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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 296-308页

作者： Yao, Yunhang Zhang, Zhiyong Zhao, Kejing Wang, Peng Wu, Ruirui Henan International Joint Laboratory of Cyberspace Security Applications College of Information Engineering Luoyang471023 China Henan University of Science and Technology Henan Intelligent Manufacturing Big Data Development Innovation Laboratory Luoyang471023 China China Nonferrous Metals Processing Technology Company Ltd. Luoyang471039 China

Industrial cyber-physical systems (CPS) are experiencing various malicious attacks and encountering increasing security challenges. Although machine learning-based intrusion detection systems can help users quickly detect attacks in industrial CPS, feature redundancy and the tuning of model hyperparameters hinder further detection performance. In this study, a Novel Geometric Mean Optimizer (NGMO) is designed to filter redundant industrial features while optimizing the hyperparameters of model. The proposed NGMO incorporates good point sets and dynamic opposition learning strategies during the population initialization and generation hopping phases to enhance the search capabilities of algorithm. Furthermore, the NGMO is combined with three gradient boosting decision tree models for intrusion detection in industrial CPS. Finally, four datasets from industrial scenarios and a real-world case are used to evaluate the effectiveness of NGMO. The experimental results show that NGMO can reduce time consumption while improving model detection accuracy. Therefore, the proposed NGMO can effectively enhance the security of industrial CPS. © 2023 IEEE.

关键词： Network intrusion

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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

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Frontiers of Computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Ancient Character Recognition: A Novel Image dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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Scheduling Multiple Mobile Agents with Abilities of Sensing and Executing Simultaneously

Scheduling Multiple Mobile Agents with Abilities of Sensing ...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Guo, Zhipeng Yu, Zhiyong Huang, Fangwan Tu, Chunyu Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

How to schedule public resources to maximize the coverage of crowds has always been a hot topic. There are mainly two types of research. One is the scheduling of static resources, whose coverage efficiency is lower because the resources cannot be moved;the other is the dynamic scheduling of movable public resources, but the existing methods require additional devices to collect crowd volume data. To address this issue, this article introduces a new category of mobile agents that can sense and execute simultaneously. This means that an agent can simultaneously perform crowd volume data collection tasks and crowds coverage tasks in its area. Taking these multi-ability agents as public resources, this article proposes a scheduling method called GE-STMC (Greedy Execute - Spatial and Temporal Monte-Carlo dropout). The method considers the spatiotemporal correlation between areas and evaluates the sensing value and the executing value of areas comprehensively to allocate the agents accordingly. Through the experimental on the real data of Beijing Happy Valley, this method can get 16% larger crowds volume coverage than other baseline methods with the same number of agents. © 2023 IEEE.

关键词： Mobile agents

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A NEW GLOBAL OPTIMIZATION ALGORITHM FOR MIXED-INTEGER QUADRATICALLY CONSTRAINED QUADRATIC FRACTIONAL PROGRAMMING PROBLEM

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Journal of Computational Mathematics 2024年第3期42卷 784-813页

作者： Bo Zhang Yuelin Gao Xia Liu Xiaoli Huang School of Civil and Hydraulic Engineering School of Mathematics and StatisticsNingxia UniversityYinchuan 750021China School of Mathematics and Information Science North Minzu UniversityYinchuan 750021China Ningxia Province Key Laboratory of Intelligent Information and Data Processing Yinchuan 750021China School of Mathematics and Statistics Ningxia UniversityYinchuan 750021China

The mixed-integer quadratically constrained quadratic fractional programming(MIQCQFP)problem often appears in various fields such as engineering practice,management science and network ***,most of the solutions to such problems are often designed for their unique *** paper puts forward a new global optimization algorithm for solving the problem *** first convert the MIQCQFP into an equivalent generalized bilinear fractional programming(EIGBFP)problem with integer ***,we linearly underestimate and linearly overestimate the quadratic functions in the numerator and the denominator respectively,and then give a linear fractional relaxation technique for EIGBFP on the basis of non-negative *** that,combining rectangular adjustment-segmentation technique and midpointsampling strategy with the branch-and-bound procedure,an efficient algorithm for solving MIQCQFP globally is ***,a series of test problems are given to illustrate the effectiveness,feasibility and other performance of this algorithm.

关键词： Global optimization Branch and bound Quadratic fractional programming Mixed integer programming

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A Privacy-friendly sequential progressive framework for segmented decision making

A Privacy-friendly sequential progressive framework for segm...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Liu, Peng Yu, Zhiyong Huang, Fangwan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

To achieve progressive and accurate decision-making for long-term time series data while meeting the needs of privacy-friendly and early, this paper proposes a universal framework for sequential progressive decision-making (SPD). This framework first segments the data and sets up multiple columns of neural networks according to the number of segments. Each column can make segmented decisions based on the inputs for the period. Additionally, without sharing the original data, the framework leverages lateral hidden layer connections between preceding and succeeding columns to obtain useful features for subsequent decision-making, gradually improving accuracy while avoiding the risk of data leakage. SPD has the advantages of privacy friendliness, column model diversification, prior knowledge reuse, and easy scalability, making it an effective framework for continuous decision-making. The effectiveness of that was validated using various network models in handwritten digit recognition and electrocardiogram classification tasks. The obtained experimental results reveal that SPD not only enables early decision-making while ensuring accuracy but also achieves accuracy levels comparable to or even surpassing those obtained using complete data, with the added benefit of privacy protection. © 2023 IEEE.

关键词： Decision making

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Point Cloud Compression Method Based on Curvature Grading and Inter-point Compression for Industrial Products 15

Point Cloud Compression Method Based on Curvature Grading an...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Ran Nanjing University of Posts and Telecommunications School of Computer Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350308693

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression. © 2023 IEEE.

关键词： Grading

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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