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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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Interference Factors and Compensation Methods when Using Infrared Thermography for Temperature Measurement: A Review

arXiv

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arXiv 2025年

作者： Pan, Dong Mo, Tan Jiang, Zhaohui Duan, Yuxia Maldague, Xavier Gui, Weihua State Key Laboratory of Precision Manufacturing for Extreme Service Performance the School of Automation Central South University Changsha410083 China School of Automation Central South University Changsha410083 China School of Physics Central South University Changsha410083 China Department of Electrical and Computing Engineering Université Laval QuébecQCG1V 0B1 Canada

Infrared thermography (IRT) is a widely used temperature measurement technology, but it faces the problem of measurement errors under interference factors. This paper attempts to summarize the common interference factors and temperature compensation methods when applying IRT. According to the source of factors affecting the infrared temperature measurement accuracy, the interference factors are divided into three categories: factors from the external environment, factors from the measured object, and factors from the infrared thermal imager itself. At the same time, the existing compensation methods are classified into three categories: Mechanism Modeling-based Compensation method (MMC), Data-Driven Compensation method (DDC), and Mechanism and Data jointly driven Compensation method (MDC). Furthermore, we discuss the problems existing in the temperature compensation methods and future research directions, aiming to provide some references for researchers in academia and industry when using IRT technology for temperature measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (temperature measurement)

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Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT

Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT

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IEEE International Conference on Web Services (ICWS)

作者： Yi Li Zhangbing Zhou Shuiguang Deng Xiao Sun Xiao Xue Sami Yangui Walid Gaaloul School of Information Engineering China University of Geosciences (Beijing) Beijing China Computer Science Department TELECOM SudParis Evry France College of Computer Science and Technology Zhejiang University Hangzhou China Department of Automation Tsinghua University Beijing China College of Intelligence and Computing Tianjin University Tianjin China University of Toulouse Toulouse France

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

关键词： Knowledge engineering Accuracy Web services Time series analysis Predictive models Graph neural networks Data models Internet of Things Forecasting Anomaly detection

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Training of Professionals on PV Systems in Bulgaria

Training of Professionals on PV Systems in Bulgaria

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Energy Efficiency and Agricultural engineering (EE&AE), International Conference on

作者： Boris I. Evstatiev Nikolay Valov Tsvetelina Mladenova Vladislav Hinkov Nicolay Mihailov Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Computing and Information Services Center University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The article presents the experience of a team from Ruse University “Angel Kanchev” (RUAK) in organizing and conducting training for installers of photovoltaic systems. These trainings are a response to the rapidly increased growth of photovoltaic (PV) plants in the country in recent years. Taking into account the qualification frameworks of the National Agency for Training and Education, a curriculum, theoretical part, and practical exercises have been developed. The offered trainings are met with interest by the business, and their results are positive.

关键词： Photovoltaic systems Renewable energy sources Companies Market research Problem-solving Qualifications

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Load Curve Based on Modeling a Photovoltaic System, Load and Weather Data - a Case Study in Brazil

Load Curve Based on Modeling a Photovoltaic System, Load and...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Teylor M. Guaitolini Ernani B. Nascimento Rafaela P. Breder Renan E. Gobbi Danilo P. e Silva Flavio B. B. Silva Renner S. Camargo Espaço Energia Laboratory Federal Institute of Espirito Santo Serra Brazil Graduate Department of Applied Computing Federal Institute of Espirito Santo Serra Brazil Electrical Engineering Department Federal Institute of Espirito Santo Vitoria Brazil Automation Engineering Dept. Federal Institute of Espirito Santo Serra Brazil

This study presents a comprehensive case study centered around the modeling of a load curve for a public education institution in Brazil that harnesses energy from distributed sources, specifically photovoltaic panels. Actual demand data, sourced from an energy analyzer, and weather data collected through an automated weather station, were integrated into the photovoltaic generation model. The model's efficacy was meticulously assessed, juxtaposing the actual and simulated photovoltaic generation data, which showcased a remarkable alignment with a percentage error of merely 6.88%. Furthermore, the study delved into weekly load curve simulations spanning the spring and summer seasons. These simulations yielded a compelling percentage error of 8.01%. These outcomes underscore the potential of the proposed load curve model, not only in strategic solar energy production planning but also in the strategic optimization of energy costs. This work not only underscores the feasibility of accurate load curve prediction but also illuminates the capacity to explore solar energy production potential, thereby opening avenues for future research in revenue generation and energy credit evaluation.

