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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Guan, Qianjun Yu, Kun Wang, Haoran Li, Qiangqiang Gao, Shihong Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an Jiangsu China

ISBN: (纸本)9798350306293

GhostNet is proposed by Huawei Noah's Ark Laboratory in CVPR2020, which can be used with the same accuracy, with less speed and less computation than SOTA method. This convolution is a low-cost operation used to decrease the amount of computation and related parameters. Ghost convolution can be used as an alternative to traditional convolution operations because they have the same accuracy. However, utilize significantly less computation and a much smaller parameter count. GSConv(Group-Shared Convolution) is a convolution operation based on group shared weights. This operation achieves efficient parallel convolution operations by grouping convolution kernels. In this way, the parameters used and calculation complexity of the model can be reduced to some extent. This makes the network more computationally efficient and smaller in model size while maintaining accuracy. The algorithm can detect the location of protective clothing quickly and efficiently, and its accuracy and efficiency have been verified. Because the algorithm adopts lightweight network structure, real-time detection can be realized on lowpower devices efficiently. The model architecture enhances the performance based on the YOLOv5 target detection algorithm by incorporating additional features. So that the model can maintain a high precision, which is crucial for the protective clothing detection algorithm. © 2023 IEEE.

关键词： Convolution

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Detection Algorithm for Safety Helmet Wearing of Chemical Plant Personnel Based on Improved YOLOv5m

Detection Algorithm for Safety Helmet Wearing of Chemical Pl...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Qiangqiang Yu, Kun Wang, Haoran Guan, Qianjun Gao, Shihong Jiang, Jiamin Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an Jiangsu China

ISBN: (纸本)9798350306293

Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve problems such as serious information loss, large number of parameters, and weak detection ability of small targets in complex scenes in the sampling process of helmet detection algorithm. In this paper, an improved lightweight safety helmet detection algorithm LP- YOLOv5m (Lightweight And High Precision YOLOv5m) based on YOLOv5m is designed. LP-YOLOv5m incorporates the Wise-IoU (Wiou) loss function to enhance model generalization. Additionally, we add a small object detection layer to improve the feature extraction ability for small objects. Furthermore, upsampling was performed using CARAFE. Finally, a lightweight module named C3-F was proposed, which replaced the Bottleneck in the C3 module with FasterNet Block, which could effectively reduce the network model size. Comparative laboratory findings demonstrate that our improved model achieves a mAP50 of 92.1%, surpassing YOLOv5m by 3 percentage points while reducing the model size by 23.3%. The LP-YOLOv5m model can be better applied to practical scene requirements, and can effectively solve practical problems. © 2023 IEEE.

关键词： Chemical plants

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YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

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computers, Materials & Continua 2024年第9期80卷 4973-4995页

作者： Yuanyuan Wang Jialong Huang Md Sharid Kayes Dipu Hu Zhao Shangbing Gao Haiyan Zhang Pinrong Lv School of Computer and Software Engineering Huaiyin Institute of TechnologyHuai’an223003China Huaiyin Institute of Technology Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu ProvinceHuai’an223001China Jiangsu Kesheng Xuanyi Technology Co. Ltd.Huai’an223300China

Printed circuit boards(PCBs)provide stable connections between electronic ***,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable ***,it is crucial to detect defective printed circuit boards during the generation *** detection methods have low accuracy in detecting subtle defects in complex background *** order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization *** neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep *** the aspect ratio of the bounding box to alleviate the issue of specificity *** training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of *** comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

关键词： Deep learning PCB defect detection large kernel noise filtering weighted fusion YOLO

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Identity-Based Key-Preserved Compacted Representative Signature as a Teaching Case of Cryptography

Identity-Based Key-Preserved Compacted Representative Signat...

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2023 International Conference on computer Network Security and software engineering, CNSSE 2023

作者： Chen, Jianhong Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

An compacted representative signature is a string that persuade the checker to believe that, for all 1 ≤ i ≤ n, the i-th representative signer approved a piece of information. The loss of clandestine keys threatens a security system. We come up with the idea of identity-based key-preserved compacted representative signature (IBKPCRS) in which representative signing keys vary over time. The usage of IBKPCRS is to meet the key-revealing challenge. In the IBKPCRS system, the user sets his telephone number or other overt string as his public key. We give the IBKPCRS scheme as a teaching case of cryptography. © 2023 SPIE.

