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A Survey of Reliability Issues Related to Approximate Circuits

Journal of computer science & technology 2023年第2期38卷 273-288页

作者：王真徐荣臣陈嘉诚肖杰 College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310000China

As one of the most promising paradigms of integrated circuit design,the approximate circuit has aroused widespread concern in the scientific *** takes advantage of the inherent error tolerance of some applications and relaxes the accuracy for reductions in area and power *** paper aims to provide a comprehensive survey of reliability issues related to approximate circuits,which covers three concerns:error characteristic analysis,reliability and test,and reliable design involving approximate *** error characteristic analysis is used to compare the outputs of the approximate circuit with those of its precise counterpart,which can help to find the most appropriate approximate design for a specific application in the large design *** the approximate design getting close to physical realization,manufacturing defects and operational faults are inevitable;therefore,the reliability prediction and vulnerability test become increasingly ***,the research on approximate circuit reliability and test is insufficient and needs more ***,although there is some existing work combining the approximate design with fault tolerant techniques,the reliability-enhancement approaches for approximate circuits are lacking.

关键词： approximate circuit error metric fault tolerance test reliability

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Overhead-free Noise-tolerant Federated Learning: A New Baseline

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Machine Intelligence Research 2024年第3期21卷 526-537页

作者： Shiyi Lin Deming Zhai Feilong Zhang Junjun Jiang Xianming Liu Xiangyang Ji Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150000China Department of Automation Tsinghua UniversityBeijing100084China

Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while keeping their data private. However, in real-world scenarios, the supervised training data stored in local clients inevitably suffer from imperfect annotations, resulting in subjective, inconsistent and biased labels. These noisy labels can harm the collaborative aggregation process of FL by inducing inconsistent decision boundaries. Unfortunately, few attempts have been made towards noise-tolerant federated learning, with most of them relying on the strategy of transmitting overhead messages to assist noisy labels detection and correction, which increases the communication burden as well as privacy risks. In this paper, we propose a simple yet effective method for noise-tolerant FL based on the well-established co-training framework. Our method leverages the inherent discrepancy in the learning ability of the local and global models in FL, which can be regarded as two complementary views. By iteratively exchanging samples with their high confident predictions, the two models “teach each other” to suppress the influence of noisy labels. The proposed scheme enjoys the benefit of overhead cost-free and can serve as a robust and efficient baseline for noise-tolerant federated learning. Experimental results demonstrate that our method outperforms existing approaches, highlighting the superiority of our method.

关键词： Federated learning noise-label learning privacy-preserving machine learning edge intelligence distributed machine learning

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Effects of sex on fat deposition through gut microbiota and short-chain fatty acids in weaned pigs

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Animal Nutrition 2024年第2期17卷 100-109页

作者： Linfang Yao Bo Wang Yu Wang Jie Bai Yiming Gao Xintong Ru Chongpeng Bi Jianping Li Anshan Shan College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030China

Nitrogen pollution resulting from excessive feed consumption poses a significant challenge for modern swine *** nutrition technology seems to be an effective way to solve this problem;therefore,understanding the law of pig body composition deposition is a *** study investigated the sex effects on growth performance,body composition,nutrient deposition,gut micro-biota,and short-chain fatty acids(SCFA)in weaned *** weaned pigs were randomly allocated to 2 treatments according to the sex of *** individual pig was considered as a treatment *** body weights(BW 5,7,11,15,20,and 25 kg)were chosen as experimental points;for each point 10 piglets close to the average BW(5 males and 5 females)were slaughtered,and there was one growth phase between each 2 BW *** indicated that the males had higher average daily gain(ADG)and average daily feed intake(ADFI)compared to the females(P<0.05)at growth phases 15 to 20 kg BW and 20 to 25 kg ***,males at 20 kg BW had higher body fat content than females(P<0.10).Males showed a higher body fat(P<0.05)deposition rate at phase 15 to 20 kg BW(P<0.05)than *** pigs at 20 kg BW,the relative abundance of Ruminococcaceae UCG-005,Clostridium,Chris-tensenellaceae_R-7_group,and Peptostreptococcaceae was significantly increased in males(P<0.05)but that of Bifidobacterium was decreased(P<0.05).At 25 kg BW,the relative abundance of Ruminococca-ceae_NK4A214_group,Fibrobacter,Ruminococcaceae UCG-009,Ralstonia,Klebsiel,and Christensenella-ceae_R-7_group in males was higher when compared with females(P<0.05).In terms of SCFA,females exhibited higher concentrations of propionate compared to males(P<0.05).The results of the current study indicated that sex influenced fat deposition through changes in the composition of gut microbiota and the content of SCFA,which has significant implications for the realization of precision nutrition in modern swine production.

