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15th International Symposium on Advanced Parallel processing Technologies, APPT 2023

作者： Chen, Jinli Li, Peixuan Xie, Ping College of Computer of Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China The Key Laboratory of Internet of Things of Qinghai Province Xining810016 China Academy of Plateau Science and Sustainability Xining810016 China

ISBN: (纸本)9789819978717

In the era of continuous advances in flash technology, the storage density of NAND flash memory is increasing, but the availability of data is declining. In order to improve data availability, low-density parity check codes (LDPC), which have been highly corrected in recent years, are used in flash memory. However, although LDPC can solve the problem of low data availability, it also brings the problem of long decoding time. Moreover, the LDPC decoding delay time is related to its decoding level, and the higher the LDPC decoding level, the longer the delay time. Long decoding delays can have an impact on the read performance of the flash. Therefore, in order to improve the speed of reading data in flash memory, this paper proposes a scheme combining cache and flushing, the main idea of which is to use the cache to reduce LDPC decoding time, and at the same time refresh the pages with high latency replaced by the cache, so that the pages with high latency can be restored to the state of low latency pages Experimental results show that this scheme can significantly reduce LDPC decoding delay and improve data availability with less overhead, and optimize the read performance of flash memory. The experimental results show that compared with the original strategy, the average response time is reduced by 24%, and the average IOPS value is increased by 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decoding

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A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior information of information Matrix

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Journal of Shanghai Jiaotong university(Science) 2022年第5期27卷 671-679页

作者： HUANG Yinghao WU Yi YAO Lixiu CAI Yunze Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai200240China Alibaba(China)Co. Ltd.Hangzhou311121China

The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking *** adaptive grid-expected-mode augmentation variable structure multiple model algorithm is *** adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model ***,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is *** the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

关键词： radar network system variable structure multiple model consistency filtering information matrix particle swarm optimization

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Review and prospect of remote sensing monitoring methods for inland black and malodorous water bodies 24

Review and prospect of remote sensing monitoring methods for...

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2024 International Conference on Image processing, intelligent Control and Computer Engineering, IPICE 2024

作者： Liao, Yansong Huang, Fenghua The Academy of Digital China Fuzhou University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China College of Artificial Intelligence Yango University China

ISBN: (纸本)9798400710285

Black and malodorous water bodies represent a severe water pollution phenomenon that urgently needs addressing due to the discomfort and health risks they pose. Since the release of the"Action Plan for Prevention and Control of Water Pollution" in 2015 and the subsequent"Action Plan for the Treatment of Black and Malodorous Water Bodies in Urban Areas during the 14th Five-Year Plan Period" in 2022 by the Chinese government, monitoring efforts have become increasingly crucial. Remote sensing technology offers advantages such as high dynamics, low cost, and a macroscopic perspective in ecological environment monitoring, making it advantageous over traditional physical and chemical methods in monitoring black and malodorous water bodies. This paper summarizes remote sensing methods for identifying black and malodorous water bodies, including extraction of information through spectral, spatial, index, and temporal features, and analyzes the monitoring feasibility of these methods while pointing out potential issues. Finally, future research directions for identifying black and malodorous water bodies are discussed. © 2024 Copyright held by the owner/author(s).

关键词： Environmental monitoring

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Gait recognition Algorithm GCGait based on Skeleton

Gait Recognition Algorithm GCGait based on Skeleton

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information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ru Ma Jikai Zhang Mengyu Jia Xiaoqi Lv School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China School of Information Engineering Inner Mongolia University of Technology Hohhot China

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method.

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Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias

arXiv

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arXiv 2023年

作者： Zhang, Zhiyuan Chen, Deli Zhou, Hao Meng, Fandong Zhou, Jie Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Pattern Recognition Center WeChat AI Tencent Inc. China

Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potentially poisonous PLMs is precisely finding poisonous dimensions. To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions. According to the relationship between parameter drifts and Hessians of different dimensions, we can detect poisonous dimensions with abnormal dynamics, purify them by resetting them to clean pre-trained weights, and then fine-tune the purified weights on a small clean dataset. To the best of our knowledge, we are the first to study the dynamics guided by the diffusion theory for safety or defense purposes. Experimental results validate the effectiveness of Fine-purifying even with a small clean dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Consistency-aware unsupervised label learning for cross-domain person re-identification

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Multimedia Tools and Applications 2024年 1-20页

作者： Geng, Yanbing Lian, Yongjian Cui, Fangshu Zhang, Xiaowei Zhou, Mingliang Zhang, Geao School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Shandong Key Laboratory of Intelligent Information Processing School of Computer Science and Technology Qingdao University Qingdao266071 China School of Computer Science Chongqing University 174 Shazheng Street Shapingba District Chongqing400044 China College of Information Science and Engineering Northeastern University 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China

Un-refined pseudo labels always disturb the cross-domain Re-ID performance in unsupervised clustering methods. In this paper, we propose a consistency-aware unsupervised label learning network to refine noisy labels for promising domain adaptation. Specifically, we use a hetero-generated label consistency constraint to mine possible label information, keep a reference consistency of the target pairs on the relative comparative characteristics. We then use a cross-granularity attention consistency constraint to refine the pseudo labels meanwhile learning multi-granularity discriminative representation, keeping a self-similarity consistency of the corresponding parts on the discriminative regions. Our model is fine-tuned by the refined pseudo-labels to reduce the domain gap while coping with the intra-domain variation. Experimental results demonstrate that the proposed method can achieve superior performance on several benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Benchmarking

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Lightweight spatial pyramid pooling network for real-time semantic segmentation

Lightweight spatial pyramid pooling network for real-time se...

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2021 International Symposium on intelligent Robotics and Systems, ISoIRS 2021

作者： Jiang, Peilong Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China

In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Energy-Efficient Virtual Machine Migration Algorithm in Data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd International conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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Spectral clustering with robust self-learning constraints

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Artificial Intelligence 2023年 320卷

作者： Bai, Liang Qi, Minxue Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Institute of Intelligent Information Processing Shanxi University Shanxi Taiyuan030006 China

Spectral clustering is a leading unsupervised classification algorithm widely used to capture complex clusters in unlabeled data. Additional prior information can further enhance the quality of spectral clustering results to satisfy users' expectations. However, it is challenging for users to find the prior information under unsupervised scenes. To get rid of the deficiency, we propose a spectral clustering model with robust self-learning constraints. In this model, we first extend the optimization problem of spectral clustering by seeing label constraints as variables to learn the constraints and the clustering result simultaneously. Furthermore, we add a robust term to the proposed model so that we can learn multiple groups of label constraints to guide the clustering process and find a robust self-constrained spectral clustering result. The robust term can reduce the impact of uncertainty in the quality of a single set of label constraints on the performance of the proposed model. An iterative strategy with update formulas for variables is proposed to solve the self-constrained spectral clustering problem. We provide the theoretical analysis to explain the importance of the learned constraints in spectral clustering. Furthermore, we analyze the convergence of our optimization scheme. Finally, we have done many experiments on benchmark data sets to illustrate the effectiveness of the proposed algorithm. © 2023 Elsevier B.V.

关键词： Cluster analysis

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