.11

Ryo Fukuoka, Noritaka Shigei, Hirofumi Miyajima, Yoshihiro Nakamura, Hiromi Miyajima . Self-driving model car acquiring three-point turn motion by using improved LSTM model . Artificial Life and Robotics26 ( 4 ) 423 - 431 2021.11Reviewed International journal

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Securely Distributed Computation with Divided Data for Particle Swarm Optimization . Proceedings of The International MultiConference of Engineers and Computer Scientists 2021 1 - 6 2021.10Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Federated Learning with Divided Data for BP . Proceedings of The International MultiConference of Engineers and Computer Scientists 2021 94 - 99 2021.10Reviewed

森山愛希,重井徳貴 . 地盤強度推定のためのニューラルネットワークモデルの最適化に関するー検討 . 電子情報通信学会九州支部学生会講演会論文集 1 - 1 2021.9

肥後佳季,重井徳貴 . セキュアマルチパーティ計算による誤差逆伝播法の高速化と精度改善の一検討 . 電子情報通信学会九州支部学生会講演会論文集 1 - 1 2021.9

山中謙志郎, 重井徳貴 . 顔の向きを考慮したテンプレートを用いた姿勢変動に頑健な個人識別法 . 電子情報通信学会九州支部学生会講演会論文集 1 - 1 2021.9

千々和航, 重井徳貴, 宮島廣美 . サーモグラフィー画像と深層学習を用いた非接触インターフェースに関する検討 . 電気・情報関係学会九州支部連合大会講演論文集 312 - 312 2021.9

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . The generalized BP learning with divided data . Proceedings of the Fuzzy System Symposium37 196 - 201 2021.9

Kazuya Arimura, Noritaka Shigei, Chihiro Morita, Yoichi Ishizuka, Hiromi Miyajima . Appearance evaluation of rust on weathering steel by multiple CNN discriminatorusing multiple patch images . Proceedings of the Fuzzy System Symposium37 694 - 698 2021.9

Miyajima Hirofumi, Shigei Noritaka, Miyajima Hiromi, Shiratori Norio . Simplified security learning using vertically partitioned data with IoT . Nonlinear Theory and Its Applications, IEICE12 ( 3 ) 412 - 423 2021.7Reviewed

松本光介, 石塚洋一, 重井徳貴, 古賀誉大 . ディープラーニングによるダブルパルステスト評価画像解析を用いたパワーデバイスの特性評価方法の基礎検討 . 電子情報通信学会技術研究報告, 電子通信エネルギー技術121 ( 40 ) 1 - 6 2021.5

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Learning algorithms for vector quantization using vertically partitioned data with IoT . Artificial Life and Robotics26 ( 3 ) 283 - 290 2021.4Reviewed International journal

Kei HIRASHIMA, Noritaka SHIGEI, Satoshi SUGIMOTO, Yoichi ISHIZUKA, Hiromi MIYAJIMA . Semi-Supervised Learning of CNN for Land-Use Classification from Aerial Photograph . Journal of Japan Society for Fuzzy Theory and Intelligent Informatics33 ( 1 ) 520 - 524 2021.2Reviewed

Ryo Fukuoka, Noritaka Shigei, Yoshihiro Nakamura, Hiromi Miyajima . CNN and LSTM models for acquiring stop and turn motion of self-driving model car . 258 - 263 2021.1Reviewed

宮島洋文, 重井徳貴, 宮島廣美, 白鳥則郎 . セキュアマルチパーティ計算によるエッジシステム上のBP学習法の提案 . 研究報告マルチメディア通信と分散処理（DPS）2020DPS185 ( 13 ) 1 - 7 2020.12

Teramura Masahiro, Shigei Noritaka, Shimoo Kosei . Fast Detection of Center of Gravity Position Using Neural Comparator Circuit Based on Artificial Neuron . IEEJ Transactions on Sensors and Micromachines140 ( 12 ) 336 - 342 2020.12Reviewed

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Institute of Electrical Engineers of Japan

This paper proposes a method to detect the center of gravity position of an object in a short time. The method may be used for real-time detection of the center of gravity position for a walking robot, a running vehicle, etc. The detection circuit employs some neural comparators based on the behavior of information processing of a biological neuron. The circuit calculates the ratio of the two signals generated from load cell sensors, and detects the center of the gravity position between the load cell sensors located at the both end of a beam. The detection error of the location is within about 3% at full-scale ratio form the circuit experiments. The detection time is about 200ns from the input signal of sensors until the output of the detection signal on average. A parallel processing circuit using a layer of neural comparators enables fast detection.

