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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Wu, Zhengjiang Li, Ruimin Lian, Tao Wu, Xuyang School of Software Henan Polytechnic University Jiaozuo China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798350318401

In the decision system, the lower approximation set keeps expanding with adding features dynamically. But when enough features are added, the lower approximation set stabilizes. This provides a criterion for feature selection in high-dimensional decision set. Semi-monolayer covering rough set is an approximate representation of uncertain knowledge in set-valued decision system. This paper proposes a feature selection algorithm for high-dimensional set-valued decision system based on incremental update theory (abbr. SMCDA). Specifically, the trend of lower DA0 approximation set under the variation of features in the set-value information system is described, and the incremental limit of the lower DA0 approximation set is illustrated. The incremental algorithm of the lower DA0 approximation under the variation of features is designed. Next, the lower DA0 approximation set is continuously updated using the incremental algorithm until stable. At this point, the already added features are the result of feature selection for SMCDA. Experimental results show that the SMCDA algorithm has a good feature reduction effect. © 2023 IEEE.

关键词： Monolayers

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State of Health Estimation of Lithium-Ion Batteries Based on Relaxation Voltage Reconstruction and AM-LSTM Method

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7261-7273页

作者： Wei, Liangliang Xu, Hongzhang Sun, Yiwen Diao, Qi Tan, Xiaojun Fan, Yuqian Liu, Han School of Intelligent Systems Engineering Shenzhen518107 China Henan Institute of Science and Technology School of Computer Science and Technology Henan Xinxiang453003 China Dongguan Institute Sun Yat-sen University Guangdong Dongguan523808 China Hong Kong Polytechnic University Department of Building and Energy Engineering Hong Kong

It is essential to accurately estimate the state of health (SOH) for lithium-ion batteries from the perspectives of safety and reliability. Most existing data-driven methods are, however, based on charging or discharging data, which is relatively difficult to apply. This article proposes a novel SOH estimation approach based on the relaxation voltage reconstruction and a long short-term memory network with an attention mechanism (AM-LSTM) method. First, based on the relaxation voltage data, a complete relaxation curve is reconstructed with a gated recurrent unit (GRU) neural network. Next, health feature (HF) extraction is carried out on the reconstructed data, and the correlation is analyzed based on Pearson correlation analysis. Then, the SOH estimation is performed based on the AM-LSTM model. Various comparative studies have been conducted to verify the effectiveness of the proposed method by comparing it with relaxation voltage reconstruction and different SOH estimation methods. The experimental results demonstrate that the proposed method can effectively reconstruct the relaxation voltage and have good accuracy in estimating the SOH with a partial relaxation voltage curve. © 2015 IEEE.

关键词： Voltage Batteries Estimation Feature extraction Long short term memory Logic gates Data models Integrated circuit modeling Data mining Transportation

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Method and Algorithm for Topology Automatic Discovery in Complicated Passive Optical Network Architecture

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Journal of Optical Communications 2022年第2期43卷 211-217页

作者： Qin, Panke Liu, Tao Ye, Qing Zhao, Zongqu Tang, Yongli College of Computer Science and Technology Henan Polytechnic University Henan454000 China

Passive Optical Network (PON) plays an important role in optical access network due to its characteristics of resources saving and network protocol transparency. In the process of running and operating a PON system, a reliable network management system (NMS) is necessary. In order to acquisition the management object, NMS should perform topology automatic discovery process with the help of embedded software. Aiming at various types of the convergence network's topology and the complicated architecture, this article proposes a topology automatic discovery algorithm to promote the network management. We have designed and verified the automatic discovery method on our simulation platform with SNMP-Agent-enabled OLTs. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Passive optical networks

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Microscale mechanism of tailing thickening in metal mines

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International Journal of Minerals,Metallurgy and Materials 2023年第8期30卷 1538-1547页

作者： Huazhe Jiao Wenbo Yang Zhu’en Ruan Jianxin Yu Juanhong Liu Yixuan Yang School of Civil Engineering Henan Polytechnic UniversityJiaozuo 454000China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China

Water-locking flocs formed by ultrafine tailings particles will damage the thickener underflow concentration in the thickening process during paste *** relationship between the mesostructure and seepage characteristics of tail mortar is typically ignored when investigating the deep dehydration stage.A shearing seepage test of an unclassified tailing-sedimentation bed was performed with copper tailings,and the morphology and geometric distribution of micropores were analyzed via X-ray computed ***,the shearing evolution of the micropore structure and seepage channel was investigated to evaluate the dewatering performance of underflow slurry using a three-dimensional reconstruction *** results show that porosity decreases considerably under *** connected-pore ratio and the average radius of the throat channel reach peak values of 0.79 and 31.38μm,respectively,when shearing is applied for 10 ***,the reverse seepage velocity and absolute permeability in the bed decrease to various extents after ***,the maximum flow rate reaches 1.537μm/s and the absolute permeability increases by 14.16%.Shearing alters the formation process and the pore structure of the seepage *** pores connect to the surrounding flocs to form branch channels,which then become the main seepage channel and create the dominant water-seepage flow channel.

