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Applied abstraction of dynamical systems based on temporal/spatial mode (本文)

永嶋, 弘樹 慶應義塾大学

2021.03.23

概要

The Japanese society faces various social problems such as a super-aged society with fewer children [1], the lack of labor power [2], economic growth at a sluggish pace [3], and the aging public infrastruc- ture [4] and so on. On the other hand, there are the important aspect that those of them are essentially coming from the progress of industrial technology [5], the improvement of medical technology [6], the development of logistics [7], and information technology [8]. In this way, Japan is in, as it were, a su- per mature society [9, 10]. In [10], it is said that two essential leverages for super mature society. One is synthetic strength to lead the global optimum of the problems. Since the various social problems in the super mature society are intricately involved, the individual resolution of them will result not in the global optimum but in the partial optimum. The other one is overhead viewpoint to conduce the global sustainability considering the refinement of the society and system.

As for sustainability in the global society, the UN (United Nations) decided the global agenda until 2030 named SDGs(Sustainable Development Goals) in 2015 [11]. On SDGs, there have established 17 goals which are globally essential social issues such as poverty, welfare, health, etc. In each goal, several targets which are more concrete and quantitative have been set, and then there are a total of 169 targets. Each target can be quantitatively evaluated through the criteria value such as amount, population, ratio, index. Then, it can be compared and evaluated the situation in each member country. Given the procedure of problem-solving in SDGs, the complicated issues have been discussed with a huge amount of background data. Then, the issues have been divided into several pieces in consideration of the big picture. Moreover, each piece in huge issue will be resolved and evaluated based on the criteria value. After all, the overall structure of the problem has been guessed based on the quantitative data and have been solved each problem.

Data-based decision making is essential in all industries, not only in policy making [12] and the process of evaluating indicators such as the SDGs [11]. In fact, many developed countries are setting up data-related visions and national strategies through the use of artificial intelligence and big data [13, 14].

In Japan, the government is proposing Society 5.0 as a new form of society [14]. In comparison to previous societies, Society 5.0 is defined as follows.
• Society 1.0 : Hunting Society
• Society 2.0 : Farming Society
• Society 3.0 : Industrial Society
• Society 4.0 : Information Society
• Society 5.0 : Super Smart Society A super sumart society is one that provides the necessary goods and services to those who need them, when they need them, and in the quantities they need; is finely tuned to the various needs of society; is accessible to all people with high quality services; and is able to overcome various differences in age, gender, region, and language to live vibrantly and comfortably. The Ministry of Economy, Trade and Industry (METI) has proposed the concept of connected industries [15] as a concrete measure to realize the concept of Society 5.0. Connected Industries is a measure to solve problems through technological innovation, productivity improvement, and skill transfer by connecting and effectively utilizing all kinds of data. The connections here include a variety of things, and the connections are assumed to be between things and things, people and machines and systems, people and people, and companies and companies.

For example, the Internet of Things (IoT), which deals with the connection between things in a factory, and the Internet, which deals with the connection of information, are technologically related, but the connection between people and people, or between companies and companies, is not only technologically related but also socially related. In other words, connections in connected industries have both technical and social aspects, and both perspectives are necessary.

In order to move on to the discussion of specific perspectives and methodologies for problem solving, the definitions of some common keywords for data-based problem solving will be organized in light of the discussion so far [16].
• Phenomenon: A fact or situation that is observed to exist or happen, especially one whose cause or explanation is in question.
• Data: Facts and statistics collected together for reference or analysis.
• Connection: A relationship in which a person or thing is linked or associated with something else.
• Model: A simplified description, especially a mathematical one, of a system or process, to assist calculations and predictions.
• Information: Facts provided or learned about something or someone.
• Structure: The arrangement of and relations between the parts or elements of something complex.

