Faculty | Department of Information Networking for Innovation and Design |
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Position | Assistant Professor |

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Birthday |

Last Updated :2020/04/04

- Discriminating between mothers' infant- and adult-directed speech: Cross-linguistic generalizability from Japanese to Italian and German, Sulpizio Simone, Kuroda Kaori, Dalsasso Matteo, Asakawa Tetsuya, Bornstein Marc H, Doi Hirokazu, Esposito Gianluca, Shinohara Kazuyuki, NEUROSCIENCE RESEARCH, NEUROSCIENCE RESEARCH, 133, 21 - 27, 08 , Refereed
- Improving throughput using multi-armed bandit algorithm for wireless LANs, Kuroda Kaori, Kato Hiroki, Kim Song-Ju, Naruse Makoto, Hasegawa Mikio, IEICE NONLINEAR THEORY AND ITS APPLICATIONS, IEICE NONLINEAR THEORY AND ITS APPLICATIONS, 9, (1) 74 - 81, Refereed
- Wireless network optimization method based on cognitive cycle using machine learning, Oshima Koji, Kobayashi Takumu, Taenaka Yuki, Kuroda Kaori, Hasegawa Mikio, IEICE COMMUNICATIONS EXPRESS, IEICE COMMUNICATIONS EXPRESS, 7, (7) 278 - 283, Refereed
- Joint Downlink and Uplink Interference Management for Device to Device Communication Underlaying Cellular Networks, Thong Huynh, Onuma Tomoyuki, Kuroda Kaori, Hasegawa Mikio, Hwang Won-Joo, IEEE ACCESS, IEEE ACCESS, 4, Refereed
- Method for Estimating Neural Network Topology Based on SPIKE-Distance, Kuroda Kaori, Hasegawa Mikio, ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I, ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I, 9886, 91 - 98, Refereed
- Reconstruction of network structures from marked point processes using multi-dimensional scaling, Kaori Kuroda, Hiroki Hashiguchi, Kantaro Fujiwara, Tohru Ikeguchi, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 415, 194 - 204, 12 , Refereed
- Identification of Neural Network Structure from Multiple Spike Sequences, Kuroda Kaori, Fujiwara Kantaro, Ikeguchi Tohru, NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II, NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II, 7664, 184 - 191, Refereed
- Estimation of network structures only from spike sequences, Kuroda Kaori, Ashizawa Tohru, Ikeguchi Tohru, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 390, (21-22) 4002 - 4011, 10 15 , Refereed
- Reconstruction of Input Information Using Transformation from Spike Trains to an Instantaneous Mean-Firing-Rate Time Series, KURODA Kaori, SHIMADA Yutaka, SUZUKI Mai, IKEGUCHI Tohru, The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese edition) A, The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese edition) A, 94, (2) 64 - 72, 02 01

- Interference Management Under Multi-channel for Device-to-Device Underlaying Cellular Networks, 2016 19TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 2016 , 査読有り
- User Association for Massive MIMO Cellular Networks with Small Cell Wireless Backhaul, 2016 19TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 2016 , 査読有り

