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Improved hungarian algorithm-based task scheduling optimization strategy for remote sensing big data processing

地球空间信息科学学报（英文版） 2024年第4期27卷 1141-1154页

作者： Sheng Zhang Yong Xue Heng Zhang Xiran Zhou Kaiyuan Li Runze Liu School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina School of Computing and Engineering University of DerbyDerbyUK Artificial Intelligence Research Institute China University of Mining and TechnologXuzhouChina Shandong Province Institute of Land Surveying and Mapping JinanChina School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina Artificial Intelligence Research Institute China University of Mining and TechnologXuzhouChina

With the development of remote sensing technology and computing science,remote sensing data present typical big data *** rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to *** computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this *** study analyzes the characteristics of batch processing of remote sensing big *** paper uses the hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to hetero-geneously distributed computing *** strategy has two core contents:the improved hungarian algorithm model and the multi-level optimal assignment task queue ***,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing *** proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational *** experimented with the aerosol optical depth retrieval algorithm workflow using this *** with the processing before optimization,the makespan of the proposed method was shortened by at least 20％.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.

关键词： Workflow hungarian algorithm optimal assignment remote sensing big data large-scale task

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Path Planning for Multiple Unmanned Surface Vehicles Using Glasius Bio-Inspired Neural Network With hungarian algorithm

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 3906-3917页

作者： Yao, Peng Wu, Keqiao Lou, Yating Ocean Univ China Coll Engn Qingdao 266100 Peoples R China

In this article, we focus on the cooperative path planning for unmanned surface vehicles (USVs) composed of single-USV path planning and multi-USVs task assignment. First, the Glasius bioinspired neural network (GBNN) is used to calculate the neural activity for the discretized working space of USV, and the ocean current is considered in the definition of neuron connection weight especially. The standard path of single USV for obstacle avoidance between start point and destination can hence be planned. Then, based on the result of neural activity values from GBNN, the cost matrix of the hungarian algorithm is built and modified for the task assignment among multi-USVs, and the unbalanced problem is well resolved. Consequently, the task points are allocated to USVs and prioritized effectively, and each USV just needs to visit the corresponding task points respectively along the standard paths by GBNN to accomplish the task. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.

关键词： Task analysis Path planning Neurons Oceans Neural activity Costs Heuristic algorithms Cost matrix Glasius bio-inspired neural network (GBNN) hungarian algorithm ocean current path planning task assignment unmanned surface vehicles (USVS)

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UNSUPERVISED LEARNING OF NEURAL SEMANTIC MAPPINGS WITH THE hungarian algorithm FOR COMPOSITIONAL SEMANTICS 49

UNSUPERVISED LEARNING OF NEURAL SEMANTIC MAPPINGS WITH THE H...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Xiang He, Shizhu Liu, Kang Zhao, Jun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Neural semantic parsing maps natural languages (NL) to equivalent formal semantics which are compositional and deduce the sentence meanings by composing smaller parts. To learn a well-defined semantics, semantic parsers must recognize small parts, which are semantic mappings between NL and semantic tokens. Attentions in recent neural models are usually explained as one-on-one semantic mappings. However, attention weights with end-to-end training are shown only weakly correlated with human-labeled mappings. Despite the usefulness, supervised mappings are expensive. We propose the unsupervised hungarian tweaks on attentions to better model mappings. Experiments have shown our methods is competitive with the supervised approach on performance and mappings recognition, and outperform other baselines.

关键词： semantic parsing hungarian algorithm attention mechanism compositional semantics

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HUNIPU: Efficient hungarian algorithm on IPUs 40

HUNIPU: Efficient Hungarian Algorithm on IPUs

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40th International Conference on Data Engineering Workshop (ICDEW)

作者： Huang, Cheng Mathiasen, Alexander Dean, Josef Mottin, Davide Assent, Ira Aarhus Univ Aarhus Denmark Graphcore Bristol Avon England

