Kuremoto's Web Page
BIP-- Lab. of Biological Information Processing System
ISE-- Dept. of Information Science and Engineering
Yamaguchi University
Publications

Last modefied: June 22, 2020


  Books (co-author)

1. Kuremoto, T., Eto, T., Kobayashi, K., and Obayashi, M., A Hippocampus-Neocortex Model for Chaotic Association. In Trends in Neural Computation (Studies in Computational Intelligence) (ed. Ke Chen and Lipo Wang), Vol.35, Chapter 5, pp.111-133, Springer, 2006.

2. Kuremoto, T., Obayashi, M., and Kobayashi, K., Neural Forecasting Systems. In Reinforcement Learning, Theory and Applications (ed. Cornelius Weber, Mark Elshaw and Norbert Michael Mayer), Chapter 1, pp.1-20, IN-TECH , 2008 (online OpenAccess).

 3. Kobayashi, K., Obayashi, M., Kuremoto, T., Objective-based Reinforcement Learning System for Cooperative Behavior Acquisition,  In Application of  Machine Learning, (ed. Yagang Zhang) , Chapter 14 , pp. 233-244,  IN-TECH , 2010 (online OpenAccess)

4. Obayashi, M., Narita, K., Okamoto, Y., Kuremoto, T., Kobayashi, K. and L. Feng, A Reinforcement Learning System Embedded Agent with Neural Network-Based Adaptive Herarchical Memory Structure, In Advances in Reinforcement Learning,(ed. Abdelhamid Mellouk), ISBN: 978-953-307-369-9, Chapter 11, pp.189-208, IN-TECH , 2011 (online OpenAccess)

 5. Obayashi, M., Nakahara, N., Yamada, K., Kuremoto, T., Kobayashi, K. and L. Feng, A Robust  Reinforcement Learning System Using Concept of Sliding Mode Control for Unknown Nonlinear Dynamical System, In Robust Control, Theory and Applications, (ed. Andrzej Bartoszewicz), ISBN: 978-953-307-229-6, Chapter 9 , pp. 197-214, IN-TECH,  2011 (online OpenAccess)

6. (Japanese Text) $BBgNS@5D>(B, $B8bK\!!6F(B, $B>.NSK.OB!'!X%$%s%F%j%8%'%s%H%3%s%T%e!<%F%#%s%0!Y!$;38}Bg3X9)3XItCNG=>pJs9)3X2J@8BN>pJs%7%9%F%`9)3X8&5f<
7. (Japanese Text) $BF#EDM*2p(B, $B8bK\!!6F(B, $BFbB<=SFs(B, $B>.NSK.OB!'!X$b$N$E$/$jAO@.pJs9)3X2J!$(BISBN978-4-902207-02-6 C3005$B!$(B2012

8. Kuremoto, T., Obayashi, M.,  Kobayashi, K., and L.-B., Feng: Instruction Learning Systems for Partner Robots.(PDF) In Advances in Robotics - Modeling, Control and Applications, (eds. Calin Ciufudean & Lino Garcia), ISBN: 978-1-461108-44-3, iConcept Press, Chapter 8, pp. 149-170, 2012 (online OpenAccess) .

9.Kuremoto, T., Obayashi, M.,  Kobayashi: A Chaotic Memory System Accelerated by an Emotional Model. In Insights into Amygdala: Structure, Functions and Implication for Disorders, (ed. Deniz Yilmazer-Hanke), Chapter 9, pp.229-254, Nova Scientific Publishers, 2012.

10. Feng, L.-B., Obayashi, M., Kuremoto, T., Kobayashi, K.: Construction and Application of Learning Petri Net. (PDF) In Petri Nets - Manufacturing and Computer Science  (ed. Pawel Pawlewski),  pp.143-176, ISBN 978-953-51-0700-2, Hard cover, 492 pages, http://dx.doi.org/10.5772/48398, IN-TECH,  2012 (online OpenAccess)

11. Kuremoto, T., Obayashi, M.,  Kobayashi: Neuro-Fuzzy Systems for Autonomous Mobile Robots. In Horizons in Computer Science Research, Vol. 8, (ed. Thomas S. Clary),Chapter 3,  pp.67-90, ISBN: 978-1-62417-413-1 Nova Scientific Publishers , March, 2013

12. (Japanese Text) $BEDCf!!L-(B, $B!!>>85N4Gn!$(B$B8bK\!!6F(B$B!'!X>pJs9)3XpJs9)3X(B $B2J!$(BISBN978-4-902207-05-2 C3005$B!$(BMarch, 2015

13. (Japanese Text) $B>>(B $BF#?.:H!$D9!!FF;V!$?e>e2E>85N4Gn!$(B$B8bK\!!6F(B$B!'(B $B!X>pJs9)3XpJs9)3X(B $B2J!$(BISBN978-4-902207-03-3 C3055, September, 2015

14.    Kuremoto, T., Baba, Y., Obayashi, M., Mabu, S., Kobayashi, K., Mental Task Recognition by EEG Signals: A Novel Approach with ROC Analysis, In Human-Robot Interaction –Theory and Application (eds. G. Anbarjafari and S. Escalera), Chapter 4, pp. 65-78, IntechOpen, ISBN: 978-1-78923-317-9, July 4, 2018 (online Open Access)

15.     Kuremoto, T., Hirata, T., Obayashi, M., Mabu, S., Kobayashi, K.,Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting, In Time Series Forecasting - Data, Methods, and Applications, (Editor: Ngan, C. K.), pp. 1-17,IntechOpen,DOI: 10.5772/intechopen.85457, Apr. 3, 2019 (online Open Access)

16.  Mabu, S., Kido, S., Hirano, Y., Kuremoto, T., Opacity Labeling of Diffuse Lung Deseases in CT images Using Unsupervised and Semi-supervised Learning, In Deep Learing in Healthcare, Paradigms and Applications (eds. Chen, Y.W. & Jain L.C.), Chapter 10, pp.165-179, https://doi.org/10.1007/978-3-030-32606-7, ISBN 978-3-030-32605-0 ISBN 978-3-030-32606-7 (eBook), Springer Nature, Nov. 19, 2019

17.     Kuremoto, T., Hirata, T., Obayashi, M., Kobayashi, K., Mabu, S., Search Heuristics for the Optimization of DBN for Time Series Forecasting, In Deep Neural Evolution, Natural Computing Series (eds. H. Iba & N. Noman), Chapter 5, pp. 131-152, DOI 10.1007/978-981-15-3685-4, Hardcover ISBN 978-981-15-3684-7, eBook ISBN 978-981-15-3685-4, Springer Nature, May 21, 2020




Journal papers (with review)

  1. Wu B. Y., Kobayashi K., and Torioka T.: Pattern Separating Conditions of a Layered Nerve Net ---The Number of Connections between Layers R=4---. Transactions on IEICE (The Institute of Electronics, Information and Communication Engineers), Vol.J80-D-II, No.9, pp.2573-2577, 1997 (in Japanese).

    2. $B8bK\6F!$>.NSK.OB!$D;2,K-;N!'!!AX>u?@7P2sO)LV$N%Q%?!<%sJ,N%>r7o!l9g!pJsDL?.3X2qO@J8;o!$(BD-II, Vol.J80-D-II, No.9, pp.2573-2577, 1997

  2. Tsukamoto S., Wu B. Y., Koga K, Miike H.: A High Accuracy Depth Measurement by a Hierarchical Phase Shifting Method. Transactions on IEICE (The Institute of Electronics, Information and Communication Engineers), Vol.J83-D-II, No.9, pp.1962-1965, 2000 (in Japanese).

    3. $BDMK\ATJe!$8bK\6F!$8E2lOBMx!$;0CS=(IR!'!!3,AX2=0LAj%7%U%HK!$K$h$k9b@:EY$J1|9T7WB,!$EE;R>pJsDL?.3X2qO@J8;o!$(BD-II, Vol.J83-D-II, No.9, pp.1962-1965, 2000

  3. Obayashi M., Omiya R., Kuremoto T., Kobayashi K.: Shapes of Non-monotonous Activation Functions in Chaotic Neural Network. IEEJ Transactions on EIS, Vol.126, No11, pp.1401-1405, 2006 (in Japanese).

    4. $BBgNS@5D>!$Bg5\M}7C!$8bK\6F!$>.NSK.OB!'!!%+%*%9%K%e!<%i%k%M%C%H%o!<%/O"A[5-21%b%G%k$K$*$1$k3h@-2=4X?t$N7A>u$H$=$NI>2A!$EE5$3X2qO@J8;o#C!$(B Vol.126, No. 11, pp. 1401-1405, 2006

  4. Kogawa N., Obayashi M., Kobayashi K., Kuremoto T.: A Reinforcement Learning Method Based on Immune Network. Transactions of SICE(The Society of Instrument and Control Engineers), Vol.43, No.6, pp.525-527, 2007 (in Japanese).

    5. $B>.@nD95W!$BgNS@5D>!$>.NSK.OB!$8bK\6F!'!!LH1V2sO)LV<06/2=3X=,!$7WB,<+F0@)8f3X2qO@J8=8!$(BVol.43, No.6, pp.525-527, 2007

  5. Hano, T., Kuremoto, T., Kobayashi, K., and Obayashi, M.: A Hand Image Instruction Learning System Using Transient-SOM. Transactions on SICE (Society of Instrument and Control Engineering), Vol.43, No.11, pp.1004-1006, 2007 (in Japanese)

    6. $B1)Ln$H$b$(!$8bK\6F!$>.NSK.OB!$(B $BBgNS@5D>!'!!(BTransient-SOM $B$rMQ$$$?AIBOExperiment.wmv(6.70MB Download:Winamp )

  6. K. Kobayashi, K. Nakano, T. Kuremoto, M. Obayashi: A State Predictor Based Reinforcement Learning System (PDF), Electronics and Communications in Japan, Vol.93, No.6, pp.8-18, 2010 (in English)
    K. Kobayashi, K. Nakano, T. Kuremoto, M. Obayashi: A State Predictor Based Reinforcement Learning System, IEEJ Transactions on EIS, Vol.128, No.8, pp.1303-1311, 2008 (in Japanese) (Abstract in English)

    $B>.NSK.OB!$CfLn9@Fs!$8bK\6F!$BgNS@5D>!'!!>uBVM=B,7?6/2=3X=,%7%9%F%`!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.128, No.8, pp.1303-1311, 2008


  7. N. Kogawa, M. Obayashi, K. Kobayashi, T. Kuremoto: A Reinforcement Learning Method Based on Immune Network Adapted to Semi Markov Decision Process, Artificial Life and Robotics, Vol. 13, No. 2, pp.538-542, 2009

  8. M. Obayashi, N. Nakahara, T. Kuremoto, K. Kobayashi: A Robust Reinforcement Learning Using Concept of Slide Mode Control, Artificial Life and Robotics, Vol. 13, No. 2,pp.526-530, 2009


  9. T. Kuremoto, T. Ohta, K. Kobayashi, M. Obayashi: A Dynami Associative Memory System by Adopting Amygdala Model, Artificial Life and Robotics, Vol. 13. No. 2, pp.478-482, 2009

  10. M. Obayashi, K. Narita, K. Kobayashi, T. Kuremoto: A Transient Chaotic Associative Memory Model with Temporary Stay Function (online PDF),Electrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi), Vol. 175, Issue 2, pp.29-36 (2011)

    $BBgNS@5D>!$@.ED820lO:!$>.NSK.OB!$(B$B8bK\(B $B6F!'!!0l;~E*BZN15!G=$r;}(B $B$D2aEOE*%+%*%9O"A[5-21%b%G%k!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.128, No.12, pp.1852-1858, 2008

  11. K. Kobayashi, T. Kuremoto, M. Obayashi: A Local Linear Wavelet Neural Network Based on a Bayesian Design Method, Transactions on IEE Japan (The Institute of Electrical Engineers of Japan), Vol.129-C, No.7, pp.1356-1362, 2009 (in Japanese) (Abstract in English)

    $B>.NSK.OB!$(B$B8bK\6F(B$B!$BgNS@5D>!'!!6I=j@~7A%b%G%k$rF3F~$7$?%&%'!<%V(B $B%l%C%H%K%e!<%i%k%M%C%H%o!<%/$N%Y%$%:E*@_7WK!(B$B!$(B$BEE(B $B5$3X2qO@J8;o(BC$B!$(BVol.129, No.7, pp.1356-1362, 2009

  12. Mizoue, H., Kobayashi, K., Kuremoto, T., and Obayashi, M., A Meta-parameter Learning Method in Reinforcement Learning Based on Temporal Difference Error, Transactions on IEE Japan (The Institute of Electrical Engineers of Japan), Vol.129-C, No.9, pp.1730-1736, 2009 (in Japanese) (Abstract in English)

    $B9B>eM5G7!$>.NSK.OB!$(B$B8bK\6F(B$B!$BgNS@5D>!'(BTD$B8m:9$K4p$E$/6/2=3X=,$N(B $B%a%?%Q%i%a!<%?3X=,K!!$(B$BEE5$3X2qO@J8;o#C(B$B!$(B Vol.129, No.9, pp.1730-1736, 2009

  13. Kuremoto, T., Obayashi, M., and Kobayashi, K., Adaptive Swarm Behavior Acquisition by a Neuro-Fuzzy System and Reinforcement Learning Algorithm, International Journal of Intelligent Computing and Cybernetic, Vol.2, No.4, pp.724-744, 2009 (Abstract).

  14. Kuremoto, T., Yamano, Y., Obayashi, M. and Kobayashi, K.: An Improved Internal Model for Swarm Formation and Adaptive Swarm Behavior Acquisition, Journal of Circuits, Systems, and Computers, Vol.18, No.8, pp.1517-1531, 2009 (Abstract).

