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共 127 条记录，以下是11-20 订阅

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Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis 25

Enhancing Web Service Anomaly Detection via Fine-grained Mul...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xixuan Yang Xin Huang Chiming Duan Tong Jia Shandong Dong Ying Li Gang Huang Peking University Beijing China Nanyang Normal University Nanyang Henan China Peking University Beijing China and National Key Laboratory of Data Space Technology and System Beijing China Alibaba Group Hangzhou Zhejiang China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400713316

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.

关键词： anomaly detection

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SMOG: Accelerating Subgraph Matching on GPUs

SMOG: Accelerating Subgraph Matching on GPUs

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

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OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs

OptiTwin: Data-Driven Machining Process Optimization Platfor...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： José Joaquín Peralta Abadía Felix Larrinaga Barrenechea Mikel Cuesta Zabaljauregui Xabier Badiola Aiestaran Aitor Duo Zubiaurre Gorka Olalde Mendia Txema Perez Lazare Software and System Engineering Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain High Performance Machining Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Electronic Power Systems Applied to the Control Electric Energy Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Data Analysis and Cybersecurity Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.

关键词： Manufacturing industries Productivity Drilling Condition monitoring Quality assurance Data acquisition Real-time systems Data models Optimization Manufacturing automation

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A Cross-Company Architecture for Cyber-Physical Production systems and its Industrial Use

A Cross-Company Architecture for Cyber-Physical Production S...

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International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)

作者： Friedrich Meyer Michael F. Benisch Florian Baueregger Armin Zimmermann R&D System Software Rohde & Schwarz GmbH & Co. KG Teisnach Germany Institute for Precision Manufacturing and High Frequency Technology Deggendorf Institute of Technology (DIT) Deggendorf Germany Digital Factory Rohde & Schwarz GmbH & Co. KG Teisnach Germany Systems and Software Engineering Group Technische Universität Ilmenau Ilmenau Germany

Cyber-physical systems are expected to be an enabler for better control of production systems. They require a combination of heterogeneous sensors and systems processing data from various sources. We present an architecture for cyber-physical production systems for cross-company sensor integration, ensuring the necessary data safety. Its design targets generalizability and performance. A real-life industrial application in the area of optical components polishing demonstrates the benefits.

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Scenario-Based Thermal Management Parametrization Through Deep Reinforcement Learning

Scenario-Based Thermal Management Parametrization Through De...

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International Conference on Intelligent Transportation

作者： Thomas Rudolf Philip Muhl Sören Hohmann Lutz Eckstein department for Software Calibration and Diagnosis Porsche Engineering Group GmbH Weissach Germany department for Thermal Management Systems Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute of Control System (IRS) Karlsruhe Institue of Technology (KIT) Karlsruhe Germany Institute for Automotive Engineering (ika) RWTH Aachen Aachen Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The thermal system of battery electric vehicles demands advanced control. Its thermal management needs to effectively control active components across varying operating conditions. While robust control function parametrization is required, current methodologies show significant drawbacks. They consume considerable time, human effort, and extensive real-world testing. Consequently, there is a need for innovative and intelligent solutions that are capable of autonomously parametrizing embedded controllers. Addressing this issue, our paper introduces a learning-based tuning approach. We propose a methodology that benefits from automated scenario generation for increased robustness across vehicle usage scenarios. Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets. We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing. The results highlight the competitive performance to baseline methods. This novel approach contributes to the shift towards virtual development of thermal management functions, with promising potential of large-scale parameter tuning in the automotive industry.

关键词： Industries Technological innovation system dynamics Thermal management Valves Deep reinforcement learning Robustness Scenario generation Tuning Testing

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Design of High-Performance Broadband Power Amplifier Based on Normal Distribution Real Frequency Matching Method 1

