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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaobo Zhao Bing Wei Ning Luo Qian Chen Yi Wu Shudong Zhang Lijuan Zhou School of Cyberspace Security Hainan University Haikou China School of International Communication and Arts Hainan University Haikou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

distributed storage systems typically use erasure codes for fault tolerance to reduce storage overhead. However, the data repair process in erasure-coded systems can generate heavy I/O overhead. Existing methods typically increase redundancy to improve repair speed, but this approach results in substantial storage overhead. To enhance repair speed while reducing storage costs, this paper proposes a Machine Learning-based Adaptive Recovery (MLAR) method. Given an application’s access patterns for a file, MLAR uses an adaptive encoding model to calculate the optimal code for each file. When applying fault tolerance with the optimal code, lower-redundancy codes can achieve faster repair speeds than higher-redundancy codes. MLAR employs machine learning to predict file access patterns. Experimental results replaying real-world I/O workloads show that, MLAR reduces storage overhead by 12.8% and recovery time by 23.7%, compared to state-of-the-art methods.

关键词： Fault tolerance distributed processing Codes Costs Fault tolerant systems Termination of employment Machine learning Maintenance engineering Encoding Pattern recognition

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Data Management Over Cloud Edge Continuum in the Context of AC3

Data Management Over Cloud Edge Continuum in the Context of ...

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IEEE International workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Dimitrios Amaxilatis Dimitrios Klonidis Adlen Ksentini Vrettos Moulos Souvik Sengupta Nikolaos Tsironis Christos Verikoukis Spark Works Ltd. Galway Ireland Ubitech Ltd. Athens Greece EURECOM Sophia-Antipolis France University of Piraeus Piraeus Greece IONOS SE Karlsruhe Germany University of Patras and ISI/ATHENA Patras Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The proliferation of Internet of Things (IoT) tech-nologies and the exponential growth of data have necessitated advanced data management solutions that can operate across distributed architectures. The AC 3 project introduces a Data Management Platform as a Service (DMPaaS), designed to address these challenges by providing a robust, scalable, and flexible platform bridging cloud and edge computing environments. This paper explores the core components of the AC 3 DMPaaS, empha-sizing its dynamic data catalog, efficient data management processes, and the seamless integration of data connectors that facil-itate data movement across the cloud-edge continuum. Through advanced metadata management, real-time data processing, and a hybrid cloud-edge data framework, the AC 3 DMPaaS supports complex data operations and analytics, ensuring data integrity and consistency. This comprehensive overview establishes the foundational elements of the AC 3 project's DMPaaS, showcasing its significant advantages for enterprises leveraging IoT data across diverse computing landscapes.

关键词： Connectors Technological innovation Protocols Data integrity Soft sensors Platform as a service distributed databases Metadata real-time systems Internet of Things

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Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP)

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JOURNAL OF parallel AND distributed COMPUTING 2021年 149卷 103-118页

作者： Kuru, Kaya Univ Cent Lancashire Sch Engn Fylde Rd Preston PR1 2HE Lancs England

The recent advances in the cyber-physical domains, cloud and edge platforms along with the advanced communication technologies play a crucial role in connecting the globe more than ever, which is creating large volumes of data at astonishing rates and a tsunami of computation within hyper-connectivity. Data analytic tools are evolving rapidly to harvest these explosive increasing data volumes. Deriving meaningful insights from voluminous geo-distributed data of all kinds as a strategic asset is fuelling the innovation, facilitating e-commerce and revolutionizing the industry and businesses in the transition from digital to the intelligent way of doing business. In this perspective, in this study, a philosophical industrial and technological direction involving Deep Insight-as-a-Service (DINSaaS) on Forged Cloud Platforms (FCP) along with Advanced Insight Analytics (AIA), primarily motivated by the global benefit is systematically analysed within sophisticated theoretical knowledge, and consequently, a conceptual geo-distributed framework is proposed to (1) guide the national/international leading organizations, governments, cloud service providers and leading companies in order to establish a scalable framework within the hyperscale geo-distributed infrastructure in which exponentially increasing voluminous Big Data (BD) can be harvested effectively and efficiently, (2) inspire the transformation of BD into wiser abstract formats in Specialized Insight Domains (SID), (3) provide fusion and networking of insights rather than BD in order to obtain globally generated distributed intelligence and help make better decisions and near-real-time predictions, in particular for time-critical latency-sensitive applications, and (4) direct all the stakeholders to rivet the high-quality products and services within Automation of Everything (AoE) by exploiting continuously created and updated insights in dedicated taxonomic SID within large-scale geo-distributed datacenters. (C)