  15. Kuremoto, T., Komoto, T., Kobayashi, K., and Obayashi, M.: Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System(online, html or pdf),  Journal of Robotics, doi: 10.1155/2010/307293, 9 pages, 2010

  16. Obayashi, M., Feng, L.-B., Kuremoto, T., and Kobayashi, K., Intelligent Agent Construction Using the Attentive Characteristic Patterns of Chaotic Neural Networks (online, pdf), Artificial Life and Robotics, Vol. 15. No. 2,pp.216-220, 2010

  17. Makino, Y., Obayashi M., Kuremoto T., Kobayashi K.: Indirect Adaptive Self-structuring Fuzzy Neural Network Control System. IEEJ Transactions on EIS, Vol., No.10, pp.1882-1887, 2010 (in Japanese) (Abstract in English)

    $BKRLn5H9(!$BgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.OB!'!!4V@\7?E,1~E*<+8J9=B$%U%!(B $B%8%#%K%e!<%i%k%M%C%H%o!<%/@)8f%7%9%F%`(B$B!$(B$BEE(B $B5$3X2qO@J8;o(B$B#C(B$B!$(BVol.130, No.10, pp. 1882-1887 , 2010

  18. Nakano, K., Obayashi M., Kuremoto T., Kobayashi K.: A Robust Reinforcement Learning Control Design Method for Nonlinear System with Partially Unknown Structure. IEEJ Transactions on EIS, Vol.130, No.10, pp.1882-1887, 2010 (in Japanese) (Abstract in English)

    $BCfLn0l9(!$BgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.OB!'!!ItJ,E*L$CN9=B$$r;}$DHs@~(B $B7A%7%9%F%`$N$?$a$N%m%P%9%H6/2=3X=,@)8f7O@_7WK!!$(B$BEE5$3X2qO@J8;o#C(B$B!$(B Vol.130, No.11 pp.2090-2091 , 2010

  19. Kuremoto, T., Watanabe S., Kobayashi, K., Feng, L.-B., Obayashi M.: The Dynamical Recollection of Interconnected Neural Networks Using Meta-Heuristics (PDF). Electronics and Communications in Japan, vol. 95, No. 6,  pp. 12-23, May,  2012, DOI 10.1002/ecj.11372 (in English)
    Kuremoto, T.    , Watanabe S., Kobayashi, K., Feng, L.-B., Obayashi M.: The Dynamical Recollection of Interconnected Neural Networks Using Meta-Heuristics. IEEJ Transactions on EIS, Vol.131, No.8, pp.1475-1484, 2011 (in Japanese) (Abstract in English)

    $B8bK\6F(B$B!$EOn4=Y!$>.NSK.OB!$qH(B $BNI_[!$BgNS@5D>!'!!Aj8_7k9g7?%M%C%H%o!<%/$K$*$1$k%a%?%R%e!<%j%9%F%#%/%9$rMQ$$$?F0E*A[5/!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.131, No.8, pp. 1475-1484, 2011


  20. Kuremoto, T., Obayashi, M., Kobayashi, K., Feng, L.-B.: An Improved Internal Model of Autonomous Robots by a Psychological Approach (Online PDF). Cognitive Computation, Vol.3, No.4, pp. 501-509, Springer, doi 10.1007/s12559-011-9102-7, (online) June, 11, 2011

  21. Obayashi, M., Uchiyama, S., Kuremoto, T.,  Kobayashi, K. : A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H$B!g(B control. IEEJ Transactions on EIS, Vol.131, No.8, pp.1467-1474, DOI: 10.1541/ieejeiss.131.1467, 2011 (in Japanese) (Abstract in English)

    $BBgNS@5D>!$Fb;3>M8c!$(B$B8bK\6F(B$B!$>.NSK.OB!'!!6/2=3X=,@)8f$HE,1~(BH$B!g@)(B $B8f$N6(F/7?@)8fJ}<0!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.131, No.8, pp.1467-1474, DOI: 10.1541/ieejeiss.131.1467, 2011

  22. Feng, L.-B., Obayashi M., Kuremoto T., Kobayashi K.: A Learning Petri net model. IEEJ Transactions on EEE, Vol.7, No.3, pp. 274-282,  info:doi/10.1002/tee.21728, John Wiley & Sons, Inc, 2012

  23. Kuremoto, T., Kinoshita, Y., Feng, L., Watanabe, S., Kobayashi, K., Obayashi M.: A Gesture Recognition System with Retina-V1 Model and One-Pass Dynamic Programming. Neurocomputing, Vol.116 , pp. 291-300, Oct. 2012 (online OpenAccess)

  24. Uchiyama, S., Obayashi, M., Kuremoto, T.,  Kobayashi, K. : A Real-time Reinforcement Learning Control System with H$B!g(B Tracking Performance Compensator. IEEJ Transactions on EIS, Vol.132-C, No.6, pp.1008-1015 , 2012 (in Japanese)

    $BFb;3>M8c!$(B     $BBgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.(B $BOB!'!!(BH$B!gDI=>@-G=Jd=~4o$rHw$($?%j%"%k%?%$%`6/2=3X=,@)8f%7%9%F%`!$(B$BEE5$3X2qO@(B $BJ8;o(B $B#C(B$B!$(BVol.132, No.6, pp.1008-1015 , 2012

  25. Obayashi, M., Sinnosuke Koga, Feng, L.-B., Kuremoto, T., and Kobayashi, K.: Handwriting Character Classification Using Freeman's Olfactory KIII Model, Artificial Life and Robotics, Vol.17, Issue 2, pp.227-232, DOI:10.1007/s10015-012-0047-z, Dec. 2012 (online OpenAccess)


  26. Kuremoto, T.,$B!!(BHashiguchi K. Morisaki K.,  Watanabe S., Kobayashi, K., Obayashi M.: Mutiple Action Sequence Learning and Automatic Generation for a Humanoid Robot Using RNNPB and Reinforcement Learning, Journal of Software Engineering and Applications, Vol.5,  pp.128 -133, doi: 10.4236/jsea.2012.512b25,  2012 (online Open Access) Video:RobotExperiment.wmv(10.5 MB Download:Winamp )

  27. Kuremoto, T., Yamano Y., Feng, L.-B., Kobayashi K., and Obayashi M.: Adaptive Swarm Behavior Acquisition Using a Neuro-Fuzzy Reinforcement Learning System. IEEJ Transactions on EIS, Vol.133-C, No.5, pp.1076-1085, 2013 (in Japanese)

    $B8bK\6F(B$B!$;3LnM4$BqH(B $BNI_[!$(B$B>.NSK.OB!$BgNS@5D>!'!!%K%e!<%m%U%!%8%#7?6/2=3X=,%7%9%F%`$rMQ$$$?729TF0(B $B$N3MF@!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.133, No.5, pp.1076-1085, 2013


  28. Uchiyama, S., Obayashi, M., Kuremoto, T.,  Kobayashi, K. : A Robust Control System Based on a Cerebellar Perceptron Improved Model. IEEJ Transactions on EIS, Vol.131-C, No.6, pp.1251-1285, 2013 (in Japanese)

    $BFb;3>M8c!$(B     $BBgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.(B $BOB!'!!>.G>%Q!<%;%W%H%m%s2~NI%b%G%kMxMQ7?%m%P%9%H@)8f%7%9%F%`!$(B$BEE5$3X2qO@J8;o(B $B#C(B    , Vol.131, No.6, pp. 1251-1285, 2013


  29. Feng, L.-B., Obayashi, M., Kuremoto, T., Kobayashi, K., and Watanabe, S.: QoS Optimization for Web Services Composition Based on Reinforcement Learning. Interational Journal of Innovative Computing, Information and Control (IJICIC), Vol.9, No. 6, pp. 2361-2376, June 2013

  30. Kuremoto, T., Tsurusaki, T., Kobayashi, K., Mabu, S., and Obayashi, M.: An Improved Reinforcement Learning System Using Affective Factors. (PDF) Robotics, Vol. 2, pp. 149-164, July, 2013 (online Open Access)

  31. Mabu, S., Hirasawa, K., Obayashi, M. and Kuremoto, T.: Enhanced Decision Making Mechanism of Rule-based Genetic Network Programming for Creating Stock Trading Signals, Expert Systems with Applications, Online Open Access, Vol.40, Issue 16, pp.6311-6320, Nov. 2013

  32. Mabu, S., Hirasawa, K., Obayashi, M. and Kuremoto, T.: A Variable Size Mechanism of Distributed Graph Programs and Its Performance Evaluation in Agent Control Problems, Expert Systems with Applications, Online Open Access, Vol.41, Issue 4 , pp.1663-1671, March 2014

  33. Watanabe, S., Kuremoto, T., Kobayashi, K., and Obayashi, M.: A Method for Analyzing the Spatiotemporal Changes of  Chaotic Neural Networks (PDF), Artificial Life and Robotics, DOI 10.1007/s10015-013-0114-0, Online Open Access, Vol. 18, No.3-4, pp. 196-203, 2013

  34. Kuremoto, T., Kimura, S., Kobayashi, K., and Obayashi, M.: Time Series Forecasting Using a Deep Belief Network with Restricted Boltzmann Machines (Online Open Access), Neurocomputing, Vol.137, No.5, pp.47-56, Aug. 2014 (Download: CATS Benchmark Data)


  35. Watada, S., Obayashi, M., Kuremoto, T.,  Kobayashi, K., Mabu, S. : Behavior Selection Method of Robots Based on a Markovian Emotional Model, IEEJ Transactions on EIS, Vol.134, No.1, pp.85-93, Jan. 2014 (in Japanese) (Abstract in English)

    $BLJED>-8g!$(B     $BBgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.(B , $B4VIa??8c!'!!!V(B$B%^%k%3%U>pF0%b%G%k$rHw$($?%m(B $B%\%C%H$N9TF07hDjK!(B$B!W!$(B$BEE5$3X2q(B $BO@(B $BJ8;o(B $B#C(B    , Vol.134, No.1, pp.85-93, 2014

  36. Shogo Watada, Masanao Obayashi, Takashi Kuremoto, Shingo Mabu, Kunikazu Kobayashi: A Decision Making System of Robots Intruducing a Re-Construction of Emotions Based on Their Own Experiences (PDF), Journal of Robotics, Networks, and Artificial Life, Online Open Access, Vol. 1, No.1, pp. 27-32, June 2014

  37. Shun Watanabe, Takashi Kuremoto, Shingo Mabu, Masanao Obayashi, Kunikazu Kobayashi: The Recollection Characteristics of Generalized MCNN Using Different Control Methods (PDF), Journal of Robotics, Networks, and Artificial Life, Online Open Access, Vol. 1, No.1, pp. 73-79, June 2014

  38. Shogo Uchiyama, Masanao Obayashi, Takashi Kuremoto,  Kunikazu Kobayashi: A Control System Based on Auto-Fusion Cerebellar Perceptron Improved Model and Its Applications to Consensus Problem. IEEJ Transactions on EIS, Vol134, No.7, pp. 990-998, 2014 (in Japanese)

    $BFb;3>M8c!$(B $BBgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.(B $BOB!'!!(B$B<+(B $B8JM;9g>.G>%Q!<%;%W(B $B%H%m%s2~NI%b%G%kMxMQ7?@)8f%7%9%F%`$H$=$N9g0ULdBj$X$N1~MQ(B$B!$(B$BEE5$3X2qO@J8;o(B $B#C(B, Vol.134, No.7, pp. 990-998, 2014

  39. Takashi Kuremoto, Takuhiro Otani, Shingo Mabu, Masanao Obayashi, Kunikazu Kobayashi: One-D-R-A-G-SOM and its Application to a Hand Shape Instruction Learning System (Online Open Access), International Journal of Networked and Distributed Computing, Vol.2, No.3, pp.166-174, Aug. 2014

  40. Watanabe, S., Kuremoto, T.,  Kobayashi, K., Mabu, S., Obayashi, M.: Dynamical Recollection and Storage of Video Images via MCNN and SOM.  IEEJ Transactions on EIS, Vol.135, No.4, pp.414-422, 2015 (in Japanese) (Abstract in English)

    $BEOn4=Y!$(B$B8bK\6F(B$B!$>.NSK.OB!$4VIa??8c!$BgNS@5D>!'!!(BMCNN $B$H(BSOM$B$rMQ$$$?F02hA|$N5-LC$HF0E*A[5/(B$B!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.135, No.4, pp. 414-422, 2015

  41. Takashi Kuremoto, Keita$B!!(BMorisaki, Kunikazu Kobayashi, Shingo Mabu, Masanao Obayashi: Elman Type Recurrent Neural Nework with Parametric Bias and its Application to Multi-Action Learning of Robot, ICIC Express Letters Part B: Applications -- An International Journal of Research and Surveys, Vol. 6, No. 9, pp., 2361-2369, Sep., 2015

  42. Shingo Mabu, Masanao Obayashi, Takashi Kuremoto: Ensemble Learning of Rule-Based Evolutionary Algorithm using Multi-Layer Perceptron for Supporting Decisions in Stock Trading Problems, Applied Soft Computing, Vol. 36, pp.357-367, 2015

  43. Shingo Mabu, Masanao Obayashi, Takashi Kuremoto: Reinforcement Learning with Symbiotic Relationship for Multi-Agent Environments, Journal of Robotics, Networks, and Artificial Life, Online Open Access, Vol. 2, No.1, pp. pp.40-45, 2015

  44. Wirarama Wedashwara, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto: On-line Rule Updating System using Evolutionary Computaion for Managing Distributed Database, Journal of Robotics, Networks, and Artificial Life, Online Open Access, Vol. 2, No.2, pp.73-78, 2015

  45. Takashi Kuremoto, Shingo Mabu, Kuniakzu Kobayashi, Masanao Obayashi: Creative Design of Robotics Education Using LEGO, International Journal of Engineering Innovation and Management, Vol.5, No. 2, pp.19-24, 2015

