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532 results on '"Cheong, Kang Hao"'

107. Two‐order cooperative optimization of swarm control based on reinforcement learning.

Author

Yu, Dengxiu, Qin, Zhenhao, Chen, Kang, Cheong, Kang Hao, and Chen, C. L. Philip

Subjects
REINFORCEMENT learning, SLIDING mode control, MULTIAGENT systems, PROPORTIONAL navigation, NONLINEAR functions, DRONE aircraft, DYNAMIC positioning systems
Abstract

This paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control. Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization. [ABSTRACT FROM AUTHOR]

Published
2024
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110. Expert System-Based Multiagent Deep Deterministic Policy Gradient for Swarm Robot Decision Making

Author

Wang, Zhen, Jin, Xiaoyue, Zhang, Tao, Li, Jiahao, Yu, Dengxiu, Cheong, Kang Hao, and Chen, C. L. Philip

Abstract

In this article, an expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) is proposed to realize the decision making for swarm robots. Multiagent deep deterministic policy gradient (MADDPG) is a multiagent reinforcement learning algorithm proposed to utilize a centralized critic within the actor–critic learning framework, which can reduce policy gradient variance. However, it is difficult to apply traditional MADDPG to swarm robots directly as it is time consuming during the path planning, rendering it necessary to propose a faster method to gather the trajectories. Besides, the trajectories obtained by the MADDPG are continuous by straight lines, which is not smooth and will be difficult for the swarm robots to track. This article aims to solve these problems by closing the above gaps. First, the ESB-MADDPG method is proposed to improve the training speed. The smooth processing of the trajectory is designed in the ESB-MADDPG. Furthermore, the expert system also provides us with many trained offline trajectories, which avoid the retraining each time we use the swarm robots. Considering the gathered trajectories, the model predictive control (MPC) algorithm is introduced to realize the optimal tracking of the offline trajectories. Simulation results show that combining ESB-MADDPG and MPC can realize swarm robot decision making efficiently.

Published
2024
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111. Complex Continuous Action Iterated Dilemma With Incremental Dynamic Model

Author

Wang, Zhen, Li, Haojing, Jin, Xiaoyue, Yu, Dengxiu, Cheong, Kang Hao, and Li, Xuelong

Abstract

In this article, we propose a complex continuous action iterated dilemma (CAID) with an incremental dynamic approach to overcome the limitations of traditional methods. In traditional CAID, the number of players was fixed. Consequently, when new players joined, computing resources were wasted due to repeated refreshes during the dynamic update process. To address these issues, we first propose a CAID model with an incremental dynamic approach. This model reflects the dynamic changes in the number of players, aligning more closely with real-world scenarios. Second, we propose an incremental updating method to prevent unnecessary refreshes of the original players’ states. When the number of players, denoted as $N$ , increases, we update and expand the evolutionary dynamic model using incremental information. This allows for an incremental connection between the original and new players. We use a weighted adjacency matrix to represent the relationships among players. The incremental updating method then updates the state matrix and the adjacency matrix. Furthermore, an analysis based on the designed Lyapunov function is proposed to prove the convergence of the CAID with incremental dynamic. The simulation results reveal the effectiveness of our proposed method.

Published
2024
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112. Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy

Author

Zhao, Jie and Cheong, Kang Hao

Abstract

As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections imperceptibly so as to protect the privacy of users. The existing evolutionary models have some successes in this type of NP-hard problem but can only be applied to small-scale datasets, rendering them inadequate for real-world applications. To alleviate this problem, we propose an original and novel CoeCo, a cooperative evolutionary community obfuscation model. In CoeCo, we leverage the divide-and-conquer strategy and put forward a co-evolutionary optimization algorithm suitable for community structure, in which two different fitness functions promote each other to find the optimal edge set. In addition, the motif hypergraph and permanence are used to improve population initialization. The experimental results indicate that our proposed method can achieve excellent efficacy in obfuscating community structure and also greatly reduces running time.

Published
2023
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113. SWITCHING BETWEEN TWO LOSING STOCKS MAY ENABLE PARADOXICAL WIN: AN EMPIRICAL ANALYSIS.

Author

WEN, TAO, LAI, JOEL WEIJIA, and CHEONG, KANG HAO

Subjects
STOCKS (Finance), INDIVIDUAL investors, PARADOX, GAME theory, TIME series analysis, MACHINE learning
Abstract

Finding strategies to profit from the stock markets is a topic of immense interest to both institutional and retail investors. Possible methods range from machine learning, time series prediction, and network science to game theory. Game theory is a useful mathematical tool to model the relationship between agents and the stocks. In this paper, we ask an important question: Why do some (non-optimal) traders end up winning despite alternating between losing stocks? Our empirical observations reveal that by using a switching strategy abstractly derived from the switching in Parrondo's paradox, we can demonstrate that losing stocks can be combined in a time-based switching scheme to achieve a winning outcome. This is akin to an automatic investment plan. Several stocks within a given time period are being analyzed. Our analysis may provide a descriptive explanation for why some traders end up winning despite switching between losing stocks. However, it is not the focus of this paper to provide any normative advice or support for switching between stocks. [ABSTRACT FROM AUTHOR]

Published
2023
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140. Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals

Author

WeiKoh, Joel En, primary, Rajinikanth, Venkatesan, additional, Vicnesh, Jahmunah, additional, Pham, The‐Hanh, additional, Oh, Shu Lih, additional, Yeong, Chai Hong, additional, Sankaranarayanan, Meena, additional, Kamath, Aditya, additional, Bairy, Giliyar Muralidhar, additional, Barua, Prabal Datta, additional, and Cheong, Kang Hao, additional

Published
2022
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141. A Gradient Model for the Spatial Patterns of Cities

Author

Chang, Jie, primary, Yang, Guofu, additional, Liu, Shun, additional, Jin, Hanhui, additional, Wu, Zhaoping, additional, Xu, Ronghua, additional, Min, Yong, additional, Zheng, Kaiwen, additional, Xu, Bin, additional, Luo, Weidong, additional, Mao, Feng, additional, Ge, Ying, additional, and Cheong, Kang Hao, additional

Published
2022
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147. A Review on Computer Aided Diagnosis of Acute Brain Stroke

Author

Inamdar, Mahesh Anil, primary, Raghavendra, Udupi, additional, Gudigar, Anjan, additional, Chakole, Yashas, additional, Hegde, Ajay, additional, Menon, Girish R., additional, Barua, Prabal, additional, Palmer, Elizabeth Emma, additional, Cheong, Kang Hao, additional, Chan, Wai Yee, additional, Ciaccio, Edward J., additional, and Acharya, U. Rajendra, additional

Published
2021
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149. Antiangiogenic Nanomicelles for the Topical Delivery of Aflibercept to Treat Retinal Neovascular Disease

Author

Zhao, Xinxin, primary, Seah, Ivan, additional, Xue, Kun, additional, Wong, Wendy, additional, Tan, Queenie Shu Woon, additional, Ma, Xiaoxiao, additional, Lin, Qianyu, additional, Lim, Jason Y. C., additional, Liu, Zengping, additional, Parikh, Bhav Harshad, additional, Mehta, Karishma N., additional, Lai, Joel Weijia, additional, Yang, Binxia, additional, Tran, Kim Chi, additional, Barathi, Veluchamy Amutha, additional, Cheong, Kang Hao, additional, Hunziker, Walter, additional, Su, Xinyi, additional, and Loh, Xian Jun, additional

Published
2021
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