关键词： Sex Body composition Nutrient deposition Gut microbiota Short-chain fatty acid Weaned piglet

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Effect of changing the proportion of C16:0 and cis-9 C18:1 in fat supplements on rumen fermentation,glucose and lipid metabolism,antioxidation capacity,and visceral fatty acid profile in finishing Angus bulls

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Animal Nutrition 2024年第3期18卷 39-48页

作者： Haixin Bai Lubo Wang Modinat Tolani Lambo Yang Li Yonggen Zhang College of Animal Science and Technology Northeast Agricultural UniversityHarbin 150030China

This study evaluated the effects of different proportions of palmitic(C16:0)and oleic(cis-9 C18:1)acids in fat supplements on rumen fermentation,glucose(GLU)and lipid metabolism,antioxidant function,and visceral fat fatty acid(FA)composition in Angus *** design of the experiment was a randomized block design with 3 treatments of 10 animals each.A total of 30 finishing Angus bulls(21±0.5 months)with an initial body weight of 626±69 kg were blocked by weight into 10 blocks,with 3 bulls per *** bulls in each block were randomly assigned to one of three experimental diets:(1)control diet without additional fat(CON),(2)CON+2.5%palmitic calcium salt(PA;90%C16:0),(3)CON+2.5%mixed FA calcium salts(MA;60%C16:0+30%cis-9 C18:1).Both fat supplements increased C18:0 and cis-9 C18:1 in visceral fat(P<0.05)and up-regulated the expression of liver FA transport protein 5(FATP5;P<0.001).PA increased the insulin concentration(P<0.001)and aspartate aminotransferase activity(AST;P=0.030)in bull's blood while reducing the GLU concentration(P=0.009).PA increased the content of triglycerides(TG;P=0.014)in the liver,the content of the C16:0 in visceral fat(P=0.004),and weight gain(P=0.032),and up-regulated the expression of liver diacylglycerol acyltransferase 2(DGAT2;P<0.001)and stearoyl-CoA desaturase 1(SCD1;P<0.05).MA increased plasma superoxide dismutase activity(SOD;P=0.011),reduced the concentration of acetate and total volatile FA(VFA)in rumen fluid(P<0.05),and tended to increase plasma non-esterified FA(NEFA;P=0.069)***,high C16:0 fat supplementation increased weight gain in Angus bulls and triggered the risk of fatty liver,insulin resistance,and reduced antioxidant *** adverse effects were alleviated by partially replacing C16:0 with cis-9 C18:1.

关键词： Palmitic acid Oleic acid Lipid metabolism Lipotoxicity Finishing bull

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An Online Exploratory Maximum Likelihood Estimation Approach to Adaptive Kalman Filtering

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 228-254页

作者： Jiajun Cheng Haonan Chen Zhirui Xue Yulong Huang Yonggang Zhang the College of Intelligent Systems Science and Engineering Harbin Engineering University the Engineering Research Center of Navigation Instruments Ministry of Education the College of Intelligent Systems Science and Engineering the College of Future Technology Harbin Engineering University IEEE

Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state ***, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.

关键词： Adaptive Kalman filtering coordinate descent maximum likelihood estimation mini-batch optimization unknown noise covariance matrix

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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Engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

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Acta Mathematica Scientia 2024年第3期44卷 1145-1164页

作者：冯立超张春艳曹进德武志辉 College of Science North China University of Science and TechnologyTangshan063210China School of Mathematics Southeast UniversityNanjing210096China Department of Mathematics Harbin University of Science and TechnologyHarbin150080China

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential ***,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential *** comparison with autonomous cases,non-autonomous systems are of great interest and represent an important ***,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are *** should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

关键词： hybrid neutral stochastic differential delay system discrete feedback control general stabilization polynomial stabilization

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A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

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computers, Materials & Continua 2024年第6期79卷 4297-4318页

作者： Jun Wang Changfu Si Zhen Wang Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production ***,there are more and more cyber-attacks targeting industrial control *** ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber *** intrusion detection methods still suffer from low accuracy and a high false alarm ***,it is important to build a more efficient intrusion detection *** paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing *** algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority *** approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority *** the experimental phase,the detection performance of the method is verified using two data *** results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State university in the United States,the accuracy rate also reaches 85.5%.

关键词： Intrusion detection convolutional neural network bidirectional long short-term memory neural network multi-head self-attention mechanism

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Microstructural evolution and mechanical properties of Ti43Al alloy by directional annealing

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China Foundry 2025年第3期22卷 273-282页

作者： Li Zhou Jie-ren Yang Yun-lu Ma Ze-dong Liu Rui-run Chen College of Materials Science and Engineering Sichuan UniversityChengdu 610065SichuanChina School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The directional annealing technique is widely used to prepare columnar grains or single *** investigate the effect of hot zone temperature and temperature gradient on the growth of columnar crystals,Ti43Al alloys were heat treated by the directional annealing technique and their mechanical properties were *** results show that columnar grains with a maximum size of 22.29 mm can be obtained at a hot zone temperature of 1,350℃ and a temperature gradient of 8 K·mm^(-1).During the directional annealing process,Ti43Al alloys are heated toαsingle-phase domain to start the phase *** grains with a microstructure of fully lamellar colonies are obtained at different hot zone temperatures and temperature *** distribution of the orientation difference for theα2 phase was found to be more random,suggesting that the growth of the columnar crystals may be stochastic in *** testing results show that the strength and elongation of directional annealed Ti43Al alloy at 1,400℃-8 K·mm^(-1) are 411.23 MPa and 2.29%,and the remaining directional annealed alloys show almost plasticity.

关键词： TiAl alloys directional annealing microstructural evolution EBSD,mechanical properties

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi-directional optimization iot devices swarm deep learning

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