DOI： 10.1541/ieejsmas.140.336

Scopus

CiNii Research

内山拓哉, 重井徳貴, 杉本知史, 石塚洋一, 宮島廣美 . 斜面モニタリングにおけるニューラルネットワークを用いた傾斜角度の推定 . 日本知能情報ファジィ学会九州支部学術講演会予稿集 16 - 20 2020.11

佐藤誠也, 重井徳貴 . 単語の条件付き確率を用いた文選択による宿泊施設のレビュー文の分類 . 日本知能情報ファジィ学会九州支部学術講演会予稿集 16 - 20 2020.11

有村和也, 重井徳貴, 森田千尋, 石塚洋一, 宮島廣美 . 複数のパッチ画像を用いたCNN による耐候性鋼材のさびの外観評価 . 日本知能情報ファジィ学会九州支部学術講演会予稿集 21 - 25 2020.11

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Fuzzy Modeling using Vector Quantization and Local Linear Mapping . IAENG International Journal of Computer Science47 ( 4 ) 1 - 8 2020.11Reviewed International journal

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Simplified Security Learning Using Vertically Partitioned Data with IoT . Proc. of 2020 International Symposium on Nonlinear Theory and Its Applications 243 - 246 2020.11Reviewed

Ryota Yamaguchi; Noritaka Shigei; Hirofumi Miyajima; Hiromi Miyajima . Personal identification by learning-based integration of facial and clothes similarities . 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 307 - 308 2020.10Reviewed International journal

Yamaguchi Ryota, Ogura Kohei, Shigei Noritaka, Miyajima Hiromi . Personal Identification Methods by Using Facial and Clothing Features for Entry and Exit Management and Its Identification Using Multiple Frames . Proceedings of the Fuzzy System Symposium36 ( 0 ) 259 - 262 2020.9

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

In this research, we develop user identification methods using facial and clothing features as methods without user operation. We propose two types of integration methods of facial and clothing features: the one based on thresholds and weights and the other using machine learning. Further, we propose an identiflcation method from multiple frames. The evaluation results demonstrate the effectiveness of the proposed methods.

DOI： 10.14864/fss.36.0_259

CiNii Research

Miyajima Hirofumi, Shigei Noritaka, Miyajima Hiromi, Shiratori Norio . Privacy preserving Particle Swarm Optimization method for Secure Multiparty Computation . Proceedings of the Fuzzy System Symposium36 ( 0 ) 379 - 384 2020.9

Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

Many studies for privacy preserving data mining have been done. Secure Multiparty Computation (SMC) has been introduced for privacy preserving data. SMC was applied to some statistical computation methods. However, SMC needs to be applied to other complex computations for data mining. In this paper, we will propose a Particle Swarm Optimization (PSO) method for SMC and show the effectiveness in numerical simulation.

DOI： 10.14864/fss.36.0_379

CiNii Research

Hirashima Kei, Shigei Noritaka, Sugimoto Satoshi, Ishizuka Yoichi, Miyajima Hiromi . Semi-Supervised Learning for Land-Use Classification from Aerial Photograph Using Convolutional Neural Network . Proceedings of the Fuzzy System Symposium36 ( 0 ) 277 - 282 2020.9

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

In recent years, GIS (geographical information system), integrating various map information, has been utilized in a wide range of flelds. Land-use is useful information in GIS data, a basic data for developing and planning public works projects. However, detailed information is not available except for some urban areas. On the other hand, it is expected that detailed estimation can be performed by using machine learning, such as a convolutional neural network (CNN) from aerial photographs. However, to improve its accuracy, a large amount of labeled data is required. In this study, we consider generating efficiently labeled data from map symbols of GIS data as a means to efficiently increase the data. Further, we propose to perform semi-supervised learning using this method. We demonstrate the effectiveness of the proposed method in 6 classes of land-use classiflcation.

DOI： 10.14864/fss.36.0_277

CiNii Research

山下悠也, 重井徳貴, 杉本知史, 高江洲竜馬, 石塚洋一 . 家屋の有無を考慮したCNNを用いた立体地図と空中写真からの土砂災害の危険箇所推定 . 土木学会西部支部研究発表会講演概要集 275 - 276 2020.3

米玉利悠也, 重井徳貴, 杉本知史, 高江洲竜馬, 石塚洋一, 宮島廣美 . ニューラルネットワークを用いた地盤強度の推定法の精度改善 . 知能と情報32 ( 1 ) 539 - 543 2020.2Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . Fast and Secure Learning Algorithms for Classification Problems using Vertically Partitioned Data with IoT . Proc. of International Symposium on Artificial Life and Robotics 321 - 326 2020.1Reviewed

Ryo Fukuoka, Noritaka Shigei, Yoshihiro Nakamura, Hiromi Miyajima . Proposal of CNN models for acquiring stop and turn motion of self-driving model car . Proc. of International Symposium on Artificial Life and Robotics 371 - 375 2020.1Reviewed

岩永英俊，重井徳貴，石塚博子 . 単語の条件付き確率とTFによる特徴量を用いた問い合わせ文書の分類 . 日本知能情報ファジィ学会九州支部学術講演会予稿集 96 - 99 2019.11

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Fuzzy Modeling using Neural Gas . Proceedings of the World Congress on Engineering and Computer Science 2019 409 - 414 2019.11Reviewed

