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26th International Conference on information and Communications Security, ICICS 2024

作者： Miao, Weijie Wu, Wenyuan Chongqing Key Laboratory of Secure Computing for Biology Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9789819787975

In the field of big data, logistic regression for binary and multi-class classification is widely used. Nowadays, there is growing concern about data privacy protection issues. This paper focuses on scenarios involving two parties participating and data being horizontally distributed. Based on homomorphic encryption, a logistic regression model training scheme is designed. This scheme reduces the number of iterations in the training process by using the second-order approximation Newton’s method. It employs the conjugate gradient method to solve the Newton’s method, and introduces a small amount of interaction to reduce ciphertext domain division operations, greatly reducing the computational overhead of the ciphertext domain. Additionally, a new encoding method is used to reduce the number of ciphertext multiplications and communication overhead. Furthermore, the "One-vs-Rest" decomposition strategy is adopted, combined with SIMD(Single Instruction, Multiple Data) technology, extending the binary model to multi-classification. Experimental results show that with the use of Privacy-preserving Logistic Regression scheme (PPLR), for most datasets, setting the number of iterations to within 3 rounds can achieve accuracy comparable to existing privacy protection schemes of 5 to 7 rounds. For sample datasets with 60 and 112 dimensions, existing similar schemes require 90 s and 165 s to complete 5 rounds of iteration, while this scheme only requires 8s and 27s. Moreover, the communication overhead is reduced to half of the original scheme, requiring only 30.8MB and 62.7MB to complete training. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Feature Intracorrelation and Weighted Mahalanobis Distance for Fuzzy Support Vector Machine 16th

Feature Intracorrelation and Weighted Mahalanobis Distance f...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Jiang, Lan Lv, Li Peng, Siwei Xie, Haihua School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819615308

Most feature-weighted fuzzy support vector machines only calculate the weight of each feature without considering inter-feature correlations, leading to redundant features that significantly impact final classification results. Additionally, designing fuzzy affiliation based on Euclidean distance overlooks the overall sample distribution and inter-feature coupling. To address these issues, we propose a fuzzy support vector machine incorporating intra-feature correlation and weighted Mahalanobis distance. First, ReliefF is employed to calculate feature weights, while the Pearson algorithm introduces a penalty factor based on the Pearson coefficient between features, updating the weights to mitigate the impact of redundant features on classification performance. Second, Mahalanobis distance replaces Euclidean distance, and a feature-weighted subordinate function based on Mahalanobis distance is constructed using the covariance matrix to eliminate variance differences between dimensions, ensuring feature independence and uniform distribution. Finally, a feature-weighted kernel function is integrated to prevent weakly correlated or uncorrelated features from dominating kernel function calculations. Accuracy and F1 value are chosen as evaluation metrics and compared with existing algorithms on the UCI dataset. Experimental results demonstrate that this algorithm effectively reduces the interference of weakly correlated, irrelevant, and redundant features while considering sample distribution, leading to superior classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Laser polishing of a high-entropy alloy manufactured by selective laser melting

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Frontiers of Mechanical Engineering 2024年第5期19卷 73-86页

作者： Xiaojun TAN Haibing XIAO Zihong WANG Wei ZHANG Zhijuan SUN Xuyun PENG Zhongmin LIU Liang GUO Qingmao ZHANG Intelligent Manufacturing and Equipment School Shenzhen Institute of Information TechnologyShenzhen 518172China Sino-German Intelligent Manufacturing School Shenzhen City PolytechnicShenzhen 518116China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices School of Information and Optoelectronic Science and EngineeringSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Industrial Ultrashort Pulse Laser Technology Shenzhen 518055China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

The selective laser melting(SLM)technique applied to high-entropy alloys(HEAs)has attracted considerable attention in recent ***,its practical application has been restricted by poor surface *** this study,the capability of laser polishing on the rough surface of a Co-free HEA fabricated using SLM was *** show that the initial SLM-manufactured(as-SLMed)surface of the Co-free HEA,with a roughness exceeding 3.0μm,could be refined to less than 0.5μm by laser ***,the microstructure,microhardness,and wear resistance of the laser-polished(LP-ed)zone were *** indicate that compared with the microhardness and wear resistance of the as-SLMed layer,those of the LP-ed layer decreased by 4%and 11%,respectively,because of the increase in grain size and reduction of the BCC *** study shows that laser polishing has an excellent application prospect in surface improvement of HEAs manufactured by SLM.

关键词： laser polishing selective laser melting high-entropy alloy surface roughness mechanical performance

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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Dual-Population-Iterating and Preys-Gathering MPA for XGBoost Optimization 16th

Dual-Population-Iterating and Preys-Gathering MPA for XGBoos...

