HORIO Keiichi





2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka

Research Fields, Keywords

Neural Network.


E-mail address

Post Graduate Education 【 display / non-display

  • 2001.03  Kyushu Institute of Technology  Graduate School, Division of Information Engineering  Doctoral Program  Completed  JAPAN

Degree 【 display / non-display

  • Kyushu Institute of Technology -  Doctor of Information Engineering  2001.03

Biography in Kyutech 【 display / non-display

  • 2019.08

    Kyushu Institute of TechnologyGraduate School of Life Science and Systems Engineering   Department of Human Intelligence Systems   Professor  

  • 2014.04

    Kyushu Institute of TechnologyGraduate School of Life Science and Systems Engineering   Department of Human Intelligence Systems   Associate Professor  

  • 2007.04

    Kyushu Institute of TechnologyGraduate School of Life Science and Systems Engineering   Department of Brain Science and Engineering   Associate Professor  

Biography before Kyutech 【 display / non-display

  • 2001.04

    Japan Society for the Promotion of Science   Special researcher of the Japan Society for the Promotion of Science   JAPAN

Specialized Field (scientific research fund) 【 display / non-display

  • Kansei informatics


Publications (Article) 【 display / non-display

  • Epilepsy EEG classification using morphological component analysis

      2018 ( 1 )   2018.12  [Refereed]

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    © 2018, The Author(s). In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic seizure using time series electroencephalogram (EEG). MCA was employed to decompose the EEG signal segments considering its morphology during epileptic events using undecimated wavelet transform (UDWT), local discrete cosine transform (LDCT), and Dirac bases forming the over-complete dictionary. Frequency-modulated time frequency features were extracted after applying the Hilbert transform. Feature root mean instantaneous frequency square (RMIFS) and its parameters and parameters ratio are used in two different pairs for classification using support vector machine (SVM), showing good and comparable results.

    DOI Scopus

  • MCA Based Epilepsy EEG Classification Using Time Frequency Domain Features

      2018-July   3398 - 3401   2018.10  [Refereed]

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    © 2018 IEEE. In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented into analytical form by using Hilbert transform. Then features, parameter's ratio of bandwidth square, mean square frequency and fractional contributions to dominant frequency were extracted to discriminate epilepsy EEG by support vector machine (SVM). These features have shown classification results comparable to previous works.

    DOI Scopus

  • Consideration of Relationship between Shape and Angular Velocity of Particles under Electrorotation

      2018-June   86 - 90   2018.08  [Refereed]

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    © 2018 TSI Press. In this study, we discuss the relationship between shape and angular velocity for cells of the same kind but with different angular velocity under electrorotation. By representing the shape of the cell as a set of distances between the center of gravity and the contour and analyzing it using a self-organizing map, we hypothesized that there is a relationship between the minor axis length and the angular velocity of the cells. In addition, the result of electromagnetic field simulation simulating the device was to affirm this hypothesis. A meaningful consideration was made toward future improvement of the device.

    DOI Scopus

  • Development of Pillar Electrode Array for Electrorotation Analysis of Single Cells

      2018-June   136 - 139   2018.08  [Refereed]

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    © 2018 TSI Press. Electrorotation is a noninvasive technique to measure the dielectric properties of single particles. In this paper, we developed an electrorotation device for high-throughput dielectric characterization of circulating tumor cells (CTCs). The device consists of array of pillar electrodes for single-cell trapping and electrorotation analysis. The practicality of the device was evaluated using barium titanate (BaTiO3) particles. The single BaTiO3particles were trapped in gaps between the four pillar electrodes and rotated by electrorotation torque, which reflects their dielectric properties.

    DOI Scopus

  • Self-calibration algorithm for a pressure sensor with a real-time approach based on an artificial neural network

      18 ( 8 )   2018.08  [Refereed]

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    © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

    DOI Scopus

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Conference Prsentations (Oral, Poster) 【 display / non-display

  • ガボール特徴量に基づく口腔内白斑形状の識別

    小野 史貴, 三澤 秀明, 堀尾 恵一, 大谷 泰志, 土生 学, 冨永 和宏, 山川 烈

    JSAI大会論文集  2018.07  -  2018.07 

     View Summary



  • Multi Layered Self-Organizing Map Propagating Semantic Information and its Application to Shape Clustering

    HORIO Keiichi, MIZUTANI Ryuki, FURUKAWA Tetsuo

    Proceedings of the Fuzzy System Symposium  2018.01  -  2018.01 


  • Reward Design Method Adapting to Agents' Learning Ability based on Self-Organizing Map with Evaluation Value

    HORIO Keiichi, MORI Ippei, FURUKAWA Tetsuo

    Proceedings of the Fuzzy System Symposium  2018.01  -  2018.01 


  • Scene Classification and Automatic Annotation Based on Multi-layered Self-Organization Map

    MIZUTANI Ryuki, FURUKAWA Tetsuo, HORIO Keiichi

    Proceedings of the Fuzzy System Symposium  2017.01  -  2017.01 


  • Hazard Anticipation System toward Realizing Autonomous Vehicle

    Horio Keiichi, Koga Hiroaki, Narain Jaiprakash, Tsuchiya Ryo, Yano Sora

    Proceedings of the Fuzzy System Symposium  2016.01  -  2016.01 


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