  46.  Takasomi Hirata, Takashi Kuremoto, Shingo Mabu, Masanao Obayashi, Kunikazu Kobayashi: Time series forecasting with deep learning, International Journal of Engineering Innovation and Management, Vol.5, No. 2, pp.13-18, 2015

  47. Wirarama Wedashwara, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto:$B!!(BCombination of Genetic Network Programming and Knapsack Problem to Support Record Clustering on Distributed Database, Expert Systems with Applications, Online Open Access, Vol.46, pp.15-23, 2016

  48.      Masanao Obayashi, Takkuya Geshi, Takashi Kuremoto, Shingo Mabu: An Optimization of Spatio-Spectral Filter Bank Design for EEG Classification, Journal of Robotics, Networking and Artificial Life, Vol. 4, No.2, pp.217-220, March, 2016

  49. .    Shingo Mabu, Masanao Obayashi, Takashi Kuremoto: An Evolutionary Algorithm for Making Decision Graphs for Classification Problems, Journal of Robotics, Networking and Artificial Life, Vol. 3, No.1, pp.45-49, June, 2016

  50. .    Shingo Mabu, Shun Gotoh, Masanao Obayashi, Takashi Kuremoto: A random-forests-based classifier using class association rules and its application to an intrusion detection system,  Artificial Life and Robotics, Springer, Vol.21, pp.371-377, June, 2016

  51. Hirata, T., Kuremoto, T.,  Obayashi, M., Mabu, S., Kobayashi, K.: A Novel Approach to Time Series Forecasting using Deep Learning and Linear Model.  IEEJ Transactions on EIS, Vol.136, No.3, pp.248-356, 2016 (in Japanese)

    $BJ?ED5.?C!$(B$B8bK\6F(B$B!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!!(B$B?$B!$(B$BEE5$3X2qO@J8;o#C(B$B!$(BVol.136, No.3, pp.348-356 , 2016

  52. Takashi Kuremoto, Takuhiro Otani, Masanao Obayashi, Kunikazu Kobayashi, Shingo Mabu: A Hand Shape Instruction Recognition and Learning System using Growing SOM with Asymmetric Neighborhood Function, Neurocomputing,  Vol.188, pp.31-41, 2016

  53.   Takashi Kuremoto, Masanao Obayashi, Kuniakzu Kobayashi, Shingo Mabu: A swarm learning system using self-organizing fuzzy neural network and reinforcement learning, International Journal of Engineering Innovation and Management, Vol.6, No. 2, pp.19-24, 2016

  54.   Mabu, S., Gotoh, S., Obayashi, M. and Kuremoto, T.: A structural optimaization method of genetic network programming for enhancing generalization ability, International Journal of Engineering Innovation and Management, Vol.6, No. 2, pp.13-18, 2016

  55. Kuremoto, T., Baba, Y., Obayashi, M., Mabu, S., Kobayashi, K.: Enhancing EEG signals recognition using ROC curve, Journal of Robotics, Networking and Artificial Life, Vol. 4, No.4, pp.283-286, June, 2018

  56.  Hirata, T., Kuremoto, T., Obayashi, M., Mabu, S., Kobayashi, K.: Forecasting real time series data using deep belief net and reinforcement learning, Journal of Robotics, Networking and Artificial Life, Vol. 4, No.4, pp.260-264, June, 2018

  57.  Mabu, S., Kobayashi, K., Obayashi, M. and Kuremoto, T.: Unsupervised image classification using multi-autoencoder and K-means++, Journal of Robotics, Networking and Artificial Life, Vol. 5, No.1, pp.75-78, March, 2018

  58. Shingo Mabu, Kohki Fujita, Takashi Kuremoto$B!'(BDisaster Area Detection from Synthetic Aperture Radar Images Using Convolutional Autoencoder and One-class SVM$B!$(BJournal of Robotics, Networking and Artificial Life$B!$(B6(1), pp. 48-51, June 2019

  59.  Kuremoto, T., Sasaki, T., Mabu, S.: Mental task recognition using EEG signal and deep learning methods, Stress Brain and Behavior, Vol. 1, pp.18-23, Dec. 2019

  60.  Shingo Mabu, Yoshiaki Nakayama, Takashi Kuremoto$B!'(BLandslide Classification from Synthetic Aperture Radar Images Using Convolutional Neural Network with Multichannel Information$B!$(BJournal of Signal Processing$B!$(B24(2), pp. 61-73, March, 2020

  61.  Shingo Mabu, Ami Atsumo, Shoji Kido, Takashi Kuremoto, Yasushi Hirano$B!'(BInvestigating the Effects of Transer Learning on ROI-based Classification of Chest CT Images: A Case Study on Difffuse Lung Diseases$B!$(BJournal of Signal Processing Systems$B!$(B92, pp. 307-313, Jan., 2020

Proceedings of international conferences (with review)


  1. M. Obayashi, K. Umesako, T.Oda, K. Kunikazu, B. Y. Wu: Actor-Critic Reinforcement Learning System Time-Varying Parameters. 2003 International Conference on Control,Automation,and Systems (ICCAS2003), pp.138-141, October 22-25, 2003 (Gyeongju, Korea)

  2. Obayashi M., Oda T., Kobayashi K., Kuremoto T., and Kitano H.: Reinforcement Learning System with Time Varying Parameters Using Neural Network. Proceedings of the 35th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS03), pp.11-16 , October 30-31, 2003 (Ube, Yamaguchi)

  3. Kuremoto T., Obayashi M., Yamamoto A., and Kobayashi K.: Neural Prediction of Chaotic Time Series Using Stochastic Gradient Ascent Algorithm. Proceedings of the 35th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS03 ), pp.17-22, October 30-31, 2003 (Ube, Yamaguchi)

  4. Kuremoto T., Obayashi M., Yamamoto A., and Kobayashi K.: Predicting Chaotic Time Series by Reinforcement Learning. Proceedings of the 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS2003), CD-ROM, December 15-18, 2003 (Singapore)
    Download:PDFCIRAS2003.pdf(357k)

  5. Kuremoto T., Koga K., Kobayashi K., and Obayashi M.: Computing Slow Optical Flow by Interpolated Quadratic Surface Matching. Proceedings of the 4th IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP2004), pp.346-351, September 6-8, 2004 (Marbella, Spain)
    Download:PDF VIIP2004.pdf(584k)

  6. Kogawa N., Obayashi M., Maeda A., Kobayashi K., and Kuremoto T.: Construction and strategy of a soccer team by the agent using immune concept. Proceedings of the 10th International Symposium on Artificial Life and Robotics (AROB2005), 342-345, February 4-6, 2005 (Beppu, Japan)

  7. Nakano K., Obayashi M., Kobayashi K., and Kuremoto T.: Cooperative behavior acquisition for multiple autonomous mobile robots. Proceedings of the 10th International Symposium on Artificial Life and Robotics (AROB2005), 543-546, February 4-6, 2005 (Beppu, Japan)


  8. Kuremoto T., Eto T., Kobayashi K., Obayashi M.: A Chaotic Model of Hippocampus-Neocortex. (ICNC'05-FSKD'05) Springer-Verlag, Lecture Notes in Computer Science Vol.3610, pp.439-448, Aug. 27-29, 2005 (Changsha, China)  (Abstract)

  9. Kuremoto T., Kobayashi K., Obayashi M.: Nonlinear Prediction by Reinforcement Learning. (ICIC 2005) Springer-Verlag, Lecture Notes in Computer Science Vol.3644, pp.1085-1094, Aug. 23-26, 2005 (Hefei, China) (Abstract)

  10. Kuremoto T., Eto T., Kobayashi K., Obayashi M.: A Multi-layered Chaotic Neural Network for Associative Memory. Proceedings of SICE Annual Conference 2005 (SICE2005), pp.1020-1023, Aug. 8-10, 2005 (Okayama, Japan)

  11. Kobayashi K., Mizuno S., Kuremoto T., Obayashi M.: A Reinforcement Learning System Based on State Space Construction Using Fuzzy ART. Proceedings of SICE Annual Conference 2005 (SICE2005), pp.3653-3658, Aug. 8-10, 2005 (Okayama, Japan)


  12. Kogawa N., Obayashi M., Maeda A., Kobayashi K., and Kuremoto T.: Learning Method of Cooperative Team Play Using the Immune System. Proceedings of the 11th International Symposium on Artificial Life and Robotics (AROB2006), pp.183-186, Jan.23 -25, 2005 (Beppu, Japan)


  13. Kuremoto T., Hano T., Kobayashi K., Obayashi M.:For Partner Robots: A Hand Instruction Learning System Using Transient-SOM. Proceedings  of  The 2nd Interenational Conference on Natural Computation and The 3rd International Conference on Fuzzy Systems and Knowledge Discovery (ICNC '06-FSKD'06), pp.403-414, Sep. 24-28, 2006 (Xi'an China) Video:AIBOExperiment.wmv(6.70MB Download:Winamp )

  14. Obayashi M., Kogawa N., Toyota S., Kobayashi K., Kuremoto T.: Controller Design Based on Immune Concept and Its Application to Chaotic Control. Proceedings of 2006  International Automatic Control Conference (CACS 2006). pp.327-331, Nov. 10-11, 2006 (Tapei, Taiwan)


  15. Kuremoto, T., Obayashi, M., and Kobayashi, K., Forecasting Time Series by SOFNN with Reinforcement Learning. Proceedings of the 27th Annual International Symposium on Forecasting (ISF2007), pp.99, June 24-27, 2007 (New York, USA) .


  16. Kuremoto, T., Hano, T., Kobayashi, K., and Obayashi, M., Robot Feeling Formation Based on Image Features. Proceedings of International Conference on Control, Automation and Systems (ICCAS2007), pp.758-761, October 17-20, 2007 (Seoul, Korea) Download:PDFKuremotoHano.pdf(1.94MB) Video:RobotExperiment.wmv(4.46MB Download:Winamp )


  17. Kobayashi K., Nakano  K., Kuremoto  T., and Obayashi  M., Cooperative Behavior Acquisition of Multiple Autonomous Mobile Robots by an Objective-based Reinforcement Learning System. Proceedings of International Conference on Control, Automation and Systems (ICCAS2007), pp.777-780, October 17-20, 2007 (Seoul, Korea)

  18. Obayashi M., Nakahara N., Kuremoto T., and Kobayashi K., A Robust Reinforcement Learning Using Concept of Sliding Mode Control. Proceedings of the 13th International Symposium on Artificial Life and Robotics (AROB2008), pp.547-550, January 31-February 2, 2008 (Beppu, Japan).


  19. Kogawa, N., Obayashi, M., Kobayashi, K., and Kuremoto, T., A Reinforcement Learning Method Based on Immune Network Adapted to Semi Markov Decision Process. Proceedings of the 13th International Symposium on Artificial Life and Robotics (AROB2008), pp.63-66, January 31-February 2, 2008 (Beppu, Japan).


  20. Kuremoto, T., Ohta, T., Kobayashi, K., and Obayashi, M., A Dynamic Associative Memory System Adopting Amygdala Model. Proceedings of the 13th International Symposium on Artificial Life and Robotics (AROB2008), pp.563-566, January 31-February 2, 2008 (Beppu, Japan).


  21. Obayashi, M., Yano, Y., Kobayashi, K., and Kuremoto, T., Chaotic Dynamical Associative Memory Model Using Supervised Learning. Proceedings of the 13th International Symposium on Artificial Life and Robotics (AROB2008), pp.555-558, January 31-February 2, 2008 (Beppu, Japan).


  22. Kuremoto T., Obayashi M., Kobayashi K., Adachi H. Yoneda K., A Reinforcement Learning System for Swarm Behaviors, Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2008 / IJCNN 2008), pp.3710-3715,June 1st-7th, 2008 (Hong Kong)

  23. Kuremoto T., Obayashi M., Kobayashi K., Adachi H.,Yoneda K., A Neuro-Fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space,ICIC 2008, Springer-Verlag, Lecture Notes in Computer Science Vol.5227, pp.675-683, Sep. 15-18, 2008 (Shanghai, China) (Abstract)


  24. M. Obayashi, K. Narita, T. Kuremoto, K. Kobayashi: A Reinforcement Learning System with Chaotic Neural Neworks-Based Adaptive Herarchical Memory Structure for Autonomous Robots, Proceedings of International Conference on Control, Automation and Systems 2008 (ICCAS 2008), pp. 69-74, Oct. 14-17, 2008 (Seoul, Korea)

  25. M. Obayashi, T. Kuremoto, K. Kobayashi: A Self-Organized Fuzzy-Neuro Reinforcement Learning System for Continuous State Space for Autonomous Robots, Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation 2008 (CIMCA08), pp.552-559, Dec. 10-12, 2008 (Vienna, Austria)

  26. K. Kobayashi, M. Obayashi and T. Kuremoto:A Bayesian Local Linear Wavelet Neural Network, Lecture Notes in Computer Science, Vol.5507, pp.147-154, Springer-Verlag, 2009, Proceedings of 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly (ICONIP 2008), pp. 113-114, Nov. 25-28, 2008 (Auckland, New Zealand)
  27. Kuremoto, T., Komoto, T., Kobayashi, K., and Obayashi, M., A Voice Instruction Learning System Using PL-T-SOM, Proceedings of the 2nd International Conference on Image and Signal Processing (CISP2009), pp.4294-4299 (IEEE eXpress Conference Publishing, ISBN: 978-1-4244-4130-3), October 17-19, 2009 (Tianjin, China) Download:PDFpdf(728k)

  28. Kuremoto, T., Ohta, T., Kobayashi, K., and Obayashi, M., A Functional Model of Limbic System of Brain, Proceedings of 2009 International Conference on Brain Informatics (BI2009),Springer-Verlag, Lecture Notes in Artificial Intelligence Vol.5819, pp.135-146, October 22-24, 2009 (Beijing, China) (Abstract)