关键词： Cyber-Physical systems (CPS) Automation of Everything (AoE) Internet of Everything (IoE) Big data analytics Cloud platform

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Ensemble Graph and Device Clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices

Ensemble Graph and Device Clustering Method based on Attenti...

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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Qingyong Deng Qiuming Li Qinghua Zuo Shujuan Tian Saiqin Long School of Computer Science and Engineering and the School of Software Guangxi Normal University Guilin China School of Computer Science Xiangtan University Xiangtan China College of Information Science and Technology Jinan University Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices.

关键词： Performance evaluation Industries distributed processing Attention mechanisms Clustering methods Scalability Microservice architectures Clustering algorithms Robustness real-time systems

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Implementation of distributed Control System for Rice Mill Using C#

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Journal of Computer Science Research 2022年第3期4卷 26-33页

作者： Hla Myo Tun Research Department of Yangon Technological University GyogoneInsein PO11011YangonMyanmar

The paper presents the distributed control system for rice mill using C#*** real-time manufacturing system can be implemented by utilizing the signal from the real time control units that is more operative than other old-fashioned control systems in the extent of modern industrial *** software-based distributed Control System(DCS)is novel fashionable than any other conventional control systems in the state-ofthe-art manufacturing *** research study emphasizes on the implementation of the DCS-based rice mill using visual C#.*** Industrial Ethernet(IE)is realized between the top level controller for the operator and the controlled station for the remote *** model of client-server approach is more appropriate for the automation and manufacturing research *** this study,the computer graphical simulation of the complete control development is depicted in real-time status quo by visual C#language under Visual Studio 2008 *** parallel ports in the computers of remote terminal level and the master terminal level controllers have been interconnected with port interface coding by visual C#program.

关键词： distributed control system Rice mill C sharp real time simulation Software development

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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工作空间测量定位系统参数模型建立方法

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光学学报 2024年第16期44卷 171-180页

作者：郑书彦林嘉睿滕明鑫张饶邾继贵天津大学精密测试技术及仪器全国重点实验室天津300072

针对工作空间测量定位系统扫描光面相较于理想平面存在变形的问题,利用高精度转台对扫描光信号进行细分采样,通过对比旋转激光扫描时间,建立了面型变形信息和仪器原始观测量之间的映射关系,从而准确评估了扫描光面型,并分析了该评估方... 详细信息

针对工作空间测量定位系统扫描光面相较于理想平面存在变形的问题,利用高精度转台对扫描光信号进行细分采样,通过对比旋转激光扫描时间,建立了面型变形信息和仪器原始观测量之间的映射关系,从而准确评估了扫描光面型,并分析了该评估方法的误差影响因素。根据面型评估结果,研究了基于差分进化算法确定最佳分割角度的分区平面模型建立方法,并设计实验对模型拟合效果及测量精度进行了评价。实验结果表明,采用本文方法评价扫描光面型更加准确,新参数模型的拟合效果及测量精度也有明显提升。

关键词：测量工作空间测量定位系统大尺寸测量参数模型差分进化算法

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Hybrid CPU-FPGA Accelerated Architecture for Hurst Surface Feature Extraction and SVM Classification of ECG Signals

Hybrid CPU-FPGA Accelerated Architecture for Hurst Surface F...

