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Congress on Evolutionary Computation

作者： Wenjing Hong Cheng Chen Zexuan Zhu Ke Tang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design.

关键词： Measurement Design methodology Evolutionary computation Search problems Linear programming Stability analysis Vaccines

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A unified analytical method to quantify three types of fast frequency response from inverter-based resources

A unified analytical method to quantify three types of fast ...

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22nd Wind and Solar Integration Workshop (WIW 2023)

作者： S. Dong X. Fang J. Tan N. Gao X. Cui A. Hoke Power Systems Energy Center National Renewable Energy Laboratory Golden USA Department of Electrical and Computer Engineering Mississippi State University Starkville USA Department of Energy Science & Engineering Stanford University Stanford USA

With more inverter-based resources (IBRs), our power systems have lower frequency nadirs following N-1 contingencies, and undesired under-frequency load shedding (UFLS) can occur. To address this challenge, IBRs can be programmed to provide at least three types of fast frequency response (FFR), e.g., step response, proportional response (P/f droop response), and derivative response (synthetic inertia). However, these heterogeneous FFR challenge the study of power system frequency dynamics. Thus, this paper develops an analytical frequency nadir prediction method that allows for the consideration of all three potential forms of FFR provided by IBRs. The proposed method provides fast and accurate frequency nadir estimation after N-1 generation tripping contingencies. Our method is grounded on the closed-form solution for the frequency nadir, which is solved from the second-order system frequency response model considering the governor dynamics and three types of FFR. The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides an accurate and fast prediction of the frequency nadir under various disturbances.

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computer Vision-Control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

arXiv

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arXiv 2023年

作者： Guo, Yangyang Wang, Hao He, Lei Pedrycz, Witold Suganthan, P.N. Song, Yanjie College of Systems Engineering National University of Defense Technology China School of Transportation Engineering Dalian Maritime University China Department of Electrical and Computer Engineering University of Alberta Canada KINDI Center for Computing Research College of Engineering Qatar University Qatar School of Electrical and Electronic Engineering Nanyang Technological University Singapore

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members’ willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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Distributed robust fuzzy control for mixed-order multi-agent systems ensuring global consensus

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Science China Technological Sciences 2025年第7期68卷 1-11页

作者： Yang, Xiaoli Chen, Jiaxi Gao, Xinbo Ge, Shuzhi Sam Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of Mathematics and Statistics Chongqing University of Posts and Telecommunications Chongqing China School of Mathematics and Statistics Xidian University Xi’an China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

This study investigates the global consensus problem of mixed-order multi-agent systems. Firstly, a control strategy based on switching functions is proposed to ensure the effective operation of fuzzy control in the fuzzy region. Secondly, the differential equation model satisfying the global Lipschitz condition is extended, and a more universal model framework is constructed. Furthermore, online learning laws with time-varying σ-modifications are introduced to effectively guarantee the global asymptotic convergence of the closed-loop multi-agent systems. Finally, the effectiveness of the proposed algorithm is validated through designed simulation experiments.

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GUEST EDITORIAL

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China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided systems

arXiv

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arXiv 2024年

作者： Li, Yunfei Luo, Yiting Wu, Xianda Shi, Zheng Ma, Shaodan Yang, Guanghua The Department of Electrical Engineering Anhui Polytechnic University Wuhu City China The School of Electronics and Information Engineering South China Normal University Foshan528000 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau Taipa China

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparser angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Jieming Zhou Tong Zhang Pengfei Fang Lars Petersson Mehrtash Harandi College of Engineering and Computer Science Australian National University Canberra Australia Image and Visual Representation Laboratory EPFL Lausanne Switzerland Imaging and Computer Vision Group CSIRO Canberra Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles’ heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs’ designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model’s performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks.

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Energy-Efficient Over-the-Air Computation for Federated Generative Model Fine-Tuning in Unmanned Vehicle-Assisted Disaster Relief

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhao, Yikun Feng, Lei Zhou, Fanqin Li, Wenjing Xiong, Zehui Du, Hongyang Wu, Celimuge Guo, Song Quek, Tony Q.S Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong University of Electro-Communications Meta-Networking Research Center Tokyo Japan Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The utilization of emergent generative artificial intelligence (GAI) within the realm of unmanned vehicles (UVs) can boost edge intelligence and potentially enhance the efficiency and effectiveness of rescue operations. However, to facilitate specialized generative edge intelligence in dynamic UV networks, GAI models need to tap into local data and conduct online fine-tuning, and the dispersed distribution of UVs makes distributed fine-tuning paradigms crucial. Although the federated generative model fine-tuning brings a potential solution, the large number of parameter transmissions involved in its fine-tuning process are often constrained by communication bottlenecks, which are more pronounced for resource-constrained UV networks. To cope with these challenges, we introduce an energy-efficient GAI model fine-tuning framework for the hierarchical UV networks, which employs over-the-air technology to save computational resource costs for unmanned aerial vehicle (UAV) during federated aggregation within a federated learning paradigm. Thereafter, we study the resource allocation problem in the fine-tuning process of the generative model and formulate a joint optimization problem for the bandwidth allocation, power control, computation resource allocation, and denoising factor control, aiming to minimize the system energy consumption. Then, we decouple the original problem into four tractable subproblems, and propose a block coordinate descent algorithm to solve them iteratively. Particularly, we derive a closed-form solution for the denoising factor to minimize the local model uploading transmission energy consumption under a specific AirComp communication error threshold. Simulation based on the state-of-the-art generative models shows that our AirComp-assisted federated generative model fine-tuning scheme can achieve satisfactory customized field-of-view image generation capability compared with the traditional fine-tuning scheme in an energy-efficient way. © 2015 I

关键词： Energy efficiency

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