山口涼太, 重井徳貴 . 入退室管理のための顔特徴と衣服特徴による個人識別に関する一考察 . 電気・情報関係学会九州支部連合大会講演論文集 380 - 380 2019.9

米玉利悠也, 重井徳貴, 杉本知史, 高江洲竜馬, 石塚洋一 . ニューラルネットワークを用いた地盤強度の推定法 . 第35回ファジィシステムシンポジウム講演論文集 70 - 73 2019.8

宮島洋文, 久冨木大夢, 重井徳貴, 宮島廣美 . ベクトル量子化を用いたファジィ推論システムの学習法 . 知能と情報（日本知能情報ファジィ学会誌）31 ( 2 ) 690 - 699 2019.4Reviewed

宮島洋文, 重井徳貴, 宮島廣美, 白鳥則郎 . 簡易秘密計算法による安全な連続値Q-learningの実現 . バイオメディカル・ファジィ・システム学会誌20 ( 2 ) 1 - 7 2019.3Reviewed

Noritaka Shigei, Kazuki Mandai, Satoshi Sugimoto, Ryoma Takaesu, Yoichi Ishizuka . Land-Use Classification Using Convolutional Neural Network with Bagging and Reduced Categories . Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2019 7 - 11 2019.3Reviewed

Noritaka Shigei, Hiroki Urakawa, Yoshihiro Nakamura, Masahiro Teramura, Hiromi Miyajima . Accuracy improvement of accelerometer-based location estimation using neural network . Advances in Intelligent Systems and Computing744 79 - 85 2018.6Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Norio Shiratori . A Proposal of Profit Sharing Method for Secure Multiparty Computation . International Journal of Innovative Computing, Information and Control14 ( 2 ) 727 - 735 2018.4Reviewed

Hirofumi Miyajima, Noritaka Shigei, and Hiromi Miyajima . Fuzzy Modeling using Vector Quantization with Supervised Learning . Proceedings of The International MultiConference of Engineers and Computer Scientists 2018 17 - 22 2018.3

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, and Norio Shiratori . Privacy Preserving Q-learning in the AnalogModel for Secure Multiparty Computation . Proceedings of the International MultiConference of Engineers and Computer Scientists 2018 374 - 379 2018.3Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro MiyanishiShinji Kitagami, Norio Shiratori . Learning Method of Fuzzy Inference Systems for Secure Multiparty Computation . Transactions on Engineering Technologies. IMECS 2017. 227 - 242 2018.2Reviewed

Miyajima H. . Analog Q-learning methods for secure multiparty computation . IAENG International Journal of Computer Science45 ( 4 ) 623 - 629 2018Reviewed International journal

Hirofumi Miyajima, Noritaka Shigei, Syunki Makino, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori . A proposal of privacy preserving reinforcement learning for secure multiparty computation . Artificial Intelligence Research6 ( 2 ) 57 - 68 2017.10Reviewed

Miyajima H. . Initial setting of effective parameters for fuzzy modeling . IAENG International Journal of Computer Science44 ( 3 ) 375 - 382 2017.8Reviewed International journal

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Effective Learning Algorithm for Fuzzy Inference Systems Using Neural Gas Network . IAENG Transactions on Engineering Sciences 96 - 110 2017.8

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori . New Privacy Preserving Clustering Methods for Secure Multiparty Computation . Artificial Intelligence Research6 ( 1 ) 27 - 36 2017.4Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori . Privacy Preserving Fuzzy Modeling for Secure Multiparty Computation . Proceedings of The International MultiConference of Engineers and Computer Scientists 2017 451 - 456 2017.3Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Fuzzy Modeling using Vector Quantization based on Input and Output Learning Data . Proceedings of The International MultiConference of Engineers and Computer Scientists 2017 1 - 6 2017.3Reviewed

Masahiro Teramura, Noritaka Shigei, Hiromi Miyajima . Design of hardware circuit based on a neural network model for rapid detection of center of gravity position . IEEE Region 10 Annual International Conference, Proceedings/TENCON 251 - 254 2017.2Reviewed

aNoritaka Shigei, Jo Kawasaki, Hiromi Miyajima . Multiple-sink approach for prolonging network lifetime of Wireless Sensor Network . IEEE Region 10 Annual International Conference, Proceedings/TENCON 2796 - 2800 2017.2Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . The Ability of Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization . Neural Information Processing, LNCS9950 479 - 488 2016.10Reviewed

Masahiro Teramura, Noritaka Shigei, Hiromi Miyajima . Design of Distinction Circuit Using Neuron-Based Comparators . International Workshop on Fundamental Research for Science and Technology 2016.8Reviewed

Y. Nakashima, N. Shigei, Hiromi Miyajima . Hybrid of Ideal Movement Method and Virtual Rail Method for Network Construction in Mobile Sensor Network . 2016 IEEE International Conference on Consumer Electronics-Taiwan 281 - 282 2016.6Reviewed