关键词： paste thickening computed X-ray tomography shear action pore structure seepage channel

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IMM-Net: Integrated Multi-stream Memory Network for real-time semantic segmentation 2

IMM-Net: Integrated Multi-stream Memory Network for real-tim...

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2nd International Conference on Big Data, Information and computer Network, BDICN 2023

作者： Huo, Zhanqiang Chen, Jian Luo, Fen Jia, Haiyang Qiao, Yingxu Henan Polytechnic University School of Software Jiaozuo China Henan Polytechnic University College of Computer Science and Technology Jiaozuo China

ISBN: (纸本)9798350321364

Real-time semantic segmentation is a challenging topic in computer vision. The popular BiSeNet utilizes twobranch approach for spatial information and context information independently. However, we find that its additional, coarser downsampling path to encode spatial information may cause structure redundancy. In this paper, an efficient and effective Integrated Multi-stream Memory module is designed and seamlessly integrated into the whole network architecture (termed IMM-Net) to obtain an extensive range of receptive fields and efficiently integrate multi-scale contextual information. Considering spatial location information is essential to preserving object boundaries, a specific Feature Aggregation Guidance Module is proposed to encode both interpixel semantic relationships and long-range location relationships. Comprehensive experiments on Cityscapes and CamVid datasets indicate our IMM-Net outperforms a few state-of-the-art real-time semantic segmentation methods, achieving a good speed-accuracy trade-off. © 2023 IEEE.

关键词： Semantic Segmentation

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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

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Intelligent Automation & Soft Computing 2023年第7期37卷 1-16页

作者： Jixun Gao Bingyi Hu Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao School of Computer Henan University of EngineeringZhengzhou451191China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Computer Hubei University of Arts and ScienceXiangyang441053China School of Software Engineering Anyang Normal UniversityAnyang455000China School of Computer and Information Engineering Henan Normal UniversityXinxiang453003China The Publicity Department Zhengzhou University of TechnologyZhengzhou450044China

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service ***,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste *** the backup virtual machine under the above circumstances can effectively improve resource ***,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for *** deployment locations have different resource utilization and average service response *** want toﬁnd a balanced solution that minimizes the average service response time of the IoT application while maximizing resource *** this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment ***,the simulation results show that the TDA can signiﬁcantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical *** experimental results conﬁrm that the perfor-mance of TDA is better than that of other two methods.

关键词： Mobile edge computing overbooking resource utilization service response time task deployment algorithm

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Implementation of Art Design Optimization Algorithm Based on Big Data and CAD technology

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computer-Aided Design and Applications 2024年第S21期21卷 150-165页

作者： Wang, Xiangqun Zhang, Weizheng School of Innovation and Entrepreneurship Henan Finance University Henan Zhengzhou450000 China College of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China

The core of this study is to use big data (BD) and computer-aided design (CAD) technology to jointly promote innovation in optimization algorithms for art and design, aiming to improve the operational efficiency and visual aesthetics of user interface (UI). To achieve this goal, we conducted an in-depth analysis of the user behaviour and design preferences revealed by BD and utilized the precise modelling ability of CAD technology for targeted design optimization. Through a detailed comparison of new and old user UIs, the results show that the new UI exhibits significant advantages in multiple dimensions. Specifically, when using the new UI, users not only complete tasks faster but also significantly reduce the error rate, resulting in a significant improvement in overall efficiency. Moreover, the new UI has also performed better in colour matching, layout, icon design, and dynamic effect display. These improvements not only optimize the user's operating experience but also make the UI more attractive. Research has shown that art design optimization algorithms based on BD and CAD technologies are practical and feasible. This algorithm not only improves the efficiency of user UI usage but also enhances its visual appeal, which has a positive impact on improving the overall quality and market competitiveness of the product. © 2024 U-turn Press LLC.

关键词： computer aided design

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Research on MapReduce Heuristic Multi Table Join Algorithm Based on Binary Optimization and Pancake Parallel Strategy

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Recent Patents on Engineering 2023年第6期17卷 49-62页

作者： Jia, Songhao Yang, Jizheng Yang, Cai Zhang, Haiyu School of Computer Science and Technology Nanyang Normal University Nanyang China Faculty of Electronic Information Engineering Henan Polytechnic Institute Nanyang China

Background: With the development of technology, the data amount has increased signif-icantly. In data processing, the multi table query is the most frequent operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper pro-poses a heuristic multi table join optimization method. By sharing information, the unconnected tu-ples are eliminated so as to reduce the amount of data transmitting. This shortens response time and improves execution performance. Methods: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying. © 2023 Bentham science Publishers.

关键词： Optimization

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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