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Infrared Single Pixel Imaging Based on Generative Adversarial Network

Journal of Shanghai Jiaotong University (science) 2023年 1-11页

作者： Jiang, Yilin Zhang, Yilong Zhang, Fangyuan College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned. It is effective to use optical lens instead of traditional optical reflection system, which can reduce the loss in optical path transmission. At the same time, deep learning neural network is used for control. An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained. The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling, the system finally can realize the single pixel recovery imaging of the infrared image, which improves the resolution of the image while ensuring high sensitivity. © 2023, Shanghai Jiao Tong University.

关键词： Generative adversarial networks

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Influence of power dissipation value and deformation activation energy on recrystallization in compression deformation behavior of Mg-Li-Zn-Y alloy

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Journal of Rare Earths 2024年第12期42卷 2341-2349,I0006页

作者： Di An Bingyu Qian Ruizhi Wu Xiang Wang Legan Hou Xiaochun Ma Jinghuai Zhang Key Laboratory of Superlight Materials&Surface Technology Ministry of EducationCollege of Materials Science&Chemical EngineeringHarbin Engineering UniversityHarbin150001China College of Materials Science and Engineering Heilongjiang University of Science and TechnologyHarbin150022China

The thermal compression deformation behavior of Mg-6Li-11Zn-2.5Y alloy was systematically investigated by using the isothermal hot compression tests using the Gleeble-3500 thermal mechanical physical simulation system with the conditions of the temperature of 513-593 K and the strain rate of *** constitutive model and thermal processing map were *** variation pattern of the unstable region under different strains was analyzed and the safe processing region was *** influence of different power dissipation values and deformation activation energies on the dynamic recrystallization mechanism was *** results demonstrate that the deformation activation energy is 156.08 k J/*** unstable region gradually expands from the low temperature and low strain region to the high temperature and high strain region with the strain *** the unstable region,adiabatic shear band pear in a-Mg phase,of which the main reason is the poor processing formability in the unstable *** dominant nucleation mechanism is discontinuous dynamic recrystallization under the conditions of high power dissipation and low deformation activation ***,under the conditions of low power dissipation and high deformation activation energy,the nucleation mechanism is mainly driven by the particle stimulated nucleation.

关键词： Mg-Li Constitutive model Processing map Dynamic recrystallization Rare earths

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Study on the adsorption performance and regeneration of lignin-derived graphitic carbon for H_(2)S

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Particuology 2024年第11期94卷 197-210页

作者： Fen Li Ruiying Wang Ying Yang Menglong Zheng Qiushuang Li Dongdong Yang Huiyu Chen Key Laboratory of Green Chemical Engineering and Technology of College of Heilongjiang Province School of Materials Science and Chemical EngineeringHarbin University of Science and TechnologyHarbin150080China

In pursuit of effective adsorption materials for malodorous gases such as H_(2)S and to broaden the utilization avenues of lignin waste,this study employed the direct pyrolysis method to synthesize three types of alkali lignin graphitized carbons,namely C-800,KC-700,and *** them,KEC-700 exhibits a high specific surface area of 1672.9 m^(2)/g,significantly superior H_(2)S adsorption performance compared to other materials,an adsorption breakthrough time of up to 220 min,and a sulfur capacity of 67.1 mg/*** analysis showed that the more oxygen-containing functional groups of lignin charcoal and the larger specific surface area facilitated the adsorption of H_(2)*** reaching adsorption saturation,the degree of graphitization of lignin carbon *** H_(2)S adsorption products primarily manifest as elemental sulfur and sulfate within the pores of lignin carbon measuring less than 2 *** thermal regeneration,the charcoal effectively eliminates the elemental sulfur adsorption ***,sulfate removal proved unsatisfactory,as the adsorption efficiency of KEC-700 following two thermal regenerations was approximately 41%of that observed for fresh samples.