Hiromi Miyajima, Shuji Yatsuki, Noritaka Shigei, Hirofumi Miyajima . Some Properties on the Capability of Associative Memory for Higher Order Neural Networks . M. Zhang (Ed.), Applied Artificial Higher Order Neural Networks for Control and Recognition 391 - 419 2016.5

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . System identifications by SIRMs models with linear transformation of input variables . Artificial Intelligence Research5 ( 2 ) 55 - 63 2016.4Reviewed

Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima . Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules . International Journal of Computer, Electrical, Automation, Control and Information Engineering10 ( 3 ) 377 - 383 2016.3Reviewed

Hirofumi Miyajima, Noritaka Shigei, Kazuya Kishida, Hiromi Miyajima . An Improved Learning Algorithm of Fuzzy Inference Systems Using Vector Quantization . Advances in Fuzzy Sets and Systems21 ( 1 ) 59 - 77 2016.3Reviewed

Noritaka Shigei, Kazuto Matsumoto, Yoshiki Nakashima, Hiromi Miyajima . Improved Network Construction Methods Based on Virtual Rails for Mobile Sensor Network . International Journal of Computer, Electrical, Automation, Control and Information Engineering10 ( 1 ) 86 - 92 2016.1Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Some Properties on Fuzzy Inference Systems Composed of Small Number of Input Rule Modules . Advances in Fuzzy Sets and Systems20 ( 2 ) 155 - 175 2015.12Reviewed

Yogi Anggun Saloko Yudo, Noritaka Shigei, Hiromi Miyajima . Multiple Route Construction with Path-overlap Avoidance for Mobile Relay on WSN . Engineering Letters23 ( 4 ) 299 - 306 2015.11Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Performance Comparison of Hybrid Electromagnetism-like Mechanism Algorithms with Descent Method . Journal of Artificial Intelligence and Soft Computing Research5 ( 4 ) 271 - 282 2015.10Reviewed

Noritaka Shigei, Yohei Yamaguchi, Hiromi Miyajima . Air Conditioner Control Learning Users' Sensations Based on Reinforcement Learning and Its Scalability Improvement . IAENG International Journal of Computer Science42 ( 3 ) 288 - 295 2015.7Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . SIRMs Fuzzy Inference Model with Linear Transformation of Input Variables and Universal Approximation . Advances in Computational Intelligence - 13th International Work-Conference on Artificial Neural Networks, 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part I 561 - 575 2015.6Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . Approximation Capabilities of Interpretable Fuzzy Inference Systems . IAENG International Journal of Computer Science42 ( 2 ) 117 - 124 2015.4Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . On the Capability of a Fuzzy Inference System with Improved Interpretability . Proceedings of The International MultiConference of Engineers and Computer Scientists 2015I 51 - 56 2015.3Reviewed

Yogi Anggun Saloko Yudo, Noritaka Shigei, Hiromi Miyajima . Path-overlap Avoidance in Multiple Route Construction for Mobile Relay on WSN . Proceedings of the International MultiConference of Engineers and Computer Scientists 2015I 107 - 112 2015.3Reviewed

Yohei Yamaguchi, Noritaka Shigei, Hiromi Miyajima . Air Conditioning Control System Learning Sensory Scale Based on Reinforcement Learning . Proceedings of the International MultiConference of Engineers and Computer Scientists 2015I 1 - 6 2015.3Reviewed

Yogi Anggun Saloko Yudo, Noritaka Shigei, Hiromi Miyajima . Effective initial route construction for mobile relay on wireless sensor network . Artificial Life and Robotics20 ( 1 ) 49 - 55 2015.1Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . A Proposal of SIRMs Model with Linear Transformation of Input Variables . Proc. of Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems 311 - 316 2014.12Reviewed

Yogi Anggun Saloko Yudo, Noritaka Shigei, Hiromi Miyajima . A* Algorithm-Based Initial Route Construction for Mobile Relay on WSN . Proc. of Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems 323 - 328 2014.12Reviewed

Noritaka Shigei, Hideaki Kumashiro, Hiromi Miyajima . Parallel Stochastic Search for PAPR Reduction of OFDM Signal . Proc. of Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems 317 - 322 2014.12Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiroki Taketatu, Hiromi Miyajima . Numerical Simulations for Hybrid Electromagnetism-like Mechanism OptimizationAlgorithms with Descent Methods . Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems2 293 - 306 2014.12Reviewed

Hirofumi Miyajima, Takehiro Kawai, Noritaka Shigei, Hiromi Miyajima . Fuzzy Inference Systems Composed of Double-Input Rule Modules for Obstacle Avoidance Problems . IAENG International Journal of Computer Science41 ( 4 ) 222 - 230 2014.11Reviewed

Hirofumi Miyajima, Fumihiro Kawai, Noritaka Shigei, Hiromi Miyajima . An Application of Fuzzy Inference System Composed of Double-Input Rule Modules to Control Problems . Proc. of IAENG International Conference on Artificial Intelligence and Applications 23 - 28 2014.3Reviewed

