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    Review of Fall Detection Technologies for Elderly
    WANG Mengxi, LI Jun
    Computer and Modernization    2024, 0 (08): 30-36.   DOI: 10.3969/j.issn.1006-2475.2024.08.006
    Abstract262)      PDF(pc) (2530KB)(161)       Save
     With the rapidly growing aging population in China, the proportion of the elderly living alone has significantly increased, and thus the aging-population-oriented facilities have received increased attention. In a domestic environment, the elderly are likely to fall down due to different reasons such as lack of care, aging, and sudden illness, which have become one of the main threats to their health. Therefore, monitoring, detecting and predicting fall down behavior of the elderly in real-time can ensure their safety to some extent, while further reducing the life and health risks caused by accidental falling down. Based on a comprehensive overview of the research on human fall detection, we categorize fall detection into two categories: vision-free technologies and computer vision based methods, depending on different kinds of sensors used for data acquisition. We summarize and introduce the system composition of different methods and explore the latest relevant research, and discuss their method characteristics and practical applications. In particular, we focus on reviewing the deep learning based schemes which have been developing rapidly in recent years, while analyzing and discussing relevant principles and research results of deep learning based schemes in details. Next, we also introduce public benchmarking datasets for human fall detection, including dataset size and storage format. Finally, we discuss the prospect for the relevant research, and come up with reasonable suggestions in different aspects.
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    Review and Discussion of Personalised News Recommendation Systems
    ZHAI Mei
    Computer and Modernization    2024, 0 (04): 12-20.   DOI: 10.3969/j.issn.1006-2475.2024.04.003
    Abstract214)      PDF(pc) (1534KB)(171)       Save

    Abstract: With the rapid development of news media technology and the exponential growth of the number of online news, personalised news recommendation plays an extremely crucial role in order to solve the problem of online information overload. It learns users' browsing behaviour, interests and other information, and actively provides user with news of interest, thus improving user's reading experience. Personalised news recommendation has become a hot research and practical problem in the field of journalism and computer science, and experts in the industry have proposed various recommendation algorithms to improve the performance of recommendation systems. In this paper, we systematically describe the latest research status and progress of personalised news recommendation. firstly, we briefly introduce the architecture of news recommendation systems, and then we study the key recommendation algorithms and common evaluation metrics in news recommendation systems. Although personalised news recommendation brings a good experience to users, it also brings a lot of unknown effects to users. Unlike other news recommendation reviews, this paper also examines the impact of current news recommendation systems on user behaviour and the problems they face. Finally, the paper proposes research directions and future work on personalised news recommendation based on the current problems encountered.

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    Survey on Gesture Recognition and Interaction
    WEI Jiakun, WANG Jiarun
    Computer and Modernization    2024, 0 (08): 67-76.   DOI: 10.3969/j.issn.1006-2475.2024.08.012
    Abstract192)      PDF(pc) (1322KB)(148)       Save
    Gesture recognition and interaction technology is the cornerstone task of frontier research in human-computer interaction technology and artificial intelligence technology. This task takes the collaborative work of computers and devices to recognize and process gesture information and give machine operations corresponding to gestures as the main goal, and integrates a number of technologies such as motion capture, image processing, image classification, and multi-terminal collaborative interaction, which is a powerful guarantee to support the command and control system, robot interaction, medical operation and other cutting-edge intelligent interaction and human-computer interaction work nowadays. At present, the research on gesture recognition and interaction has become more and more mature with a wide range of application fields and rich application scenarios. This paper mainly provides a review of the gesture recognition development and interaction related technologies and hardware. Firstly, it sorts the research progress of gesture recognition and interaction technology out comprehensively, and categories the key steps of gesture recognition at the same time. Secondly, it classifies and elaborates the related work of the current mainstream gesture recognition depth sensors used for 3D gesture interaction. Subsequently, it analyses and discusses the real sense recognition technology for 3D gesture recognition. Finally, it analyses the deficiencies and urgent problems in gesture recognition and interaction technology, proposes the integration of such cutting-edge technologies as deep learning, pattern recognition and other feasible research ideas and methods, and makes predictions and prospects for the future research direction, technology development and application areas in this field.