Taken together, these keywords can express a common sense of the problem as follows. First, information on connections from data on phenomena related to humans and things is obtained, clarify their structures, construct models, analyze them for new considerations, and synthesize them according to the desired purpose. In order to advance the discussion on the basis of the above issues, three points of contention are listed.
(1) What connections should be discussed?
(2) How can be expressed the information and structure obtained in each connection?
(3) How will the information and structure obtained be used concretely?

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参考文献

[1] Cabinet Office, Government of Japan, “Annual report on the ageing society,” 2019.

[2] Small and Medium Enterprise Agency, Government of Japan, “White paper on small and medium enterprises in japan,” 2019.

[3] Ministry of Health, Labour and Welfare, Government of Japan, “White paper on the labour econ- omy,” 2019.

[4] Ministry of Land, Infrastructure, Transport and Tourism, Government of Japan, “White paper on land, infrastructure, transport and tourism in japan,” 2019.

[5] Ministry of Economy, Trade and Industry, Government of Japan, “White paper on manufacturing industries,” 2019.

[6] Ministry of Health, Labour and Welfare, Government of Japan, “Annual health, labour and welfare report,” 2019.

[7] Ministry of Land, Infrastructure, Transport and Tourism, Government of Japan, “White paper on transport policy,” 2019.

[8] Ministry of Internal Affairs and Communications, Government of Japan, “White paper on informa- tion and communications in japan,” 2019.

[9] “Keio Program for Leading Graduate School.” http://plgs.keio.ac.jp/?lang=en (ac- cessed 2020-05-03).

[10] K. Ohnishi, “Human resource development for super mature society,” The Journal of Institute of Electronics, Information and Communication Engineers, vol. 97, pp. 21–26, January 2014.

[11] “Sustainable Development Goals Knowledge Platform.” https:// sustainabledevelopment.un.org/ (accessed 2020-05-03).

[12] S. Tanabe, “The history and substance of evidence based policy making,” in Proceedings of the annual conference of the Japan Evaluation Society, (Yokohama, Japan), pp. 1–8, December 2018.

[13] Ministry of Finance and Ministry of Industry, Business and Finance Affairs, “National strategy for artificial intelligence,” tech. rep., The Danish Government, March 2019.

[14] M. Fukuyama, “Society 5.0: Aiming for a new human-centered society,” in ECONOMY, CULTURE & HISTORY Japan SPOTLIGHT Bimonthly, pp. 47–50, July/August 2018.

[15] “METI Released a Policy Concept Titled “Connected Industries” as a Goal that Japanese Indus- tries Should Aim for.” http://www.meti.go.jp/english/press/2017/0320_001. html.

[16] “LEXICO.” https://www.lexico.com/.

[17] T. Murakami, F. Yu, and K. Ohnishi, “Torque sensorless control in multidegree-of-freedom manip- ulator,” IEEE Transactions on Industrial Electronics, vol. 40, pp. 259–265, April 1993.

[18] E. Saito and S. Katsura, “Vibration control of two-mass resonant system based on wave compen- sator,” IEEJ Transactions on Industry Applications, vol. 132, no. 4, pp. 473–479, 2012.

[19] T. D. Murphey and J. W. Burdick, “On the stability and design of distributed manipulation control systems,” in Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), vol. 3, pp. 2686–2691 vol.3, 2001.

[20] M. Nakamura, “Study on ict system today and future for engineering chain connection in manufac- turing from product design to production,” Journal of the Japan Society for Precision Engineering, vol. 81, no. 3, pp. 220–224, 2015.

[21] E. Saito and S. Katsura, “Compensation of integrated resonant and time delay system by using wave compensator,” Automatika, vol. 54, no. 1, pp. 28–38, 2013.

[22] V. Villani, F. Pini, F. Leali, and C. Secchi, “Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications,” Mechatronics, vol. 55, pp. 248–266, 2018.

[23] C. Mitsantisuk, K. Ohishi, and S. Katsura, “Control of interaction force of twin direct-drive mo- tor system using variable wire rope tension with multisensor integration,” IEEE Transactions on Industrial Electronics, vol. 59, pp. 498–510, January 2012.