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima . A Proposal of Hybrid Electromagnetism-like Mechanism Method . Proc. of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing 281 - 284 2014.2Reviewed

Yogi Anggun Saloko Yudo, Noritaka Shigei, Hiromi Miyajima . Effective Initial Route Construction for Mobile Relay on Wireless Sensor Netowrk . Proc. of International Symposium on Artificial Life and Robotics 311 - 316 2014.1Reviewed

Noritaka Shigei, Kentaro Araki, Hiromi Miyajima . Effective Selection and Mutation in Genetic Algorithm for PAPR Reduction of OFDM Signal . Proc. of International Symposium on Advanced Intelligent Systems 1675 - 1687 2013.11Reviewed

Hiromi Miyajima, Masataka Fujisaki, Noritaka Shigei . An Associative Model Based on Quantum Search Algorithms . Proc. of International Conference on Digital Enterprise and Information Systems 98 - 107 2013.3Reviewed

Noritaka Shigei, Masahiro Teramura, Hiromi Miyajima, Naohiro Tateishi . An Area Reduction Technique for Logic Synthesis of Neural Networks . Proc. of International Conference on Digital Enterprise and Information Systems 108 - 118 2013.3Reviewed

Noritaka Shigei, Issei Fukuyama, Hiromi Miyajima, Yogi A.S. Yudo . Battery-Aware Algorithms for Mobile Relay and Route Construction on Wireless Sensor Network . IAENG International Journal of Computer Science39 ( 3 ) 321 - 328 2012.8Reviewed

Hiromi Miyajima, Masataka Fujisaki, Noritaka Shigei . Quantum Search Algorithms in Analog and Digital Models . IAENG International Journal of Computer Science39 ( 2 ) 182 - 189 2012.6Reviewed

寺村正広, 重井徳貴, 宮島廣美 . 蓄電池の残容量推定に適したニューラルネットワークの回路設計と推定特性 . 電子情報通信学会論文誌J95-B ( 4 ) 556 - 567 2012.4Reviewed

Masataka Fujisaki, Hiromi Miyajima, Noritaka Shigei . Data Search Algorithms based on Quantum Walk . Proc. of International MultiConference of Engineers and Computer ScientistsI 164 - 169 2012.3Reviewed

Noritaka Shigei, Issei Fukuyama, Hiromi Miyajima, Yogi A.S. Yudo . Battery Aware Mobile Relay for Wireless Sensor Network . Proc. of International MultiConference of Engineers and Computer ScientistsI 368 - 373 2012.3Reviewed

H. Miyajima, N. Shigei, T. Shiiba . Numerical Evaluation of Clustering Methods with Kernel PCA . Convergence and Hybrid Information Technology Lecture Notes in Computer Science6935 677 - 684 2011.9Reviewed

重井徳貴，宮島廣美，大迫司 . スモールワールドモデルを用いた市場指向プログラミングの空調制御への応用 . システム制御情報学会論文誌24 ( 4 ) 77 - 87 2011.4Reviewed

N. Shigei, H. Miyajima, K. Ozono, K. Araki . Acceleration of Genetic Algorithm for Peak Power Reduction of OFDM Signal . IAENG International Journal of Computer Science38 ( 1 ) 32 - 37 2011.2Reviewed

N. Shigei, H. Miyajima, K. Ozono . Time-Efficient Genetic Algorithm for Peak Power Reduction of OFDM Signal . Proc. of The World Congress on Engineering and Computer Science 2010 186 - 191 2010.11Reviewed

三池聡明，宮島廣美，重井徳貴，野尾健太郎 . ルール削除機能をもつ少数入力ルール群結合型ファジィ推論モデル . 知能と情報（日本知能情報ファジィ学会誌）22 ( 5 ) 81 - 89 2010.10Reviewed

N. Shigei, H. Miyajima, H. Morishita . Energy Consumption Reduction of Clustering Communication Based on Number of Neighbors for Wireless Sensor Networks . IAENG International Journal of Computer Science37 ( 3 ) 296 - 303 2010.8Reviewed

H. Miyajima, N. Shigei, K. Arima . Some quantum search algorithms for arbitrary initial amplitude distribution . Proc. of International Conference on Natural Computation2 603 - 608 2010.8Reviewed

N. Shigei, H. Morishita, H. Miyajima . Energy Efficient Clustering Communication Based on Number of Neighbors for Wireless Sensor Networks . Proc. of International MultiConference of Engineers and Computer ScientistsII 762 - 767 2010.3Reviewed

N. Shigei, H. Miyajima, M. Maeda, L. Ma . Bagging and AdaBoost Algorithm for Vector Quantization . Neurocomputing73 ( 42007 ) 106 - 114 2009.12Reviewed

K. Arima, N. Shigei, H. Miyajima . A Proposal of a Quantum Search Algorithm . Proc. of International Conference on Computer Sciences and Convergence Information Technology 1559 - 1564 2009.11Reviewed