ISBN: (纸本)9798350384048;9798350384031

The hungarian algorithm is a fundamental approach for a number of matching problems that find corresponding elements in two sets. For instance, the optimal alignment of proteins. Due its computational complexity, this algorithm takes several hours for only a few thousand elements on a desktop computer. The most common solution to increase the efficiency is parallelization using GPUs. However, GPUs run the same operation in all the threads in a warp. This constraint is a limitation for the hungarian algorithm that requires to find, in multiple steps, the best matching among a variable number of candidates. In this paper, we introduce HUNIPU, a parallel version of the hungarian algorithm optimized for a novel architecture called Intelligence Processing Unit (IPU). IPUs is a promising avenue for the hungarian algorithm since each core can independently perform a distinct operation. To benefit from the computational power of the IPU, we design HUNIPU to exploit the computational model of the IPU. We provide a smooth introduction to the IPU model and its challenges and show its flexibility and potential for numerous algorithmic advances. In our extensive experiments, the HUNIPU outperforms the best GPU algorithm on cuttingedge A100 GPU running 6 Chi faster on synthetic datasets and up to 32 Chi on real datasets for graph alignment.

关键词： IPU GPU hungarian algorithm Linear assignment problem

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Infrared target detection based on Gaussian model and hungarian algorithm

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ENTERPRISE INFORMATION SYSTEMS 2022年第10-11期16卷 1573-1586页

作者： Zhu, Zhiyu Lou, Kang Ge, Huilin Xu, Qingqing Wu, Xuedong Jiangsu Univ Sci & Technol Sch Elect Informat Zhenjiang 212003 Jiangsu Peoples R China

Based on Gaussian distribution, a method for modelling targets is proposed. The trajectories of infrared targets are predicted by Kalman filtering. The Munkres version of the hungarian algorithm is utilized to find the optimum assignment between the trajectories and the positions of real targets. Finally, based on the open dataset, we prove that the proposed algorithm is able to find and track infrared dim targets more effectively, while satisfying the requirements of real-time detection.

关键词： Gaussian model hungarian algorithm Kalman filter detection tracking infrared weak target

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HBP: AN EFFICIENT BLOCK PERMUTATION SOLVER USING hungarian algorithm AND SPECTROGRAM INPAINTING FOR MULTICHANNEL AUDIO SOURCE SEPARATION 47

HBP: AN EFFICIENT BLOCK PERMUTATION SOLVER USING HUNGARIAN A...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Li Kameoka, Hirokazu Seki, Shogo NTT Corp NTT Commun Sci Labs Tokyo Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi Japan

ISBN: (纸本)9781665405409

This paper proposes a method called "hungarian Block Permutation (HBP)" to solve the block permutation problem in frequency-domain multichannel audio source separation. Many methods for frequency-domain multichannel audio source separation are designed to simultaneously solve frequency-wise source separation and permutation alignment in determined cases. However, in practice, separation can fail due to permutation inconsistencies in different frequency blocks for various reasons, such as convergence to a locally optimal solution as a result of bad initialization. To correct permutation inconsistencies, the proposed HBP method first masks, for each separated signal, the frequency bands where the components from other sources are likely to be dominant, and then restores the components in those bands so that the restored spectrogram becomes closer to the original spectrogram of the corresponding source. The hungarian algorithm is then used to perform permutation realignment in those bands in accordance with the restored spectrogram. The experimental results show that the proposed method can solve the permutation realignment and improve the separation performance even in the case of 18 speakers.

关键词： Multichannel source separation block permutation hungarian algorithm audio inpainting

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View-shuffled clustering via the modified hungarian algorithm

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NEURAL NETWORKS 2024年 179卷 106602页

作者： Dong, Wenhua Wu, Xiao-Jun Xu, Tianyang Feng, Zhenhua Ahmed, Sara Atito Ali Awais, Muhammad Kittler, Josef Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn Guildford GU2 7XH England Univ Surrey Ctr Vis Speech Signal & Proc Guildford GU2 7XH England

In the majority of existing multi-view clustering methods, the prerequisite is that the data have the correct cross-view correspondence. However, this strong assumption may not always hold in real-world applications, giving rise to the so-called View-shuffled Problem (VsP). To address this challenge, we propose a novel multi- view clustering method, namely View-shuffled Clustering via the Modified hungarian algorithm (VsC-mH). Specifically, we first establish the cross-view correspondence of the shuffled data utilizing strategies of the global alignment and modified hungarian algorithm (mH) based intra-category alignment. Subsequently, we generate the partition of the aligned data employing matrix factorization. The fusion of these two processes facilitates the interaction of information, resulting in improved quality of both data alignment and partition. VsCmH is capable of handling the data with alignment ratios ranging from 0 to 100%. Both experimental and theoretical evidence guarantees the convergence of the proposed optimization algorithm. Extensive experimental results obtained on six practical datasets demonstrate the effectiveness and merits of the proposed method.