[24] B. Hannaford, “A design framework for teleoperators with kinesthetic feedback,” IEEE Transac- tions on Robotics and Automation, vol. 5, pp. 426–434, August 1989.

[25] D. A. Lawrence, “Stability and transparency in bilateral teleoperation,” IEEE Transactions on Robotics and Automation, vol. 9, pp. 624–637, October 1993.

[26] K. Hashtrudi-Zaad and S. E. Salcudean, “Bilateral parallel force/position teleoperation control,”Journal of Robotic Systems, vol. 19, no. 4, pp. 155–167, 2002.

[27] T. Chakraborty, A. Dalmia, A. Mukherjee, and N. Ganguly, “Metrics for community analysis: A survey,” ACM Computing Surveys, vol. 50, no. 4, 2017.

[28] J. LeSage and R. Pace, Introduction to Spatial Econometrics. Statistics: A Series of Textbooks and Monographs, CRC Press, 2009.

[29] Y. Ohtsuka, “Estimation of regional bussiness cycle in japan with markov switching spatial autoregressive-ar model,” Journal of the Japan Statistical Society, vol. 40, no. 2, pp. 89–109, 2011.

[30] K. Kakamu and H. Wago, “Bayesian spatial panel probit model with an application to business cycle in japan,” MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, pp. 856–863, 2005.

[31] H. Nagashima and S. Katsura, “Functional mode estimation using principal component analysis of grasping/manipulating motion,” IEEJ Journal of Industry Applications, vol. 2, no. 4, pp. 211–220, 2013.

[32] H. Nagashima and S. Katsura, “Alignment estimation of actuators forgrasping/manipulating motion using principal component analysis,” in Mechanical Engineering Congress 2011,MECJ ’12, pp. 1–5, September 2012.

[33] H. Nagashima and S. Katsura, “Human-motion analysis of grasping/manipulating motion including time-variable function using principal component analysis,” in Proceedings of the 2012 IEEE/SICE International Symposium on System Integration, SII ’12-FUKUOKA, (Fukuoka, Japan), pp. 798– 803, December 2012.

[34] H. Nagashima and S. Katsura, “Function estimation of grasping/manipulating motions in scaled bilateral control using principal component analysis,” in Proceedings of the 38th Annual Confer- ence of the IEEE Industrial Electronics Society, IECON ’12-MONTREAL, (Montreal, Canada),pp. 4392–4397, October 2012.

[35] H. Nagashima and S. Katsura, “Motion analysis of interaction mode control using principal com- ponent analysis,” in Proceedings of the IEEE International Conference on Mechatronics, ICM ’13- VICENZA, (Vicenza, Italy), pp. 540–545, February 2013.

[36] H. Nagashima and S. Katsura, “Multi-dof motion representation using principal component analysis of haptic information,” in The IEEJ Papers of Technical Meeting on Industrial Instrumentation and Control, pp. 31–36, March 2012.

[37] H. Nagashima and S. Katsura, “Motion representation using principal component analysis of haptic information,” in The 2011 Annual Meeting of the Institute of Electrical Engineers of Japan, pp. 329– 330, March 2012.

[38] H. Nagashima and S. Katsura, “Abstraction and analysis of human motion based on real-world haptics,” in SICE SI Division Technical Meeting on Mechatronics, March 2012.

[39] H. Nagashima and S. Katsura, “Accuracy analysis of function estimation based on human-motion information,” in The 55th Administration Committee of Japan Joint Automatic Control Conference, JACC12, pp. 295–300, November 2012.

[40] H. Nagashima and T. Nakatsuma, “Bayesian tempo-spatial estimation of the japanese prefectural business cycle indicators,” in 8th International Conference on Computational and Financial Econo- metrics (CFE 2014), December 2014.