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16th International Conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Li, Jiacheng Lv, Li Kang, Ping Tian, Xiumei School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819615308

The marine predator algorithm (MPA) often struggles with issues such as low population diversity, susceptibility to local optima, and population aggregation when optimizing XGBoost, which is sensitive to numerous parameters. To address these challenges, we propose the dual-population-iterating and prey-gathering marine predator algorithm (DPGMPA) and apply it to XGBoost parameter optimization. Initially, we utilize a dual-population-iterating strategy to create the original population and an opposition-based learning population. Brownian motion and Laplace distribution respectively control the step size to guide population updates toward the global optimum, enhancing population diversity. Subsequently, the preys-gathering strategy is employed, selecting prey with the highest accuracy in each iteration and having neighboring preys converge towards them to mitigate local optima. Furthermore, we optimize the Fish Aggregating Devices (FADs) by introducing variable weight control, addressing the population aggregation issue. To validate the parameter optimization capability of this algorithm for XGBoost, we conducted experiments on six datasets, using accuracy and F1 score as evaluation metrics, and compared the results with five other algorithms. The experimental results demonstrate that the DPGMPA algorithm more effectively escapes local optima when optimizing the XGBoost classifier, significantly improving XGBoost's classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Laplace distribution Marine Predator Algorithm normal distribution parameter optimization population diversity XGBoost

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASYMMETRIC RESIDUAL CROSS TRANSFORMER

JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASY...

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IET International Radar Conference 2023, IRC 2023

作者： Sun, Yaxiu Wang, Minhui Sun, Rui Hao, Lihong Xiang, Jianhong National Key Laboratory of Communication Anti-Jamming Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

With the increasing amount of remote sensing (RS) data obtained from satellites or airborne platforms, The combined use of multimodal remote sensing data to improve classification performance has been widely concerned. Therefore, a new transformer-based hyperspectral and synthetic aperture radar (HS-SAR) data joint classification network ACCT was proposed in the paper. Combining the strengths of convolutional neural networks (CNN) and transformer, focus on extracting specific and common features from HS and SAR data. In addition, adding layer scale to the output of each residual block can learn diagonal matrix and improve the dynamics of training. Then, combining the advantages of residual join and asymmetric convolution kernel, the parameters are reduced while the feature information is fully learned. Experiments in this paper are carried out on two famous multimodal fusion datasets: Augsburg (AU), and Berlin (BL) datasets. Results show that ACCT achieves better classification accuracy than several existing models. OA reaches 97.20%, and 85.02% on the two datasets. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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Three-phase Voltage PWM Rectifier Based on EID and Improved ADRC Control 50

Three-phase Voltage PWM Rectifier Based on EID and Improved ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Lu, Junjie Huang, Zixin Wang, Wei School of Electrical and Information Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan China

ISBN: (纸本)9781665464543

In high-precision occasions, three-phase voltage PWM rectifier based on conventional voltage-current dual closed-loop control suffer from current harmonics and poor interference immunity. In order to effectively alleviate the above problems, partial feedback linearisation control is introduced in the current inner loop, and equivalent input disturbance (EID) based active disturbance rejection control (ADRC) control is introduced in the voltage outer loop, and the fal function is changed to qin function to solve the jitter problem that occurs when the inflection point is not smooth. Firstly, the mathematical model of the three-phase voltage PWM rectifier in the dq coordinate system is established, and its underactuated characteristics are analysed;Then, the feedback linearisation controller is designed with current as the actuated variables, and the improved EID\ADRC controller is designed with voltage as the underactuated variable;Finally, simulation experiments are carried out by Matlab/Simulink to verify the feasibility of the proposed method. © 2024 IEEE.

关键词： Disturbance rejection

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A Triangular Six-Line Oversampling Method for Unbalanced Datasets 6

A Triangular Six-Line Oversampling Method for Unbalanced Dat...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Liu, Jing Jing Liu, Yefeng Wang, Shuai Shenyang Institute of Technology Liaoning Key Laboratory of Information Physics Fusion and Intelligent Manufacturing for Cnc Machine Basic Courses Department China Shenyang Institute of Technology Liaoning Key Laboratory of Information Physics Fusion and Intelligent Manufacturing for Cnc Machine China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350356618

An improved triangle oversampling technique is introduced to address the challenge of data imbalance in industrial classification tasks. In this method, the new sample is synthesized precisely along the median line of the triangle, ensuring that it aligns with the mathematical expectation of the triangle's center. Furthermore, the variance of this newly synthesized sample, under the action of the regulator, closely approximates the variance of the original sample, effectively simulating the density distribution of raw data. The samples produced by this method exhibit a distribution similar to the original samples, and the efficacy of this proposed method has been validated through numerical experiments. © 2024 IEEE.

关键词： Oversampling SMOTE Triangular six-line method Unbalanced dataset

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