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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Wang, Zhengbing Ruan, Shanhui Feng, Xugang Zhang, Keqi Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan243032 China School of Electrical and Information Engineering Anhui University of Technology Anhui Maanshan243032 China Anhui Province Engineering Laboratory of Intelligent Demolition Equipment Anhui University of Technology Anhui Maanshan243032 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The matching and fusion of visible and infrared images of power equipment are essential for real-time monitoring. Nevertheless, the computation of an accurate transform model between images for alignment remains a tough challenge due to notable changes in spectrum, resolution, and intensity between visible and infrared images. To address this issue, this paper proposes a hierarchical matching algorithm for visible and infrared images of power equipment based on multi-scale local normalized filtering. First, the input image is preprocessed by multi-scale local normalized filtering to enhance the image structures. Then, the contours of the filtered image are extracted by the Canny operator. A k-cosine curvature-based multi-scale corner detection algorithm is used to detect the feature points on the contours and the scale-invariant PIIFD is constructed for each feature point. Finally, a hierarchical matching strategy is proposed for feature matching, and the consistency check algorithm is applied to eliminate the false matches. The experiment is carried out on four groups of visible and infrared images of power equipment. The results show that our method can effectively achieve a large number of correct matches despite of scale and viewpoint changes. © 2025 SPIE.

关键词： Infrared imaging

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SOKD: A Soft Optimization Knowledge Distillation Scheme for Surface Defects Identification of Hot-Rolled Strip

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ISIJ International 2025年第1期65卷 104-110页

作者： Wang, Wenyan Ren, Zheng Wang, Cheng Lu, Kun Tao, Tao Pan, Xuejuan Wang, Bing School of Electrical and Information Engineering Anhui University of Technology Ma’anshan243032 China Wuhu Technology Innovation Research Institute Anhui University of Technology Anhui Wuhu241000 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China School of Electronics and Information Engineering Anhui Jianzhu University Anhui Hefei230601 China School of Chemistry and Chemical Engineering Anhui University of Technology Anhui Ma’anshan243032 China

The surface defect of hot-rolled strip is a significant factor that impacts the performance of strip products. In recent years, convolutional neural networks (CNNs) have been extensively used in strip surface defect recognition to ensure product quality. However, the existing CNNs-based methods confront the challenges of high complexity, difficult deployment and slow inference speed. Accordingly, this work proposes a soft optimization knowledge distillation (SOKD) scheme to distill the ResNet-152 large model and extract a compact strip surface recognition model. The SOKD scheme utilizes Kullback-Leibler (KL) divergence to minimize the error between the soft probability distributions of the student network and the teacher network, and gradually reduces the weight of "Hard loss" during the training process. The operation significantly reduces the learning constraints that the prior knowledge of the teacher network on the student network in the original KD, which improves the recognition performance of the model. Additionally, SOKD is applicable to most CNNs for identify surface defect of hot-rolled strip. The experimental results on NEU-CLS dataset show that the SOKD outperforms state-of-the-art methods. © 2025 The Iron and Steel Institute of Japan.

关键词： Convolutional neural networks

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2025年第4期9卷 1-10页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui University Hefei 230000 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Ma'anshan 243032 China School of Computer science School of Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing 210000 China

In the realm of the internet of things (IoT), radio frequency identification (RFID) technology is essential for linking physical objects to digital data. The introduction of harmonic technology has expanded RFID's applications by improving sensitivity and enabling communication with wireless fidelity (Wi-Fi) networks. However, this trend faces challenges, notably interference from strong Wi-Fi signals, which impacts RFID-based sensing systems. This paper proposes the WiFID (Wi-Fi and RFID) algorithm, which enables Wi-Fi devices to sense weak RFID harmonic signals. Adaptive subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic sensing. Experimental validation on the universal software radio peripheral (USRP) platform demonstrates that WiFID successfully detects 90% of RFID harmonics at −30 dBm, with only a minimal 4% decrease in Wi-Fi throughput.

关键词： Radiofrequency identification Harmonic analysis Wireless fidelity Power harmonic filters Sensors Interference Throughput Filtering Correlation Vibration measurement

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Solving partial differential equations based on preconditioning-pretraining physics-informed neural network

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Computer Physics Communications 2025年 312卷

作者： Wang, Zihan Hu, Ziyue Yang, Mingwei Dong, Yalin Huang, Wenlong Ren, Haijun School of Computer Science and Technology Anhui Engineering Laboratory for Industrial Internet Intelligent Applications and Security Anhui University of Technology Anhui Ma'anshan243002 China Department of Mechanical Engineering The University of Akron AkronOH44325 United States CAS Key Laboratory of Geospace Environment Department of Engineering and Applied Physics School of Physical Sciences University of Science and Technology of China Anhui Hefei230026 China

Physics-Informed Neural Network (PINN) is a deep learning framework that has been widely employed to solve spatial-temporal partial differential equations (PDEs) across various fields. However, recent numerical experiments indicate that the vanilla-PINN often struggles with PDEs featuring high-frequency solutions or strong nonlinearity. To enhance PINN's performance, we propose a novel strategy called the Preconditioning-Pretraining Physics-Informed Neural Network (PP-PINN). This approach involves transforming the original task into a new system characterized by low frequency and weak nonlinearity over an extended time scale. The transformed PDEs are then solved using a pretraining approach. Additionally, we introduce a new constraint termed "fixed point", which is beneficial for scenarios with extremely high frequency or strong nonlinearity. To demonstrate the efficacy of our method, we apply the newly developed strategy to three different equations, achieving improved accuracy and reduced computational costs compared to previous approaches which incorporate the pretraining technique. The effectiveness and interpretability of our PP-PINN are also discussed, emphasizing its advantages in tackling high-frequency solutions and strong nonlinearity, thereby offering insights into its broader applicability in complex mathematical modeling. © 2025 Elsevier B.V.