  29. K. Kobayashi,H. Mizoue, T. Kuremoto and M. Obayashi: A Meta-learning Method Based on Temporal Difference Error, Lecture Notes in Computer Science, Vol.5863, pp.530-537, Springer-Verlag, 2009 (Abstract), Proceedings of 16th International Conference on Neural Information Processing (ICONIP 2009), pp. 373-374, Dec. 1-5, 2009 (Bangkok, Thailand)

  30. Obayashi M., Yamada K.,Kuremoto T., Kobayashi K.: A Robust Reinforcement Learning System Using Sliding Mode Control with Variable Filters, Proceedings of 2009  International Automatic Control Conference (CACS 2009), CD-ROM, Nov. 27-29, 2009 (Taipei, Taiwan)

  31. Feng, L., Obayashi, M., Kuremoto, T., and Kobayashi, K.: A Learning Petri Net Model Based on Reinforcement Learning, Proceedings of the 15th International Symposium on Artificial Life and Robotics (AROB2010), pp.290-293, February 4-6, 2010 (Beppu, Japan)

  32. Obayashi, M., Kuremoto, T., and Kobayashi, K.:Intelligent Agent Construction Using the Idea of the Attention Characteristic Pattern in A Chaotic Neural Network Proceedings of the 15th International Symposium on Artificial Life and Robotics (AROB2010), pp.597-600, February 4-6, 2010 (Beppu, Japan)

  33. Kuremoto, T., Obayashi, M., Kobayashi, K., and Feng, L.: Autonomic Behaviors of Swarm Robots Driven by Emotion and Curiosity, Proceedings of 2010 International Conference on Life System Modeling and Simulation 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS & ICSEE 2010),Springer-Verlag, Lecture Notes in Bioinformatics (LNBI), Vol. 6330, pp.541-547, Sep. 17-20, 2010 (Wuxi, China)

  34. Obayashi, M., Nishida, T., Kuremoto, T., Kobayashi, K., and Feng, L., A Reinforcement Learning System Embedded Agent with Neural Network-Based Multi-Value Pattern Memory Structure, Proceedings of International Conference on Control, Automation, and Systems 2010 (ICCAS 2010), pp.176-181, Oct. 27-30, 2010 (Gyeonggi-do, Korea)

  35. Feng, L., Obayashi, Kuremoto, T., and Kobayashi, K.:An Intelligent Control System Construction Using High-Level Time Petri Net and  Reinforcement Learning, Proceedings of International Conference on Control, Automation, and Systems 2010 (ICCAS 2010), pp.535-539, Oct. 27-30, 2010 (Gyeonggi-do, Korea)


  36. Kuremoto, T., Yamane, T., Feng, L.-B., Kobayashi, K., Obayashi, M.: A Human-Machine Interaction System: A Voice Command Learning System Using PL-G-SOM. Proceedings of International Conference on  Industrial Engineering and Management Special Session Within Mass (IEEE-IEM 2011), pp. 83-86, Aug. 12-14, 2011 (Zhengzhou, China) Download:PDFAIBO-PLGSOM.pdf (385KB) Video:AIBO-PLGSOM.wmv (4.55MB Download: Winamp )

  37. Kuremoto, T., Kinoshita, Y., Feng, L., Watanabe, S., Kobayashi, K., Obayashi M.: A Gesture Recognition System Using One-Pass DP Method. Proceedings of the 7th International Conference on Intelligent Computing (ICIC 2011),  Lecture Note in Artificial Intelligence (LNAI), Vol.6839, pp.581-587, Springer-Verlag, Aug. 12-14, 2011 (Zhengzhou, China)


  38. Obayashi, M., Yokoji, Y., Uchiyama, S., Feng, L-B., Kuremoto, T., and Kobayashi, K.: Intelligent Tracking Control Method of A Target by Group of Agents with Nonlinear Dynamics, Proceedings of International Conference on Control, Automation, and Systems 2011 (ICCAS 2011), pp.928-933, Oct. 26-29, 2011 (Gyeonggi-do, Korea)


  39. Uchiyama, S., Obayashi, M., Kuremoto, T., and Kobayashi, K.: Robust Reinforcement Learning Control System with H-infinity Tracking Performance Compensator, Proceedings of International Conference on Control, Automation, and Systems 2011 (ICCAS 2011), pp.248-253, Oct. 26-29, 2011 (Gyeonggi-do, Korea)


  40. Kobayashi, K., Kanehira, R., Kuremoto, T., and Obayashi, M.: An Action Selection Method Based on Estimation of Other's Intention in Time-Varying Multi-Agent Environments, Prodeedings of International Conference on Neural Information Proccessing (ICONIP 2011),Lecture Note in Computer Science (LNCS) Vol. 7064, pp.76-85,Springer-Verlag, Nov., 14-17, 2011 (Shanghai, China)

  41. Uchiyama, S., Obayashi, M., Kuremoto, T., and Kobayashi, K.:H_infinity Robust Reinforcement Learning Control System with Auto-Structuring  Fuzzy Neural Network, Proceedings of the 3rd International Symposium on Digital Manufacturing (ISDM 2011), Nov. 30-Dec. 2, 2011 (Kitakyushu, Japan)

  42. Feng, L., Obayashi, Kuremoto, T., and Kobayashi, K.:Optimization and Verification for a Robot Control System on Leraning Petri Net Model, Proceedings of the 3rd International Asia Conference on Informatics in Control, Automation, and Robotics (CAR 2011), Lecture Note in Electronic Engineering (LNEE), Vol. 133, pp.815-823,  Springer-Verlag, Dec. 24-25, 2011 (Shenzhen, China)

  43. Kuremoto, T., Yamano, Y., Feng, L., Kobayashi, K., and Obayashi M.: A Neuro-Fuzzy Network with Reinforcement Learning Algorithm for Swarm Learning. Proceedings of 2011 International Conference on Future Wireless Networks and Information Systems, Lecture Note in Electronic Engineering (LNEE), No.144,  Springer, (ICFWI 2011), pp.101-108,  Dec. 1-2. 1, 2011 (Macau, China)

  44. Obayashi, M., Watanabe, K., Kuremoto, T., and Kobayashi, K.: Development of a Brain Computer Interface Using Inexpensive Commercial EEG Sensor with One Channel, Proceedings of the 17 International Symposium on Artificial Life and Robotics (AROB2012), pp.1040-1043, Jan. 19-21, 2012 (Beppu, Japan)


  45. Obayashi, M., Koga, S, Kuremoto, T., and Kobayashi, K.: Handwriting Character Classification Using Freeman's Olfactory Model, Proceedings of the 17th International Symposium on Artificial Life and Robotics (AROB2012), pp.714-717, Jan. 19-21, 2012 (Beppu, Japan)


  46. Kuremoto, T., Otani, T., Feng, L., Kobayashi, K., and Obayashi M.: A Hand Image Instruction Learning System Using PL-G-SOM, Proceedings of the 12th International Conference on Artificial Intelligence (ICAI 2012 / WORLDCOMP 2012), CD-ROM, Jul. 16-19, 2012 (Las Vegas, U.S.A.)


  47. Kuremoto, T., Kimura, S., Kobayashi, K., and Obayashi M.: Time Series Forecasting Using Restricted Boltzmann Machine. Proceedings of the 8th International Conference on Intelligent Computing (ICIC 2012),  Communications in Computer and Information Science (CCIS), Vol. 304, pp.17-22, Springer-Verlag, July 26-29, 2012 (Huangshan, China)


  48. Obayashi, M.,Takuno, T, Kuremoto, T., and Kobayashi, K.: An Emotional Model Embedded Reinforcement Learning System, Proceedings of the IEEE  International Conference on System, Man, and Cybernetics (IEEE SMC 2012), pp. 1058-1063, Oct. 14-17, 2012 (Seoul, Korea)


  49. Kobayashi, K., Kurano, T., Kuremoto, T., and  Obayashi, M.: Cooperative Behavior Acquisition in Multi-agent Reinforcement Learning System Using Attention Degree, Proceedings of the 19th  International Conference on Neural Information Processing (ICONIP 2012), Lecture Notes in Computer Science, Vol.7665, pp.537-544, Springer-Verlag, 2012, Nov. 12-15, 2012 (Doha, Qatar)


  50. Watada, S., Obayashi, M., Kuremoto, T., and Kobayashi, K.: A New Decision-Making System of an Agent Based on Emotional Models in Multi-Agent System, Proceedings of the 18 the International Symposium on Aritificial Life and Robotics (AROB 18th '13), pp. 452-455, Jan. 30-Feb. 1, 2013 (Daejeon, Korea)


  51. Watanabe, S., Kuremoto, T.,  Kobayashi, K., and Obayashi, M.: The Effect of the Internal Parameterson Association Performance of A Chaotic Nerual Network, Proceedings of the 18 the International Symposium on Aritificial Life and Robotics (AROB 18th '13), pp. 464-467, Jan. 30-Feb. 1, 2013 (Daejeon, Korea)

  52. Mabu, S., Hirasawa, K., Obayashi, M., Kuremoto, T.: A Variable Size Mechanism of Distributed Graph Programs for Creating Agent Behaviors, 2013 IEEE Congress on Evolutionary Computation (CEC 2013), pp. 1756-1762, June 20-23, 2013 (Cancun, Mexico)

  53. Obayashi, M., Otomi Y., Kuremoto, T., and Kobayashi K.: Decentralized Adaptive Control Using an Affine plus Self-organizing Fuzzy Neural Networks for Multi-Agent System Consensus Problem, 2013 IEEE International Conference on System Science and Engineering (ICSSE 2013), pp. 247-252, July 4-6, 2013 (Budapest, Hungary)

  54. Kuremoto, T., Tsurusaki, T., Kobayashi, K., and Obayashi, M.: A Model of Emotional Intelligent Agent for Cooperative Goal Exploration, Lecture Note in Computer Science (LNCS), Vol. 7995, pp. 21-30, 2013; Proceedings of International Conference on Intelligent Computing (ICIC 2013), July 28-31, 2013 (Nanning, China)

  55. Watanabe, S., Kuremoto, T., Kobayashi, K., Mabu, S., and Obayashi, M.: The Recollection Characteristics of a Generalized MCNN, in Proceedings of SICE Annual Conference (SICE 2013), pp.1375-1380, Sep. 14-17, 2013 (Nagoya, Japan)

  56. Obayashi, M., Kamikariya, T. Uchiyama, S., Watada, S., Kuremoto, T., Mabu, S. and Kobayashi, K.: Adaptive Control System Based on Self-organizing Wavelete Neural Network with H_infinity Tracking Performance Compensator, in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013), pp. 3232-3237, Oct. 13-16, 2013 (Manchester, UK)

  57. Watanabe, S., Kuremoto, T., Kobayashi, K., Mabu, S., and Obayashi, M.: Dynamical recollection and Storage of Video Images via MCNN and SOM, in Proceedings of International Conference on Innovative Application Research and Education (ICIARE 2013), pp.66-69, Dec., 2-5, 2013 (Dalian, China)

  58. Kuremoto, T., Watanabe, S., Kobayashi, K., Mabu, S., and Obayashi, M.: Innovative Practice of Robotics Education Usnig LEGO Mindstorm NXT, in Proceedings of International Conference on Innovative Application Research and Education (ICIARE 2013), pp.74-77, Dec., 2-5, 2013 (Dalian, China)

  59. Obayashi, M., Shinkawa, M., Kuremoto, T., Kobayashi, K., Mabu, S.: An Innovative Metal Task Classification Method with a Hierarchical Structure Based on EEG Data, in Proceedings of International Conference on Innovative Application Research and Education (ICIARE 2013), pp.92-95, Dec., 2-5, 2013 (Dalian, China)

  60. Watanabe, S., Kuremoto, T., Kobayashi, K., Mabu, S., and Obayashi, M.: The Recollection Characteristics of Generalized MCNN Using Different Control Methods, in Proceedings of International Conference on Artificial Life and Robotics (ICAROB 2014), CD_ROM, 6 pages. Jan. 11-13, 2014 (Oita, Japan)

  61. Watada, S., Obayashi, M., Kuremoto, T., Kobayashi, K.: Decision Making System of Robots Introducting a Reconstruction of Emotions Based on Their Own Experiences, in Proceedings of International Conference on Artificial Life and Robotics (ICAROB 2014), CD_ROM, 4 pages, Jan. 11-13, 2014 (Oita, Japa)


  62. Kuremoto, T., Otani, T., Obayashi, M., Kobayashi, K., Mabu, S.:One Dimensional Ring Type Growing SOM with Asymmetric Neighborhood Function and its Application to a Hand Shape Instruction Learning System, in Proceedings of 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2014), 6 pages, June 30-July 2, 2014 (Las Vegas, USA)

  63. Kuremoto, T., Otani, T., Obayashi, M., Kobayashi, K., Mabu, S.: A Hand Shape Instruction Recognition and Learning System usign Growing SOM with Asymmetric Neighborhood Function, in Lecture Note in Computer Science (LNCS), Vol. 8588, pp. 269-276, 2014, Proceedings of 10th International Conference on Intelligent Computing (ICIC 2014),  Aug. 3-6, 2014 (Taiyuan, China)

  64. I Gde Putu Wirarama Wedashwaran, Shingo Mabu, Masanao Obayashi, Kuremoto, T.: Implementation of Genetic Network Programming and Knapsack Problem for Record Clustering on Distributed Database, in Proceedings of SICE Annual Conference 2014 (SICE 2014), pp.935-940, Sep. 9-12, 2014 (Sapporo, Japan)
    65.
  65. Kuremoto, T., Morisaki,  Kobayashi, K., Mabu, S., K., Obayashi, M.: A Modified Recurrent Neural Network with Parametric Bias and its Application to Action Learning of a Humanoid Robot, in Proceedings of 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP 2014), pp.414-418, Sep. 26-29, 2014 (Kitakyusyu, Japan)

  66. Kuremoto, T., Hirata, T., Obayashi, M.,  Mabu, S, Kobayashi, K.,.: Forecast Chaotic Time Series Data by DBNs, 2014 Proceedings of 7th International Conference on Image and Signal Processing (CISP '14-BMEI'14), Oct. 14-16, 2014 (Dalian, China)