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    GAN-generated Fake Images Recognition Based on Improved ConvNeXt
    XIAO Mengsi1, WU Jianbin1, TU Yameng1, YUAN Linfeng2
    Computer and Modernization    2024, 0 (04): 38-42.   DOI: 10.3969/j.issn.1006-2475.2024.04.007
    Abstract182)      PDF(pc) (3317KB)(119)       Save

    Abstract: In order to distinguish the authenticity of face images in social networks, a recognition method based on ConvNeXt for face image generated by Generative adversarial networks (GAN) is proposed. The ConvNeXt network structure is used as the main body, using the color features and spatial texture features of the face image, and multi-channel combination input (Multichannel Input, MCI) with multi-color space is used to expand the learning range of the network, while channel attention mechanism and spatial attention mechanism are introduced to highlight the differences between real and fake face images in color components and spatial features, and then the detection and recognition of fake face images are achieved. The experimental results show that the recognition accuracy of face images generated by GAN with improved ConvNeXt (I-ConvNeXt) network structure reaches 99.405%, with an average accuracy improvement of 1.455 percentage points compared with the original ConvNeXt algorithm. The results validate the feasibility and reasonableness of the proposed scheme.

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    Intelligent Identification Method of Debris Flow Scene Based on Camera Video Surveillance
    HU Mei-chen1, 2, LIU Dun-long1, 2, SANG Xue-jia1, 2, ZHANG Shao-jie3, CHEN Qiao4
    Computer and Modernization    2024, 0 (03): 41-46.   DOI: 10.3969/j.issn.1006-2475.2024.03.007
    Abstract144)      PDF(pc) (2573KB)(327)       Save
    Abstract: Camera video surveillance is widely used in debris flow disaster prevention and mitigation, but the existing video detection technology has limited functions and can not automatically judge the occurrence of debris flow disaster events. To solve this problem, using transfer learning strategy, this paper improves a video classification method based on convolutional neural network. Firstly, with the help of TSN model framework, the underlying network architecture is changed to ResNet-50, which is utilized for motion feature extraction and debris flow scene identification. Then, the model is pre-trained with ImageNet and Kinetics 400 datasets to make the model have strong generalization ability. Finally, the model is trained and fine-tuned with the pre-processed geological disaster video dataset, so that it can accurately identify debris flow events. The model is tested by a large number of moving scene videos, and the experimental results show that the identification accuracy of the method for debris flow movement video can reach 87.73%. Therefore, the research results of this paper can to the play a full role of video surveillance in debris flow monitoring and warning.

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    Survey on Group-level Emotion Recognition in Images
    GAO Shuaipeng, WANG Yifan
    Computer and Modernization    2024, 0 (08): 98-107.   DOI: 10.3969/j.issn.1006-2475.2024.08.016
    Abstract140)      PDF(pc) (1434KB)(99)       Save
     In recent years, image-based group emotion recognition has received widespread attention, which aims to accurately determine the overall emotional state of groups in different scenes and with different numbers of people. Since group emotion recognition involves the analysis and fusion of multiple group emotion clues such as facial emotional features, scene features, and human posture features in pictures, this field is very challenging. At this stage, there is a lack of relevant review articles in this field to sort out the existing research, so as to better conduct the next step of research. This article carefully sorts out and categorizes group emotion recognition models with different emotional cues and different processing methods in this field. At the same time, the processing methods and characteristics of existing models are reviewed and analyzed, and models with different fusion methods and mainstream databases in this field are sorted out. Finally, a brief summary and outlook on the development of this field are given.
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    Enhanced Big Language Model Dual Carbon Domain Services Based on Knowledge Graph
    QI Jun1, 2, QU Ruiting2, JIAO Chuanming2, ZHOU Qiaoni2, GUO Yanliang3, TAN Wenjun3
    Computer and Modernization    2024, 0 (09): 8-14.   DOI: 10.3969/j.issn.1006-2475.2024.09.002
    Abstract136)      PDF(pc) (1796KB)(124)       Save
    With the continuous development of the large language model, it has been widely applied in many fields. Due to the lack of knowledge in the dual carbon field in the big language model, the accuracy of the response results is low if the large language model is directly applied to the field of double carbon. Therefore, the method of constructing dual carbon knowledge graph as a knowledge base is adopted to enhance the application of large language models in the field of carbon peaking and carbon neutrality. The LoRA method is used to fine-tune the large language model to improve its ability to extract keywords in the carbon peaking and carbon neutrality fields. A dual carbon knowledge graph is constructed as local knowledge base to provide dual carbon domain knowledge for the model. The knowledge is used as the context of the problem, allowing the large language model to learn, and a prompt engineering assistance model is designed to generate responses. Finally, the effectiveness of the responses is evaluated. The experimental results show that, compared with the direct use of large language model, the method based on knowledge graph to enhance the dual carbon domain service of large language model has a high accuracy of intelligent response results in the field of carbon peaking and carbon neutrality, and provides an effective assistance for the construction of carbon peaking and carbon neutrality.