关键词： Hydrogen sulfide Alkali lignin Lignin carbon Regeneration

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Improved Harris Hawks Algorithm and Its Application in Feature Selection

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computers, Materials & Continua 2024年第10期81卷 1251-1273页

作者： Qianqian Zhang Yingmei Li Jianjun Zhan Shan Chen School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China College of Systems Engineering National University of Defense TechnologyChangsha410073China

This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration ***,and a lack of thorough exploitation *** tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation *** effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test *** findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving ***,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional *** analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight *** results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature ***,IHHO-FS shows strong competitiveness relative to numerous algorithms.

关键词： HHO IHHO population diversity energy factor update strategy deep exploitation strategy feature selection

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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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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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Interpretable machine learning for chiral induced symmetry breaking of spin density boosting hydrogen evolution

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Journal of Energy Chemistry 2025年第4期103卷 68-78页

作者： Xin Song Zhonghua Li Li Sheng Yang Liu School of Chemistry and Chemical Engineering Key Laboratory of Microsystems and Microstructures ManufacturingMinistry of EducationHarbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions(HER).This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs(M=In,Bi,and Sb)single-atom catalysts(SACs).The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C_(2V)in achiral SACs to C_(2)in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superiorα-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity(CISS)effect,with spin-upα-electron density reaching 3.43×10^(-3)e/Bohr^(3)at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.

关键词： Symmetry breaking Machine learning Spin density CISS DFT Hydrogen evolution reaction

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Steering the energy sharing of electrons in nonsequential double ionization with orthogonally polarized two-color field

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Chinese Physics B 2024年第8期33卷 248-252页

作者：樊光琦杨志杰孙烽豪郑金梅韩云天黄明谦刘情操 College of Science Harbin Institute of TechnologyWeihai 264209China School of Information Science and Engineering Harbin Institute of TechnologyWeihai 264209China Harbin Institute of Technology Weihai 264209China

Using the semiclassical ensemble model,the dependence of relative amplitude for the recollision dynamics in nonsequential double ionization(NSDI)of neon atom driven by the orthogonally polarized two-color field(OTC)laser field is theoretically *** the dynamics in two typical collision pathways,recollision-impact-ionization(RII)and recollisionexcitation with subsequent ionization(RESI),is systematically *** results reveal that the V-shaped structure in the correlated momentum distribution is mainly caused by the RII mechanism when the relative amplitude of the OTC laser field is zero,and the first ionized electrons will quickly skim through the nucleus and share few energy with the second *** the relative amplitude increases,the V-shaped structure gradually disappears and electrons are concentrated on the diagonal in the electron correlation spectrum,indicating that the energy sharing after electrons collision is symmetric for OTC laser fields with large relative *** studies show that changing the relative amplitude of the OTC laser field can efficiently control the electron–electron collisions and energy exchange efficiency in the NSDI process.

关键词： nonsequential double ionization correlated electron–electron momentum distribution energy sharing of electrons orthogonally polarized two-color field laser field semiclassical ensemble models

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Voice Spoofing Detection using Feature Adaptive One-Class Classification Based on Graph Neural Network 24

Voice Spoofing Detection using Feature Adaptive One-Class Cl...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Lan, Haiyan Li, Yike Wang, Yuhua Dong, Linfan Han, Zhiying Yang, Kai College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710834

To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unknown attacks, this paper combines the representation ability of graph neural networks with the learning objective of one-class classification, and proposes a feature adaptive one-class voice detection framework based on graph neural networks. The core idea of the feature adaptive one-class classification loss (FAOC) in the framework is to cluster the real voices affected by similar factors more closely, learn several classification boundaries of the real voices, and optimize the distance of the real voices with different distributions, so that the model can obtain more general features of the real voices. At the same time, the relationship between different frequency bands or time segments of the voice is modeled by using graph neural networks, and the graph embedding features with strong recognition ability are obtained, which can better exploit the performance advantages of FAOC. A series of experiments are conducted on the ASVspoof2019 LA and ASVspoof2021 LA datasets, and the results on the ASVspoof2021 LA show that the EER and min-tDCF are improved by 45.8% and 48.2% compared to the baseline. The results show that the proposed method has good performance for voice synthesis attack detection. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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