  67. Kuremoto, T., Obayashi, M., Kobayashi, K., Mabu, S.: How an Adaptive Learning Rate Benefits Neuro-Fuzzy Reinforcement  Learning Systems, in Lecture Note in Computer Science (LNCS), Vol. 8794, pp. 324-331, 2014 Proceedings of 5th International Conference on Swarm Intelligence (ICSI 2014), 8 pages, Oct. 17-20, 2014 (Hefei, China)

  68. Aridome, T., Obayashi, M., Kuremoto, T., Mabu, S.: A Tracking Control Method for A Two-Wheeled Robot Using Wavelet Neural Network-based Sliding Mode Control. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp. 30-33, Dec. 1-3, 2014 (Chonbuk National University, Korea)

  69. Gotoh, S., Mabu, S., Obayashi, M., Kuremoto, T..: A Study on Effective Structural Evolution of Genetic Network Programming for Improving Generalization Performance. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp. 39-42, Dec. 1-3, 2014, The BEST PAPER AWARD of ICIARE 2014 (Chonbuk National University, Korea)

  70. Hikino, W., Obayashi, M., Kuremoto, T., Mabu, S.: An Odor Recognition System in Real Environments Using KIII Olfactory Function Model. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp. 43-46, Dec. 1-3, 2014 (Chonbuk National University, Korea)

  71. Hirata, T., Kuremoto, T., Obayashi, M., Kobayashi, K., Mabu, S.:Time Series Prediction using DBN and ARIMA.
    72. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp.54-57, Dec. 1-3, 2014 (Chonbuk National University, Korea)

  72. Kuremoto, T., Obayashi, M., Kobayashi, K., Mabu, S.: A Reinforcement Learning System with Neuro-Fuzzy Network and its Applications. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp. 68-71, Dec. 1-3, 2014 (Chonbuk National University, Korea)

  73. Kuremoto, T., Kuzukami, Y., Obayashi, M., Kobayashi, K., Mabu, S.: A Voice Instruction Learning System using GSOM with Asymmetric Neighborhood Function. In The Proceedings of The 2nd International Conference on Innovative Application Research and Education (ICIARE 2014), pp.72-75, Dec. 1-3, 2014 (Chonbuk National University, Korea)

  74. Mabu, S., Obayashi, M., Kuremoto, T.: Ensemble Learning of Rule-based Evolutionary Algorithm Using Multi Layer Perceptron for Stock Trading Models. In The Proceedings of Joint 7th  International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2014), pp. 624-629, Dec. 3-6, 2014 (Kitakyushu, Japan)

  75. I Gde Putu Wirarama Wedashwaran, Shingo Mabu, Masanao Obayashi, Kuremoto, T : Online Rule Updating System Using Evolutionary Computation for Managing Distributed Database, in Proceedings of International Conference on Artificial Life and Robotics (ICAROB 2015), pp.98-101, Jan. 10-12, 2015 (Oita, Japan)

  76. Mabu, S., Obayashi, M.,  Kuremoto, T.: Reinforcement Learning with Symbiotic Relationships for Multiagent Environments, The BEST PAPER AWARD of ICAROB 2015, in Proceedings of International Conference on Artificial Life and Robotics (ICAROB 2015), pp.102-106, Jan. 10-12, 2015 (Oita, Japan)

  77. Obayashi, M., Shinkawa, K., Kuremoto, T., Mabu, S., Kobayashi, K.: Learder-Follower Formation Control with an Adaptive Linear and Terminal Sliding Mode Combined Controller Using Auto-Structuring Fuzzy Neural Network, in Proceedings of The Seventh International Conference on Advanced Cognitive Technologies and Applications (Cognitive 2015), ISBN: 978-1-61208-390-2, pp.130-136, Mar., 22-27, 2015 (Nice, France)

  78. Kuremoto T., Ko K., Obayashi M., Mabu S., Kobayashi, K. : Neural Networks using Reinforcement Learning and Their Applications to Time Series Forecasting, Proceedings of 3rd International Conference on Automatic Control, Softcomputing, and Human-Machine Interactions (WSEAS/ASME'15) , Recent Researches in Electrical and Computer Engineering, ISBN 978-1-61804-315-3,pp.69-74, June 27-29, 2015 (Salerno, Italy)

  79. Kuremoto T., Baba Y., Obayashi M., Mabu S., Kobayashi K.:To Extract the Feature of EEG Signals for Mental Task Recognition, Proceedings of SICE Annual Conference (CCC-SICE 2015), pp,353-358, July 28-31, 2015 (Hangzhou, China)

  80. Wirarama Wedashwaran, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto: Evotionary Rule Based Clustering for Making Fuzzy Object Oriented Database Models, Proceedings of 4th International Congress on Advanced Applied Informatics (IIAI-AAI 2015), pp.517-522, July-12-16, 2015 (Okayaama, Japan)

  81. Hirata T., Kuremoto T., Obayashi M., Mabu S., Kobayashi K.: Time Series Forecasting using DBN and ARIMA, The Outstanding Award Paper of 2015 ICCAT, Proceedings of 2015 International Conference on Computer Application Technologies (2015 ICCAT), August 31-September 2, 2015 (Matsue, Japan)

  82. Obayashi, M., Uto, S., Kuremoto, T., Mabu, S., Kobayashi, K.: An Extended Q Learning System with Emotion State to Make Up an Agent with Individuality, Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 3: NCTA 2015, pp. 70-78, Nov. 12-14, 2015 (Lisbon, Portugal)

  83. Obayashi, M., Yamane, T., Kuremoto, T., Mabu, S., Kobayashi, K.: An Autonomous Mobile Robot with Functions of Action Learning, Memorizing, Recall and Identifying the Environment Using Gaussian Mixture Model, Proceedings of the 22th Conference on Neural Informatino Processing (ICONIP2015) , Lecture Note in Computer Science (LNCS), Vol. 9489, pp. 272-282, Nov. 9-12, 2015 (Istanbul, Turkey)

  84. Kuremoto T., Baba Y., Obayashi M., Mabu S., Kobayashi, K. : A Method of Feature Extraction for EEG Signal Recognition, Proceedings of 3rd International Conference on Innovative Application Research and Education (ICIARE2015) , ,pp, 67-69, Dec. 20-22, 2015 (Zhenjiang, China)

  85. Watada, S., Obayashi, M., Kuremoto, T., Mabu, S.: Efficient Reinforcement Learning Using an Adaptive Control of Meta-parameters Based on a Markov Emotional Model, Young Author Award in Proceedings of 21th International Symposium on Artificial Life and Robotics (AROB 21st 2016), pp.14-19, Jan. 20-22, 2016 (Beppu, Japa)

  86. Wirarama Wedashwaran, Shingo Mabu, Masanao Obayashi, Takashi Kuremoto:  Evolutionary Rule Based Clustering with Fuzzy Feature Selection for High Dimensional Database, in Proceedings of 21th International Symposium on Artificial Life and Robotics (AROB 21st 2016), pp.42-47, Jan. 20-22, 2016 (Beppu, Japa)

  87. Gotoh, S., Mabu, S., Obayashi, M., Kuremoto, T..: An Instrusion Detection System Using Random Forest Based on Class Association Rules, in Proceedings of 21th International Symposium on Artificial Life and Robotics (AROB 21st 2016), pp.148-153, Jan. 20-22, 2016 (Beppu, Japa)

  88. Obayashi, M., Geshi, T., Kuremoto, T., Mabu, S.: An Optimization of Spatio-Spectral Filter Bank Design for EEG Classification, in Proceedings of 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), pp.397-400, Jan. 29-31, 2016 (Okinawa, Japa)

  89. Mabu, S., Obayashi, M.,  Kuremoto, T.: An Evolutionary Algorithm for Making Decision Graphs for Classification Problems, in Proceedings of 2016 International Conference on Artificial Life and Robotics (ICAROB 2016), pp.458-462, Jan. 29-31, 2016 (Okinawa, Japa)

  90. Kuremoto T., Tsubaki K., Obayashi M., Mabu S., Kobayashi, K. : A Neuro-Fuzzy Reinforcement Learning System for Autonomous Robot Dealing with Continuous Space, Proceedings of 2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing  (NCSP'16 ),pp. 258-261, March 6-9, 2016 (Hawaii, U.S.A.)

  91. Obayashi M., Aridome T., Kuremoto T., Mabu S.: Leader-Follower Formation Control Using Cerebellar Perceptron Improved Model with Auto-Structuring, In Proc. 2016 IEEE International Conference on Computational Science and Engineering (2016 CSE), pp. 423-431, Aug. 24-26, 2016 (Paris, France)

  92. Kuremoto T., Kuzukami Y., Obayashi M., Kobayashi K., Mabu S.: RP-AG-SOM: A Growing Self-Organizing Map with Assymetric Neighborhood Function and Variable Radius, In Proc. SAI Intelligent Systems Conference 2016 (IntelliSys 2016), pp. 245-252, Sep. 21-22, 2016 (London, U.K.)

  93. Hirata T., Kuremoto T., Obayashi M., Mabu S., Kobayashi K.: Deep Belief Network using Reinforcement Learning and its Applications to Time Series Forecasting, In Proc. Intern. Conf. Neural Inform. Processing, (ICONIP 2016), Lecture Notes in Computer Science (LNCS), Springer, Vol. 9949, pp. 30-37, Oct. 18-21, 2016 (Kyoto, Japan)

  94. Cannon R. N., Kuremoto T., Obayashi M., Mabu S.: Learning Embedding Function for Instance-based Reinforcement Learning with Matching Networks, In Proc. Intern. Conf. Innovative Appli. Research & Education 2016 (ICIARE 2016), Dec. 22-25, Chungbuk, Korea (The BEST PAPER AWARD)

  95. Azakami K., Mabu S., Obayashi M., Kuremoto T.: A Rule-Based Classification System Enhanced by Multi-Objective Genetic Algorithm, Proceedings of The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), pp.650-653, Jan. 19-22, 2017 (Miyazaki, Japan)

  96. Hirata T., Kuremoto T., Obayashi M., Mabu S.: Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning, Proceedings of The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), pp.658-661, Jan. 19-22, 2017 (Miyazaki, Japan)

  97.  Kuremoto T., Baba Y., Obayashi M., Mabu S., Kobayashi K.: A Method of Feature Extraction for EEG Signals Recognition Using ROC Curve, Proceedings of The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017), pp.654-657, Jan. 19-22, 2017 (Miyazaki, Japan)

  98. Kuremoto T., Tokuda S., Obayashi M., Mabu S., Kobayashi K.: An experimental comparison of deep belief nets with different learning methods, Proceedings of 2017 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2017), pp. , 637-640, Feb. 28-Mar. 3, 2017 (Guam, U.S.A.)

  99. Obayashi M., Aridome T., Kuremoto T., Mabu S.: Leader-Follower Adaptive Formation Control without Observation of Agent$B!G(Bs Velocity Using Wavelet Neural Network, Proceedings of 2017 9th International Conference on Machine Learning and Computing (ICMLC 2017), C083, pp.1-8, Feb. 24-26, 2017 (Singapor)

  100. Kuremoto T., Matsusaka H., Obayashi M., Mabu S., Kobayashi K.: A Reinforcement Learning System with Multi-Layered Fuzzy Neural Network, Proceedings of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017 (ICISIP 2017), pp. 444-449, Sep. 7-12 (Hawaii, U.S.A.)

  101. Kuremoto T., Tsuruda T., Obayashi M., Mabu S.:  A Sentence Summarizer using Recurrent Neural Network and Attention-Based Encoder, Proceedings of 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017), pp.245-248, Nov. 26-27, 2017 (Shanghai, China)

  102. Fujita K., Mabu S., , Kuremoto T.: Anomaly Dectection of Disaster Areas from Satellite Images Using Convolutional Autoencoder and One-class SVM, Proceedings of The 2018 International Conference on Artificial Life and Robotics (ICAROB 2018), pp.116-119, Feb. 1-4, 2018 (Beppu, Japan)

  103. Shingo Mabu, Shoji Kido, Noriaki Hashimoto, Yasushi Hirano, Takashi Kuremoto: Opacity annotation of diffuse lung deseases using deep convolutional neural network with multi-channel information, Proc. SPIE Medical Image 2018, Houston, U.S.A., Mar. 1, 2018

  104. Mabu S., Kobayashi K., Obayashi M., Kuremoto T.: Unsupervised Image Classification Using Multi-Autoencoder and K-means++, Proceedings of The 2018 International Conference on Artificial Life and Robotics (ICAROB 2018), pp.112-115, Feb. 1-4, 2018 (Beppu, Japa)

  105. Yanping Liu, Changhui Yang, Huang Ling, Shingo Mabu, Takashi Kuremoto: A Visual System of Citrus Picking Robot using Convolutional Neural Networks, The 2018 5th International Conference on System and Informatics (ICSAI2018), pp. 329-334, Nov. 10-12, 2018


  106. Ami Atsumo, Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto$B!'(BAnalysis of the effects of transfer learning on opacity classification of diffuse lung diseases using convolutional neural network$B!$(BProc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, Singapore, Jan. 6-9, 2019 (The BEST PAPER AWARD)


  107. Shingo Mabu, Shoji Kido, Yasushi Hirano, Takashi Kuremoto$B!'(BUnsupervised and Semi-Supervised Learning for Efficient Opacity Annotation of Diffuse Lung Diseases$B!$(B Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, Singapore, Jan. 6-9, 2019


  108. Takashi Kuremoto, Tsuruda Takushi, Shingo Mabu: Summarizing Articles into Sentences by Hierarchical Attention Model and RNN Language Model, Proc. The 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2019), pp. 1-6, Oct. 19-21, 2019, Suzhou, China 