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    Path Planning of Parking Robot Based on Improved D3QN Algorithm
    WANG Jian-ming1, WANG Xin1, LI Yang-hui2, WANG Dian-long1
    Computer and Modernization    2024, 0 (03): 7-14.   DOI: 10.3969/j.issn.1006-2475.2024.03.002
    Abstract131)      PDF(pc) (2440KB)(210)       Save
    Abstract: The parking robot emerges as a solution to the urban parking problem, and its path planning is an important research direction. Due to the limitations of the A* algorithm, the deep reinforcement learning idea is introduced in this article, and improves the D3QN algorithm. Through replacing the convolutional network with a residual network and introducing attention mechanisms, the SE-RD3QN algorithm is proposed to improve network degradation and convergence speed, and enhance model accuracy. During the algorithm training process, the reward and punishment mechanism is improved to achieve rapid convergence of the optimal solution. Through comparing the experimental results of the D3QN algorithm and the RD3QN algorithm with added residual layers, it shows that the SE-RD3QN algorithm achieves faster convergence during model training. Compared with the currently used A*+TEB algorithm, SE-RD3QN can obtain shorter path length and planning time in path planning. Finally, the effectiveness of the algorithm is further verified through physical experiments simulating a car, which provides a new solution for parking path planning.
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    A mmWave Massive MIMO Channel Estimation Based on Joint Weighted#br# and Truncated Nuclear Norm
    ZHANG Zhineng, HUANG Xuejun
    Computer and Modernization    2024, 0 (04): 1-4.   DOI: 10.3969/j.issn.1006-2475.2024.04.001
    Abstract125)      PDF(pc) (1413KB)(166)       Save
    Abstract: In this paper, a millimeter-wave massive multiple input multiple output (MIMO) channel estimation algorithm based on joint weighted and truncated nuclear norm is proposed. Aiming at the problem of high training and feedback overhead in millimeter-wave massive MIMO channel estimation, firstly, the channel estimation problem is transformed into a low-rank matrix recovery problem by using the sparse antenna angle domain of millimeter-wave channel. An effective and flexible rank function, the joint weighted and truncated kernel norm, is adopted as the relaxation of the nuclear norm, and a new matrix recovery model is constructed for channel estimation. The optimization objective is to minimize the weighted and truncated nuclear norm, and it is solved by an alternating optimization framework. The simulation results show that this method can effectively improve the accuracy of channel estimation and has reliable convergence.
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    Collaborative Recommendation Algorithm with Implicit Roles
    YU Tianyi, LI Jianfeng, CHEN Hailong, ZHAI Jun
    Computer and Modernization    2024, 0 (09): 1-7.   DOI: 10.3969/j.issn.1006-2475.2024.09.001
    Abstract125)      PDF(pc) (1594KB)(121)       Save
    This article aims to improve the effectiveness of the algorithm, starts from the psychological needs of users, locates the implicit role group of users, and researches the personalized recommendation algorithms. From a theoretical point of view, the research in this paper effectively ensures the diversity requirements of recommendation systems and improves the accuracy of algorithms to a certain extent. It expands the relevant theory of implicit preference to address the phenomenon of preference evolution. Through verification in real data, multiple experimental evaluation indicators have been significantly improved. This not only provides a theoretical basis and reference for recommendation systems, but also improves the accuracy of recommendation results. It has broad application prospects. From a practical point of view, the classification of users in this article is no longer limited to ordinary social attributes, but can further explore users’ psychological needs, obtains more accurate and diverse recommendation results, improves user satisfaction and experience. Enterprises can guide users to change their interests, increase their loyalty and value, improve their lifecycle, and increase their profits.
