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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Fault line selection for small current grounding system based on ICEEMDAN and improved VGG

Fault line selection for small current grounding system base...

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IEEE International conference on Energy Internet (ICEI)

作者： Yadong Liu Naiyu Liu Hao Zhang Yunhai Cui Zhe Shi Chenggang Li Xiaochen Hu Dahai Zhang Electric Power Research Institute State Grid Jilin Electric Power Co. Ltd. Changchun China School of Electrical Engineering Beijing Jiaotong University Beijing China State Grid Jilin Electric Power Co. Ltd. Changchun China

ISBN: (数字)9798331523558

ISBN: (纸本)9798331523565

In order to solve the problem of low accuracy of single-phase grounding fault line selection method in traditional low-current grounding system, this paper proposes a single-phase grounding fault line selection method for small-current grounding distribution network based on improved VGG. Firstly, the original signal is transformed into an Intrinsic Mode Function component by using the ICEEMDAN algorithm, and then the entropy value of each IMF component is calculated by using the distributed entropy algorithm and differentiated processing, and the processed IMF component is reconstructed to highlight the frequency domain characteristics and amplitude characteristics of the original data, and the influence of noise is removed to a certain extent. The processed zero-sequence current data is converted into a two-dimensional image by the Gramian Angular Field algorithm, and on the basis of retaining the data features, a single-phase grounding fault dataset is prepared, and the fault characteristics are more prominent, which is convenient for neural network processing. Then, the line selection results were obtained by improving the training iterative optimization parameters. Finally, an experimental platform was built by using Simulink, and the proposed algorithm was compared with two line selection methods. Experimental results show that the proposed algorithm has higher line selection accuracy and better noise immunity.

关键词： Training Accuracy Grounding Software packages Frequency-domain analysis Noise Transforms Entropy Mathematical models image reconstruction

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A parallel Dual-Task Learning Network for InSAR Phase Retrieval

A Parallel Dual-Task Learning Network for InSAR Phase Retrie...

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IEEE National conference on Radar

作者： Xu Zhan Xiaoling Zhang Xiangdong Ma Jun Shi Shunxin Zheng Jiaping Chen Shunjun Wei Tianjiao Zeng University of Electronic Science and Technology of China

This work focuses on the problem of InSAR phase retrieval. Current methods consist of two cascaded tasks: phase filtering and phase unwrapping. Unavoidable accumulated errors cause precision loss, and serial computations cause efficiency loss. We propose a parallel dual-task learning work to address these issues for high-quality and efficient InSAR phase retrieval. Methodologically, we retrieve the InSAR phase in a parallel manner instead of a serially cascaded one. Specifically, three core phases throughout the whole processing chain are considered, including feature attraction, task learning, and task balance. First, for feature attraction, considering the InSAR image characteristics, we propose a hybrid Trans-Encoder module to attract features locally and nonlocally. Second, regarding the dual-task needs for feature learning, we propose a dual-decoder to denoise and unwrap parallelly. Third, considering the dual-task's different attributes for task balance, we propose an uncertainty-weighted loss to make balances between tasks. Experiments on both simulated and measured data verify the proposed method's higher precision and efficiency compared to other methods. An ability study is conducted that confirms the effectiveness of the proposed modules.

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Fast distributed Complex Join processing 37

Fast Distributed Complex Join Processing

引用

37th IEEE International conference on Data Engineering (IEEE ICDE)

作者： Zhang, Hao Qiao, Miao Yu, Jeffrey Xu Cheng, Hong Chinese Univ Hong Kong Hong Kong Peoples R China Univ Auckland Auckland New Zealand