  109.  Takashi Kuremoto, Daisuke Shitamoto, Shingo Mabu: A Recursive Attention Model for Text Summarization, InThe Proceedings of The 7th International Conference on Innovative Application Research and Education (ICIARE 2019), pp. 55-58, Dec. 20-23, 2019 (Inje University, Korea)

  110.  Yuya Mori, Takashi Kuremoto, Shingo Mabu: Facial Expression Recognition with DCNN and SVM, InThe Proceedings of The 7th International Conference on Innovative Application Research and Education (ICIARE 2019), pp. 64-68, Dec. 20-23, 2019, (Inje University, Korea)

  111.  Takashi Kuremoto, Hiroki Matsusaka, Shingo Mabu, Masanao Obayashi, Kunikazu Kobayashi: An Improved Fuzzy Neural Network for Reinforcement Learning, Proc. The 3rd International Conference on Big Data Research (ICBDR 2019), pp. 88-93, Nov. 20-22 2019, Paris, France (The BEST PRESENTATION AWARD)

    Proceedings of symposium or domestic conferences (in Japanese)


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    2. $BD9(B $B9b90(B, $B8b(B $BK\6F(B, $B8E2lOBMx(B: $B!H6JLLJd4V%^%C%A%s%0K!$K$h$k%*%W%F%#%+%k%U%m!<$NCj=P!I(B, $BBh(B12$B2sF02hA|7WB,=hM}8&5f2qO@J8MW;]=8(B(1997-12), pp. 61-62, 1997

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    8. $BDMK\ATJe(B, $B8b(B $BK\6F(B, $B8E2lOBMx(B, $B;0CS=(IR(B: $B!H3,AX2=0LAj%7%U%HK!$K$h$k9b@:EY$J1|9T$-7WB,!I(B, $B2hA|$NG'<1!&M}2r%7%s%](BMIRU2000, pp. 185-190 , 2000

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    12. $BBg5\M}7C(B, $BBgNS@5D>(B, $B>.NSK.OB(B, $B8bK\(B $B6F(B: $B!I4X?t7?5-21MWAG$r;}$D%+%*%9%K%e!<%i%k%M%C%H%o!<%/O"A[5-21%7%9%F%`!I(B, $BJ?@.(B15$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B, pp.1016-1019, 2003

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    14. $BKLLn42L@(B, $BBgNS@5D>(B, $B>.NSK.OB(B, $B8bK\(B $B6F(B: $B!I;k3P>pJs$K4p$E$/0\F0%m%\%C%H$N6(D4=d2sK,Ld$K$D$$$F!I(B , $BBh(B12$B2s7WB,<+F0@)8fCf9q;YIt3X=Q9V1i2q(B, pp.146-147, 2003

    15. $B>.@nD95W(B,$BBgNS@5D>(B,$B>.NSK.OB(B, $B8bK\6F(B:$B!I%5%C%+!<%7%_%e%l!<%?$r(B $BMQ$$$?6(D49TF0$NAOH/$K4X$9$k4pACE*8!F$!I(B , $BBh(B12$B2s7WB,<+F0@)8fCf9q;YIt3X=Q9V1i2q(B, pp.262-263, 2003

    16. T. Kuremoto, K. Koga, K. Kobayashi, M. Obayashi: $B!I(BAn Optical Flow Technique's Performance$B!I(B, Proceedings of the 2004 IEICE General Conference, 2004

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    19. $B1)Ln$H$b$((B, $B>.NSK.OB(B, $B8bK\(B $B6F(B, $BBgNS@5D>(B: $B!I2hA|FCD'$K4p$E$/%m%\%C%H$N46>p7A@.$H$=$NI=8=$K$D$$$F!I(B , $BJ?@.(B16$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B, pp. 512-517, 2004

    20. $B8bK\(B $B6F(B, $B>.NSK.OB(B, $BBgNS@5D>(B: $B!I%U%!%8%#%K%e!<%i%k%M%C%H%o!<%/$K$*$1$k6/2=3X=,5Z$SHs@~7AM=B,$X$N1~MQ!I(B, $BJ?@.(B16$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B,pp. 518-519, 2004

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    22. $BBg5\M}7C(B, $BBgNS@5D>(B, $B8bK\(B $B6F(B, $B>.NSK.OB(B: $B!IHsC1D43h@-2=4X?t$rMQ$$$?%+%*%9%K%e!<%i%k%M%C%H%o!<%/O"A[5-21%b%G%k!I(B, $BBh(B13$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 132-133, 2004

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    24. $B>.@nD95W(B, $BBgNS@5D>(B, $B>.NSK.OB(B, $B8bK\(B $B6F(B: $B!ILH1V35G0$rMQ$$$?%(!<%8%'%s%H$K$h$k%5%C%+!<%A!<%`$N3X=,$K$D$$$F!I(B, $BBh(B13$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 140-141, 2004

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    27. $B>.@nD95W(B, $BA0ED(B $B>O(B, $BBgNS@5D>(B, $B>.NSK.OB(B, $B8bK\(B $B6F(B: $B!ILH1V35G0$rMQ$$$?%(!<%8%'%s%H$K$h$k%5%C%+!<%A!<%`$N9=C[$H@oN,!I(B, $BBh(B6$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 186-187, 2004

    28. $B>.@nD95W(B, $BBgNS@5D>(B, $B>.NSK.OB(B, $B8bK\(B $B6F(B: $B!ILH1VE*pJs!&%7%9%F%`ItLgBg2qO@J8=8(B, pp.1025-1028, 2005

    29. $BB-N)9@0l(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB(B,$B!!BgNS@5D>!'!I%(%$%j%"%7%s%0$r9M(B $BN8$7$?J#?tL\I8
    30. $B@.ED820lO:(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I?@7P?6F0;R$rF3F~(B $B$7$?3,AX7?5-215!9=$r$b$D6/2=3X=,%7%9%F%`!I(B,$B!!Bh(B14$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X(B $B=Q9V1i2qO@J8=8(B,$B!!(Bpp.164-165, 2005

    31. $BAjIt1Q5*(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!IItJ,4QB,%^%k%3%U4D(B $B6-$K$*$1$k3F
    32. $BK-ED??Li(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I6/2=3X=,$rMQ$$$?LH(B $B1V7?@)8f4o$N@_7W$H$=$N1~MQ!I(B,$B!!Bh(B14$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8(B $B=8(B,$B!!(Bpp.256-257, 2005

    33. $B1)Ln$H$b$((B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB(B,$B!!BgNS@5D>!'!I(BAIBO$B$N
    34. $BCf8665Gn(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!ILH1V35G0$rMQ$$$?(B $B%5%C%+!<%(!<%8%'%s%H%A!<%`$N9=@.$HI>2A!I(B,$B!!Bh(B14$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V(B $B1i2qO@J8=8(B,$B!!(Bpp.172-173, 2005

    35. $B>.@nD95W(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!ILH1VE*
    36. $BCfLn9@Fs(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I6/2=3X=,%7%9%F%`$K(B $B$*$1$kF0E*4D6-$KE,MQ2DG=$J>uBV6u4V$N9=C[K!!I(B,$B!!Bh(B14$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.236-237, 2005

    37. $BJ?ED??0l(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\(B,$B6F(B,$B!!BgNS@5D>!'!I(Bactor- critic$B7?6/2=3X=,$rMQ$$$?#A#I#B#O$N>c32J*2sHr9TF0$N3MF@!I(B,$B!!Bh(B15$B2s7WB,<+F0@)(B $B8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.34-35, 2006

    38. $BLpLn1p9>(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\(B,$B6F(B$B!'!I3NN(E*%+%*%9%@%$(B $B%J%_%C%/%9$rMQ$$$?O"A[5-21%b%G%k$N@-G=$K$D$$$F!I(B,$B!!Bh(B15$B2s7WB,<+F0@)8f3X2qCf9q(B $B;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.60-61, 2006

    39. $BBgEDCRHO(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB(B,$B!!BgNS@5D>!'!I>pF0!
    40. $B>.ED?N(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I>pJs6&M-5!G=(B $B$r;}$D#M#A#X#Q$NDs0F!I(B,$B!!Bh(B15$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.154-155, 2006

    41. $BAjIt1Q5*(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I2rA|EY$N35(B $BG0$rMQ$$$?3,AX7?6/2=3X=,%7%9%F%`!I(B,$B!!Bh(B15$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.156-157, 2006

    42. $B>.@nD95W(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!ILH1V%M%C%H(B $B%o!<%/$r4p$K$7$?6/2=3X=,K!$NDs0F!I(B,$B!!Bh(B15$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.158-159, 2006

    43. $BEDn5J~;R(B,$B!!LnDEB?H~;R(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I1#$l(B $B%^%k%3%U%b%G%k$rMxMQ$7$?2;@<$K4p$E$/46>pH=JLK!$K$D$$$F!I(B,$B!!Bh(B15$B2s7WB,<+F0@)(B $B8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.166-167, 2006

    44. $BCf8665Gn(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I6/2=3X=,7?(B $B%9%i%$%G%#%s%0%b!<%I@)8f!I(B,$B!!Bh(B15$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.184-185, 2006

    45.     $B2OB<2BBe;R(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!ILH1V%M%C%H%o!<%/(B $B$N35G0$rMQ$$$?%+%*%9@)8f%7%9%F%`!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.60-61, 2007

    46.     $BHx:jCR9a(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B $B8bK\6F(B$B!'!I%Y%$%8%"%s%M%C%H%o!<%/$K$h$k>uBV?dDj$rMxMQ$7$?6/2=3X=,%7%9%F%`!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.64-65, 2007

    47.     $BGHB?Ao(B, $B8bK\6F(B, $B>.NSK.OB(B, $BBgNS@5D>!'!I%O%$%V%j%C%I%b%G%k$K$h$k;~7ONsM=B,!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.66-67, 2007

    48.     $B>.ED?N(B,$B!!>.NSK.OB(B, $B8bK\6F(B, $BBgNS@5D>!'!I7hDjLZ$rMQ$$$?3,AX7?6/2=3X=,K!$NDs0F!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.70-71, 2007

    49. $BJ?ED??0l(B, $B>.NSK.OB(B, $B8bK\6F(B, $BBgNS@5D>!'!I(B$B@)8f7ONs$r<+8JJ,3d$9$k3,AX7?6/2=3X=,%7%9%F%`(B$B!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.72-73, 2007

    50. $B9B>eM5G7(B, $B>.NSK.OB(B, $B8bK\6F(B, $BBgNS@5D>!'!I(B$B%a%?%Q%i%a!<%?$N3X(B $B=,$rF3F~$7$?6/2=3X=,%b%G%k(B$B!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q(B $B;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.80-81, 2007

    51. $B>.@nD95W(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I%;%_%^%k%3%U7hDj2a(B $BDx$K$*$1$kLH1V%M%C%H%o!<%/$rMQ$$$?6/2=3X=,K!$NDs0F!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X(B $B2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.82-83, 2007

    52. $BLpLn(B$B1p9>(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I8{G[K!$rMxMQ$7$?3X=,7?O"A[5-21(B $B%b%G%k!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.88-89, 2007

    53. $B8EK\N4?M(B, $B8bK\6F(B, $B>.NSK.OB(B, $BBgNS@5D>!'!IDI2C3X=,5!G=$r;}$D2;@
    54. $BBgEDCRHO(B, $B8bK\6F(B, $B>.NSK.OB(B, $BBgNS@5D>!'!IY(EmBN!<3$GO%b%G%k$K$h$kF0E*O"A[5-21%7%9%F%`!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.162-163, 2007

    55. $B;3ED>!L&(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I%9%i%$%G%#%s%0%b!<(B $B%I@)8f$N35G0$rMxMQ$7$?6/2=3X=,%7%9%F%`!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.184-185, 2007

    56. $B1JED>;I'(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I%"%s%H%3%m%K!<:GE,(B $B2=$K$*$1$k>e0L%i%s%/8BDj%i%s%@%`A*BrJ}<0$NDs0F!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf(B $B9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.204-205, 2007

    57. $B>>0fM5G7(B, $B>.NSK.OB(B, $B8bK\6F(B, $BBgNS@5D>!'!I(B$B@/:v?dDjK!$K$h$k%^%k%A%(!<%8%'%s%H6/2=3X=,(B$B!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.212-213, 2007

    58. $BEDn5J~;R(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I(B$BSL3P5!(B $BG=$N(BFreeman$B%b%G%k$N8!>Z$H$=$N1~MQ(B$B!I(B,$B!!Bh(B16$B2s7WB,<+(B $BF0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.234-235, 2007

    59. $B5WJ];{??Bg(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I(B$B%Y%$%8%"%s%M%C%H%o!<%/$rMxMQ$7$?%b%8%e!<%k@Z49$(7?6/2=3X=,%7%9%F%`(B$B!I(B,$B!!Bh(B16$B2s7WB,<+F0@)8f3X2qCf9q;YIt(B $B3X=Q9V1i2qO@J8=8(B,$B!!(Bpp.290-291, 2007

    60. $B8bK\6F(B$B!$BgNS@5D>!$>.NSK.OB!$?yLn855*!$>>:jMN0lO:!'!I46(B $B>pM65/7?J#?t%m%\%C%H$NE,1~9TF0$N2~A1!I!$EE;R>pJsDL?.3X2qAm9gBg2q(B2008, pp.S39-S40, 2008

    61. $B1JED>;I'(B,$B!!BgNS@5D>(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB!'!I%"%s%H%3%m%K!<:GE,(B $B2=K!$K$*$1$k>e0L%i%s%/8BDj%i%s%@%`A*BrJ}<0!I!$J?@.(B20$BG/EE5$3X2qEE;R>pJs%7%9%F%`(B $BItLgBg2q(B, pp. 926-931, 2008