[41] H. Nagashima and S. Katsura, “Vibration control of flexible arm by input shaping based on wave model,” Journal of the Japan Society for Precision Engineering, vol. 83, pp. 593–598, June 2017.

[42] J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor, Dynamic Mode Decomposition. Society for Industrial and Applied Mathematics, 2016.

[43] A. Aitken, “On bernoulli s numerical solution of algebraic equations,” in Proc. Roy. Soc. Edinburgh Ser A 46, pp. 289–305, 1926.

[44] L. M. Capisani and A. Ferrara, “Trajectory planning and second-order sliding mode mo- tion/interaction control for robot manipulators in unknown environments,” IEEE Transactions on Industrial Electronics, vol. 59, pp. 3189–3198, August 2012.

[45] C. Ma, J. Cao, and Y. Qiao, “Polynomial-method-based design of low-order controllers for two- mass systems,” IEEE Transactions on Industrial Electronics, vol. 60, pp. 969–978, March 2013.

[46] S. Li, J. Yang, W. H. Chen, and X. Chen, “Generalized extended state observer based control for systems with mismatched uncertainties,” IEEE Transactions on Industrial Electronics, vol. 59,pp. 4792–4802, December 2012.

[47] N. Tsunashima and S. Katsura, “Spatiotemporal coupler: Storage and reproduction of human finger motions,” IEEE Transactions on Industrial Electronics, vol. 59, pp. 1074–1085, February 2012.

[48] Y. Kang, H. Kim, S. H. Ryu, N. L. Doh, Y. Oh, and B. j. You, “Dependable humanoid naviga- tion system based on bipedal locomotion,” IEEE Transactions on Industrial Electronics, vol. 59,pp. 1050–1060, February 2012.

[49] S. Y. Chen, “Kalman filter for robot vision: A survey,” IEEE Transactions on Industrial Electronics, vol. 59, pp. 4409–4420, November 2012.

[50] Y. Fang, X. Liu, and X. Zhang, “Adaptive active visual servoing of nonholonomic mobile robots,”IEEE Transactions on Industrial Electronics, vol. 59, pp. 486–497, January 2012.

[51] V. B. Zordan and J. K. Hodgins, “Motion capture-driven simulations that hit and react,” in In Pro- ceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 89 – 96, ACM Press, January 2002.

[52] J. H. Cho, H. I. Son, D. G. Lee, T. Bhattacharjee, and D. Y. Lee, “Gain-scheduling control of teleoperation systems interacting with soft tissues,” IEEE Transactions on Industrial Electronics, vol. 60, pp. 946–957, March 2013.

[53] R. Kubo, T. Shimono, and K. Ohnishi, “Flexible controller design of bilateral grasping systems based on a multilateral control scheme,” IEEE Transactions on Industrial Electronics, vol. 56,pp. 62–68, January 2009.

[54] H. Kuwahara, T. Shimono, H. Tanaka, D. Yashiro, and K. Ohnishi, “Abstraction of action com- ponents unconstrained by alignment of haptic sensing points,” IEEE Transactions on Industrial Electronics, vol. 58, pp. 3196–3204, August 2011.

[55] R. Kubo, A. Kato, T. Tsuji, and K. Ohnishi, “A structure of bilateral control systems based on grasp- ing/manipulating control scheme,” IEEJ Transactions on Industry Applications, vol. 127, no. 6,pp. 563–570, 2007.

[56] T. Tsuji, K. Ohnishi, and A. Sabanovic, “A controller design method based on functionality,” IEEE Transactions on Industrial Electronics, vol. 54, pp. 3335–3343, December 2007.

[57] T. Gibo, S. Aoki, T. Miyamoto, M. Iwata, and A. Shiozaki, “Sequential learning and recognition of comprehensive behavioral patterns based on flow of people,” IEEJ Transactions on Industry Applications, vol. 131, no. 6, pp. 820–828, 2011.

[58] X. Ding, L. He, and L. Carin, “Bayesian robust principal component analysis,” IEEE Transactions on Image Processing, vol. 20, pp. 3419–3430, December 2011.