ISBN: (纸本)9781728191843

Big data analytics often requires processing complex join queries in parallel in distributed systems such as Hadoop, Spark, Flink. The previous works consider that the main bottleneck of processing complex join queries is the communication cost incurred by shuffling of intermediate results, and propose a way to cut down such shuffling cost to zero by a one-round multi-way join algorithm. The one-round multi-way join algorithm is built on a one-round communication optimal algorithm for data shuffling over servers and a worst-case optimal computation algorithm for sequential join evaluation on each server. The previous works focus on optimizing the communication bottleneck, while neglecting the fact that the query could be computationally intensive. With the communication cost being well optimized, the computation cost may become a bottleneck. To reduce the computation bottleneck, a way is to trade computation with communication via pre-computing some partial results, but it can make communication or pre-computing becomes the bottleneck. With one of the three costs being considered at a time, the combined lowest cost may not be achieved. Thus the question left unanswered is how much should be traded such that the combined cost of computation, communication, and pre-computing is minimal. In this work, we study the problem of co-optimize communication, pre-computing, and computation cost in one-round multiway join evaluation. We propose a multi-way join approach ADJ (Adaptive distributed Join) for complex join which finds one optimal query plan to process by exploring cost-effective partial results in terms of the trade-off between pre-computing, communication, and *** analyze the input relations for a given join query and find one optimal over a set of query plans in some specific form, with high-quality cost estimation by sampling. Our extensive experiments confirm that ADJ outperforms the existing multi-way join methods by up to orders of magnitude.

关键词： conferences Prototypes Estimation Big Data Data engineering Servers Sparks

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PQMPV: parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi

PQMPV: Parallel and Quantified Dense Stereo Disparity Estima...

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International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Rongqi Gu Tianhang Wang Tianpei Zou Bo Zhang Zhijun Li Haotian Zhang Guang Chen Department of Automotive Engineering Tongji University Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Automation University of Science and Technology of China Hefei China Department of Computer Science and Technology Tongji University Shanghai China

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications.

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The Stabilization Condition for Interval Type-2 Fuzzy Systems via Relaxed Membership-Parameter Matrix Inequalities

The Stabilization Condition for Interval Type-2 Fuzzy System...

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IEEE conference on Decision and Control

作者： Kyung Soo Kim PooGyeon Park Department of Electrical Engineering POSTECH Signal Processing & Control lab Pohang Gyeongbuk KOREA Department of Electrical Engineering POSTECH Pohang Gyeongbuk KOREA

This paper aims to investigate the relaxed stability condition for interval type-2 Takagi-Sugeno fuzzy systems via membership-parameter matrix inequalities. The membership framework of interval type-2 fuzzy sets is structured in convex polytopes with a straightforward method. The stabilization syn-thesis with a non-parallel distributed compensator controller in-corporating lower and upper membership functions is achieved in the sense of a matrix. Moreover, this paper introduces the relaxation strategy for the orthogonal complement, effectively reducing the number of decision variables related to the linear matrix inequalities. In conclusion, examples are presented to demonstrate the effectiveness and applicability of the proposed methods.

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Pyramidal Connected Component Labeling by Irregular Graph Pyramid 5

Pyramidal Connected Component Labeling by Irregular Graph Py...

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5th International conference on Pattern Recognition and image Analysis, IPRIA 2021

作者： Banaeyan, Majid Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9781665426596

This paper presents a new logarithmic-time algorithm which simultaneously assigns labels to all connected components of a binary image in parallel. The irregular graph pyramid of an input binary image is constructed based on the optimized combinatorial structure. The novel small built pyramid has only three levels and is created at worst case with O(log(N2)) complexity for a N× N-sized (2D) binary image. To assign a label to each connected component, instead of the common linear-time raster scan techniques (with complexity O(N2)), only two important movements, namely bottom-up and top-down traversing, are needed. First, in the bottom-up traversing, the redundant connections are removed and the contraction kernels are contracted. This results in a simpler graph at top of the pyramid each of its vertices has a unique label identifying corresponding connected component. Such reduced graph preserves all connecting relations including inclusions. Second, in the top-down traversing, each unique label propagates down into each individual corresponding pixel at the base level. The complexity of the labeling propagation procedure in worst cases is O (log(image - size)). The GPU implementation of the algorithm has high performance and the bottleneck is the bandwidth of the memory or equivalently the number of available independent processing elements. Finally, the experimental results show the proposed algorithm outperforms the other state-of-the-art methods. © 2021 IEEE.

关键词： Binary images

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

引用

IEEE International conference on Big Data

作者： Bożena Małysiak-Mrozek Paulina Pawłowicz Vaidy Sunderam Che-Lun Hung Andrzej Kwiecień Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data.

关键词： Proteins Solid modeling Three-dimensional displays Pipelines Cluster computing Big Data Biology Organisms Decoding Sparks

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A versatile distributed MCMC algorithm for large scale inverse problems

A versatile distributed MCMC algorithm for large scale inver...