    62. $B>.NSK.OB(B,$B!!BgNS@5D>(B,$B!!(B$B8bK\6F(B$B!'!I6I=j@~7A%b%G%k$rF3F~$7$?%&%'!<(B $B%V%l%C%H%K%e!<%i%k%M%C%H%o!<%/$N%Y%$%:E*@_7WK!!I!$J?@.(B20$BG/EE5$3X2qEE;R>pJs%7%9(B $B%F%`ItLgBg2q(B, pp.738-743, 2008

    63. $B9B>eM5G7(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I#T#D8m:9$K4p$E$/6/(B $B2=3X=,$N%a%?%Q%i%a!<%?3X=,!I!$J?@.(B20$BG/EE5$3X2qEE;R>pJs%7%9%F%`ItLgBg2q(B, pp. 873-878, 2008

    64. $B>.NSK.OB(B,$B!!BgNS@5D>(B,$B!!(B$B8bK\(B$B6F!'!I6I=j@~7A%&%'!<%V%l%C%H%K%e!<%i(B $B%k%M%C%H%o!<%/$N%Y%$%:E*@_7WK!!I!$Bh(B18$B2sF|K\?@7P2sO)3X2q9gF1Bg2q%W%m%0%i%`!&>6(B $BO?=8!$(B146-147,2008
    65. $B1JED>;I'(B,$B!!BgNS@5D>(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB!'!I%"%s%H%3%m%K!<:GE,(B $B2=$K$*$1$k%i%s%@%`A*BrN(@_DjJ}K!!I!$Bh(B17$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 132-133, 2008

    66. $B9B>eM5G7(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I#T#D8m:9$rMQ$$$?6/(B $B2=3X=,$N%a%?%Q%i%a!<%?3X=,K!!I!$Bh(B17$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8(B $B=8(B, pp. 124-125,$B!!(B2008
    67. $BHx:jCR9a(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I3X=,J,N`;R%7%9%F%`(B $B!J#X#C#S!2#Q#T!K$rMQ$$$?6/2=3X=,%7%9%F%`$N@-G=I>2A!I!$Bh(B17$B2s7WB,<+F0@)8f3X2qCf(B $B9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 120-121,$B!!(B2008

    68. $B2OB<2BBe;R(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!I<+N'0\F0%m%\%C%H(B $B$K$h$k%^%C%W:n@.$H%4!<%kC5:w!I!$Bh(B17$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8(B $B=8(B, pp.84 -85,$B!!(B2008

    69. $B>>0fM5G7(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B,$B!!BgNS@5D>!'!I%i%s%@%`%?%$%`%j%s(B $B%0$rMQ$$$?%b%8%e!<%k7?6/2=3X=,!I!$Bh(B17$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8(B $B=8(B, pp.122 -123,$B!!(B2008
    70. $B;3ED>!L&(B,$B!!BgNS@5D>(B,$B!!>.NSK.OB(B,$B!!(B$B8bK\6F(B$B!'!IIT40A44QB,4D6-2<$K(B $B$*$1$k%9%i%$%G%#%s%0%b!<%I@)8f$N35G0$rMQ$$$?6/2=3X=,%7%9%F%`!I!$Bh(B17$B2s7WB,<+F0(B $B@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 188-189,$B!!(B2008
    71. $B8bK\6F(B, $BBgNS@5D>(B, $B>.NSK.OB!'!IBgG>JU1o7O%b%G%k$N9=C[!I!$Bh(B21$B2s<+N'J,;6%7%9%F%`%7%s%]%8%&%`O@J8=8!$(Bpp.111-116 , 2009

    72. $BGHB?Ao(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB(B,$B!!BgNS@5D>!'!I3,:9;~7ONs$rMQ$$$?(B $B%K%e!<%i%k%M%C%H%o!<%/$K$h$k;~7ONsM=B,!I!$Bh(B21$B2s<+N'J,;6%7%9%F%`%7%s%]%8%&%`O@J8(B $B=8!$(Bpp.135-138, 2009

    73. $B8EK\N4?M(B,$B!!(B$B8bK\6F(B,$B!!>.NSK.OB(B,$B!!BgNS@5D>!'!IDI2C3X=,5!G=$r;}$D(B $B2;@
    74. $B7sJ?(B $BN6(B, $B>.NS(B $BK.OB(B, $B8bK\(B $B6F(B, $BBgNS(B $B@5D>(B: "$BItJ,4QB,4D6-$K$*$1$kM=B,5!G=$rHw$($?%b%8%e!<%k7?6/2=3X=,%7%9%F%`(B",     $BBh(B 11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 285-288, 2009

    75. $B0f>e(B $BM@0t(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B0dEAE*%"%k%4%j%:%`$rMQ$$$?(BLEGO $B%m%\%C%H$N:GE,9TF07PO)C5:w(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 289-292, 2009

    76. $B8E2l(B $B?.G72p(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$BSL3P%b%G%k$rMQ$$$?2;@
    77. $B2,K\(B $BN4;V(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B<+N'7?%(!<%8%'%s%H$N9TF03X=,$K$*$1$k2ACM%7%9%F%`$N9=C[$K4X$9$k8&5f(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 299-301, 2009

    78. $B@>ED(B $BJ~9-(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$BB?CM%Q%?!<%s5-215!9=$rHw$($?6/2=3X=,%7%9%F%`$K4X$9$k8&5f(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 308-310, 2009

    79. $BKRLn(B $B5H9((B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B6/2=3X=,$rMQ$$$?E,1~(B FNN $B@)8f%7%9%F%`(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 322-325, 2009

    80. $B;3Ln(B $BM4$B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B, $BBgNS(B $B@5D>(B: "$B729TF0$N$?$a$N%K%e!<%m%U%!%8%#6/2=3X=,%7%9%F%`$K4X$9$k8&5f(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 365-368, 2009

    81. $BLZ2<(B $B9/90(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B, $BBgNS(B $B@5D>(B: "$B?@7P2sO)%b%G%k$K$h$kF02hA|=hM}$K4X$9$k8&5f(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 382-384, 2009

    82. $B0$It(B $B9'>4(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B<+8JAH?%2=%^%C%W$rMQ$$$?E,1~E*6/2=3X=,%7%9%F%`(B", $BBh(B11$B2s(BIEEE$B9-Eg;YIt3X@8%7%s%]%8%&%`O@J8=8(B, pp. 419-420, 2009

    83. $B2,K\(B $BN4;V(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B<+N'7?%(!<%8%'%s%H$N9TF03X=,$K$*$1$k2ACM%7%9%F%`$N9=C[$K4X$9$k8&5f(B", $BBh(B18$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 22-23, 2009

    84. $B@>ED(B $BJ~9-(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$BB?CM%Q%?!<%s5-215!9=$rHw$($?6/2=3X=,%7%9%F%`$K4X$9$k8&5f(B", $BBh(B18$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 24-25, 2009

    85. $BKRLn(B $B5H9((B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B6/2=3X=,$rMQ$$$?E,1~(B $B%U%!%8%#%K%e!<%i%k%M%C%H%o!<%/@)8f%7%9%F%`(B", $BBh(B18$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 70-71, 2009

    86. $B7sJ?(B $BN6(B, $B>.NS(B $BK.OB(B, $B8bK\(B $B6F(B, $BBgNS(B $B@5D>(B: "$BItJ,4QB,4D6-$K$*$1$k%b%8%e!<%k7?6/2=3X=,$NM=B,5!G=$NpJs4XO"3X2qCf9q;YItBh(B60$B2sO"9gBg2q(B, pp. 533-534, 2009

    87. $B7sJ?(B $BN6(B, $B>.NSK.OB(B, $B8bK\6F(B, $BBgNS@5D>(B: "$B%^%k%A%(!<%8%'%s%H4D6-$K$*$1$kB>
    88. $BFb;3>M8c(B, $BBgNS@5D>!$(B$B8bK\(B $B6F(B, $B>.NSK.OB(B: $B@)8f$H6/2=3X=,$NM;9g$K$h$k%m%P%9%H$J7W2h9TF0@)8fJ}<0$N3+H/(B", $BJ?@.(B22$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B, pp. 1518-1523, 2010

    89. $BBpLnM:Bg!$BgNS@5D>(B, $B8b(B $BK\(B $B6F(B, $B>.NSK.OB(B, $BqH(B $BNI_[(B: "$B>pF0%b%G%kM;9g7?6/2=3X=,%7%9%F%`(B", $BJ?@.(B22$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B $BO@J8=8(B, pp.126-131, 2010

    90. $B2,K\N4;V(B, $BBgNS@5D>(B, $B8b(B $BK\(B $B6F(B, $B>.NSK.OB(B: "$B2ACM$N35G0$rF3F~$7$?6/2=3X=,%7%9%F%`(B",$B%U%!%8%#%7%9%F%`%7%s%]%8%&%`(B (FSS 2010)$BO@J8=8(B, pp. -, 2010

    91. $BBgIY9/90(B, $BBgNS@5D>(B, $B8bK\(B $B6F(B, $B>.NSK.OB(B: "$B<+8JAH?%2=%U%!%8%#%K%e!<%m%sE,1~@)8f$K$*$1$k%9%i%$%G%#%s%078?tD4@0K!(B", $BBh(B 19$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 44-45, 2010

    92. $B>.NSK.OB(B, $BBgNS@5D>(B,$B!!(B$B8b(B $BK\6F(B$B!'(B"$B%^%k%A%(!<%8%'%s%H%7%9%F%`$K$*$1$k9TF0M=B,$H0U?^?dDj$K4X$9$k8&5f(B $BF08~(B", $BEE5$3X2q8&5f2q;qNA%7%9%F%`8&5f2q(B, ST-11-015, pp. 5-10, 2011

    93. $BEOn4=Y(B, $B8bK\(B $B6F(B,  $B>.NSK.OB(B, $BBgNS@5D>(B: "$B%a%?%R%e!<%j%9%F%#%/%9pJs!&%7%9%F%`ItLgBg2qO@J8=8(B, pp.1294-1298, 2011

    94. $BEOn4=Y(B, $B8bK\(B $B6F(B,  $B>.NSK.OB(B, $BBgNS(B $B@5D>(B: "$B?J2=E*7W;;pJs4XO"3X2qCf9q;YItBh(B62$B2sO"9gBg2q(B, pp.234-235 , 2011

    95. $BFb;3>M8c(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B,$B>.NSK.OB(B: "$BDI=>@-G=Jd=~4o$rHw$($?%*%s%i%$%s7?6/2=3X=,@)(B $B8f%7%9%F%`(B", $BEE5$!&>pJs4XO"3X2qCf9q;YItBh(B62$B2sO"9gBg2q(B, pp.373-374 , 2011

    96. $BBpLnM:Bg(B, $BBgNS@5D>(B, $B8bK\6F(B, $B>.NSK.OB!'(B"$B3,AX7?3X=,K!$rMQ$$$?@oN,A*Br%"%k%4%j%:%`(B", $BBh(B20$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp.220-221, 2011

    97. $BBgIY9/90(B, $BBgNS@5D>(B, $B8bK\6F(B, $B>.NSK.OB!'(B"$B%^%k%A%(!<%8%'%s%H%7%9%F%`9g0ULdBj$KBP$9$k<+8JAH?%2=%U%!%8%#%K%e!<%i%k%M%C%H%o!<%/$rMQ$$$?J,;67?E,1~@)8f(B", $BBh(B20$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp.234-235 , 2011

    98. $BFb;3>M8c(B, $BBgNS(B $B@5D>(B,$B8bK\(B $B6F(B, $B>.NSK.OB!'(B"$B%a%b%j@8@.5!9=$rMQ$$$?%Q(B $B%iL\%H%j%C%/(BCMAC", $BEE5$3X2q8&5f2q;qNA%7%9%F%`8&5f2q(B, ST-11-025, pp.1-6, 2011

    99. $BEOn4=Y(B, $B8bK\(B $B6F(B,$B>.NSK.OB(B, $BBgNS(B $B@5D>!'(B"$B?J2=E*7W;;
    100. $BFb;3>M8c(B, $BBgNS@5D>(B, $B8bK\6F(B, $B>.NSK.OB!'(B"$B%U%#!<%I%P%C%/8m:93X=,$K4p$E$/<+8JM;9g7?>.G>%Q!<%;%W%H%m%s%b%G%kMxMQ7?%m%P%9%H@)8f%7%9%F%`(B", $BJ?@.(B24$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B, pp., 2012

    101. $B;3:,(B $BBg!$(B$B8bK\6F(B$B!$>.NSK.OB!'!H<+N'0\F0%m%\%C%H$K$*$1$k4D6-(B $B5Z$S9TF0$N5-21$H$=$NH=JLJ}<0!I(B,$BBh(B28$B2s%U%!%8%#%7%9%F%`%7%s%]%8(B $B%&%`!J#F#S#S(B)$B9V1iO@J8=8!$(Bpp.1223-1228, 2012

    102. $BLJED(B $B>-8g!$BgNS@5D>!$(B$B8bK\6F(B$B!$>.NSK.OB!'(B"$B>pF0A+0\%b%G%k$rHw$($?%(!<(B $B%8%'%s%H$N9TF07hDjK!$H4D6-F1DjJ}<0(B",$BBh(B28$B2s(B $B%U%!%8%#%7%9%F%`%7%s%]%8%&%`(B(FSS)$B9V1iO@J8=8!$(Bpp.448-451, 2012

    103. $B>e2>20BsLi(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "H$B!gDI=>@-G=Jd=~4o$rHw$($?%&%'!<%V%l%C%H%K%e!<%i%k%M%C%H%o!<%/E,1~@)8f%7%9%F%`(B", $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 88-89, 2012

    104. $B?9:j7.NSK.OB(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B: "$B%P%$%"%9@Z497?(BRNN$B$rMQ$$$?%R%e!<%^%s%N%$%I%m%\%C%H$N9TF03X=,(B",  $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 90-91, 2012

    105. $BFb;3>M8c(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$B>.G>%Q!<%;%W%H%m%s%b%G%k$rMxMQ7?%m%P%9%H@)8f%7%9%F%`$NDs0F$H$=$N9g0ULdBj$X$N1~MQ(B", $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 92-93, 2012

