Kyushu University [Kimura Hajime (Professor) Faculty of Engineering, ]

Kimura Hajime

Last modified date：2023.11.22

Professor / Naval Architecture and Marine Systems Engineering Human Resource Development Course / Faculty of Engineering

Papers

1.	Automatic Routing System for Multiple Pipe-lines.
2.	An Automatic Piping Algorithm including Elbows and Bends, [URL].
3.	Automatic Designing System for Piping and Instruments Arrangement including Branches of Pipes: Multi-objective Optimization of Piping Material Costs and Valve Operability Automatic designing of piping layout is challenging since it is composed of several numerical and/or combinational optimization problems, e.g., routing problems of pipes including branches, and arrangement problems of equipments. This paper presents a new approach based on a simple idea that the branches of pipes are considered to be a variety of equipment. Accordingly, the pipe routing problems are fairly simplified by removing the branches, and it derives a lot of efficient algorithms to solve the pipe arrangement problems. One is a multi-objective genetic algorithm (MOGA) in which the gene represents both the locations of the equipments and the arrangement of the pipes. And a new simple and efficient crossover operation which appropriately merges two different piping layouts (but of course the PID is the same) is proposed. In order to provide a fairly good initial population for the MOGA, a new heuristics making use of self-organization techniques to arrange equipments is proposed. The efficiency of the proposed approach is demonstrated through two experiments, one is a designing problem including five valves, one pump, and five branches, and the other includes seven valves, one pump, and six branches. The objective of the optimization in the experiments is to minimize the length of the pipes, the number of elbows, and the valve operability cost. The algorithms are programmed using Java language. Although the automatic arrangement system used in the experiments is academic, the concept of the proposed approach will be accepted in practical systems. , [URL].
4.	Planning for Delivering Steel Plates in a Stockyard Using Hierarchical Reinforcement Learning, [URL].
5.	Koichiro Shiraishi and Hajime Kimura, A unified motion planning method for a multifunctional underwater robot, Artificial Life and Robotics, Vol.14, No.3, pp.405--409, 2009.12.
6.	Automatic Design Algorithm for Pipe Arrangement Considering Valve Operationality.
7.	Automatic Design Algorithm for Pipe Arrangement based on Equipment Arrangement Figure and pipe Diagram.
8.	Optimal Path Planning of an Autonomous Underwater Vehicle in a Sea Current field.
9.	Hajime Kimura, Shigenobu Kobayashi, An Actor-Critic Algorithm using a Binary Tree Action Selector,--Reinforcement Learning to Cope with Enormous Actions--, Transaction of the Society of Instrument and Control Engineers , No.1, pp.73--81, 2004., 2007.12, [URL].
10.	An Extension of The Rational Policy Making algorithm to Continuous State Spaces.
11.	Reinforcement Learning in multi-dimensional state-action space using random tiling and Gibbs sampling.
12.	Satoshi Ikehira and Hajime Kimura, Multi-objective Genetic Algorithms for Pipe Arrangement Design, Proceedings of the 2006 Genetic and Evolutionary Computation Conference (GECCO 2006), pp.1869--1870 (2006), 2006.07.
13.	Automatic Design for Pipe Arrangement using Multi-objective Genetic Algorithms.
14.	Satoshi Ikehira, Hajime Kimura and Hiroyuki Kajiwara, Automatic Design for Pipe Arrangement using Multi-objective Genetic Algorithms, The 12th International Conference on Computer Applications in Shipbuilding (ICCAS 2005), pp.97--110, 2005., 2005.06.
15.	An improvement of sorting efficiency in a ship-building stockyard.
16.	Kimura, H. and Kobayashi, S., Reinforcement Learning by Policy Improvement Making Use of Experiences of The Other Tasks, The 8th Conference on Intelligent Autonomous Systems (IAS-8), pp.413--421 (2004), 2004.03.
17.	Tuchiya, C., Kimura, H. and Kobayashi,S., Policy Learning by GA using Importance Sampling, The 8th Conference on Intelligent Autonomous Systems (IAS-8), pp.385--394 (2004), 2004.03.
18.	Aoki, K., Kimura, H. and Kobayashi, S., Distributed Reinforcement Learning using Bi-directional Decision Making for Multi-criteria Control of Multi-Stage Flow Systems, The 8th Conference on Intelligent Autonomous Systems (IAS-8), pp.281--290 (2004), 2004.03.

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