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    Underwater Trash Detection Method Based on Improved YOLOv5
    PANG Mei, WANG Gong, ZHAN Yong, HUANG Zhefa
    Computer and Modernization    2024, 0 (07): 120-126.   DOI: 10.3969/j.issn.1006-2475.2024.07.018
    Abstract116)      PDF(pc) (3845KB)(114)       Save
    To address the limitations of underwater image acquisition such as insufficient light, high noise and unclear object recognition, which lead to the ineffectiveness of existing object detection algorithms, an underwater garbage object detection algorithm based on improved YOLOv5 is proposed. The purpose of the improved object detection algorithm is to achieve more accurate detection and removal of underwater plastic trash from the ocean. The improved algorithm containes some improvements:using the Contrast Limited Adaptive Histogram Equalization(CLAHE) algorithm to enhance data features, which reduces the difficulty of feature extraction and enables the network to be detected more flexibly and more accurately; introducing a parameter-free attention module SimAM, using the lightweight convolution method GSConv to enhance network extraction capability while reducing model computation; At the same time, multi-scale feature fusion detection is added to solve the problem of small target location of underwater debris. Numbers of experiments are conducted based on MarineTrash which is a self-built real underwater environmental litter dataset, the results show that the improved method has good performance, in which the accuracy is increased by 4.3 percentage points, the mAP is increased by 3.5 percentage points, the GFLOPs is reduced by 0.3, and the model weight is only 13.9 MB, which is 0.6 MB lower than the baseline. The research on the underwater trash detection algorithm based on the improved YOLOv5 provides sufficient technology for deploying and installing detectors in Autonomous Underwater Vehicles (AUVs) to achieve detection and automatic removal of marine underwater trash and maintain the marine ecosystem.
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    Automated Drawing Psychoanalysis Based on Image Classification
    ZHAO Xiaoming, PAN Ting, LIU Weifeng
    Computer and Modernization    2024, 0 (08): 92-97.   DOI: 10.3969/j.issn.1006-2475.2024.08.015
    Abstract115)      PDF(pc) (3358KB)(96)       Save
     Drawing psychoanalysis method is widely used in the discovery and treatment of psychological illness and mental disorders. The House-Tree-Person (HTP) test is the most representative drawing psychoanalysis method, which projects the individual’s psychological state through the houses, trees, and persons drawn by the patient. Compared with the psychological health questionnaire, it has the advantages of being non-verbal, projective, and creative, and can systematically release the subconscious. At present, the HTP test is tested and evaluated by the therapist, which takes a long time in large-scale psychological screening, and the evaluation results will be affected by the experience and subjectivity of the therapist. Therefore, it is necessary to establish an automated method to improve the objectivity, reliability, and efficiency of the HTP test. The paper proposes an automated drawing screening method for the HTP test based on the relationship between psychological states and drawing features. The method extracts key features such as the size, position, and shadow of the drawing, and combines these features to build a psychological state classifier. This method can effectively screen out negative drawings for further diagnosis and treatment. At the same time, this paper collects the test drawing of HTP from the psychological counseling centers of the college and makes HTP dataset for experiments. Experimental results prove the superiority and application value of this method.
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    Encryption Traffic Classification Method Based on AHP-CNN
    YOU Jiajing1, 2, HE Yueshun1, HE Linlin1, ZHONG Hailong1, 2
    Computer and Modernization    2024, 0 (04): 83-87.   DOI: 10.3969/j.issn.1006-2475.2024.04.014
    Abstract115)      PDF(pc) (895KB)(137)       Save
    Abstract: To address the insufficient feature extraction of existing methods for encrypted traffic, this study proposes an encrypted traffic classification method based on an Attention-based Hybrid Pooling Convolutional Neural Network (AHP-CNN). This method improves the pooling layers of Convolutional Neural Networks (CNNs) by combining average pooling and max pooling in a parallel manner, forming a dual-layer synchronized pooling pattern. This enables the capturing of both global and local features of network encrypted traffic. Furthermore, a self-attention module is incorporated into the model to enhance the extraction of dependency relationships among encrypted traffic features, leading to more accurate classification. Experimental results demonstrate a significant improvement in the accuracy of encrypted traffic identification using the proposed model, with an F1 score exceeding 0.94. This research provides a more effective and precise approach for the classification of network encrypted traffic, contributing to advancements in research and applications in the field of network security.
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    A Source Code Security Vulnerability Detection Method Using ChatGPT
    YU Lihui, HU Shaowen, HUANG Langxin, LUO Shuhuan
    Computer and Modernization    2024, 0 (04): 88-91.   DOI: 10.3969/j.issn.1006-2475.2024.04.015
    Abstract115)      PDF(pc) (3675KB)(138)       Save
    Abstract: As the security issues of software and information systems become more and more prominent, as an important part, the security of source code is the bottom key point. How to quickly and accurately detect security vulnerabilities of source code is particularly important. This paper proposes a source code security vulnerability detection method based on ChatGPT, which takes advantage of ChatGPT in the field of natural language processing, converts source code into natural language form, and then uses ChatGPT to process it to identify potential security vulnerabilities. This method can detect various types of security vulnerabilities, such as insecure design, SQL injection and so on. We demonstrate the superiority and accuracy of our approach through experimental analysis of security vulnerability detection on source codes of publicly available datasets.

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    Debris Flow Infrasound Signal Recognition Approach Based on Improved AlexNet
    YUAN Li1, 2, LIU Dun-long1, 2, SANG Xue-jia1, 2, ZHANG Shao-jie3, CHEN Qiao4
    Computer and Modernization    2024, 0 (03): 1-6.   DOI: 10.3969/j.issn.1006-2475.2024.03.001
    Abstract112)      PDF(pc) (3108KB)(234)       Save
    Abstract: Environmental interference noise is the main challenge for on-site monitoring of debris flow infrasound, which greatly limits the accuracy of debris flow infrasound signal identification. In view of the performance of deep learning in acoustic signal recognition, this paper proposes a debris flow infrasound signal recognition method based on improved AlexNet network, which effectively improves the accuracy and convergence speed of debris flow infrasound signal recognition. Firstly, the original infrasound data set is preprocessed such as data expansion, filtering and noise reduction, and wavelet transform is used to generate a time-frequency spectrum image. Then the obtained time-frequency spectrum image is used as input, and an improved AlexNet network model is built by reducing the convolution kernel, introducing a batch normalization layer and selecting the Adam optimization algorithm. Experimental results show that the improved AlexNet network model has a recognition accuracy of 91.48%, achieves intelligent identification of debris flow infrasound signals and provides efficient and reliable technical support for debris flow infrasound monitoring and early warning.
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    An LLM-based Method for Automatic Construction of Equipment Failure Knowledge Graphs
    ZHANG Kun1, ZHANG Yongwei1, WU Yongcheng1, ZHANG Xiaowen2, ZHAI Shichen2
    Computer and Modernization    2024, 0 (11): 46-53.   DOI: 10.3969/j.issn.1006-2475.2024.11.008
    Abstract111)      PDF(pc) (5470KB)(96)       Save
    Fault operation and maintenance is an important research topic in the field of industrial production. The research of fault prediction, fault diagnosis, question-answering systems based on the fault knowledge graph have been greatly developed and applied. However, a high-quality fault operation and maintenance knowledge graph is the foundation for these methods. Considering that traditional knowledge graph construction techniques require data preprocessing, entity recognition, relationship extraction and entity alignment of raw data, to improve the efficiency of knowledge graphs, this paper focuses on using large language models for unsupervised knowledge extraction from fault operation and maintenance data to achieve automatic construction of large-scale fault operation and maintenance knowledge graphs. This method mainly includes two parts: 1) Two zero-shot Prompts oriented towards the construction of fault operation and maintenance knowledge graphs are proposed. These Prompts can guide large language models to generate conceptual layers and extract elemental knowledge for the fault operation and maintenance knowledge graph represented and output in RDF syntax; 2) A method based on large language models for constructing knowledge graphs is proposed. This method can guide large language models to extract knowledge from fault operation and maintenance data through zero-shot Prompts and complete the construction of large-scale fault operation and maintenance knowledge graphs iteratively. Experimental results show that the proposed method is scientific and effective.
