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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Zhang, Lingyu Chen, Yongqi Qin, Xinkun He, Wanlin Qi, Jia Sun, Yongquan School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781837242672

Accurate prediction of the life degradation trajectory of the clock battery, a critical component of the power meter, is essential for assessing the reliability of the device. Addressing the computational inefficiency and limited generalization capabilities of traditional empirical recession models, this paper introduces a prediction model based on the Informer neural network. The battery degradation data are initially segmented and reassembled using a sliding window strategy. The encoder-decoder structure is then employed to achieve multilevel feature extraction from the input sequences. By setting various prediction starting points, the model demonstrates high sensitivity in predicting battery degradation under temperature conditions identical to those in the training set. Additionally, the model is capable of predicting the degradation trajectories of batteries under different temperature conditions, such as 60℃. © The Institution of engineering & technology 2024.

关键词： Decoding

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme

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Digital Communications and Networks 2025年第1期11卷 60-70页

作者： Zhuo Chen Bowen Zhu Chuan Zhou College of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China

Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.

关键词： Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network

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Co-modulated interface binding energy and electric field distribution of layer-structured PVDF–LDPE dielectric composites with BaTiO3: experiment and multiscale simulations

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Frontiers of Materials science 2023年第3期17卷 155-167页

作者： Ruitian Bo Chunfeng Wang Yongliang Wang Peigang He Zhidong Han School of Materials Science and Chemical Engineering Harbin University of Science and TechnologyHarbin 150040China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Engineering Dielectrics and Its Application(Ministry of Education) Harbin University of Science and TechnologyHarbin 150080China

The layer-structured composites were built by the dielectric and insulating layers composed of polyvinylidene fluoride(PVDF)and low-density polyethylene(LDPE)composites containing barium titanate(BT)to modulate the dielectric and energy storage properties of the *** simulations on the interface models for molecular dynamics and the geometric models for finite element analysis were performed together with the experimental characterization of the morphology,dielectric,and energy storage properties of the *** results revealed that polyethylene as an insulating layer played a successful role in modulating dielectric permittivity and breakdown strength while BT particles exerted positive effects in improving the miscibility between the composed layers and redistributing the electric *** strong interface binding energy and the similar dielectric permittivity between the PVDF layer and the BT20/LDPE layer made for the layer-structured composites with a characteristic breakdown strength(Eb)of 188.9 kV·mm^(−1),a discharge energy density(Ud)of 1.42 J·cm^(−3),and a dielectric loss factor(tanδ)of 0.017,which were increased by 94%,141%,and decreased by 54%in comparison with those of the BT20/PVDF composite,respectively.

关键词： dielectric composite layer structure low-density polyethylene polyvinylidene fluoride molecular dynamics simulation finite element analysis

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Review of Research on Improved PID Control in Electro-hydraulic Servo System

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Recent Patents on engineering 2024年第1期18卷 54-68页

作者： Li, Jianying Li, Weidong Liang, Heng Kong, Lingbing Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin China College of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China

Background: Proportional-integral-differential (PID) controller is widely used in the engineering field because of its simple structure, high control accuracy, and easy operation. Different patented PID control technologies have their own advantages, disadvantages, and application scenarios. Objective: By analyzing and discussing the improved PID control techniques in the electro-hydraulic servo system, some valuable conclusions have been drawn to predict the future research and development of PID control techniques. Methods: The improved PID control techniques applied to electro-hydraulic servo systems are clas-sified into three categories: PID parameter tuning technology, PID parameter online adjustment strategy and compound control strategy combined with PID algorithm. Results: By comparing the principles and characteristics of the above techniques, the advantages, disadvantages and application scenarios of each are analyzed, and the further development of PID control technology is discussed. Conclusion: Based on the characteristics of electro-hydraulic servo systems, the further combination of the three types of technology can make up for the shortcomings of PID algorithms to form a control strategy with high control accuracy, high robustness, and fast response speed. © 2024, Bentham science Publishers. All rights reserved.

