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Hajime Kimura Last modified date:2017.12.11

Professor / Department of Marine Systems Engineering
Faculty of Engineering

Graduate School

Hajime Kimura
Dept. of Marine Engineering, Graduate School of Engineering .
Academic Degree
Field of Specialization
Machine Learning, Artificial Intelligence, Robotics, Reinforcement learning, Statistics, piping, ship building
Outline Activities
Research activities:

(1) Automatic pipe arrangement for ships and its evaluation
(2) statisticsOptimization in Logistics of Manufacturing
(3) Performance estimation of ship hulls using

Education activities:
(1) Systems design engineering
(2) Advanced systems design
(3) Marine statistics
(4) Programming exercise
Research Interests
  • A development of automatic piping design system
    keyword : piping, pipe routing, automatic design, equipment arrangement, Dijkstra method, Touch and Cross method
  • Measurement of shapes making use of 3D scannar in shipbuilding
    keyword : 3D-scannar, point clouds, shape recognition, shape measurement
  • Optimization in shipbulding
    keyword : optimization scheduling
    2004.04~2017.03Investigation of optimization techniques in logistics.
  • Assist system for decision of main hull parameters on ground design for shipbuilding
    keyword : Shipbuilding, ground design, main hull parameters
  • Behavior learning of underwater robots using Reinforcement learning
    keyword : Reinforcement learning, underwater robots
  • Mine search robot using electrical sensor
    keyword : robotics, mine search, electrical sensor
    2004.07mine detection using an electrical method.
  • underwater sensor using electrical search
    keyword : electrical search, underwater sensor
  • Development of Reinforcement learning
    keyword : reinforcement learning, Robotics
    1995.03Reinforcement Learning: Finding control rules through trial and error interactions in the unknown environments.
  • Decision making under uncertainty
    keyword : decision making, Markov decision process
    1995.03Decision making under uncertainty.
Current and Past Project
  • Development of decision support system for early stage of ship hull design
Academic Activities
1. (Relay Review: Recent Developments in Reinforcement Learning) A Basis of Reinforcement Learning.
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. Reinforcement Learning in multi-dimensional state-action space using random tiling and Gibbs sampling.
1. Yuto Ando, Hajime Kimura, Automatic Pipe Routing to Avoid Air Pockets, International Conference on Computer Applications in Shipbuilding, 2013.09.25, [URL].
2. Kimura Hajime, An Automatic Pipe Arrangement Algorithm Considering Elbows and Bends, International Conference on Computer Applications in Shipbuilding, 2012.06.14, [URL].
3. Reinforcement Learning in Multi-Dimensional State-Action Space Using Random Rectangular Coarse Coding and Gibbs Sampling, [URL].
Membership in Academic Society
  • Japan Society of Naval Architects and Ocean Engineers
  • The Society of Instrument and Control Enginerrs
  • Robotics Society of Japan