关键词： Cryptography

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An enterprise portrait tag extraction method based on context embedding and knowledge distillation

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Soft Computing 2024年第20期28卷 11823-11834页

作者： Li, Xiang Ding, Xingshuo Zhu, Quanyin Ma, Jialin Zhou, Hong Sun, Jizhou Faculty of Computer and Software Huaiyin Institute of Technology Jiangsu Huaian223003 China Qingdao Ocean Shipping Mariners College Shandong Qingdao266400 China

Enterprise portraits are reliant on the meticulous process of tag extraction, with the accuracy and efficiency of this foundational task being pivotal for the construction of high-quality portraits and their subsequent applicability in varied real-world scenarios. However, current methodologies, chiefly semantic clustering and deep neural networks, are constrained by significant shortcomings: the former struggles with intricate text feature discernment, while the latter, although high-performing, are marked by their extensive computational requirements. In response to these challenges, we present EPCEKD, a novel approach leveraging context embedding and knowledge distillation, designed to refine the tag extraction process. EPCEKD utilizes structured enterprise context data to facilitate feature expansion and impose tag constraints, enabling the efficient vectorization and integration of enterprise context data and text. Further, EPCEKD amalgamates a large-scale BERT network and a streamlined GRU network, embedding the branch network into the backbone network to forge a unified tag extraction model. To validate the efficacy of EPCEKD, comprehensive experiments were conducted on the Ente-pku dataset and benchmarked against eight prevailing models, with EPCEKD demonstrating superior performance, achieving 95.17% in precision, recall, and F1-score, significantly surpassing models like BiLSTM-CNN (94.25%) and Transformer (91.46%). Importantly, EPCEKD not only excels in precision and F1-score but also enhances the computing speed of large-scale neural networks by 4-11.4 times, showcasing its substantial potential for practical enterprise portrait applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Embeddings

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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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A Bearing Fault Diagnosis Method Based on Improved Deep Residual Contraction Network

A Bearing Fault Diagnosis Method Based on Improved Deep Resi...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Guo, Shuxin Chen, Xiaobing Zhang, Chuangchuang Wang, Rongrong Lu, Heng Lu, Chengfa Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

In this paper, a method for bearing fault diagnosis based on an improved deep residual contraction network is proposed. The method utilizes the residual contraction module in the deep residual contraction network, which is improved in combination with the Inception network, in order to enhance the diagnostic accuracy and efficiency of bearing faults. The method divides the bearing fault diagnosis problem into several sub-problems and designs the corresponding residual contraction module and Inception network structure for each sub-problem. Through experimental validation using an actual bearing fault dataset, the results demonstrate that the method achieves high accuracy and stability in bearing fault diagnosis, providing a new idea and method for research in the field of bearing fault diagnosis. © 2023 IEEE.

关键词： Failure analysis

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A Multi-constraint 3D Mesh Models Deformation Method

A Multi-constraint 3D Mesh Models Deformation Method

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Chuangchuang Chen, Xiaobing Lin, Jianchu Guo, Shuxin Wang, Rongrong Gu, Yinxi Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model. © 2023 IEEE.

关键词： Mesh generation

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A Student engineering Competence Level Prediction Method Based on RNN-GRU and Dynamic Bayesian Network

A Student Engineering Competence Level Prediction Method Bas...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Xu, Xiang Li, Guanxiao Xiao, Shaozhang Zhang, Zheyi Huaiyin Institute of Technology School of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students' engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students' progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students' engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy. © 2023 IEEE.

关键词： Students

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Application of data twinning based on deep time series model in smart city traffic flow prediction

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Gao, Li Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an223003 China

This paper introduces an intelligent traffic flow prediction system that combines data twinning and deep learning, aiming to improve the prediction accuracy and model adaptability by integrating grey prediction model (GM(1,1)), long-short-term memory network (LSTM) and particle swarm optimization (PSO). The system construction starts from physical layer data acquisition, deals with missing data through smoothing and interpolation, and applies the GM(1,1) model to construct a digital twin layer for preliminary prediction. The information fusion mechanism combines real-time data and forecast data to optimize the model inputs, and the PSO algorithm is used to optimize the model parameters. The LSTM model is used as a deep time-series model, and combined with the output of the data twin prediction to make a comprehensive prediction, and at the same time, feature engineering is utilized to enhance the model performance. The system implements dynamic feedback adjustment to optimize the model in real time according to the prediction error. Empirical analysis shows that the LSTM model with integrated data twin (LSTM + DT) outperforms the traditional model in prediction accuracy, stability, and generalization ability, especially in peak hours, holidays, and emergency response, and provides a reliable solution for intelligent traffic management despite the long training time. © The Author(s) 2025.

关键词： Deep learning

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