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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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GSArch: Breaking Memory Barriers in 3D Gaussian Splatting Training via Architectural Support 31

GSArch: Breaking Memory Barriers in 3D Gaussian Splatting Tr...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： He, Houshu Li, Gang Liu, Fangxin Jiang, Li Liang, Xiaoyao Song, Zhuoran Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331506476

3D Gaussian Splatting (3DGS) introduces a novel methodology for representing scenes with anisotropic 3D Gaussian primitives, achieving exceptional quality and rendering speed in neural scene representation (NSR). However, the insufficient training speed of 3DGS limits its applicability in tasks that require online learning to perceive dynamic environments, such as autonomous driving and embodied intelligence. Although recent work, GSCore, has introduced a specialized accelerator for the rendering process of 3DGS, it overlooks the time-consuming backward propagation during 3DGS *** this paper, we propose GSArch, a hardware architecture designed to overcome memory barriers and boost the efficiency of 3DGS training. Through a thorough characterization of 3DGS training, we identify three root causes of inefficiency: redundant data loading from off-chip memory, time-consuming atomic write operations, and severe bank conflicts during on-chip buffer reading. To address these challenges, GSArch introduces three architectural innovations. First, acknowledging that Gaussians vary in shape and often span multiple pixels, with larger Gaussians causing more repetitive data loading, GSArch employs hybrid memory management. This approach categorizes Gaussians into 'hot' and 'cold' ones, storing hot Gaussians in a fast but small on-chip buffer to reduce redundant loading while minimizing hardware costs. Second, GSArch leverages the informativeness variability of Gaussians' gradients to filter out low-contribution gradients, significantly reducing atomic operations. Lastly, a rearrangement unit is designed to pack conflicting memory read requests into non-conflicting bundles. Our evaluation results demonstrate that GSArch achieves up to 6.49 × and 15.42 × speedups compared to Nvidia A100 and Jetson AGX Xavier, respectively, with substantially lower energy consumption and negligible image quality loss. © 2025 IEEE.

关键词： Gaussian distribution

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FPGA-implementation of the Decision Block for Numerical Prediction System Based on Artificial Neural Networks

FPGA-implementation of the Decision Block for Numerical Pred...

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2023 Seminar on Information Computing and Processing, ICP 2023

作者： Krutikov, Alexander K. Meltsov, Vasily Yu. Strabykin, Dmitry A. Vyatka State University Faculty of Automation and Computer Engineering Department of Electronic Computers Kirov Russia

ISBN: (纸本)9798350369830

This paper considers the method of cascading neural network modules to build the operating device of a specialized computing system. Each module implements some model of neural network. The cascade of modules is formed strictly in accordance with the logical links, found after the analysis of training samples. The structure of the input vector is fragmented according to the logical dependencies of the parameter set. Individual nodes of the cascade process certain fragments of the input vector, taking into account the features of neural network models used in them. The library containing the basic elements was prepared in the Quartus II CAD. The prototype with the ensemble of the main modules is implemented on the Cyclone III FPGA. Comparative assessments of the hardware- and software- implementation of the system for predicting numerical results in sports disciplines and multisport competitions are presented. © 2023 IEEE.

关键词： Neural networks

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Parameters Design of a Digital VSG Control Strategy for Grid-forming DG Systems Applications 15

Parameters Design of a Digital VSG Control Strategy for Grid...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： do Nascimento, Thiago F. Salazar, Andres O. Department of Computer Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil

ISBN: (纸本)9798350314182

This paper presents a controller design procedure for a digital virtual synchronous generator (VSG) strategy applied to grid-forming distributed generation (DG) systems. The main contribution of the design methodology is to support researchers who are implementing DG systems with VSG-based power flow control strategies in a digital environment. For this purpose, an impedance model describes the DG power flow dynamic characteristics. Then, the digital VSG controllers parameters design uses the root-locus method (RLM) to achieve the desired dynamic performance. A case study demonstrates the effectiveness of the DG system with a VSG control strategy designed based on the proposed design method. Furthermore, the results evaluate the VSG controller's dynamic performance under the system's equivalent impedance variations. ©2023 IEEE.

