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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

IEEE Internet of Things Journal 2025年

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

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Theoretical Characterization of the Electro-optical Properties of New Molecules for OLED Devices

Theoretical Characterization of the Electro-optical Properti...

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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Imane El Mhamedi Zakariae El Madani Abdelhafid Yahya Zakaria El Malki Materials and Systems Control (MMSC) Computer Engineering and Intelligent Electrical Systems (2ISEI) Laboratory High School of Technology (ESTM) University Moulay Ismail Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This study focuses on the exploration of five novel π-conjugated organic compounds for potential incorporation into organic light-emitting diodes (OLEDs). The molecular structure of these compounds follows a D-Di-D setup, with a carbazole acting as the D component, and the specific Di component varying based on the donor utilized. Through analysis at the DFT/B3LYP/6-31G (d, p) level, including the determination of HOMO–LUMO energy values, after optimizing the molecules' ground state, the frontier molecular orbital (FMO) was studied using Egap band gap. Utilizing the TD-DFT and ZINDO method the optical properties like absorption (λmax) and emission (λem) were explored. Based on SILVACO-TCAD software, the estimated current-voltage (I-V) characteristics of the two most efficient molecules were obtained. The indices of the studied molecules calculated from these interactions indicates that the molecules have sufficient properties which allow these molecules to be considered promising candidates for use in OLED devices as high-performing active materials.

关键词： Photonic band gap Absorption Stimulated emission Stationary state Organic light emitting diodes Market research Software Orbits Optical devices Organic compounds

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Robust Jumping of A Bipedal Wheel Legged Robot in Gusty Weather 24

Robust Jumping of A Bipedal Wheel Legged Robot in Gusty Weat...

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Proceedings of the 2024 6th International Conference on Mechatronics Systems and control engineering

作者： Yiyu Guo Minghui Zhang Qihong Guo Jingyan Sun School of Mechanical Engineering Southwest Jiaotong University Chengdu Sichuan China Institute of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing Beijing China School of Mechanical Engineering Fuzhou University Fuzhou Fujian China School of Control and Computer Engineering North China Electric Power University Beijing Beijing China

ISBN: (纸本)9798400710346

Currently, bipedal wheeled-leg robots are considered to have significant potential for various applications. Current research lacks an in-depth study of the performance of bipedal wheeled-leg robots in high-wind conditions. However, researching robots in such conditions is crucial for their application in outdoor environments, such as disaster rescue. Therefore, enhancing the balance performance of these robots to improve their mobility in complex environments is crucial. This paper explores the jumping performance of bipedal wheeled-leg robots in strong wind conditions and proposes a method for optimizing the robot's jumping trajectory using a genetic algorithm. This approach enables the robot to successfully perform jumps and maximize the jump distance even in windy environments. To make the simulation environment more realistic, a detailed simulation of strong wind conditions was conducted, where a function was established to model the variation in wind force on the robot's surface under different tilt angles during a jump. Additionally, the maximum torque limitations of the motors and the weight of the electromechanical components were taken into account. Ultimately, successful jumps were achieved in windy conditions, with a great improvement in jump distance compared to before optimization.

关键词： Bipedal wheeled-leg robot

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Multi-Agent Distributed DQN and Transfer Learning for Energy-Efficient Power Management in Solar Energy-Harvested Small-Cell Networks

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IEEE Internet of Things Journal 2025年

作者： Cho, Hyebin Kim, Hyungsub Na, JeeHyeon Lim, Seung-Chan Lee, Howon Hankyong National University School of Electrical Electronic and Control Engineering Anseong17579 Korea Republic of Electronics and Telecommunications Research Institute Daejeon34141 Korea Republic of Hankyong National University School of ICT Robotics and Mechanical Engineering and IITC Anseong17579 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

The integration of solar energy harvesting into small-cell networks is a promising solution for achieving energy-efficient and sustainable wireless communications. However, the inherent variability and intermittency of solar energy, coupled with precise inter-cell interference management, significantly hinder efficient network operation. To resolve these challenges, we propose a multi-agent distributed deep Q-network (MA-DDQN) framework, where the distributed base stations learn the optimal transmit power control policies. To further enhance adaptability under varying solar conditions, we present a daily model transfer with a fine-tuning approach, enabling efficient deployment without extensive training overhead. Simulation results demonstrate that the proposed methods remarkably improve energy efficiency while maintaining robust adaptability under dynamic solar conditions, revealing their potential for sustainable small-cell network deployments. © 2014 IEEE.

