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Name JINNO Kenya
Official Title Professor
Affiliation Industrial Systems
E-mail kjinno@tcu.ac.jp
Web
  1. http://www.comm.tcu.ac.jp/nel/
  2. https://researchmap.jp/kjinno?lang=en
  3. https://www.risys.gl.tcu.ac.jp/Main.php?action=profile&type=detail&tchCd=5002085
Profile My research field is nonlinear systems engineering, and I am working on the analysis of nonlinear dynamical systems and nonlinear optimization. In my research on nonlinear dynamical systems, I analyze the dynamics of artificial neural networks and synthese them based on our research results. In the area of nonlinear optimization, I am working on swarm intelligence optimization and evolutionary computation. These are applied to the optimal design of power supply circuits, automatic design of analog circuits, and image processing using neural networks. In recent years, I am focusing my research on machine learning theory and its applications. In machine learning, we are studying theories of semi-supervised learning, clustering, and reservoir computing. I am applying these techniques to control in various fields. Our research results are presented at international conferences and published in academic journals.
Research Field(Keyword & Summary)
  1. 1) Nonlinear theory and its applications

    There are a variety of nonlinear systems in the world. The primary objective of this research is to analyze the dynamics of these nonlinear systems and to clarify their behavior. We aim to clarify the mechanism of various phenomena caused by nonlinearity, and to develop new systems using these phenomena.

  2. 2) Swarm optimization and Evolutionary computation

    For black-box optimization problems where the objective function to be optimized is not explicitly given, we are developing an algorithm to efficiently search for the optimal solution using only function value information. The optimization of such black-box problems is very important in real problems, and our goal is to develop algorithms that are as efficient as possible.

  3. 3) Analysis and Application of Machine Learning Theory

    We are conducting research on the analysis of new algorithms for machine learning and their applications. In particular, we are focusing on the latent variable space when extracting features from data, and conducting research on algorithms that are efficient and can successfully extract features from the original data. We also aim to apply these algorithms to a variety of new applications.
Representative Papers
  1. 1) Analysis of particle swarm optimization by dynamical systems theory, NOLTA IEICE, vol. 12, no. 2, pp. 118-132, 2021.
  2. 2) Local Search Method for Multiple-Vehicle Bike Sharing System Routing Problem, Journal of Signal Processing, vol. 22, Issue 4, pp. 157-160, 2018.
  3. 3) Learning Algorithm with Nonlinear Map Optimization for Neural Network, vol. 22, Issue 4, pp. 153-156, 2018.
  4. 4) Analysis of the Dynamical Characteristics of the Firefly Algorithm, nternational Journal of Swarm Intelligence Research (IJSIR), vol. 8, no. 4, pp. 18-33, 2017.
  5. 5) An Effective Construction Algorithm for the Steiner Tree Problem Based on Edge Betweenness, Journal of Signal Processing, vol. 20, no. 4, pp. 145-148, 2016.
  6. 6) Synchronization of Relaxation Oscillators having Individual Difference by Non-periodic Signal Injection, IEICE Transaction on Fundamentals, vol. E99-A, no. 6, pp. 1188-1197, 2016
  7. 7) Optimization of Switching Phase of a Single-Phase PWM dc-ac Inverter, Electrical Engineering in Japan, vol. 195, Issue 4, pp. 16-25, 2016.
  8. 8) Common noise-induced synchronization of relaxation oscillators, IFAC-PapersOnLine, volume 48, Issue 18, pp. 233-238, 2015.
  9. 9) Harmonic Elimination of Three phase PWM DC-AC Inverter using Particle Swarm Optimization, NOLTA IEICE, vol. E6-N, no. 4, pp. 512-519, 2015.
  10. 10) Neural-based routing strategy with transmitting information for complex communication networks, NOLTA IEICE, vol. 6, no. 2, pp. 263-274 , 2015.
Patent
  1. 1) DATA CONVERSION METHOD BASED ON SCALE-ADJUSTED .beta.-MAP, 2011JP001664, WO 2011/125296, 2011.
  2. 2) DATA CONVERSION METHOD BASED ON NEGATIVE .beta.-MAP, 2011JP001666, WO 2011/125297, 2011.
  3. 3) MULTI-HYSTERESIS VOLTAGE CONTROLLED CURRENT SOURCE SYSTEM, 2010JP000571, WO 2010/089983, 2011.
  4. 4) MULTI-SCREW CHAOTIC OSCILLATOR CIRCUIT, 2010JP001687, WO 2010/109793, 2011.
Award 1) Young Researcher's Award, IEICE, 1996.
2) Research Encouragement Award, IEICE Karuizawa CAS Workshop, 1996.
3) Planning Award, IEEJ, 2007
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS) https://nrid.nii.ac.jp/en/nrid/1000050286762/
Affiliated academic society (Membership type) 1) IEICE (Fellow)
2) IEEE (Member)
3) JSAI (Member)
4) IPSJ (Member)
5) ISCIE (Member)
Education Field (Undergraduate level) Machine Learning, Data Science, Programming, Digital Circuits, Circuit Theory, Information Theory, Linear System Theory. Object Programming, Algorithm
Education Field (Graduate level) Adavanced Machine Learning, Adavanced Nonlinear System Engineering

Affiliation