关键词： Two term control systems

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Microstructure evolution of extruded Mg-RE-Ag alloy during short-time heat treatment

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Journal of Rare Earths 2024年第12期42卷 2350-2361,I0006页

作者： Zehua Li Jinghuai Zhang Shujuan Liu Jiaming Hao Liyuan Liu Yuying He Haodong Zhang Xingkai Jiao Xiaobo Zhang Ruizhi Wu Key Laboratory of Superlight Material and Surface Technology Ministry of EducationCollege of Material Science and Chemical EngineeringHarbin Engineering UniversityHarbin150001China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology Nanjing Institute of TechnologyNanjing211167China

Microstructure regulation via short-time heat treatment is conducive to the optimization in the microstructure and properties of precipitable magnesium(Mg)alloys,but there is currently a lack of relevant *** this work,the microstructure evolution of a Mg-RE-Ag alloy during different short-time heat treatments was characterized and *** results show that extreme short-time heat treatment(ESHT,e.g.,2 min)at 450-480℃can greatly increase solute concentration in Mg matrix through the rapid re-dissolution of the second-phase and simultaneously maintain fine grains,while the ESHT at a too high temperature(e.g.,510℃)is not suitable due to excessive grain growth and coarse second phase regenerated at grain *** is found that 480℃is the approximate critical temperature for appropriate ESHT,and further prolongation of the time will lead to excessive grain *** is suggested that in addition to grain boundary migration,grain rotation is activated,resulting in the annihilation of high-angle grain boundaries with relatively low misorientation,as well as the reduction in the ability of the residual second phase to pin grain *** addition,the reasons for the abnormal grain boundary segregation and grain boundary continuous phase were analyzed from the perspective of interfacial *** study provides a basis for effective microstructure regulation of Mg-RE alloys.

关键词： Magnesium alloy Rare earths Microstructure Heat treatment Precipitates

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Indoor Uncertain Semantic Trajectory Similarity Join

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Journal of computer science & technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

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Optimization of porous structures via machine learning for solar thermochemical fuel production

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Progress in Natural science:Materials International 2024年第5期34卷 895-906页

作者： Da Xu Lei Zhao Meng Lin Harbin Institute of Technology Department of Mechanical and Energy Engineering Southern University of Science and Technology SUSTech Energy Institute for Carbon Neutrality Southern University of Science and Technology

Porous reactant is the key component in solar thermochemical reactions, significantly affecting the solar energy conversion and fuel production performance. Triply periodic minimal surface（TPMS） structures, with analytical expressions and predictable structure-property relationships, can facilitate the design and optimization of such structures. This work proposes a machine learning-assisted framework to optimize TPMS structures for enhanced reaction efficiency, increased fuel production,and reduced temperature gradients. To mitigate the computational cost of conventional high-throughput optimization, neural network regression models were used to for performance prediction based on input features. The training dataset was generated using a three-dimensional multiphysics model for the thermochemical reduction driven by concentrated solar energy considering fluid flow, heat and mass transfer, and chemical reacions. Both uniform and gradient structures were initially assessed by the three-dimensional model showing gradient design in c and ω were necessary for performance enhancement. Further, with our proposed optimization framework, we found that structures with parameters c1= c2= 0.5（uniform in c） and ω1= 0.2, ω2= 0.8（gradient in ω） achieved the highest relative efficiency(fchem/fchem,ref) of 1.58, a relative fuel production(Δδ/Δδref) of 7.94, and a max relative temperature gradient（dT/dy）/（dT/dy）refof 0.26. Kinetic properties,i.e., bulk diffusion and surface exchange coefficient, were also studied showing that for materilas with slow kinetics, the design space in terms of c and ω were highly limited compared to fast kinetics materials. Our framework is adaptable to diverse porous structures and operational conditions, making it a versatile tool for screening porous structures for solar thermochemical applications. This work has the potential to advance the development of efficient solar fuel production systems and scalable industrial applications in renewable energy

关键词： Regression analysis

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