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    Temporal Knowledge Graph Question Answering Method Based on#br# Semantic and Structural Enhancement
    HUANG Zheng-lin, DONG Bao-liang
    Computer and Modernization    2024, 0 (03): 15-23.   DOI: 10.3969/j.issn.1006-2475.2024.03.003
    Abstract106)      PDF(pc) (1330KB)(141)       Save
    Abstract: Knowledge graphs, as one of the popular research topics in the field of natural language processing, have consistently received widespread attention from the academic community. In reality, the knowledge quiz process often carries temporal information. Consequently, in recent years, the application of temporal knowledge graphs for knowledge question answering has gained popularity among scholars. Traditional methods for temporal knowledge graph question answering primarily encode the question information to facilitate the inference process. However, they are unable to deal with the more complex entities and temporal relationships contained in the questions. To address this, semantic and structural enhancement for temporal knowledge graph question answering is proposed. This method aims to simultaneously consider both semantic and structural information in the inference process to improve the probability of providing correct answers. Firstly, implicit temporal expressions in the questions are parsed, and the questions are rewritten using direct representations based on the information in the temporal knowledge graph. Additionally, the temporal information in the temporal knowledge graph is aggregated according to different time granularities based on the question set. Secondly, the semantic information of the questions is represented and fused based on entity and time information to enhance the learning of entity and time semantics. Subsequently, subgraphs are extracted based on the extracted entities, and the structural information of the subgraphs is captured using graph convolutional networks. Finally, the fused semantic and structural information of the questions are concatenated, and candidate answers are scored, with the entity receiving the highest score selected as the answer. Comparative tests on MultiTQ data sets show that the proposed model outperforms other baseline models.

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    Multiple Unmanned Aerial Vehicles Three-dimensional Cooperative Route Planning Based on Improved GWO Algorithm
    JIAO Jian, JI Yuanfa, SUN Xiyan, WU Jianhui, LIANG Weibin
    Computer and Modernization    2024, 0 (10): 1-6.   DOI: 10.3969/j.issn.1006-2475.2024.10.001
    Abstract105)      PDF(pc) (2694KB)(107)       Save
    To overcome the problems of poor cooperation, immersing local minimization, low convergence speed and poor solving accuracy in solving the collaborative route by GWO algorithm for multiple unmanned aerial vehicles, an improved GWO-based three-dimensional collaborative route planning algorithm for multiple unmanned aerial vehicles is proposed. Firstly, a three-dimensional collaborative trajectory planning mathematical model for multiple unmanned aerial vehicles is established, using the weighted sum of consumption cost, height cost, threat cost, spatial constraint, time constraint, and penalty term as the objective function. Secondly, the Greedy algorithm and Tent mapping are combined to improve the fitness of the population and preserve the diversity of the population to reduce the possibility of falling into local optima; then we optimize the convergence factor to improve the rate of convergence of the algorithm. Afterwards, we design a dynamic weight position update method to enhance the exploration and development capabilities of the algorithm. Finally, the improved GWO algorithm is applied to solve the trajectory planning problem of multiple unmanned aerial vehicles, and compared with GWO algorithm and CSGWO algorithm. The simulation results indicate that the proposed improved GWO algorithm enhance the solution accuracy by 64.8% and 16.7%, as well as the convergence speed by 28.5% and 25.4%, respectively. Additionally the synergy ability is significantly better than that of the comparison algorithms.
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    Survey on Multimodal Information Processing and Fusion Based on Modal Categories
    HUANG Wendong, WANG Yifan
    Computer and Modernization    2024, 0 (07): 47-62.   DOI: 10.3969/j.issn.1006-2475.2024.07.008
    Abstract105)      PDF(pc) (1939KB)(102)       Save
     With the continuous advancement of artificial intelligence and deep learning technologies, research in the field of multimodal information processing and fusion has garnered widespread attention from researchers. This paper provides a comprehensive overview of the development history and milestone works of multimodal information processing, along with strategies and models for multimodal fusion. Based on different modalities,mainstream datasets for multimodal information processing and fusion are systematically classified and summarized. Using modality type as the classification criterion, this paper systematically reviews the research progress in multimodal information processing and fusion, emphasizing the distinctions between different modalities. Multimodal information processing and fusion are categorized into four types: audio-visual processing and fusion, audio-text processing and fusion, visual-text processing and fusion, and visual-audio-text processing and fusion. Detailed investigations are conducted on methods and models for processing and fusing different input modalities. Finally, a summary and outlook on the development of multimodal processing and fusion are provided.