- Wireless Network Optimization Method based on Cognitive Cycle using Machine Learning, 大島浩嗣, 大島浩嗣, 小林拓夢, 妙中佑基, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2018 02 21
- Estimation of Network Topology for Detecting a Cause of Trouble in Layer2 Networks, 黒田佳織, 松本拓也, 長谷川幹雄, 電子情報通信学会技術研究報告, 2017 08 03
- Improving Performance of UCB1-tuned Algorithm by Lebesgue Spectrum Filter, CHO Xinyu, 黒田佳織, 村田侑雄, KIM Song‐Ju, 成瀬誠, 長谷川幹雄, 電子情報通信学会技術研究報告, 2017 07 06
- Design and Implementation of the Routing Protocol in Power Packet Network, 山本悠真, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2017 06 22
- Solving Asymmetric Traveling Salesman Problems by Coherent Ising Machine, 村田侑雄, 安田裕之, 黒田佳織, 合原一幸, 長谷川幹雄, 電子情報通信学会技術研究報告, 2017 03 03
- Optimization of Device-to-Device Communication by Coherent Ising Machine, 林航平, 安田裕之, HUYNH Thong, 黒田佳織, 合原一幸, 長谷川幹雄, 電子情報通信学会技術研究報告, 2016 10 28
- A study on protocol for power packet routing, 膽熊優介, HUYNH Thong, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2016 08 02
- コグニティブサイクルに基づく無線LANネットワーク最適化手法の設計と実装, 小林拓夢, 佐藤雅季, 黒田佳織, 長谷川幹雄, 電子情報通信学会大会講演論文集(CD-ROM), 2016 03 01
- A Study on Location Estimation Method by Wi-SUN Using Machine Learning, 坂本博, 安田裕之, HUYNH Thong, 黒田佳織, 荘司洋三, 長谷川幹雄, 電子情報通信学会技術研究報告, 2016 02 25
- Exact Optimization Method for Radio Resource Allocation in MIMO-OFDM System, 光岡高宏, HUYNH Thong, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2016 01 21
- Throughput Optimization Method and its Evaluation in Wireless Mesh Networks, 宝崎康平, HUYNH Thong, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2015 11 02
- Estimation of connectivity between neurons using SPIKE-distance (非線形問題), 黒田 佳織, 長谷川 幹雄, 池口 徹, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 2015 10 31
- Design and Implementation of Optimization Scheme for Wireless Networks using Cognitive Cycle, 佐藤雅季, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2015 10 24
- Application of Improvement Method of Wireless LAN Throughput based on TOW Algorithm, 加藤拓樹, KIM Song‐Ju, 黒田佳織, 長谷川幹雄, 電子情報通信学会技術研究報告, 2015 10 24
- コグニティブサイクルに基づく無線ネットワーク最適化手法の設計と実装, 佐藤雅季, 黒田佳織, 長谷川幹雄, 電子情報通信学会大会講演論文集(CD-ROM), 2015 08 25
- 無線LANを用いたDevice‐to‐Deviceの切り替え手法の設計と実装, 野口耕平, 黒田佳織, 長谷川幹雄, 電子情報通信学会大会講演論文集(CD-ROM), 2015 08 25
- A Study on Perfect Synchronization Method using Environmental Fluctuations, 安田 裕之, 黒田 佳織, 長谷川 幹雄, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 2015 08 06
- Design and Implementation of Improvement Method of Wireless LAN Throughput Using the TOW Algorithm, 加藤 拓樹, 金 成主, 黒田 佳織, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 2015 08 06
- A Study on Data Input Interval of Time Synchronization Scheme using Natural Environmental Noises, 安田 裕之, 黒田 佳織, 長谷川 幹雄, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 2015 07 21
- Longitudinal study on the facilitatory effect of motherese on language development, 久永 聡子, 土居 裕和, 黒田 佳織, 発達研究 : 発達科学研究教育センター紀要, 2015
- Application of tabu strategy to an adaptive local search for solving quadratic assignment problem, WATANABE Akio, KURODA Kaori, FUJIWARA Kantaro, KEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2014 03 10 , We proposed an adaptive local search algorithm for solving quadratic assignment problems. The proposed algorithm decides the number of exchange elements during searching process. We also applied the tabu search to the proposed algorithm to realize effective escape from undesirable local minima, and showed that the performance of the proposed algorithm is improved.
- AS-1-7 Estimation of connectivity of systems from distance relation between marked point processes, Kuroda Kaori, Fujiwara Kantaro, Ikeguchi Tohru, Proceedings of the Society Conference of IEICE, 2013 09 03
- AS-1-2 Improvement by Chaotic Neuro Dynamics of an Adaptive Local search Algorithm for solving Combinatorial Optimization Problems, Watanabe Akio, Kuroda Kaori, Fujiwara Kantaro, Ikeguchi Tohru, Proceedings of the Society Conference of IEICE, 2013 09 03
- Experimental observation of common noise-induced synchronization in chaotic oscillators, KAWAI Tsubasa, KUSUMI Ryoya, KURODA Kaori, KATO Hideyuki, FUJIWARA Kantaro, JIN'NO Kenya, IKEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2013 03 14 , Synchronization is a ubiquitous phenomenon that has been widely observed in various nonlinear dynamical systems. Among them, noise-induced synchronization has been observed theoretically and experimentally. In this report, we conducted experiments by electric circuits to reproduce the common noise-induced synchronization in chaotic oscillators. We used three chaotic oscillators: a piecewise linear Rossler oscillator[18], the Rossler oscillator[15]and the Lorenz oscillator[16]. We applied the common white noise to two uncoupled chaotic oscillators to explore whether noise-induced synchronization can be observed in these real physical systems. We also investigated how chaotic oscillators synchronize in terms of noise intensity.
- Prediction of growth of complex networks, YAGINUMA Suguru, KURODA Kaori, SHIMADA Yutaka, FUJIWARA Kantaro, IKEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2013 03 14 , In this report, we propose a method to predict growth of a complex network. In our method, first, we transformed a complex network into a time series. Next, we predicted the time series by using a method of nonlinear time series prediction. Then, we reconstructed a complex network from the predicted time series. Finally, we evaluated growth of the predicted networks by comparing them with the real networks. Through numerical simulations, we show that we can predict growth of complex networks with high accuracy using the proposed method.
- Analysis on real networks by classical multidimensional scaling, GAO Yong, KURODA Kaoli, SHIMADA Yutaka, FUJIWARA Kantaro, IKEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2013 03 14 , In the real world, we have a wide variety of complex networks, such as Internet, neural networks, human relationships and so on. To understand characteristics and structure of these complex networks, a new framework of combining the complex network theory and nonlinear time series analysis has been proposed. One of the frameworks uses the classical multidimensional scaling to transform complex networks to time series. In this report, by using this transforming method, we investigated the distribution of coordinate values of the time series transformed from the networks. We compared the distribution of coordinate values of real networks with that of network models such as the Watts-Strogatz model and the Barabasi-Albert model.
- A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems, WATANABE Akio, KURODA Kaori, FUJIWARA Kantaro, IKEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2013 03 14 , The quadratic assignment problem(QAP)is one of the NP-hard combinatorial optimization problems. Then, it is required to develop approximate algorithms for finding near optimal solutions in realistic time. 0n the other hand, to solve traveling salesman problems which is a special case of QAP, the Lin-Kernighan algorithm has been proposed. In this report, we propose an algorithm for solving QAPs by introducing characteristic property of searching process in the Lin-Kernighan algorithm. We also in- troduced chaotic dynamics into the proposed algorithm to escape undesirable local minima. To evaluate solving performance of the proposed method, we compared the performance of the proposed method with that of the conventional methods. As a result, the solving performance of the proposed method with chaotic dynamics exhibits smaller gaps from optimal solutions than the conventional algorithms.
- A-2-19 Estimation of complex neural network structures and direction of couplings, Kuroda Kaori, Fujiwara Kantaro, Ikeguchi Tohru, Proceedings of the IEICE General Conference, 2013 03 05
- Estimation of network structures from marked point process using spike time metric, KURODA Kaori, FUJIWARA Kantaro, IKEGUCHI Tohru, IEICE technical report. Neurocomputing, 2013 01 24 , In the real world, additional information with event sequences can be essential for several complex phe-nomena, for example, seismic events, transactions in stock markets and so on. Such sequences are often referred as a marked point process. It is important to understand network structures of systems to analyze, model or predict such marked point processes. In this report, we propose a method for estimating the network structures from marked point processes using spike time metric extended for marked point processes and applying partiahzation analysis to the spike time metric.
- Estimation of network structures from marked point process using spike time metric, KURODA Kaori, FUJIWARA Kantaro, IKEGUCHI Tohru, IEICE technical report. Nonlinear problems, 2013 01 24 , In the real world, additional information with event sequences can be essential for several complex phe-nomena, for example, seismic events, transactions in stock markets and so on. Such sequences are often referred as a marked point process. It is important to understand network structures of systems to analyze, model or predict such marked point processes. In this report, we propose a method for estimating the network structures from marked point processes using spike time metric extended for marked point processes and applying partiahzation analysis to the spike time metric.
- A-2-10 Spike Time Metric for Marked Point Process, Kuroda Kaori, Ikeguchi Tohru, Proceedings of the Society Conference of IEICE, 2011 08 30
- A-2-4 Adaptive Reconstruction of Input Information Applied to Neurons from Spike Trains, Kuroda Kaori, Ikeguchi Tohru, Proceedings of the IEICE General Conference, 2011 02 28
- A-2-3 A method for transforming marked point process to continuous time-series, Kuroda Kaori, Ikeguchi Tohru, Proceedings of the Society Conference of IEICE, 2010 08 31
- Reconstruction of input information using transformation from spike train to continuous-time series, SUZUKI Mai, KURODA Kaori, SHIMADA Yutaka, IKEGUCHI Tohru, IEICE technical report, 2010 03 02 , Neurons code input information and generate spike trains. The generated spike trains reflect the input information. In this report, we use the Hanning window to transform the spike trains to a continuous instantaneous firing-frequency time series. Then, we reconstruct hidden input information through this transformation. In the numerical simulations, we use periodic time series (sinusoidal wave), quasi-periodic time series generated from the Langford equations, and chaotic time series generated from the Lorenz equations. In addition, we use a real time series of a Japanese vowel /a/. Using the cross-correlation coefficient, we compare reconstructed time series with input time series. As a result, we find that strong correlation exists between the input time series and its reconstructed time series.
- A-2-21 Transforming spike sequence to continuous time series to estimate neural network structure, Kuroda Kaori, Ikeguchi Tohru, Proceedings of the IEICE General Conference, 2010 03 02
- Estimation of connectivity of nonlinear dynamical systems by partial correlation analysis, KURODA Kaori, UCHIDA Atsushi, IKEGUCHI Tohru, IEICE technical report, 2010 01 14 , In the real world, nonlinear dynamical systems often produce complex behaviors due to interactions between elements. To model such nonlinear dynamical systems, one of the important issues is how to estimate connectivity between their elements only from observed time series. In this report, we use the partialization analysis to estimate the connectivity between the elements of nonlinear dynamical systems. We apply our estimation method to mathematical models: the Henon map, the chaotic neural network, and the Duffing equation. As a result, our method can estimate the connectivity between the elements in the mathematical models only from the observed time series. We also show that its estimation accuracy is high.
- Partialization Analysis for Nonlinear Connections of Second Order, KURODA Kaori, IKEGUCHI Tohru, IEICE technical report, 2009 11 04 , Real systems often produce very complicated behavior due to complex interactions between elements in the system. In order to analyze such complex systems, it is important to clarify connections between the elements in the system. In this report, we propose nonlinear partial correlation coefficient which removes spurious effects of nonlinear connections of the second order. The proposed nonlinear partial correlation coefficient is derived from a nonlinear multivariable regression model. To investigate the validity of the proposed coefficient, we used mathematical models which have nonlinear connections of the second order. As a result, the proposed coefficient can remove the spurious nonlinear correlations.

- 2012 10 , Student Award on International Symposium on Nonlinear Theory and its Applications 2012