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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Investigating the 3D cardiomyocyte arrangement in human interventricular septum sample using X-ray phase-contrast microtomography 17

Investigating the 3D cardiomyocyte arrangement in human inte...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Li, Wenfeng Li, Zhaorui Du, Yongbin Wang, Shunli Zhengzhou University of Science and Technology Institute of Big Data and Artificial Intelligence Zhengzhou China Harbin Institute of Technology School of Instrumentation Science and Engineering Harbin China Heilongjiang University of Science and Technology School of Electrical and Control Engineering Harbin China

ISBN: (纸本)9798350387384

A good knowledge of the three-dimensional cardiomyocyte arrangement in interventricular septum is crucial for understanding both ventricle's functions. In this preliminary study, we investigate the local myocardial architecture in a human interventricular septum sample imaged by X-ray phase-contrast micro-tomography. This imaging modality allows us to visualize the complex structure of myocardial tissue at high resolution. We first cut the reconstruction data into sub-volume of 32×32×32 voxels (i.e. 112×112×112 μm3) in size, and compute the elevation angle of the local cardiomyocyte using Fast Fourier Transform based method. Then, we describe the distribution of cardiomyocyte elevation angle in the sample globally and locally. We note that, in part of regions, the running of cardiomyocyte aggregates is winding. Though the function of this specific cardiomyocyte arrangement manner is unclear, its existence is inspiring and merits further investigation. © 2024 IEEE.

关键词： Fast Fourier transforms

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Research on hyperspectral image classification based on improved deep cross-domain few-shot learning 5

Research on hyperspectral image classification based on impr...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Luo, Xinhua Wang, Qingyan Liang, Xintao Zhang, Zhen School Of Measurement And Control Technology And Tele Communications Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

In this study, an enhanced method and system for hyperspectral image classification are presented, based on deep cross-scene few-shot learning. This pertains to the domain of remote sensing image processing technology and addresses the prevalent issues of inadequate classification performance in existing techniques for hyperspectral image categorization. The core aspects of this invention encompass the following: utilization of two mapping layers to standardize the input dimensions between the source and target domains;the deployment of an embedded feature extractor to incorporate the image cubes from both the source and target domains into a space-spectral embedding environment simultaneously, ensuring that like samples are closely aligned and dissimilar ones are distanced. Through gauging the distances between each class of unlabeled and labeled samples in this space-spectral embedding zone, learning with a few number of examples in both the source and target domains is achieved. Furthermore, a conditional domain discriminator is employed to mitigate domain shifts between domains, thus solidifying the domain stability of the extracted spatial-spectral embedding features. This innovative approach allows for high-precision hyperspectral data categorization, even when only a few examples are available. © 2023 ACM.

关键词： Image enhancement

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Sampled-Data Output Voltage Practical Tracking for Uninterruptible Power Supply Systems with Transmission Delays

Sampled-Data Output Voltage Practical Tracking for Uninterru...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Jia, Mengqi Liu, Wei Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331504755

This paper aims at addressing output voltage practi-cal tracking problem for uninterruptible power supply systems by devising an aperiodic sampled-data control scheme. We consider the presence of transmission delay in the input channel. We demonstrate that the problem can be solved by a predictor-based sampled-data dynamic output feedback control law. Moreover, the tracking error can converge to a prescribed neighborhood of the origin. © 2024 IEEE.

关键词： Delay control systems

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Dosage- and site-dependent retention of black carbon and polycyclic aromatic hydrocarbons in farmland soils via long-term biochar addition

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Carbon Research 2024年第1期3卷 1-14页

作者： Zhang, Jun Wang, Yinghui Shi, Yameng Yang, Biwei Zhang, Aiping Du, Zhangliu Zhong, Guangcai Luo, Chunling Zhang, Gan Wang, Junjian Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences Beijing100081 China College of Resources and Environmental Sciences China Agricultural University Beijing100193 China State Key Laboratory of Organic Geochemistry and Guangdong-Hongkong-Macao Joint Laboratory for Environmental Pollution and Control Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou510640 China

Biochar, a soil conditioner containing significant amounts of polycyclic aromatic hydrocarbons (PAHs), has gained widespread popularity in agricultural practices due to its advantages in improving soil fertility and carbon sequestration. While biochar may increase soil black carbon (BC) and PAH contents, the quantitative accumulation of BC and PAHs in different soil environments under varying biochar addition dosages remains poorly understood. Here, we investigated the content and composition of black carbon (evaluated using benzene polycarboxylic acids, BPCAs) and PAHs in soils treated with different biochar addition dosages from two long-term experimental farmlands in Ningxia (5-year) and Shandong (7- and 11-year), China. Results showed that increasing cumulative biochar dosage caused elevated contents of black carbon and PAHs, accompanied by decreases in their retention efficiencies. Contrasting retention was observed between sites, with the Shandong site characterized by higher retention efficiencies of BPCAs and lower retention efficiencies of PAHs, possibly owing to its higher temperature, more sandy soil texture, less irrigation, and lower sunlight intensity. Despite both black carbon and PAHs originating from biochar and sharing similar condensed aromatic structures, there was no significant correlation between the contents of black carbon and PAHs, indicating distinct behaviors and fates of these compounds. These findings emphasize the importance of optimizing biochar addition dosages and considering site-specific environmental factors for effective soil black carbon sequestration through biochar application. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Aromatization

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P-tuned FeN_(2)binuclear sites for boosted CO_(2)electro-reduction

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Journal of Energy Chemistry 2025年第2期101卷 816-824,I0018页

作者： Cao Guo Sanshuang Gao Jun Li Menglin Zhou Abdukader Abdukayum Qingquan Kong Yingtang Zhou Guangzhi Hu School of Materials and Energy Institute for Ecological Research and Pollution Control of Plateau LakesSchool of Ecology and Environmental ScienceYunnan UniversityKunming 650504YunnanChina Xinjiang Key Laboratory of Novel Functional Materials Chemistry College of Chemistry and Environmental SciencesKashi UniversityKashi 844000XinjiangChina School of Mechanical Engineering Chengdu UniversityChengdu 610106SichuanChina National Engineering Research Center for Marine Aquaculture Marine Science and Technology CollegeZhejiang Ocean UniversityZhoushan 316004ZhejiangChina

The recycling of CO_(2)through electrochemical processes offers a promising solution for alleviating the greenhouse effect;however,the activation of CO_(2)and desorption of^(*)CO in electrocatalytic CO_(2)reduction(ECR)frequently encounter high energy barriers and competitive hydrogen evolution reactions(HERs),which are urgent problems that need to be *** this study,a catalyst(P100-Fe-N/C)with homogeneous P-tuned FeN_(2)binuclear sites(N_(2)PFe-FePN_(2))was successfully synthesised,demonstrating satisfactory performance in the ECR to CO.P100-Fe-N/C attains a peak FECOof 98.01%and a normalized TOF of 664.7 h-1at-0.7 VRHE,surpassing the performance of the Fe binuclear catalyst without P and singleatoms *** the MEA cell,a FECOexceeding 90%can still be *** functional theory analysis indicates that the asymmetric coordination configuration induced by the incorporation of P facilitates a reduction in the system's *** modulation of P results in the d-band centre of the catalyst being positioned closer to the Fermi level,which facilitates the interaction of the catalyst with CO_(2),allowing more electrons to be injected into the CO_(2)molecule at the Fe binuclear sites and inhibiting the *** P-tuned FeN_(2)binuclear sites effectively lower the^(*)CO desorption barrier.

关键词： Electrocatalysis Diatomic catalyst Heteroatom doping CO_(2)reduction reaction DFT calculation

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Predictive Analytics of Industrial Quality KPIs Using Scalable Gaussian Mixture Modeling 6

Predictive Analytics of Industrial Quality KPIs Using Scalab...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Zhai, Xuanming Tang, Keye Chen, Ru Tang, Siyuan Shen, Bingbing Jiang, Xiaoyu Wei, Chihang Yao, Le School of Mathematics Hangzhou Normal University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China School of Information Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350356618

The Variational Inference Gaussian Mixture Model (VI-GMM) has been widely employed in multimode process modeling due to its inherent ability to automatically ascertain the optimal number of mixture components. Despite its advantages, the VI-GMM faces a significant computational challenge when dealing with large datasets, as it processes all data samples in each parameter update iteration. To address this issue, this paper introduces a novel approach: the Stochastic Variational Inference Gaussian Mixture Model (SVI-GMM). By leveraging the SVI algorithm to maximize the Evidence Lower Bound (ELBO), the scalability of the traditional VI-GMM is significantly enhanced, making it suitable for handling large- scale datasets. Furthermore, this study extends the application of SVI-GMM to its regression variant, SVI-GMR, for the forecasting of quality-related Key Performance Indicators (KPIs). The efficacy of the proposed methodology is demonstrated through a numerical example and a case study involving real-world industrial quality prediction modeling with extensive process data. These examples underscore the effectiveness and robustness of the SVI-GMM and SVI-GMR algorithms in providing accurate and scalable solutions for quality-related KPI predictions in big data environments. © 2024 IEEE.

关键词： Prediction models

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Kinematic analysis of inverted planetary roller screw considering skew error 9

Kinematic analysis of inverted planetary roller screw consid...

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2023 9th International Forum on Manufacturing Technology and engineering Materials, ICMTEM 2023

作者： Zhang, Changhui Fan, Yuanxun Zhang, Ming Xu, Zhiwei Yan, Cheng School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China Shanghai Aerospace Control Technology Institute Shanghai201109 China

To reveal the motion characteristics of inverted planetary roller screws under the influence of axial deviation error, the working principle of inverted planetary roller screws was elaborated, and a motion analysis model of inverted planetary roller screws based on deviation error was established. This paper analyzed the angle error, axial displacement error, and relative velocity error of the roller, and comprehensively analyzed the influence of errors and parameters on the motion state. © Published under licence by IOP Publishing Ltd.

关键词： Screws

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Carbon Energy 2025年第2期7卷 121-130页

作者： Tianyi Xu Dongxu Jiao Jinchang Fan Yilong Dong Zhaoyong Jin Lei Zhang Wei Zhang Jingxiang Zhao Weitao Zheng Xiaoqiang Cui State Key Laboratory of Automotive Simulation and Control Key Laboratory of Automobile MaterialsMinistry of EducationSchool of Materials Science and EngineeringJilin UniversityChangchunChina College of Chemistry Jilin UniversityChangchunChina Key Laboratory of Photonic and Electronic Bandgap Materials Ministry of EducationCollege of Chemistry and Chemical EngineeringHarbin Normal UniversityHarbinChina

Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of *** this study,we propose a novel approach called“similar stacking”for stress modulation,achieved through the loading of Co_(2)P on Ni_(2)P(Ni_(2)P/Co_(2)P).Theoretical simulations reveal that the compressive strain induced by Co_(2)P influences orbital overlap and electron transfer with hydrogen ***,the number of stacked layers can be adjusted by varying the precursor soaking time,which further modulates the strain range and hydrogen *** a 2-h soaking condition,the strain effect proves favorable for efficient hydrogen *** characterizations using X-ray diffraction,high-angel annular dark-field scanning transmission election microscope(HAADF-STEM),and X-ray absorption near-edge structure spectroscopy successfully demonstrate lattice contraction of Co_(2)P and bond length shortening of ***,our catalyst shows an ultrahigh current density of 1 A cm^(-2) at an overpotential of only 388 mV,surpassing that of commercial Pt/C,while maintaining long-term *** material design strategy of similar stacking opens up new avenues of strain modulation and the deeper development of electrocatalysts.

关键词： compressive strain heterogeneous phosphide hydrogen evolution reaction similar stacking

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