[59] K. Ohnishi, M. Shibata, and T. Murakami, “Motion control for advanced mechatronics,”IEEE/ASME Transactions on Mechatronics, vol. 1, pp. 56–67, March 1996.

[60] J. Yang, S. Li, and X. Yu, “Sliding-mode control for systems with mismatched uncertainties via a disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 60, pp. 160–169, January 2013.

[61] J. N. Yun, J. Su, Y. I. Kim, and Y. C. Kim, “Robust disturbance observer for two-inertia system,”IEEE Transactions on Industrial Electronics, vol. 60, pp. 2700–2710, July 2013.

[62] S. Katsura, Y. Matsumoto, and K. Ohnishi, “Modeling of force sensing and validation of distur- bance observer for force control,” IEEE Transactions on Industrial Electronics, vol. 54, pp. 530– 538, February 2007.

[63] W. S. Huang, C. W. Liu, P. L. Hsu, and S. S. Yeh, “Precision control and compensation of servomo- tors and machine tools via the disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 57, pp. 420–429, January 2010.

[64] T. Murakami, R. Nakamura, F. Yu, and K. Ohnishi, “Force sensorless compliant control based on reaction force estimation observer in multi-degrees-of-freedom robot.,” Journal of the Robotics Society of Japan, vol. 11, pp. 765–768, January 1993.

[65] S. Katsura, K. Irie, and K. Ohishi, “Wideband force control by position-acceleration integrated disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 55, pp. 1699–1706, April 2008.

[66] S. Katsura and K. Ohishi, “Modal system design of multirobot systems by interaction mode con- trol,” IEEE Transactions on Industrial Electronics, vol. 54, pp. 1537–1546, June 2007.

[67] K. Asako and T. Onodera, “Business cycle analyses with prefectural business index,” Economic Review, vol. 60, no. 3, pp. 266–285, 2009.

[68] J. H. Stock and M. W. Watson, “A probability model of the coincident economic indicators,” Work- ing Paper 2772, National Bureau of Economic Research, November 1988.

[69] J. H. Stock and M. W. Watson, “New indexes of coincident and leading economic indicators,” Working Paper 1380, National Bureau of Economic Research, April 1990.

[70] C.-J. Kim and C. R. Nelson, “Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching,” The Review of Economics and Statistics, vol. 80, no. 2, pp. 188–201, 1998.

[71] J. D. Hamilton, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, vol. 57, pp. 357–384, March 1989.

[72] T. Watanabe, “Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switch- ing Factor Model,” Monetary and Economic Studies, vol. 21, pp. 35–68, February 2003.

[73] C. K. Carter and R. Kohn, “On gibbs sampling for state space models,” Biometrika, vol. 81, no. 3,pp. 541–553, 1994.

[74] S. Fru¨hwirth-Schnatter, “Data augmentation and dynamic linear models,” Journal of Time Series Analysis, vol. 15, no. 2, pp. 183–202, 1994.

[75] J. Durbin and S. Koopman, Time Series Analysis by State Space Methods: Second Edition. Oxford Statistical Science Series, OUP Oxford, 2012.

[76] P. de Jong and N. Shephard, “The simulation smoother for time series models,” Biometrika, vol. 82, no. 2, pp. 339–350, 1995.

[77] A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari, and D. Rubin, Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science, Taylor & Francis, 2013.

[78] D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. Van Der Linde, “Bayesian measures of model complexity and fit,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 64, no. 4, pp. 583–639, 2002.

[79] J. Geweke, “Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,” Bayesian Statistics, vol. 4, pp. 169–193, 1992.

[80] S. Chib, “Chapter 57 - markov chain monte carlo methods: Computation and inference,” vol. 5 ofHandbook of Econometrics, pp. 3569 – 3649, Elsevier, 2001.

[81] G. Bry and C. Boschan, Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. National Bureau of Economic Research, Inc, 1971.