关键词： Solar cell efficiency

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Knowledge-driven inverse diffusion prediction algorithm for flexible job shop scheduling problem considering transportation resources and multiple breakdowns

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Swarm and Evolutionary Computation 2025年 96卷

作者： Wang, Cong Wei, Lixin Sun, Hao Hu, Ziyu Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei 066004 China School of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

The complex coupling between transportation and production poses significant challenges for scheduling algorithms, particularly in dynamic environments where contingencies are frequent. The traditional Flexible Job Shop Scheduling Problem (FJSP) does not fully account for these factors. Therefore, a two-stage knowledge-driven inverse diffusion prediction algorithm (TKIDP) is proposed for FJSP with transportation resources and multiple breakdowns (MBFJSPT). In the first phase, TKIDP operates in a static environment and aims to minimize the makespan and load imbalance, thereby reducing the risk of machine breakdowns. When a machine breakdown occurs, TKIDP enters a rescheduling phase and minimizes total energy consumption as an additional objective. An inverse diffusion prediction strategy is proposed to address the occurrence of multiple machine breakdowns. By learning from optimization processes of historical breakdowns, the proposed strategy identifies potential evolutionary patterns and predicts the evolutionary trends of future breakdowns. A similarity measure based on mutual entropy is introduced to select the most relevant historical breakdowns for learning. This method enhances prediction accuracy. To further improve exploration and exploitation capabilities, an adaptive competitive reconfiguration strategy is proposed. This strategy incorporates multiple deletion and reconstruction operators to guide the algorithm toward the optimal solution. To verify the effectiveness of MBFJSPT, a dedicated MB test set is constructed. On the MB1-18 test, TKIDP is compared with five other algorithms. Based on the Inverted generational distance (IGD), hypervolume (HV), and relative deviation (RD) metrics, TKIDP achieves the best results on 15, 18, and 18 out of the 18 test instances, respectively. © 2025 Elsevier B.V.

关键词： Inverse diffusion prediction Knowledge-driven Machine breakdown Multi-objective optimization Transportation resources

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Business Process Representation Based on Graph Convolutional Network 1st

Business Process Representation Based on Graph Convolutiona...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Zeng, Qingtian Zhang, Lin Cao, Rui Guo, Wenyan Li, Chao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao Shandong China School of Information and Control Engineering Qingdao University of Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789819794119

The effective representation of business processes is a key problem of predictive monitoring based on deep learning networks. Most of the existing business process representation methods are unable to accurately reflect the characteristics and node information aggregation of events in the graph structure. Therefore, a business process representation method based on graph convolutional network is proposed. Firstly, the diagram structure is constructed based on the same prefix window event of the business process. Secondly, on the basis of the constructed graph structure data, the node vectorization representation based on graph convolutional network model is given, and the information is aggregated through the graph convolutional network. Then, the depth representation of nodes is applied to the downstream prediction task of the business process. Finally, five real datasets are used for experimental evaluation. The experimental results show that the proposed event log graph construction strategy and process representation method are effective in business process prediction. The source code of this work is available at https://***/LinZhang0/GCNp. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Data-Driven Nonconvex Reachability Analysis using Exact Multiplication

arXiv

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arXiv 2025年

作者： Zhang, Zhen Niazi, M. Umar B Chong, Michelle S. Johansson, Karl H. Alanwar, Amr School of Computation Information and Technology Technical University of Munich Germany Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper addresses a fundamental challenge in data-driven reachability analysis: accurately representing and propagating non-convex reachable sets. We propose a novel approach using constrained polynomial zonotopes to describe reachable sets for unknown LTI systems. Unlike constrained zonotopes commonly used in existing literature, constrained polynomial zonotopes are closed under multiplication with constrained matrix zonotopes. We leverage this property to develop an exact multiplication method that preserves the non-convex geometry of reachable sets without resorting to approximations. We demonstrate that our approach provides tighter over-approximations of reachable sets for LTI systems compared to conventional methods. © 2025, CC BY.