M. Maeda, N. Shigei, H. Miyajima . Learning Algorithms with Neighboring Inputs in Self-Organizing Maps for Image Restoration . Proc. of International Joint Conference on Computational Intelligence 331 - 338 2009.10Reviewed

T. Shiiba, N. Shigei, H. Miyajima . A Consideration on Vector Quantization Clusterings with Kernel Functions . Proc. of the 24th International Technical Conference on Circuits/Systems, Computers and Communications 637 - 640 2009.7Reviewed

H. Miyajima, N. Shigei, S. Yatsuki . Higher Order Neurodynamics of Associative Memory for Sequential Patterns . Proc. of International Symposium on Neural Networks, Lecture Notes in Computer Science 5552 886 - 894 2009.5Reviewed

N. Shigei, H. Miyajima, S. Nagamine . A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules . Proc. of International Symposium on Neural Networks, Lecture Notes in Computer Science 5552 118 - 126 2009.5Reviewed

N. Shigei, H. Miyajima, H. Morishita, M. Maeda . Centralized and Distributed Clustering Methods for Energy Efficient Wireless Sensor Networks . Proc. of International MultiConference of Engineers and Computer Sientists 2009I 423 - 427 2009.3Reviewed

N. Shigei, H. Miyajima . Bagging and Boosting Algorithms for Support Vector Machine Classifiers . Proc. of International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases 372 - 377 2009.2Reviewed

M. Maeda, K. Suzaki, N. Shigei, H. Miyajima . Properties of Creation and Reduction According to the Equinumber Principle for Adaptive Vector Quantisation . International Journal of Automation and Control2 ( 42038 ) 232 - 246 2008.12Reviewed

N. Shigei, H. Miyajima, S. Hashiguchi, M. Maeda, L. Ma . Hardware Implementation of Multiple Vector Quantization Decoder . International Journal of Computer Science and Network Security8 ( 11 ) 54 - 61 2008.11Reviewed

H. Miyajima, N. Shigei, S. Fukumoto, T. Miike . Parallel Fuzzy Reasoning Models with Ensemble Learning . Proc. of International Symposium on Neural Newtorks, LNCS 5263 512 - 530 2008.9Reviewed

M. Maeda, N. Shigei, H. Miyajima . Learning Model in Relaxation Algorithm Influenced by Self-Organizing Maps for Image Restoration . IEEJ Trans. on Electrical and Electronic Engineering3 ( 4 ) 404 - 412 2008.7Reviewed

S. Nagamine, N. Shigei, H. Miyajima . Fuzzy Inference Models Appropriate for Digital Circuit . Proc. of International Technical Conference on Circuit/Systems Computers and Communications 1113 - 1116 2008.7Reviewed

K. Arima, H. Miyajima, N. Shigei, M. Maeda . Some Properties of Quantum Data Search Algorithms . Proc. of International Technical Conference on Circuit/Systems Computers and Communications 1169 - 1172 2008.7Reviewed

Hiromi Miyajima, Noritaka Shigei, Michiharu Maeda, Shuji Hosoda . Learning Algorithms with Boosting for Vector Quantization . Proc. of the 2008 International Symposium on Communications, Control and Signal Processing 352 - 356 2008.3Reviewed

M. Maeda, N. Shigei, H. Miyajima, K. Suzaki . Reduction Models in Competitive Learning Founded on Distortion Standards . Journal of Advanced Computational Intelligence and Intelligent Informatics12 ( 3 ) 314 - 323 2008.3Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda, Lixin Ma . Effective Multiple Vector Quantization for Image Compression . Journal of Advanced Computational Intelligence and Intelligent Informatics11 ( 10 ) 1189 - 1196 2007.12Reviewed

Michiharu Maeda, Noritaka Shigei, Hiromi Miyajima, Kenichi Suzaki . Characteristics of Adaptive Vector Quantization According to the Equinumber Principle . Proc. of the 8th International Conference on Intelligent Technologies 21 - 26 2007.12Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Multiple Vector Quantization Based on the Error Distribution in Image Compression . Proc. of the 8th International Conference on Intelligent Technologies 324 - 331 2007.12Reviewed

Hiromi Miyajima, Noritaka Shigei, Shinya Fukumoto, Nobuya Nakatsu . A Learning Algorithm with Boosting for Fuzzy Reasoning Model . Proc. of International Conference on Fuzzy Systems and Knowledge Discovery2 85 - 90 2007.8Reviewed

重井徳貴，宮島廣美，山口和彦 . 領域間結合が疎な領域分割に基づくモバイルエージェントのメッセージ伝達法 . 電子情報通信学会論文誌DJ90-D ( 6 ) 1495 - 1508 2007.6Reviewed

Michiharu Maeda, Hiromi Miyajima, Noritaka Shigei . Parallel Learning Model and Topological Measurement for Self-Organizing Maps . Journal of Advanced Computational Intelligence and Intelligent Informatics11 ( 3 ) 327 - 334 2007.3Reviewed