    106. $B;3:,Bg(B, $B8bK\(B $B6F(B, $B>.NS(B $BK.OB(B: "$BJ#?t$N4{9TF03X=,4D6-$rMxMQ$7$?<+N'%(!<%8%'%s%H$NL$3X=,4D6-LBO)C5:wJ}<0(B",  $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 94-95, 2012

    107. $BLJED>-8g(B, $BBgNS(B $B@5D>(B, $B8bK\(B $B6F(B, $B>.NSK.OB(B: "$B>pF0%b%G%k$K4p$E$/%(!<%8%'%s%H$N9TF07hDjK!$H$=$N1~MQ(B",  $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 116-117, 2012

    108. $BBgC+Bs42(B, $B8bK\(B $B6F(B, $B>.NSK.OB(B, $BBgNS@5D>!'(B"PL-G-SOM$B$rMQ$$$?u;XNa3X=,%7%9%F%`$N9=C[(B", $BBh(B21$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@(B $BJ8=8(B, pp. 168-169, 2012

    109. $BLJED>-8g!$BgNS@5D>!$8bK\6F(B$B!$(B $B>.NSK.OB!'(B"$B%^%k%3%U>pF0%b%G%k$rHw$($?%m(B $B%\%C%H$N9TF07hDjK!(B",$BEE5$3X2q#CItLgBg2q!$(Bpp.400-405, 2013

    110. $BBgC+Bs42(B, $B8bK\(B $B6F(B $B>.NSK.OB(B, $B4VIa??8c(B, $BBgNS@5D>(B:$B!IHsBP>N6aK54X?t$r;}$D(BG-SOM$B$*$h$S$=$l$rMQ$$$?%m%\%C%H$NL?Na3X=,%7%9%F%`!I(B, $BBh(B4$B2s%3%s%T%e!<%F!<%7%g%J%k!&%$%s%F%j%8%'%s%98&5f2q9V1iO@J8=8(B, pp. 1-8, 2013

    111. $BLJED>-8g(B, $BBgNS@5D>(B,$B8bK\(B $B6F(B $B4VIa??8c!$>.NSK.OB!'!I7P83$K4p$E$/>pF0$N:F9=@.$rF3F~$7$?%m%\%C%H$N9TF0A*Br%7%9%F%`!I(B, $BBh(B4$B2s%3%s%T%e!<%F!<%7%g%J%k!&%$%s%F%j%8%'%s%98&5f2q9V1iO@J8=8(B, pp.9-15, 2013

    112. $BLJED>-8g!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$>.NSK.OB!$4VIa??8c!'!I>p(B $BF07A@.$K4p$E$/9TF0A*Br%7%9%F%`$H$=$N
    113. $B;3:,Bg!$(B$B8bK\(B $B6F(B$B!$>.NSK.OB!$4VIa??8c!'!IJ#(B $B?t4D6-$KBP1~2DG=$J<+N'0\F0%m%\%C%H%7%9%F%`!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8!$(Bpp.76-77, 2013

    114. $BDX(B $BOBBg!$(B$B8bK\(B $B6F(B$B!$BgNS@5D>!$>.NSK.OB!$4VIa??8c!'!I(B Neuro-Fuzzy$B7?6/2=3X=,%7%9%F%`$K$*$kCNE*%(!<%8%'%s%H$N9TF03X=,!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8!$(Bpp.84-85, 2013

    115. $BEOn4(B $B=Y!$(B$B8bK\(B $B6F(B$B!$>.NSK.OB!$4VIa??8c!$BgNS@5D>!'!I(B PSO$B$rMQ$$$?%+%*%9E*3$GO!=?7Hi
    116. $B1'ET=SM$!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$4VIa??8c!$>.NSK.OB!'!I?'(B $B:LFCD'$K$h$k>pF0$r9MN8$7$?6/2=3X=,%7%9%F%`!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8!$(Bpp.90-91, 2013

    117. $B2<;JBsLi!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$4VIa??8c!$>.NSK.OB!'!I(B $B%K%e!<%i%k%M%C%H%o!<%/$rMQ$$$?%9%F%l%*2hA|$K$*$1$kHo
    118. $B4_(B $BC#Li!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$4VIa??8c!$>.NSK.OB!'!I>p(B $BF0$rMQ$$$?%m%\%C%H$N7PO)7hDjK!$N9M0F!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 158-159, 2013

    119. $B@P@n9,J?!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$>.NSK.OB!$4VIa??8c!'!IHs(B $B@~7A%@%$%J%_%/%9$r;}$D%(!<%8%'%s%H72$K$h$k%?!<%2%C%H$NCNE*DI=>@)8fJ}<0!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp.166-167, 2013

    120. $BBgC+Bs42!$(B$B8bK\(B $B6F(B$B!$>.NSK.OB!$4VIa??8c!$BgNS@5D>!'!IHs(B $BBP>N(BGSOM$B$rMQ$$$?u;XNa3X=,%7%9%F%`$N9=C[$K4X$9$k8&5f!H!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 200-201, 2013

    121. $BJ?ED5.?C!$(B$B8bK\(B $B6F(B$B!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!I(B RMB$B$H(BMLP$B$rMQ$$$?(BDBN$B$K$h$k;~7ONsM=B,$K4X$9$k8&5f!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 208-209, 2013

    122. $B?9:j7$B8bK\(B $B6F(B    $B!$4VIa??8c!$>.NSK.OB!$BgNS@5D>!'!I(B Elman$B7?(BRNNPB$B$rMQ$$$?%R%e!<%^%s%N%$%I%m%\%C%H$N65<(3X=,$K4X$9$k8&5f!H(B,$BBh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 210-211, 2013

    123. $BFAED?5B@O:!$(B$B8bK\(B $B6F(B$B!$4VIa??8c!$>.NSK.OB!$BgNS@5D>!'!I(B RBM$B$rMQ$$$?(BDeep Belief Net$B$K$h$k%+%*%9;~7ONsM=B,!I!$Bh(B22$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8(B, pp. 212-213, 2013

    124. $B8bK\!!6F(B$B!$BgNS@5D>!$>.NSK.OB!$4VIa??8c!'!I3NN(79
    125. $BJ?ED5.?C!$(B$B8bK\!!6F(B$B!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!I(B RBM$B$H(BMLP$B$rMQ$$$?(BDeep Belief Net$B$K$h$k;~7ONsM=B,!I!$J?@.#2#6G/EY!JBh#6#52s!KEE5$!&>pJs4XO"3X2qCf9q;YItO"9gBg2q!$(Bpp.338-339, 2014

    126. $BLJED>-8g!$BgNS@5D>!$(B$B8bK\(B $B6F(B$B!$4VIa??8c!'!I%Y%i2J5{N`$N(B $B@8BV$K4p$E$/0dEAE*%"%k%4%j%:%`$N2~NI!I!$J?@.#2#6G/EE5$3X2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q9V1iO@J8=8(B, pp.44-49, 2014

    127. $BLJED>-8g!$BgNS@5D>!$(B$B8bK\!!6F(B$B!$4VIa??8c!'!I%Y%i2J5{N`$N(B $B@8BV$K4p$E$/0dEAE*%"%k%4%j%:%`$N2~NI
    128. $BDX!!OBBg!$(B$B8bK\!!6F(B$B!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!I<+(B $B8JAH?%2=(B $B%K%e!<%m%U%!%8%#6/2=3X=,%7%9%F%`$K$h$kF;4D6-$NC5:w!I!$Bh(B23$B2s7WB,<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8!$(Bpp. 136-137, 2014

    129. $B1'ET=YM$!$BgNS@5D>!$(B$B8bK\!!6F(B$B!$4VIa??8c!'!I?':L>pJs$r9MN8$7$?6/2=(B $B3X=,%7%9%F%`!I!$Bh(B23$B2s<+F0@)8f3X2qCf9q;YIt3X=Q9V1i2qO@J8=8!$(Bpp. 138-139, 2014

    130. $BJ?ED5.?C!$(B$B8bK\!!6F(B$B!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!I?<(B $BAX3X=,$H@~7A%b%G%k$rJ;MQ$7$?;~7ONsM=B,K!!I!$(B$BJ?@.(B27$BG/EE5$3X(B $B2qEE;R!&>pJs!&%7%9%F%`ItLgBg2q(B$B!$(Bpp. 68-73,2015
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140.         $BRo>e7z;0!$(B $B4VIa??8c!$BgNS@5D>!$(B$B8bK\(B $B6F(B:$B!H(B$B%/%i%9Aj4X%k!<%k$N%/%i%9%?%j%s%0$K$h$k(B $B<1JL%7%9%F%`$N2~NI$H$=$N@-G=I>2A(B$B!I(B, $BJ?@.(B28$BG/EY(B($BBh(B67$B2s(B)$BEE5$!&>pJs4XO"3X2qCf9q;YItO"9gBg2q(B , R16-21-07, $B9-EgBg3X(B, Oct. 22, 2016

141.     $B8bK\(B $B6F(B$B!$DX(B $BCNBg!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'!HO"B3F~=PNO6u4V$K$*$1$k<+8JAH?%2=7?%K%e!<%m%U%!%8%#6/2=3X=,%7%9%F%`!I(B,$BJ?@.(B28$BG/EE5$3X2qEE;R!&>pJs!&%7%9%F(B $B%`ItLgBg2q9V1iO@J8=8(B, pp. 153-154, $B?@8MBg3X!$(BAug. 31-Sep. 3, 2016

142. Shingo MABU, Masanao OBAYASHI, Takashi KUREMOTO, NoriakiHASHIMOTO, Yasushi HIRANO, Shoji KIDO: $B!H(BUnsupervised Class Labeling of Diffuse Lung Diseases Using Evolutionary Data Mining and K-means$B!I(B, Proceedings of the 35th JAMIT Annual Meeting (JAMIT 2016), OP6-3, July 21-23, Chiba University, 2016

143. Takashi Kuremoto: "A Glance at Deep Learning$B!I(B, Keynote Speech, 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS2017), $B>e3$(B, 2017$BG/(B11$B7n(B26$BF|!=(B27$BF|(B

144. Kuremoto T., $B!H(BDeep Reinforcement Learning: A Promised Way to AI$B!I(B, Keynote Speech, The 5th International Conference on Intelligent Systems and Image Processing 2017 (ICISIP 2017), Hawaii, U.S.A., Sep.8, 201

145. $BHu8}BsO:!$(B $B4VIa??8c!$BgNS@5D>!$(B$B8bK\(B $B6F(B:$B!H(B$BH>65;U$"$j%G!<%?%^%$%K%s%0$rMQ$$$?%"%s%5%s%V(B $B%k3X=,7?<1JL4o$N9=C[(B$B!I(B, $BJ?@.(B29$BG/EY(B($BBh(B68$B2s(B)$BEE5$!&>pJs4XO"3X2qCf9q;YItO"9gBg2q(B , R16-21-05, $B2,;3M}2JBg3X(B, Oct. 21, 2017

146. $B8bK\(B $B6F(B$B!$J?ED5.?C!$BgNS@5D>!$4VIa??8c!$>.NSK.OB!'(B $B!H6/2=3X=,$rMQ$$$?%G%#!<%W%S%j!<%U%M%C%H5Z$S$=$N1~MQ!I(B,$BEE5$3X2q%7%9%F%`8&5f2q(B, $BEE5$3X2q8&5f2q;qNA(BST-17-013$B!A(B017, pp. 17-20,$B!!(BJune 10, 2017

<>147. $BCf;3NIIK!$4VIa??8c!$(B$B8bK\!!(B $B6F(B$B!'!H%^%k%A%A%c%s%M(B $B%k>pJs$rMxMQ$7$?>v$_9~$_%K%e!<%i%k%M%C%H%o!<%/$K$h$k(BSAR$B2hA|2r@O!I!$Bh(B59$B2s%7%9%F%`9)3XIt2q8&5f2q(B, pp.36-43, $BFaGF;T!$(B2018$BG/(B3$B7n(B15$BF|!A(B16$BF|(B

148. Kuremoto T., Sasaki T., Mabu S., $B!H(BThe performance of EEG signal classification using hybrid machine learning methods$B!I(B, 6th International Symposium on Sensor Science (I3S2018), Taiwan,  2018$BG/(B8$B7n(B6$BF|!A(B8$BF|(B

 149.  Kuremoto T., Sasaki T., Mabu S.: $B!H(BMental task recognition using EEG signal and deep learning methods$B!I(B, 15the International Neuroscience and Biological Psychiatry Regional (Asia ) ISBS Conference $B!H(BStress and Behavior: Yamaguchi 2018$B!I(B, $B1'It;T!$(B2018$BG/(B9$B7n(B9$BF|!A(B10$BF|(B

150     $B8b(B $BK\!!6F(B$B!$EDCfBg

151        $B8b(B $BK\!!6F(B$B!$:4!9LZ7IIK!$4VIa??8c!'!H%K%e!<%i%k%M%C%H%o!<%/$H%5%]!<%H%Y%/%H%k%^%7%s$rJ;MQ$7$?(BEEG$B?.9f<1JLpJs!&%7%9%F%`ItLgBg2qO@J8=8!$(BTC16-13, pp.593-597, $B;%KZ;T!$(B2018$BG/(B9$B7n(B6$BF|!A(B8$BF|(B

152.  Kuremoto T., Mori Y., Mabu S., $B!H(BAn online facial expression recognition system using deep con volutonal neural networks$B!I(B, Invited Speaker, International Conference on Soft Computing and Machine Learning (SCML 2019), Wuhan, China, 2019$BG/(B4$B7n(B26$BF|!A(B29$BF|(B

  1. Kuremoto's Web Page

  2. BIP-- Lab. of Biological Information Processing System
  3. ISE-- Dept. of Information Science and Engineering
  4. Yamaguchi University