关键词： High frequency solution Physics informed neural network Preconditioning-pretraining Strong nonlinearity

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pFedCal: Lightweight Personalized Federated Learning with Adaptive Calibration Strategy

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IEEE Transactions on Services Computing 2025年

作者： Deng, Dongshang Wu, Xuangou Zhang, Tao Xiang, Chaocan Zhao, Wei Xu, Minrui Kang, Jiawen Han, Zhu Niyato, Dusit Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243002 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Provincial Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Chongqing University College of Computer Science Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology School of Automation Guangzhou510062 China 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

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an aggregation gap caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose pFedCal, a novel personalized federated learning with lightweight adaptive calibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods. © 2008-2012 IEEE.

关键词： Federated learning

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Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

arXiv

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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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A Near-Optimal Category Information Sampling in RFID Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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RL-Based Load Balancing for Software-Defined WiFi Networks 12

RL-Based Load Balancing for Software-Defined WiFi Networks

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12th International Conference on Electronics, Communications and Networks, CECNet 2022

作者： Li, Jun School of Computer and Information Anhui Polytechnic University China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet China

ISBN: (纸本)9781643683683

Due to the popularity of mobile devices, more WIFI access points (APs) are being installed in public spaces, including train stations, airports, campuses, and shopping centers. Any station in WiFi networks will often connect to the AP that is closest to it based on the RSSI value that is the strongest. This user association approach will result in unfair bandwidth distribution and load imbalance when WiFi networks have many stations and an uneven distribution of those stations. By separating the control plane and data plane, software-defined networks (SDN) enable WiFi networks that are programmable, controllable, measurable, and manageable. This paper represents a novel load balancing mechanism based on reinforcement learning and SDN. In our proposed scheme, the center controller collects the number of associated stations with APs and traffic information on each AP's backhaul link. Then the controller decides the optimal station migration scheme to achieve load balancing among APs, based on collected information and reinforcement learning algorithm. The simulation results illustrate that our algorithm outperforms the RSSI method in terms of throughput of stations and average station number of each AP. © 2022 The authors and IOS Press.

关键词： Reinforcement learning

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Matched averaging federated learning gesture recognition with WiFi signals 7

Matched averaging federated learning gesture recognition wit...

引用

7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Wang, Zexin Zhang, Weidong Wu, Xuangou Wang, Xiujun Anhui University of Technology Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet School of Computer Science and Technology Maanshan China

ISBN: (纸本)9781665442527

WiFi signals based gesture recognition plays an important role in smart homes, such as human-computer interaction, identity authentication and so on. Most of existing approaches need a large number of channel state information dataset for model training to recognized human gestures. However, the robustness of the model to new users will be seriously affected, when the number of users participating in the training dataset is lacking. To address this problem, we propose a gesture recognition system based on matched averaging federated learning framework (WiMA). WiMA exploits parameter matching federated learning to training gesture prediction model, instead of traditional parameter aggregation. Experimental results show that the average accuracy of WiMA can be up to 90.4%, when 7 users participate in model training in 2 different rooms. © 2021 IEEE.

关键词： Channel state information

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An improved defect recognition framework for casting based on DETR algorithm

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Journal of Iron and Steel Research International 2023年第5期30卷 949-959页

作者： Long Zhang Sai-fei Yan Jun Hong Qian Xie Fei Zhou Song-lin Ran Anhui Province Key Laboratory of Metallurgical Engineering&Resources Recycling Anhui University of TechnologyMa’anshan 243002AnhuiChina School of Metallurgical Engineering Anhui University of TechnologyMa’anshan 243002AnhuiChina Technical Department Anhui Highly Precision Casting Co.Ltd.Ma’anshan 238100AnhuiChina Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032AnhuiChina

The current casting surface defect detection algorithms suffer from poor small target defect recognition and imbalance between detection performance and detection *** improved algorithmic framework for casting defect detection was proposed based on the DEtection TRansformer(DETR)*** algorithm takes ResNet with an efficient channel attention(ECA)-Net module as the backbone *** addition,based on the original algorithm architecture,dynamic anchor boxes,improved multi-scale deformable attention module,and SIoU loss function are introduced to improve the sensitivity of transformer structure to input location information and scale size,and the small target defect detection performance is effectively *** recognition performance of the algorithm in a self-built casting defect dataset was *** improved DETR algorithm has 97.561% accuracy in recognizing two defects,namely sandinclusion and notch,with the detection rate being improved by 65.854% and 17.073% compared with the original DETR and you only look once(Yolo)-V5,*** algorithm verifies the applicability of the transformer architecture target detection algorithm for casting defect detection tasks and provides new ideas for detecting other similar application scenarios.

关键词： Casting defect recognition DEtection TRansformer Small target detection Deep learning Attention mechanism

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