关键词： Multi-view View-shuffled clustering Matrix factorization Global alignment hungarian algorithm

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A hungarian algorithm Based Hybrid Precoding Scheme for mmWave Massive MIMO Systems

A Hungarian Algorithm Based Hybrid Precoding Scheme for mmWa...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Wang, Xuehan Wang, Jintao Shi, Xu Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Elect Engn Beijing Peoples R China Tsinghua Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781665442664

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with the hybrid precoder employed have been regarded as a reliable option to raise the capacity and coverage of the cellular network. Extensive studies have demonstrated that the capacity achieved by the hybrid precoder with fixed architectures cannot satisfy the requirement of the ever-increasing data traffic. However, the performance of the hybrid precoding can be further promoted by exploiting the flexibility of the precoding structure, where the adaptively-connected architecture can provide a critical enhancement. In this paper, we investigate the adaptively-connected structure and propose a near-optimal hybrid precoding scheme based on the hungarian algorithm. Simulation results demonstrate the significant advantage of the proposed scheme over existing hybrid precoding algorithms.

关键词： massive MIMO hybrid precoding adaptively-connected hungarian algorithm

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Temperature Matrix-Based Data Placement Using Improved hungarian algorithm in Edge Computing Environments 22nd

Temperature Matrix-Based Data Placement Using Improved Hunga...

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22nd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Zhao, Yuying Wang, Pengwei Huang, Hengdi Zhang, Zhaohui Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9783030967727;9783030967710

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are problems such as network latency and power costs. The emergence of edge computing brings data close to the edge of the network, making edge computing a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To address this, we propose a temperature matrix-based data placement method using an improved hungarian algorithm (TEMPLIH). A temperature matrix reflects the influence of data characteristics on its placement. A replica selection algorithm based on a temperature matrix (RSA-TM) can meet latency requirements. An improved hungarian algorithm (IHA-RM) is proposed on the basis of replica selection, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with commonly used data placement strategies, experiments show that TEMPLIH can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers.

关键词： Edge computing Data placement Data temperature hungarian algorithm Load balancing

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A Kalman filter-hungarian algorithm with a postprocessor for tracking aeolian saltating particle in high-speed video

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EARTH SURFACE PROCESSES AND LANDFORMS 2024年第15期49卷 5086-5097页

作者： Mei, Fanmin Zhou, Hongji Su, Jin Chen, Jinguang Xian Polytech Univ Sch Environm & Chem Engn Xian 710048 Peoples R China Xian Polytech Univ Sch Sci Xian Peoples R China Xian Polytech Univ Sch Comp Sci Xian Peoples R China

Saltating particle tracking (SPT) is an essential visualized channel to understand the dynamics of aeolian saltation at sand particle size scale, while the published SPTs could have low recall or accuracy rate and misestimate further saltation intensity. Hence, a Kalman filter-hungarian algorithm with a postprocessor (KF-H-k) was proposed, where the Kalman filter was employed for predicting particle motion, and the hungarian algorithm for optimizing global assignment, as well as the postprocessor with k-means cluster for correcting the erroneous recovered tracks by Kalman filter-hungarian algorithm. The new SPT was validated in a digital high-speed video with various particle concentrations from a wind tunnel experiment. It demonstrated that compared with the previous SPTs, KF-H-k kept the highest and most stable accuracy (85% similar to 93%), the best spatial resolution, the moderate recall rate (50% similar to 70%) and time cost. The present work offers a new hybrid scheme for tracking sand particles accurately but alsodatasets for automatically identifying saltating tracks via machine learning models, very critical for insight into new hypothesis on sand ripple formation.

关键词： high-speed video hungarian algorithm Kalman filter k-means cluster saltating particle tracking

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