关键词： Geometry

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A novel internal crack detection method for photovoltaic (PV) panels based on the air-coupled ultrasonic

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Sensors and Actuators A: Physical 2025年 392卷

作者： Sun, Tianyi Bai, Tianyang Cao, Shengxian Fan, Siyuan Zhang, Yanhui School of Automation Engineering Northeast Electric Power University Jilin132012 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Accurately assessing the potential risk of cracks in photovoltaic (PV) panels is crucial for improving the system's energy conversion efficiency and safety. This paper develops a novel internal crack detection device for PV panels based on air-coupled ultrasonics and establishes a dedicated model for PV panel crack detection. Considering the anisotropy of silicon, the Rayleigh–Lamb equation is first used to obtain the dispersion curve of Lamb waves. The continuous wavelet transform (CWT) is then applied to analyze the A0 mode Lamb wave echo signals within a single cycle to distinguish between cracked and non-cracked regions. Further, the original signal is enhanced using singular value decomposition (SVD) to strengthen the signal features, and a direct relationship model between the signal echo and the crack length is proposed. Finally, the model's validity is verified by measuring actual crack data from PV panels. The results show that the optimal frequency of Lamb waves for crack detection in PV panels is 200 kHz, with an attenuation coefficient of 6.4 dB/mm and incidence and exit angles of 11°. Compared to electroluminescence (EL) and inductive thermal imaging methods, the proposed approach achieves higher accuracy, reaching 84.26%. © 2025

关键词： Image enhancement

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Two-Stage Incentive Mechanism Based on a Cooperative Mode: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Long Wang, Yingjie Teng, Haojun Jiao, Xiuzhen Sun, Meimei Yang, Jishen Cai, Zhipeng Yantai University School of Computer and Control Engineering Yantai264005 China China Judicial Big Data Research Institute Co. Ltd China Naval Aviation University China Georgia State University Department of Computer Science AtlantaGA30303 United States

With the explosive growth of mobile data, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection. The difficulty of data collection and the gaps in workers’ sensing capabilities are key factors to consider in worker recruitment and task assignment. To address these issues, we designed a two-stage cooperative incentive mechanism. In the first stage, a shelving level is introduced to assess task difficulty. Tasks are divided into high-quality and low-quality groups based on quality scores, while workers are categorized into high-ability and low-ability groups based on their historical performance. The Improved Chaotic Particle Swarm Optimization (ICPSO) algorithm is then applied to generate optimal task combinations. In the second stage, we address benefit distribution among cooperating workers by introducing time-dependent rewards and employing Stackelberg game theory to analyze the optimal completion time for both high-ability and low-ability workers. This analysis determines the optimal reward distribution and ability value allocation, with proof of the existence and uniqueness of a Nash equilibrium. Comparative experiments conducted on real-world datasets demonstrate that our cooperative task mechanism outperforms existing task bundling methods, validating its rationality and effectiveness. © 2014 IEEE.

关键词： Nash equilibrium

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Acoustic feature processing strategy for leak degree identification in non-metallic pipelines

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Applied Acoustics 2025年 238卷

作者： Yu, Yuebo Cui, Xiwang Gao, Yan Han, Xiaojuan Song, Lijiang Lu, Fangcheng School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

In response to the challenge of accurately identifying the degree of leaks in non-metallic pipelines, this study proposes acoustic feature processing strategy based on the Principal Component Analysis (PCA) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM). The hydrophone sensors are deployed to collect acoustic pressure signals generated by different degrees of pipeline leak. Acoustic signal features of varying leak degrees are extracted from the perspectives of the time domain, the spectral domain, and waveform shape. Subsequently, PCA is employed to streamline and reduce the dimensionality of the extracted features. The optimal number of principal components is ascertained based on the Cumulative Variance Contribution Rate (CVCR), which eliminates redundant features and preserves principal components. Furthermore, the PCA-Bi-LSTM model is designed to identify the degree of pipeline leak. The principal components are organized chronologically into cells for input into the Bi-LSTM. Comparative analysis is performed using different statistical features and neural network architectures, with additional experiments conducted under varying pressures and leak locations to validate the generalizability of the proposed method. The results indicate that the PCA-Bi-LSTM approach achieves a leak identification accuracy of 98.6 %, which is significantly higher than the 91. 5 % accuracy of the unidirectional LSTM network and the 93.2 % accuracy of the traditional Bi-LSTM network. The results demonstrate that the designed strategy effectively improves the accuracy of identifying the degree of non-metallic pipeline leaks and demonstrates a certain level of generalizability. © 2025 Elsevier Ltd

关键词： Leak detection

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