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IEEE International Conference on High Performance Computing workshops (HiPCW)

作者： Basab Bijoy Purkayastha Shovan Barma Department of ECE Indian Institute of Information Technology Guwahati Guwahati India Department of Physics Indian Institute of Technology Guwahati Guwahati India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this paper, a hybrid CPU-FPGA architecture is proposed to accelerate the extraction of contour-based features from ECG-derived Hurst surface images, coupled with an FPGA-accelerated Support Vector Machine (SVM) for arrhythmia detection. Traditional CPU-based systems, while efficient for large-scale data processing, face challenges in real-time applications due to their sequential nature, which can lead to delays. To address these limitations, a custom FPGA kernel was developed to leverage hardware-level parallelism for faster processing. The CPU is responsible for extracting global features, such as entropy, skewness, and kurtosis, and managing data flow and higher-level statistical analyses. In parallel, the FPGA efficiently handles computationally intensive tasks, including contour detection and the extraction of geometric features such as area and perimeter, as well as texture features like Hu moments and Haralick texture descriptors. This division of labor allows the system to optimize resource utilization, with the CPU handling sequential, control-oriented tasks and the FPGA accelerating pixel-based operations. The features extracted by the hybrid CPU-FPGA system are then processed by the FPGA-accelerated SVM classifier, enabling real-time arrhythmia detection. This architecture not only reduces computational latency and enhances processing speed but also maintains high classification accuracy, making it suitable for large-scale, time-sensitive medical diagnostics. The results validate its effectiveness for real-time ECG analysis, presenting a scalable and efficient framework for advancing medical diagnostics and other biomedical signal processing applications.

关键词： Support vector machines Arrhythmia Computer architecture parallel processing Electrocardiography Feature extraction real-time systems Medical diagnosis Field programmable gate arrays Surface treatment

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Research on distributed PV consumption control method

Research on distributed PV consumption control method

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CIRED 2024 Vienna workshop

作者： Ge Liang Gao Zhuo Zhao Fengqing Song Xiaozhou Beijing Sifang Automation Co. LTD. Beijing People's Republic of China

ISBN: (数字)9781837241224

The traditional distribution network has been developed into a new distribution network with multi-power supply, bidirectional power current and a large number of new power electronic equipment access, after large number of distributed resources such as photovoltaic, wind power and energy storage are connected to the medium and low voltage distribution system. The active power of distributed generators such as photovoltaic wind generators is greatly affected by meteorological factors and has strong randomness and volatility, which brings difficulties for the safe and stable operation control of the active distribution networks. Sometimes photovoltaic or wind generators have to be decreased output or even given up, which reduces the economy incomes of DGs’ owners and reliability of distribution system. To solve this problem, an operational control method has been proposed to improve the load-bearing capacity of the distribution system in this paper . The method aims to maximize the consumption ability of distributed generators, and establishes an active power coordination control model for distribution system meeting with the safe operation constraint. This model has the characteristics of multi spatiotemporal comprehensive optimization and it is a multidimensional comprehensive optimization method. In terms of time dimension, it is divided into day-ahead strategy optimization, intraday ultra short-term strategy optimization, and real-time control optimization. In terms of space dimension, it is divided into three levels: regional autonomy, inter regions collaboration, and global optimization. The specific implementation method is as follows: regional autonomy includes coordinated control of distributed energy sources such as photovoltaic generators, wind power generators, and energy storage systems within one MV/LV transformer supplying area to achieve power autonomy within the MV/LV transformer supplying area, flexible interconnection of the low-voltage areas power

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real-time Deep-Learning-Driven parallel MPC

Real-Time Deep-Learning-Driven Parallel MPC

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IEEE Conference on Decision and Control

作者： Roman Kohút Erika Pavlovičová Kristina Fedorová Juraj Oravec Michal Kvasnica Faculty of Chemical and Food Technology Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava

A novel real-time approximated MPC control policy based on deep learning is proposed to address the high computational burden of model predictive control (MPC) for large-scale systems and those with fast dynamics. This control method approximates the optimal solution of the distributed optimization problems in the ALADIN-based parallel MPC design framework, resulting in a highly effective approach that outperforms other well-known methods for solving the MPC design problem. The numerical case study shows promising results, demonstrating the potential of this approach for real-time implementation.

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