Hiromi Miyajima, Noritaka Shigei . Associative Ability of Higher Order Correlation Models for Sequential Patterns . Journal of Knowledge-Based and Intelligent Engineering Systems10 ( 6 ) 403 - 416 2006.12Reviewed

Michiharu Maeda, Noritaka Shigei, Hiromi Miyajima . Construction of Competitive Learning by Reduction with Distortion Standards . Proc. of International Conference on Intelligent Technologies 45 - 52 2006.12Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Learning Algorithm for Multiple Vector Quantization Based Image Compression . Proc. of International Symposium on Nonlinear Theory and its Applications 836 - 839 2006.9Reviewed

Lixin Ma, Hiromi Miyajima, Noritaka Shigei . Multi-Layered Neural Networks with Learning of Output Functions . International Journal of Computers Science and Network Security6 ( 3A ) 140 - 145 2006.3Reviewed

Michiharu Maeda, Hiromi Miyajima, Noritaka Shigei . A Model of Parallel Learning in Self-Organizing Maps and Topological Measurement . Proc. of International Conference on Intelligent Technologies 133 - 140 2005.12Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Compression Rate Improvement of Multiple Vector Quantization Based Image Compression . Proc. of International Conference on Intelligent Technologies 228 - 233 2005.12Reviewed

Hiromi Miyajima, Noritaka Shigei . Robustness and Capacity of Higher Order Correlation Associative Memory for Sequential Patterns . Proc. of International Conference on Neural Information Processing 388 - 393 2005.11Reviewed

Michiharu Maeda, Noritaka Shigei, Hiromi Miyajima . Adaptive Vector Quantization with Creation and Reduction Grounded in the Equinumber Principle . Journal of Advanced Computational Intelligence and Intelligent Informatics9 ( 6 ) 599 - 606 2005.11Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Competitive Learning with Fast Neuron-Insertion . Journal of Advanced Computational Intelligence and Intelligent Informatics9 ( 6 ) 590 - 598 2005.11Reviewed

Lixin Ma, Hiromi Miyajima, Noritaka Shigei . Learning Characteristics of Multi-Layered Higher Order Neural Networks . International Conference of Computational Methods in Sciences and Engineering 2005, Lecture Series on Computer and Computational Sciences4 361 - 364 2005.10Reviewed

Hiromi Miyajima, Noritaka Shigei, Shuji Yatsuki . Shift-Invariant Associative Memory Based on Homogeneous Neural Networks . IEICE Trans. FundamentalsE88-A ( 10 ) 2600 - 2606 2005.10Reviewed

Hiromi Miyajima, Noritaka Shigei . Absolute Capacities for Higher Order Associative Memory of Sequential Patterns . Proc. of International Conf. on Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science3683 547 - 553 2005.9Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . A Multiple Vector Quantization Approach to Image Compression . Proc. of International Conference on Natural Computation, Lecture Notes in Computer Science3611 361 - 370 2005.8Reviewed

Michiharu Maeda, Noritaka Shigei, Hiromi Miyajima . Characteristics of Equinumber Principle for Adaptive Vector Quantization . Proc. of International Conference on Natural Computation, Lecture Notes in Computer Science3611 415 - 424 2005.8Reviewed

Shinya Fukumoto, Hiromi Miyajima, Noritaka Shigei, Keita Uchikoba . Decision Procedure of Initial Values of Fuzzy Inference System Using Counterpropagation Networks . Journal of Signal Processing9 ( 4 ) 335 - 342 2005.7Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Competitive Learning with Fast Neuron-Insertion . Proc. of the International Symposium on Computational Intelligence and Industrial Application, WM1B-2, CD-ROM 2004.12Reviewed

Michiharu Maeda, Noritaka Shigei, Hiromi Miyajima . Creation and Reduction Methods of Adaptive Vector Quantization According to Equinumber of Inputs . Proc. of the International Symposium on Computational Intelligence and Industrial Application, TA3A-1, CD-ROM 2004.12Reviewed

Noritaka Shigei, Hiromi Miyajima, Shuji Yatsuki . The Associative Ability of Homogeneous Neural Networks . Proc. of the 2004 International Symposium on Nonlinear Theory and Its Applications 645 - 648 2004.11Reviewed

Hiromi Miyajima, Noritaka Shigei, Yasuo Hamakawa . Transition Properties of Higher Order Associative Memory of Sequential Patterns . Proc. of International Conference on Knowledge-Based Intelligent Information & Engineering Systems, LNAI3215 855 - 861 2004.9Reviewed

Noritaka Shigei, Hiromi Miyajima, Michiharu Maeda . Numerical Evaluation of Incremental Vector Quantization using Stochastic Relaxation . IEICE Trans. FundamentalsE87-A ( 9 ) 2364 - 2371 2004.9Reviewed