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European Signal processing conference (EUSIPCO)

作者： Pierre-Antoine Thouvenin Audrey Repetti Pierre Chainais Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL Lille France Department of Actuarial Mathematics & Statistics Heriot-Watt University Edinburgh UK Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh UK

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

For large scale inverse problems, inference can be tackled with distributed algorithms, dividing the task over multiple computing nodes or cores referred to as workers. Since random sampling methods yield not only estimates but also credibility intervals, we leverage data augmentations and MCMC algorithms to design a distributed sampler. In contrast with usual approaches relying on a client-server architecture, we propose a flexible distributed sampler relying on a Single Program Multiple Data implementation, in which all workers have a similar task. This distributed strategy allows the computing time and volume of communications to be reduced by separately handling blocks of data and parameters on different workers. Experiments on a large synthetic image inpainting problem illustrate the performance of the proposed approach to produce high quality estimates in a small amount of time.

关键词： Monte Carlo methods Inverse problems distributed databases Signal processing algorithms Computer architecture Signal processing Markov processes

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A Mathematical Framework for Optimizing Lexical Analysis on Multi-Core Architectures

A Mathematical Framework for Optimizing Lexical Analysis on ...

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Sustainable Expert Systems (ICSES), International conference on

作者： Vaikunta Pai T Priyadarshini P B Sunil Kamath Shreyas Vivek Manjunatha Nethravathi P S Department of ISE NITTE (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India Department of Computer Applications St Joseph Engineering College Mangaluru India School of Information Technology Deakin University Waurn Ponds VIC Australia Department of CSE Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Manipal India Department of CSE Shree Devi Institute of Technology Mangaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In the field of multi-core computing systems, optimizing the distribution of tasks across multiple cores is essential to harness maximum performance potential. Traditional static task allocation methods, often employed in lexical analysis-the initial and vital phase of compiling and interpreting source code-frequently result in imbalanced workloads and suboptimal resource utilization. This paper introduces an innovative and sophisticated mathematical model for dynamic task allocation specifically tailored to lexical analysis, aiming to revolutionize how tasks are distributed across multiple cores. By employing advanced dynamic allocation techniques, our proposed model not only ensures balanced workload distribution but also scales efficiently with varying computational demands. This novel approach significantly enhances the efficiency of lexical analysis in multi-core environments, leading to substantial improvements in processing times and resource utilization. Extensive experimental results validate the superiority of our method, showcasing dramatic reductions in processing latency and a more effective exploitation of multi-core capabilities. This dynamic model not only advances the field of lexical analysis but also sets a new benchmark for task allocation strategies in multi-core computing systems, promising broader applicability and future innovations in compiler design and resource management.

关键词： Adaptation models Analytical models Multicore processing Computational modeling Scalability parallel processing Dynamic scheduling Mathematical models Computational efficiency Resource management

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Software and Hardware processing Method for Blockchain Light Node SPV Verification

Software and Hardware Processing Method for Blockchain Light...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhan’Gang Ma Jiandong Yan Feng Zou Xixin Cao dept. Integrated Circuits and Intelligent Systems School of Software and Microelectronics (of Peking University) Beijing China dept. Development Planning and Strategic Inverstment Ruiyuan Venture Capital Co. Ltd(of Changzhou Zhonglou Jinlong Holding Group) Changzhou China

With the application of blockchain light nodes in embedded devices, how to alleviate computing pressure brought by complex operations such as transaction’s SPV Verification for CPU of embedded devices and improve the performance of devices in these aspects has gradually become a research topic in industry and academia. This paper proposes a series of methods to improve the performance of blockchain SPV Verification from the perspectives of system architecture and hash computing unit: (1) According to the computational characteristics of SPV Verification, this paper customizes macro instructions and microinstructions for the coprocessor to meet the requirements of flexibility; The built-in dedicated cache holds transaction data fetched from external memory and intermediate data generated by internal Hash Computing Unit, which not only prepares transaction data for hash computation, but also avoids frequent access to the bus and external memory. (2) Techniques like two-round unfolded computing, timing-balanced pipeline architecture and optimized adders are adopted to improve the performance of SHA256 computation. (3) When double hash computing is required for transactions, Hash Computing Unit can directly perform the second hash computation based on the first hash computation, reducing the frequency of accessing to external memory, thereby improving the performance to a certain extent. Through these methods, the performance of the hardware coprocessor for SVP verification of transactions is more than double that of traditional solutions.

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