引用

1st International Artificial Intelligence Conference, IAIC 2023

作者： Li, Zhenbing Wu, Xiangrui Huang, Junjie Jia, Shilin Zhang, Jinrong Fang, Xiaochuan Chu, Chu Li, Gang Centre for RFIC and System School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Antennas and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom College of Computer Science Sichuan Normal University Sichuan Chengdu610066 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In order to improve the working bandwidth of the power amplifier and make it suitable for a variety of application scenarios, a real frequency matching method based on normal distribution generating weights is proposed in this paper. Based on the working frequency of the power amplifier, a normal distribution function is constructed to generate frequency-related function values as the weights of the cost function of the real frequency method (that is, the weights of each frequency point are different), and sideband frequency points are mainly considered according to the actual frequency band. The matching circuit with weak normal distribution is obtained. The simulation results based on MATLAB algorithm show that the average insertion loss of the proposed real frequency matching method in the frequency band is reduced by 9.1%@1.7 GHz compared with the traditional real frequency matching method. The parameters obtained by MATLAB simulation are brought into ADS for circuit simulation. The simulation results show that when the designed power amplifier is less than 1.7 GHz, S21 is greater than 17 dB. It is proved that the proposed real frequency matching method can improve the bandwidth of power amplifier, and can provide theoretical and experimental reference for the design of high broadband power amplifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Power amplifiers

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Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure

Cutting Force Similarity Calculation in Milling Process Usin...

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Mechatronics and Electrical engineering (MEEE), International Conference on

作者： Juheon Kwak Wonkeun Jo Soomin Lee Hyein Kim Jeongin Koo Dongil Kim Division of Artificial Intelligence and Software Ewha Womans University Seoul South Korea Computer Science and Engineering Chungnam National University Daejeon South Korea Manufacturing System R&BD Group Korea Institute of Industrial Technology Cheonan South Korea Department of Data Science Ewha Womans University Seoul South Korea

Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

关键词： Training Electrical engineering Mechatronics Force measurement Force Time series analysis Process control

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Xie, Yongyi Lai, Kang Geng, Xiao Song, Yijun Jiang, Linshan National University of Singapore School of Computing Singapore National University Health System Group Technology Office Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Tongji University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine China Nanyang Technological University School of Mechanical & Aerospace Engineering Singapore Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Orthopaedic Department China Zhejiang University of Finance & Economics Dongfang College China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nalively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential. © 2024 IEEE.

关键词： Confounding factors genome-wide association study mixed model variable selection

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Smart Grid Cyber Attacks: Overview, Threats, And Countermeasures

Smart Grid Cyber Attacks: Overview, Threats, And Countermeas...

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International Conference on Intelligent systems Applications to Power systems (ISAP)

作者： Muhammad Adnan Khan Ahmed Mohammed Saleh Muhammad Waseem Vokony István Department of Electrical Engineering HITEC University Taxila Rawalpindi Pakistan Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BME V1 Building Egry József u. 18 Budapest Hungary Department of Electronic Engineering International Renewable and Energy System Integration Research Group (IRESI) Maynooth University Co. Kildare Ireland Lero - Science Foundation Ireland Research Centre for Software School of Business Innovation Value Institute (IVI) Maynooth University Co. Kildare Ireland

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

The smart grid paradigm brings about a significant transformation in conventional power grid networks through the seamless integration of cutting-edge computer, communication, and sensing technologies. This integration promotes optimal bidirectional power circulation, cultivating a digitally interconnected power grid that offers a multitude of advantages. The aforementioned benefits encompass improved inter-system communication, granularized control capabilities, bidirectional energy transmission, and the capacity to efficiently adapt to demand fluctuations. Notwithstanding its capacity for revolutionization, the smart grid presents a pivotal apprehension: cybersecurity. It is of the utmost importance to prioritize information security; however, the implementation of smart grid technology presents novel susceptibilities, specifically with regard to cyber threats. This article delves into the dynamic and changing environment of cybersecurity risks as they pertain to smart grid technology. Beginning with established methods of securing conventional power infrastructures, this section lays the groundwork for comprehending the ever-changing cybersecurity challenges of the future. Following this, an analysis is conducted on the progressively intricate cyber threats that the smart grid encounters. The article delineates a multitude of prevalent cyberattack vectors and suggests preventative measures to alleviate the associated hazards. By conducting an extensive investigation, this study seeks to illuminate the critical significance of cybersecurity in safeguarding the dependability and robustness of the smart grid infrastructure.

关键词： Vectors Robustness Smart grids Power system reliability Sensors Stakeholders Computer security Protection Cyberattack Resilience

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Yongyi Xie Kang Lai Xiao Geng Yijun Song Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine Tongji University School of Mechanical & Aerospace Engineering Nanyang Technological University Orthopaedic Department Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Dongfang College Zhejiang University of Finance & Economics Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

关键词： Adaptation models Input variables Biological system modeling High performance computing Noise Genomics Medical services Data models Robustness Bioinformatics

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