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Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic Key

Communication Encryption Scheme of 5G Power Trading Private ...

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Electrical, Power and Computer engineering (EPCE), Asia Conference on

作者： Rui Zhou Yiyang Song Ying Xu Electrical Engineering and Automation School Harbin Institute of Technology Harbin China Information Data Department Guangdong Power Exchange Center Guangzhou China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency.

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Surface Extraction and Boundary Detection Based on DBSCAN Clustering in 3D Point Clouds

Surface Extraction and Boundary Detection Based on DBSCAN Cl...

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International Conference on Electronic Measurement and Instruments, ICEMI

作者： Hui Chen Tingting Xie Muhammad Llyas Menhas Bilal Ahmad Heping Huang Qilin Li College of Automation Engineering Shanghai University of Electric Power Shanghai China Department of Electrical Engineering Mirpur University of Science and Technology Mirpur Pakistan College of Electrical Engineering University of Poonch Rawalakot Mirpur Pakistan Zhengtai Instrument Co. Ltd Hangzhou China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

Surface extraction from point clouds has many applications in product design, reverse engineering, industrial manufacturing and more. One of the key challenges is the detection and segmentation of overlapping areas of different surfaces. Existing methods are mainly on local judgment of similarity features, which can lead to either over- or under-segmentation. The proposed method in this paper utilizes DBSCAN for 3D point clouds surface extraction and boundary detection. Prior to extraction, the normal vector at sharp features is estimated based on a voting method. The surface extraction process is comprises two stages. First, the local quadric surface is determined based on the candidate sample points, and the points belonging to the same local surface are clustered. Second, in the merging stage, where the local surfaces are combined into a single smooth surface using three merging conditions. To demonstrate its effectiveness, the proposed approach is accessed on synthetic and real datasets, showing that the method can address the problem of insufficient and excessive segmentation of curved point clouds, and achieve high extraction accuracy.

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Terms Development of Additive Manufacturing 2

Terms Development of Additive Manufacturing

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2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

作者： Tamir, Tariku Sinshaw Xiong, Gang Jiang, Jingchao Shen, Zhen Lodhi, Ehtisham Ali, Hub Wan, Li Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266000 China Technology Co. Ltd Foshan528225 China

ISBN: (纸本)9781665492270

Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development. © 2022 IEEE.

关键词： 3D printers

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引用

Chinese automation Congress (CAC)

作者： Yumin Lang Jiande Wu Xun Lang Wanke Yu Chuanyan Yang Hongye Su Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Oscillation is a significant indication of the deterioration of control system performance and needs to be carefully monitored to ensure safe and stable process operations. However, industrial processes are often prone to noisy artifacts and nonstationary trends, resulting in low signal-to-noise ratios. In order to tackle this problem, this study introduces a novel framework for enhanced trend removal and noise reduction, which is featured by the following steps:(i) The circulant singular spectrum analysis (CiSSA) is used to decompose oscillation data into several reconstructed components (RCs). (ii) The estimated power spectral density (PSD) of these RCs is then smoothed using the Savitzky-Golay filter (SGF) to eliminate noise and identify the RCs corresponding to oscillatory components. (iii) These identified RCs are subsequently reconstructed to produce purified oscillatory data. The effectiveness of the suggested approach is validated using simulations and real-world industrial case studies. The findings indicate that the method notably enhances the quality of oscillatory data.

关键词： Power filters Noise reduction Process control Market research Information filters Noise measurement Oscillators Spectral analysis Monitoring Signal to noise ratio

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