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    Helmet Detection Algorithm Based on CE-YOLOv5s
    WANG Zhibo, MA Han, FENG Jinliang, LIU Guoming
    Computer and Modernization    2024, 0 (04): 55-59.   DOI: 10.3969/j.issn.1006-2475.2024.04.010
    Abstract104)      PDF(pc) (2091KB)(107)       Save

    Abstract: In the complex environment of construction sites, there are many dangerous factors, so the protection of the safety of workers has become a focus. Due to the chaotic environment and fixed information collection points at construction sites, there are problems of missed and false detection in safety helmet-wearing detection. Therefore, this paper proposes a safety helmet detection algorithm based on CE-YOLOv5s. The algorithm combines the SE attention mechanism with the C3 module, replaces the C3 module in the original network, assigns a higher weight to key features, and suppresses general features. Meanwhile, an object detection neural network based on Bi-directional Feature Pyramid Network (BiFPN) is introduced, which performs both upward and downward feature fusion, adds additional weights to each channel, and better preserves detailed information under low-resolution images. The SIoU loss function is introduced to improve the accuracy of boundary box positioning and accelerate convergence speed. Experimental results show that the improved network model has significantly improved in precision, recall, mAP@0.5, and mAP@0.5:0.95, effectively improving the detection accuracy of safety helmets and improving the detection accuracy of small targets and obscured targets in cluttered backgrounds. When applied to construction sites, it can timely detect whether workers have taken protective measures, and better protect their safety.

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    Wind Power Prediction Method Based on STAGCN-Informer Spatiotemporal Fusion Model
    YANG Shaozu1, 2, WANG Haicheng1, 2, WU Jinya1, 2, MA Jiying1, 2
    Computer and Modernization    2024, 0 (07): 13-20.   DOI: 10.3969/j.issn.1006-2475.2024.07.003
    Abstract103)      PDF(pc) (3491KB)(90)       Save
     Aiming at the problem that the spatial information cannot be effectively extracted due to the influence of spatiotemporal fluctuation and randomness in wind power forecasting, resulting in insufficient prediction accuracy, a model named STAGCN-Informer-DCP is proposed based on Variational Mode Decomposition (VMD),fusion of Spatiotemporal Attention Graph Convolutional Network (STAGCN) and improved Informer combination model. Firstly, VMD is used to perform modal decomposition on the original features, and the feature information on different time scales is extracted. At the same time, the selection of core parameters (penalty factor and K value) of VMD is optimized by using northern goshawk optimization (NGO). Secondly, the STAGCN module that integrates spatio-temporal attention is used to dynamically capture the spatio-temporal features of the target wind turbine and its neighbors, and fuses them with the original signal components to obtain a feature vector carrying spatial scale information. Finally, the improved Informer model is used to extract the long-term dependencies of temporal context and realizes multi-step output prediction. The experimental results show that the combination model can better capture the dynamic space-time dependence, and effectively improve the accuracy of medium and long-term wind power forecasting.
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    Information Extraction for Aircraft Fault Text
    QIAO Lu, SUN You-chao, WU Hong-lan
    Computer and Modernization    2024, 0 (03): 61-66.   DOI: 10.3969/j.issn.1006-2475.2024.03.010
    Abstract102)      PDF(pc) (1248KB)(154)       Save
    Abstract: In view of the problems of large workload, low efficiency and high cost of manual extraction of aircraft fault information, a method of information extraction based on domain dictionary, rules and BiGRU-CRF model is proposed. Combining the characteristics of aircraft domain knowledge, domain dictionary and template rules are constructed based on aircraft fault text information, and semantic labeling of fault information is carried out. The BiGRU-CRF deep learning model is used for named entity recognition. BiGRU obtaines the semantic relationship of context, and CRF decodes and generates the entity label sequence. The experimental results show that the information extraction method based on domain dictionary, rules and BiGRU-CRF model has an accuracy of 95.2%, which verifies the effectiveness of the method. It can accurately identify the key words in the aircraft fault text, such as time, aircraft type, fault part name, fault part manufacturer and other information. At the same time, according to the domain dictionary and rules to correct the recognition results, effectively improves the efficiency and accuracy of information extraction, and solves the problem of traditional entity extraction model long-term dependence on manual features.