关键词： Distributed generation impedance model power flow control root-locus method virtual synchronous generator

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Misaligned Visible-Thermal Object Detection: A Drone-based Benchmark and Baseline

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Song, Kechen Xue, Xiaotong Wen, Hongwei Ji, Yingying Yan, Yunhui Meng, Qinggang School of Mechanical Engineering and Automation Northeastern University ShenyangLiaoning 110819 China Department of Computer Science Loughborough University LE11 3TU Loughborough U.K

Multispectral object detection has achieved remarkable results due to its ability to fuse information from visible and thermal modalities in recent years. However, the existing visible-thermal datasets are constructed based on manually aligned image pairs, which cannot fully represent the challenges of real-world scenarios where image pairs are often misaligned. Existing methods for visible-thermal object detection are based on aligned data and are limited by the accuracy of registration. To address the above issues, we propose a dataset, namely DVTOD, which is a misaligned visible-thermal object detection dataset captured by drones. DVTOD includes 16 challenging attributes and 54 capture scenes. Furthermore, we introduce a cross-modal alignment detector (CMA-Det) for misaligned visible-thermal object detection. Firstly, we design an alignment network to estimate the visible-to-thermal deformation field, which is used to correct for misalignment of the corresponding visible and thermal features. Secondly, we propose a strategy called Object Search Rectification (OSR) to improve the robustness of feature alignment. To better remove the interference of complex backgrounds, a bi-directional feature correction fusion module (BFCFM) is designed to calibrate bimodal features by exploiting the correlation of channel and spatial information between two modalities. CMA-Det outperforms existing methods on the DVTOD dataset and two other visible-thermal object detection datasets. The dataset and code will be published at https://***/VDT-2048/DVTOD. IEEE

关键词： Real time systems

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Open-Set Recognition in Unknown DDoS Attacks Detection With Reciprocal Points Learning

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IEEE Access 2024年 12卷 56461-56476页

作者： Shieh, Chin-Shiuh Ho, Fu-An Horng, Mong-Fong Nguyen, Thanh-Tuan Chakrabarti, Prasun National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung82444 Taiwan Nha Trang University Department of Electronics and Automation Engineering Nha Trang650000 Viet Nam Sir Padampat Singhania University Department of Computer Science and Engineering Rajasthan Udaipur313601 India

The internet, a cornerstone of modern life, has profound implications across personal, business, and society. However, its widespread use has posed challenges, especially concerning privacy and cybersecurity. Besides, the threats on the internet are increasing in terms of danger, intensity, and complexity. Distributed denial-of-service (DDoS) attacks have emerged as a common and dangerous cybersecurity threat capable of disabling the network systems of targeted organizations and services. Therefore, various security strategies, such as firewalls and intrusion detection systems (IDS), are employed to protect against DDoS attacks. Enhancing the defensive capabilities of IDS systems through machine learning (ML) and deep learning (DL) technologies is a significant trend nowadays. However, despite notable successes, detecting DDoS attacks using ML and DL technologies still faces challenges, especially with Unknown DDoS Attacks. In this research, the primary goal is to address the unknown DDoS detection problem through efficient and advanced techniques. Our proposed method, CNN-RPL, integrates Convolutional Neural Network (CNN) with Reciprocal Points Learning (RPL), a novel Open-Set Recognition (OSR) technology. This model can effectively handle both known and unknown attacks. The CNN-RPL model demonstrates excellent results, achieving an accuracy exceeding 99.93% against known attacks in the CICIDS2017 dataset. Simultaneously, the model achieves a commendable average accuracy of up to 98.51% against unknown attacks in the CICDDoS2019 dataset. In particular, the CNN-RPL model simplifies the architecture of the deep neural network by significantly reducing the number of training parameters without compromising defense capabilities. Therefore, our proposed method is genuinely efficient, particularly flexible, and lightweight compared to traditional methods. This can equip organizations and businesses with a highly applicable yet powerful security approach against the evolv

关键词： Denial-of-service attack

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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A Method for Detecting Depression in Adolescence Based on an Affective Brain‑computer Interface and Resting‑State Electroencephalogram Signals

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Neuroscience Bulletin 2025年第3期41卷 434-448页

作者： Zijing Guan Xiaofei Zhang Weichen Huang Kendi Li Di Chen Weiming Li Jiaqi Sun Lei Chen Yimiao Mao Huijun Sun Xiongzi Tang Liping Cao Yuanqing Li School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China The Affiliated Brain Hospital Guangzhou Medical UniversityGuangzhou 510370China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China

Depression is increasingly prevalent among adolescents and can profoundly impact their ***,the early detection of depression is often hindered by the timeconsuming diagnostic process and the absence of objective *** this study,we propose a novel approach for depression detection based on an affective brain-computer interface(aBCI)and the resting-state electroencephalogram(EEG).By fusing EEG features associated with both emotional and resting states,our method captures comprehensive depression-related *** final depression detection model,derived through decision fusion with multiple independent models,further enhances detection *** experiments involved 40 adolescents with depression and 40 matched *** proposed model achieved an accuracy of 86.54%on cross-validation and 88.20%on the independent test set,demonstrating the efficiency of multi-modal *** addition,further analysis revealed distinct brain activity patterns between the two groups across different *** findings hold promise for new directions in depression detection and intervention.

关键词： Depression detection Brain-computer interface EEG Multimodal

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