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DRLO: Optimizing edge server placement in dynamic MEC scenarios using deep reinforcement learning

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Computer Networks 2025年 268卷

作者： Guo, Yingya Chen, Cen College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian China

As an emerging computing paradigm, Mobile Edge computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios. © 2025 Elsevier B.V.

关键词： Reinforcement learning

Correcting the Bias: Mitigating Multimodal Inconsistency Contrastive Learning for Multimodal Fake News Detection

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Correcting the Bias: Mitigating Multimodal Inconsistency Con...

IEEE International Conference on Multimedia and Expo (ICME)

Multimodal fake news detection has become a topical research of fake news detection. Existing models have made great efforts in capturing and fusing multimodal semantics of news for classification. However, they overlooked mitigating inconsistency between different modalities, which may result in learning biased statistical information. Therefore, we propose a mitigating multimodal inconsistency contrastive learning framework (MMICF), which mitigates inconsistency in multi-modal relations for fake news detection. Inspired by various forms of artificial fake news, we summarize two patterns of multimodal inconsistency: local and global inconsistency. To mitigate local inconsistency in multimodal relations, we use a causal-relation reasoning module by causally removing the direct effects of the textual and visual entities. Considering the influence of global inconsistency in multimodal semantics, our contrastive learning framework mitigates the semantic deviation of contrastive text-image objectives, which are constrained into a unified semantic space by a modal unified module. Thus, our MMICF can jointly mitigate local and global inconsistency for further maximally exploiting multimodal consistent semantics for fake news detection. The extensive experimental results show that the MMICF can improve the performance of multimodal fake news detection and provide a novel paradigm for mitigating multimodal inconsistency contrastive learning.

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MINDFLOW: A NETWORK TRAFFIC ANOMALY DETECTION MODEL BASED ON MINDSPORE

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arXiv 2025年

作者： Xiang, Qiuyan Wu, Shuang Wu, Dongze Liu, Yuxin Qin, Zhenkai School of Information Technology Guangxi Police College 530028 China College of Computer Science and AI Southwest Minzu University 610213 China Institute of Software Chinese Academy of Sciences 100190 China School of Computing and Information China Network Security Research Center China Big Data and Policing Technology Laboratory Guangxi Police College Guangxi Nanning China

With the wide application of IoT and industrial IoT technologies, the network structure is becoming more and more complex, and the traffic scale is growing rapidly, which makes the traditional security protection mechanism face serious challenges in dealing with high-frequency, diversified, and stealthy cyber-attacks. To address this problem, this study proposes MindFlow, a multi-dimensional dynamic traffic prediction and anomaly detection system combining convolutional neural network (CNN) and bi-directional long and short-term memory network (BiLSTM) architectures based on the MindSpore framework, and conducts systematic experiments on the NF-BoT-IoT dataset. The experimental results show that the proposed model achieves 99% in key metrics such as accuracy, precision, recall and F1 score, effectively verifying its accuracy and robustness in network intrusion detection. © 2025, CC0.

关键词： Convolutional neural networks

A prediction-correction based iterative convolution-thresholding method for topology optimization of heat transfer problems

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arXiv 2024年

作者： Chen, Huangxin Dong, Piaopiao Wang, Dong Wang, Xiao-Ping School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

Bridging VLM and KMP: Enabling Fine-grained robotic manipulation via Semantic Keypoints Representation

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arXiv 2025年

作者： Zhu, Junjie Liu, Huayu Wang, Jin Wen, Bangrong Huang, Kaixiang Li, Xiaofei Zhang, Haiyun Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Research Center of Yuyao City Ningbo315400 China

From early Movement Primitive (MP) techniques to modern Vision-Language Models (VLMs), autonomous manipulation has remained a pivotal topic in robotics. As two extremes, VLM-based methods emphasize zero-shot and adaptive manipulation but struggle with fine-grained planning. In contrast, MP-based approaches excel in precise trajectory generalization but lack decision-making ability. To leverage the strengths of the two frameworks, we propose VL-MP, which integrates VLM with Kernelized Movement Primitives (KMP) via a low-distortion decision information transfer bridge, enabling fine-grained robotic manipulation under ambiguous situations. One key of VL-MP is the accurate representation of task decision parameters through semantic keypoints constraints, leading to more precise task parameter generation. Additionally, we introduce a local trajectory feature-enhanced KMP to support VL-MP, thereby achieving shape preservation for complex trajectories. Extensive experiments conducted in complex real-world environments validate the effectiveness of VL-MP for adaptive and fine-grained manipulation. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

Programming Variational Quantum Circuits with Quantum-Train Agent

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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

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SSRN

SSRN 2024年

关键词： Convolution

Adaptive Control for Multi-Agent Systems Based on Dynamic Event-triggered Mechanism: In Different Topologies

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Adaptive Control for Multi-Agent Systems Based on Dynamic Ev...

International Symposium on Autonomous Systems (ISAS)

作者： Yu Huang Huiyan Zhang Zixian Chen Jiangyan Zou School of Mechanical Engineering Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. Chongqing China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Under different communication topologies, this paper investigates the consensus problem for multi-agent systems with general linear dynamics under leader following. A distributed dynamic event-triggered control strategy with output feedback adjustment is proposed by using adaptive control technology of auxiliary variables, and it is applied to different follower communication topologies. The simulation results show that the leader-following systems under different communication modes of the followers can solve the completely distributed consensus problem. Moreover, the Zeno behavior of each multi-agent can be excluded. Finally, the effectiveness of the proposed control strategy is verified by taking multi-vehicle cooperative tracking control as an example.

关键词： Trajectory tracking Simulation Consensus control Control systems Topology Vehicle dynamics Adaptive control

An improved LED junction temperature test method based on FCM 5

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An improved LED junction temperature test method based on FC...

2022 5th International Conference on Power Electronics and Control Engineering, ICPECE 2022

作者： Zhang, Jie Zhang, Junchao Xu, Bingshe Fu, Qiang Wang, Chen Zhao, Haodi College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials. Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

High junction temperature (Tj) of LED will affect the performance and life of LED, so this paper proposes a high-power LED junction temperature test method. Mainly aiming at the problem that the accuracy of the forward current method (FCM) will be affected by the calibration procedure, equipment aging, and technical differences when batch testing LED Tj, this paper uses the relationship between relative current and Tj to calibrate the procedure based on this method, to limit the negative affection of sample heterogeneity. Ten LED samples were tested in the experiment, and the Tj errors under two test methods were obtained. The results showed that the improved method was more accurate than the traditional method. Then, the Tj error of the improved method was compared with that of the forward voltage method, and the error of the new method was smaller. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes