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Restoring Missing Slices of Serial Section Electron Microscopy Using Diffusion Models 1

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Yang, Hongyu Zhang, Ruobing Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China University of Science and Technology of China No. 96 Jinzhai Road Anhui Baohe District Hefei230022 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (数字)9789819619047

ISBN: (纸本)9789819619030

Serial section electron microscopy (ssEM), an advanced three-dimensional imaging technique, has played a crucial role in studying neuronal connections and microstructures of the brain. However, imperfect sample preparation and image acquisition randomly cause degradation of ssEM images, where Missing Slices (MS) is the most significant one. The previous deep learning methods such as interpolation model using spatially-adaptive convolutions have been proven to outperform conventional approaches, but those methods fail to recover some high-frequency information of missing slices. In this work, we propose a novel approach leveraging Denoising Diffusion Probabilistic Models (DDPM) for MS restoration in ssEM images. Our method improves the U-Net backbone with asymmetric and symmetric 3D convolutions, adapting to the anisotropic characteristic of ssEM images. Then, we introduce the Adaptive and Learnable Reconstruction module (ALR) and First and Last slices Attention Block (FLAB) to extract the features of slices effectively. Moreover, we employ a diffusion correction method, reducing training-testing data discrepancies. Experiment results demonstrate the effectiveness of our method in generating realistic missing slices, surpassing previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Joint advertising and ordering decisions under the pre-sales crowdfunding

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management System Engineering 2023年第1期2卷 1-14页

作者： Zeng, Kuan Shen, Yaohan School of Management Wuhan Textile University Wuhan China Research Center of Enterprise Decision Support Key Research Institute of Humanities and Social Sciences Wuhan China School of Management Wuhan University of Technology Wuhan China Research Center for Data Science and Intelligent Decision Making Wuhan University of Technology Wuhan China

Our study considers a company launching new product on pre-sales crowdfunding platform and explores the joint advertising and ordering decisions in mass market thereafter. Under the “AON” mechanism, we construct profit-maximizing models to examine the impact of pledged amount on the company’s decision on advertising expense, order quantity and funding target. The results show that the optimal order quantity increases with the pledged amount in crowdfunding, and the company won’t promote new product in mass market until the pledged amount reaches a funding threshold, and the advertising expense increases with the pledged amount since then. In addition, we figure out the feasible scope for the funding target and suggest the company set the target within the scope, to ensure the project profitability. Furthermore, we demonstrate that the expected pledged amount won’t increase with the funding target, and the optimal funding target would be the lower bound in its feasible scope, to improve the campaign success. Finally, we conduct numerical analysis to verify our theoretical results.

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Path analysis of influencing factors on the masses of celebrities carrying goods based on the AMOS model 21

Path analysis of influencing factors on the masses of celebr...

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4th International Conference on Information management and management Science, IMMS 2021

作者： Qu, Ying Yin, Shaoya School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Shijiazhuang050000 China

ISBN: (纸本)9781450384278

Under the Internet celebrity economy, live streaming has become a hot topic. This article explores the impact of the introduction of celebrity marketing methods in the live broadcast room on the consumption of the masses. Through the user behavior analysis model, find out the influence factors of the "star guest live room"model on the public's purchase intention. Based on AMOS model and factor analysis, Through 7 dimensions, Get live recognition, Celebrity popularity, Degree of trust, product quality, They are positively influencing people's purchase and consumer sharing. Consumer viewing attitude as an intermediary variable positively affects the results shared by consumers. By increasing the visibility of the live broadcast space, Pay attention to live broadcast quality, Increase interaction, etc. It can promote the purchase of consumers, Provide reference for the construction of the platform, and formulate reasonable marketing strategies in the live broadcast room, Reduce the blind purchase of consumers and promote rational purchase. Open up a new situation of live streaming shopping. © 2021 ACM. Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Factor analysis

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Single Cell Clustering Based on Multi-view Generation 2nd

Single Cell Clustering Based on Multi-view Generation

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wang, Yueyue Wu, Zheyu Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819619061

The rapid development of single-cell RNA sequencing technology has opened new horizons for biomedical research. As the initial step in single-cell data analysis, clustering forms the foundation for downstream analyses. However, due to the inherent high dimensionality, noise, and sparsity of single-cell data, most current clustering methods exhibit poor stability. To address these limitations, we propose a novel clustering method based on multi-view generation (scCMVG) coupled with noise perturbation. Specifically, scCMVG introduces disturbances at both gene level and cellular level to disrupt the original associations between genes and cells. It then employs autoencoder and graph convolutional network to reconstruct the disturbed data and generate new feature representations from it. Through this strategy, the model's noise resistance can be enhanced, increasing the ability to capture diverse cellular states and gene expression patterns. Experimental results demonstrate scCMVG superior clustering performance across multiple single-cell RNA sequencing datasets, outperforming existing methods. Consequently, scCMVG emerges as a valuable tool for clustering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gene expression

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Parallel Systems Framework: A Safe and Efficient Testbed for Unmanned Ships 4

Parallel Systems Framework: A Safe and Efficient Testbed for...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Fu, Long Shen, Zhen Dong, Xisong Han, Yunjun Yang, Shuguang Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Shandong Jiaotong University Schoool of Rail Transportation Jinan250399 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements. © 2024 IEEE.

关键词： Virtual environments

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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