[82] H. Asama, “Service engineering and service robotics,” Journal of the Japan Society for Precision Engineering, vol. 78, no. 3, pp. 196–200, 2012.

[83] T. Sasaki, K. Shinsen, N. Itoh, Y. Ikemoto, and M. Jindai, “Vibration control for high-speed minia- ture assembling using correction speed after starting,” Journal of the Japan Society for Precision Engineering, vol. 80, no. 5, pp. 479–483, 2014.

[84] A. Yamamoto, H. Miyagawa, H. Hamamatsu, S. Goto, and M. Nakamura, “High-speed positioning control for linear motor driving table without base vibration,” Journal of the Japan Society for Precision Engineering, Contributed Papers, vol. 70, no. 5, pp. 645–650, 2004.

[85] Y. Noh, K. Lee, Y. Arai, and W. Gao, “A force sensor integrated fast tool control system,” Journal of the Japan Society for Precision Engineering, vol. 77, no. 1, pp. 85–89, 2011.

[86] M. Balas, “Feedback control of flexible systems,” IEEE Transactions on Automatic Control, vol. 23,pp. 673–679, August 1978.

[87] T. Yoshikawa, A. Ohta, and K. Kanaoka, “State estimation and parameter identification of flexible manipulators based on visual sensor and virtual joint model,” in Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, vol. 3, pp. 2840–2845 vol.3, 2001.

[88] Y. Iwashita, T. Nakamura, S. Ikai, and K. ichi Takayama, “A study on low frequency vibration suppression control by two-mass system model for feed axes of nc machine tools,” Journal of the Japan Society for Precision Engineering, vol. 82, no. 8, pp. 745–750, 2016.

[89] Z. Mohamed and M. Tokhi, “Command shaping techniques for vibration control of a flexible robot manipulator,” Mechatronics, vol. 14, no. 1, pp. 69–90, 2004.

[90] W. Singhose, “Command shaping for flexible systems: A review of the first 50 years,” International Journal of Precision Engineering and Manufacturing, vol. 10, pp. 153–168, October 2009.

[91] M. Iwasaki and H. Nakamura, “Suppression of resonant vibration due to angular transmission errors of harmonic drive gearings by variable notch filter,” Journal of the Japan Society for Precision Engineering, vol. 78, no. 10, pp. 887–893, 2012.

[92] K. Yuki, T. Murakami, and K. Ohnishi, “Vibration control of 2 mass resonant system by resonance ratio control,” in Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON ’93., International Conference on, pp. 2009–2014 vol.3, November 1993.

[93] M. Iwasaki, Y. Asai, and N. Matsui, “Vibration suppression of robot arm by feedforward con- trol with auto-measurement of system parameter,” IEEJ Transactions on Industry Applications, vol. 114, no. 10, pp. 1046–1052, 1994.

[94] J. M. Hyde and W. P. Seering, “Using input command pre-shaping to suppress multiple mode vibra- tion,” in Proceedings. 1991 IEEE International Conference on Robotics and Automation, pp. 2604– 2609 vol.3, April 1991.

[95] S. Katsura and K. Ohnishi, “Absolute stabilization of multimass resonant system by phase-lead compensator based on disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 54,pp. 3389–3396, December 2007.

[96] C. Wagner-Nachshoni and Y. Halevi, “Control of multi-link flexible structures,” in Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005., pp. 507–512, June 2005.

[97] E. Saito and S. Katsura, “Vibration control of a two-mass resonant system using wave compen- sator,” in SICE Annual Conference 2011, pp. 2672–2677, September 2011.

[98] K. Ohishi, K. Ohnishi, and K. Miyachi, “Torque-speed regulation of dc motor based on load torque estimation method,” in Proceedings of International Power Electronics Conference (IPEC-TOKYO ’83), (Tokyo, Japan), pp. 1209–1218, March 1983.

参考文献をもっと見る