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システム/制御/情報 Vol. 37 (2024), No. 8

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  1. Vol. 37 (2024)

    1. No.8
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    3. No.6
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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  9. Vol. 29 (2016)

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  19. Vol. 19 (2006)

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  20. Vol. 18 (2005)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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キーワードランキング

21 Nov. (Last 30 Days)

システム/制御/情報 Vol. 37 (2024), No. 8

Deep Q-Networkとグラフ探索を組み合わせた複数台移動ロボットの経路計画法

福島 昂之介, 西 竜志, 劉 子昂, 藤原 始史

pp. 207-215

DOI:

10.5687/iscie.37.207

抄録

In recent years, automated multiple mobile robots are introduced for transporting luggage and inspecting final products in factories to reduce the burden of human labor shortage. There is a need for mobile robots to develop autonomous systems that make flexible decisions like human operators. We propose a route planning method that combines deep reinforcement learning and graph search methods. In the proposed method, the routing is firstly determined by a graph search algorithm, and deep Q-network (DQN). A deep reinforcement learning method is used to avoid collisions. The proposed method is applied to the multiple drones route planning problem. As a result, a near-optimal routing is obtained that can reach to the destination while avoiding collisions between drones. These results suggest that the route planning problem in a three dimensional environment is successfully solved by using DQN that can process multidimensional states. We generate a learning model for collision avoidance using DQN for both whole observation and partial observation ranges to verify the usefulness of path planning with partial observations through computational experiments.

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Deep Q-Networkとグラフ探索を組み合わせた複数台移動ロボットの経路計画法

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Ris

Surrogate-Assisted Self-Adaptive MBEANNの 提案とその評価

小村 真央, 宮本 明治, 平賀 元彰, 森本 大智, 大倉 和博

pp. 216-224

DOI:

10.5687/iscie.37.216

抄録

This paper proposes an extension for Mutation-Based Evolving Artificial Neural Networks (MBEANN) algorithm. The proposed method consists of two parts: a surrogate model and a self-adaptive mutation. Firstly, the surrogate-assisted mechanism is introduced to MBEANN for reducing the cost of fitness evaluations. This mechanism employs approximated fitness values predicted by a surrogate model instead of true fitness functions. Secondly, the self-adaptive mutation is applied to MBEANN for adjusting the exploring area in parameter space. The performance of the proposed method is compared with the normal MBEANN and NEAT algorithms by using the three benchmarks of OpenAI Gym. The experimental results showed that the proposed method outperformed other algorithms in all benchmarks.

mendeley

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Surrogate-Assisted Self-Adaptive MBEANNの 提案とその評価

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ワクチン接種年齢を考慮した確率SVIRモデルの安定性解析

石川 昌明

pp. 225-233

DOI:

10.5687/iscie.37.225

抄録

The stochastic bifurcations of the infectious model with vaccination age are studied in this paper. It should be noted that biological parameters such as infectious and recovery rates randomly fluctuate by the random noise caused by environmental changes and individual differences. Moreover, since the effectiveness of vaccination varies greatly depending on vaccination age, we propose the stochastic infectious disease model with vaccination age. The prevalence of infectious diseases is closely related to the stability of the two types of the steady states, the disease-free state (DFS) and the endemic steady one (ES), of the infectious disease model. Hence, we study the stability of the DFS and the ES using the stochastic stability analysis and the bifurcation theory. By numerical simulations, we consider the influence of the random noise on the stability of the DFS and the ES.

mendeley

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ワクチン接種年齢を考慮した確率SVIRモデルの安定性解析

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21 Nov. (Last 30 Days)

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