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RETRACTED ARTICLE: Research on North Gulf distributed big data submarine 3D terrain computing system based on remote sensing and multi-beam

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Soft Computing 2019年第8期24卷 5847-5857页

作者： Dong, Yuan Hu, BaoQing Zhang, ShiLun Huang, YuanLin Nong, GuoCai Xin, Han College of Resource and Environment Beibu Gulf University Qinzhou People’s Republic of China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin People’s Republic of China The Key Laboratory for Advanced Technology to Internet of Things Beibu Gulf University Qinzhou China Key Laboratory of Environment Evolution and Resources Utilization in Beibu Gulf (Nanning Teachers Education University) Ministry of Education Nanning People’s Republic of China

North Gulf is China’s important sea route to Asia and Europe, and the seabed is rich in resources. For various reasons, the detection and mapping of submarine terrain are blank. The traditional multi-beam measurement method has high accuracy but uneven spatial distribution. Although the detection line process has high sampling and measurement accuracy, there is a large blank area between adjacent routes, which is difficult to achieve full-scale large area measurement due to environmental, time and budget constraints. On the other hand, the accuracy of the remote sensing inversion depth method is not high, but it can achieve a wide range of coverage, which is characterized by low cost, short cycle and convenient dynamic monitoring. This paper takes North Gulf as the research area, uses remote sensing data to construct the seabed topography, uses multi-beam data for checksum correction and uses distributed computing technology to distribute big data in different computing nodes, and the data are refined and reconstructed in three dimensions to form a 3D seabed topography. The method improves the efficiency and accuracy of multi-beam data processing and can construct a large area, high-resolution submarine DEM and realize database management.

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A Survey of Stochastic Computing in Energy-Efficient DNNs On-Edge

A Survey of Stochastic Computing in Energy-Efficient DNNs On...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Danghui Wang Zhaoqing Wang Linfan Yu Ying Wu Jiaqi Yang Kuizhi Mei Jihe Wang Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China Northwestern Polytechnical University Engineering Research Center of Embedded System Integration Ministry of Education Xi'an China Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

In recent years, "edge intelligent computing power deployment" has become a key and difficult problem in key areas such as system structure and artificial intelligence. Some independent decision-making scenario requires the edge devices to have the ability to handle high energy efficient and high throughput tasks. However, restricted by the traditional accurate computing mode and memory-computation separation structure, the edge devices often fail to provide the computing power guarantee of high throughput and high energy efficiency, which seriously hinders the edge intelligent deployment. This paper summarizes the new computing model integrating memory and computation of deploying stochastic circuits around memory, and constructs an edge intelligent system taking stochastic computing as a core from three levels of microstructure of circuit, architecture of near-memory stochastic computing and system-level software/hardware cooptimization. It strives to break through the power consumption and performant bottleneck of traditional computing model, and greatly improves the deploying ability of complex intelligent computing on edge equipment.

关键词： Power demand Computational modeling Architecture Stochastic processes Computer architecture Throughput Energy efficiency

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Hypotheses Tree Building for One-Shot Temporal Sentence Localization

arXiv

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arXiv 2023年

作者： Liu, Daizong Fang, Xiang Zhou, Pan Di, Xing Lu, Weining Cheng, Yu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Peking University China Nanyang Technological University Singapore ProtagoLabs Inc United States Tsinghua University China Microsoft Research United States

Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on dense video frame annotations, which require a tremendous amount of human effort to collect. In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. Particularly, we propose an effective and novel tree-structure baseline for one-shot TSL, called Multiple Hypotheses Segment Tree (MHST), to capture the query-aware discriminative frame-wise information under the insufficient annotations. Each video frame is taken as the leaf-node, and the adjacent frames sharing the same visual-linguistic semantics will be merged into the upper non-leaf node for tree building. At last, each root node is an individual segment hypothesis containing the consecutive frames of its leaf-nodes. During the tree construction, we also introduce a pruning strategy to eliminate the interference of query-irrelevant nodes. With our designed self-supervised loss functions, our MHST is able to generate high-quality segment hypotheses for ranking and selection with the query. Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration

arXiv

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arXiv 2024年

作者： Cheng, Xinxing Jia, Xi Lu, Wenqi Li, Qiufu Shen, Linlin Krull, Alexander Duan, Jinming School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom National Engineering Laboratory for Big Data System Computing Technology Shenzhen University 518060 China

Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. However, due to the cascaded nature and repeated composition/warping operations on feature maps, these methods negatively increase memory usage during training and testing. Moreover, such approaches lack explicit constraints on the learning process of small deformations at different scales, thus lacking explainability. In this study, we introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales, utilizing the wavelet coefficients derived from the original input image pair. By exploiting the properties of the wavelet transform, these estimated coefficients facilitate the seamless reconstruction of a full-resolution displacement/velocity field via our devised inverse discrete wavelet transform (IDWT) layer. This approach avoids the complexities of cascading networks or composition operations, making our WiNet an explainable and efficient competitor with other coarse-to-fine methods. Extensive experimental results from two 3D datasets show that our WiNet is accurate and GPU efficient. Code is available at https://***/x-xc/WiNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image registration

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NONSMOOTH INTERVAL-VALUED MULTIOBJECTIVE OPTIMIZATION PROBLEMS AND GENERALIZED VARIATIONAL INEQUALITIES ON HADAMARD MANIFOLDS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2023年第1期5卷 69-84页

作者： Upadhyay, B.B. Li, Lijie Mishra, Priyanka Department of Mathematics Indian Institute of Technology Patna Bihar Patna801103 India Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Mathematics Division School of Advanced Sciences and Languages VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

In this paper, we study the classes of approximate Minty and Stampacchia type vector variational inequalities along with their local and weak versions on a Hadamard manifold, and a class of nonsmooth interval-valued multiobjective optimization problems, respectively. We obtain the equivalence between the solutions of considered approximate vector variational inequalities and LU-efficient solutions of nonsmooth interval-valued multiobjective optimization problems in which the cost function is assumed to be geodesic approximately LU-convex or geodesic LU-α-convex. ©2023 Applied Set-Valued Analysis and Optimization.

关键词： Cost functions

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Chinese Named Entity Recognition in Power Domain Based on Bi-LSTM-CRF

Chinese Named Entity Recognition in Power Domain Based on Bi...

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作者： Zhenqiang Zhao Zhenyu Chen Jinbo Liu Yunhao Huang Xingyu Gao Fangchun Di Lixin Li Xiaohui Ji School of Information Engineering China University of Geosciences Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology(China Electric Power Research Institute) Big Data Center State Grid Corporation of China Power Dispatching and Control Center State Grid Corporation of China Institute of Microelectronics Chinese Academy of Sciences

Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power *** power domain Named Entity Recognition(NER) methods rely on feature engineering seriously,which unable to learn power entity features *** order to learn entity features automatically and extract power domain named entities efficiently,a model based on Bidirectional Long Short-Term Memory Neural Networks(Bi-LSTM) and Conditional Random Field(CRF) was proposed in this *** representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain *** results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%,which confirm the method based on BiLSTM and CRF is effective for named entity recognition in the power domain.

关键词： Named Entity Recognition Power Domain Bi-LSTM CRF

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BLOWN: A Blockchain Protocol for Single-Hop Wireless Networks under Adversarial SINR

arXiv

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arXiv 2021年

作者： Xu, Minghui Zhao, Feng Zou, Yifei Liu, Chunchi Cheng, Xiuzhen Dressler, Falko The School of Computer Science and Technology Shandong University Qingdao266510 China The Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China Ernst & Young Shanghai200120 China The School of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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