福元伸也，宮島廣美，長澤庸二，重井徳貴 . エントロピー項付き正則化ファジィ推論モデルの提案 . 知能と情報(日本知能情報ファジィ学会誌)16 ( 4 ) 372 - 380 2004.8Reviewed

Lixin Ma, Hiromi Miyajima, Noritaka Shigei, Shuma Kawabata . Neural Network Determining Injury by Salt Water for Distribution Lines . Proc. of International Symposium on Neural Networks, LNCS3174 436 - 441 2004.8Reviewed

Hiromi Miyajima, Noritaka Shigei, Yasuo Hamakawa . Higher Order Differential Correlation Associative Memory of Sequential Patterns . Proc. of the 2004 IEEE International Joint Conference on Neural Networks2 891 - 896 2004.7Reviewed

Yasuo Hamakawa, Hiromi Miyajima, Noritaka Shigei, Takeshi Tsuruta . On Some Properties of Higher Order Correlation Associative Memory of Sequential Patterns . Journal of Signal Processing8 ( 3 ) 225 - 234 2004.5Reviewed

Hiromi Miyajima, Noritaka Shigei, Nobuaki Kiriki . Higher Order Multidirectional Associative Memory with Decreasing Energy Function . Neural Information Processing: Research and Development (Studies in Fuzziness and Soft Computing) Springer-Verlag152 128 - 149 2004.5Reviewed

重井徳貴，宮島廣美 . ゲートアレイを対象としたモジュール配置へのSOM-SA混成手法の適用 . 第17回回路外システム軽井沢ワークショップ論文集 523 - 528 2004.4Reviewed

Hiromi Miyajima, Shinya Fukumoto, Noritaka Shigei, Kazuya Kishida . A Self-Tuning Method of Fuzzy Inference Rules Using Counter Propagation Networks . WSEAS Trans. Circuits and Systems3 ( 1 ) 21 - 24 2004.1Reviewed

Shinya Fukumoto, Noritaka Shigei, Michiharu Maeda, Hiromi Miyajima . A Hybrid Learning Approach to Self-Organizing Neural Network for Vector Quantization . IEICE Trans. FundamentalsE86-A ( 9 ) 2280 - 2286 2003.9Reviewed

Noritaka Shigei, Hiromi Miyajima . Reconfiguration Classes and an Optimal Reconfiguration Method within a Reconfiguration Class . IEICE Trans. Information and SystemsE85-D ( 12 ) 1909 - 1917 2002.12Reviewed

Noritaka Shigei, Masahiro Kanda . Overlapped-Two-Phase Broadcast and Its Evaluation on a Cluster of PCs . IEICE Trans. Information and SystemsE85-D ( 6 ) 1039 - 1042 2002.6Reviewed

重井徳貴，田谷基教，宮島廣美 . 木の動的成長抑制による遺伝的プログラミングの計算量軽減 . 電子情報通信学会論文誌J83-D-I ( 3 ) 395 - 398 2000.3Reviewed

Noritaka Shigei, Hiromi Miyajima . Embedding Chordal Rings and Pyramids into Mesh-Connected Computers with Multiple Buses . IEICE Trans. Information and SystemsE83-D ( 2 ) 142 - 150 2000.2Reviewed

宮島廣美，馬立新，重井徳貴 . 高次ランダム神経回路網のパターン間の距離の変換特性 . 電気学会論文誌C118 ( 10 ) 1516 - 1521 1998.10Reviewed

Noritaka Shigei, Hiromi Miyajima . On the Search for Effective Spare Arrangement of Reconfigurable Processor Arrays Using Genetic Algorithm . IEICE Trans. FundamentalsE81-A ( 9 ) 1898 - 1901 1998.9Reviewed

Noritaka Shigei, Hiromi Miyajima, Sadayuki Murashima . Maximum finding on one-way mesh-connected computers with multiple buses . IEICE Trans. FundamentalsE80-A ( 6 ) 1076 - 1079 1997.6Reviewed

Noritaka Shigei, Hiromi Miyajima, Sadayuki Murashima . On efficient spare arrangements and an algorithm with relocating spares for reconfiguring processor arrays . IEICE Trans. FundamentalsE80-A ( 6 ) 988 - 995 1997.6Reviewed

Noritaka Shigei, Hiromi Miyajima, Takayuki Ishizaka, Sadayuki Murashima . On methods for reconfiguring processor arrays . IEICE Trans. Information and SystemsE79-D ( 8 ) 1139 - 1146 1996.8Reviewed

重井徳貴，宮島廣美，村島定行 . 網目結合計算機の再構成アルゴリズムとその評価 . 電子情報通信学会論文誌J79-D-I ( 7 ) 475 - 478 1996.7Reviewed

Hiromi Miyajima, Shuji Yatsuki, Noritaka Shigei, Sadayuki Murashima . On some dynamical properties of threshold and homogeneous networks . IEICE Trans. FundamentalsE77-A ( 11 ) 1823 - 1830 1994.11Reviewed

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