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    Transmission Line Faults Detection Algorithm Based on YOLOX
    WU Hengfeng, HOU Xingsong, WANG Huake
    Computer and Modernization    2024, 0 (05): 5-10.   DOI: 10.3969/j.issn.1006-2475.2024.05.002
    Abstract101)      PDF(pc) (2405KB)(146)       Save
    Abstract:Power system is an important foundation of national life, intelligent detection of transmission line faults has great social and economic value. Aiming at the problem of lack of public datasets in transmission line faults detection scenarios, poor performance when there are multiple scale targets simultaneously, and difficulty in detecting high IoU bounding boxes, a transmission line faults detection method based on improved YOLOX was proposed. First, a transmission line faults detection dataset was set up through acquisition and simulation; then an adaptive multi-scale feature fusion method was proposed to fully use multi-scale features; finally a new loss was proposed to improve the optimization ability of the network for high IoU bounding boxes and solve sample imbalance problem, which effectively improved the detection accuracy. The experimental results show that in the dataset collected in this paper, the proposed algorithm can still achieve 67.48% mAP50:95 while ensuring real-time performance, outperforming the classical algorithms such as EfficientDet and YOLOV5.
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    Recommendation Algorithm Model Based on DNN and Attention Mechanism
    ZHOU Chao, CONG Xin, ZI Lingling, XIAO Guping
    Computer and Modernization    2024, 0 (06): 1-7.   DOI: 10.3969/j.issn.1006-2475.2024.06.001
    Abstract100)      PDF(pc) (916KB)(122)       Save
    Abstract: In order to solve the defect of factorization machine in extracting high-order combination features and learn more useful feature information better, this paper attempts to use factorization machine to extract cross-feature and learn key feature information from low and high-order combination features by combining attention network, deep neural network, multi-head self-attention mechanism and other methods. Finally, the weighted fusion results were obtained according to the importance of the combination features of different orders, and the click-through rate of advertisements was estimated. The experiment was mainly carried out based on the advertising data set Criteo, and the analogy experiment was carried out with MovieLens data set to verify the effectiveness of the proposed algorithm model. The experimental results showed that compared with the benchmark model, in the two data sets, the AUC index increased by 2.32 percntage points and 0.4 percntage points.

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    Combining Knowledge Tracing and Graph Convolution for Knowledge Concept#br# Recommendation
    WANG Yan, CONG Xin, ZI Lingling
    Computer and Modernization    2024, 0 (08): 17-23.   DOI: 10.3969/j.issn.1006-2475.2024.08.004
    Abstract100)      PDF(pc) (1848KB)(76)       Save
    The innovative development of technology has led to the flourishing advancement of online education platforms, which provide a huge amount of educational resources, each type of which contains rich knowledge concepts. The current research mainly focuses on personalized course resource recommendation by knowledge graph, which is vulnerable to the data sparsity problem and difficult to be extended. Difficulty in matching learners’ learning status with learning resources, the model KT-GCN (Knowledge Tracing-Graph Convolution Network) is proposed. Firstly, the overall modeling of learners’ knowledge level is performed using knowledge tracing, getting the learner’s current learning status. Then path encoding is performed using graph convolutional network, accessing to learner-adapted learning paths, path selection is performed using TransE method and multi-hop path. Finally, predictive scoring is performed to obtain a recommended list of the most matching learning resources. To validate the performance of the model, comparison experiments are conducted with the baseline model on multiple datasets, and corresponding ablation experiments are performed to verify the performance of each component of the model.
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    Breast Cancer Immunohistochemical Image Generation Based on Generative Adversarial Network
    LU Zi-han1, ZHANG Dong1, YANG Yan1, YANG Shuang2
    Computer and Modernization    2024, 0 (03): 92-96.   DOI: 10.3969/j.issn.1006-2475.2024.03.015
    Abstract99)      PDF(pc) (2044KB)(243)       Save

    Abstract: Breast cancer is a dangerous malignant tumor. In medicine, human epidermal growth factor receptor 2(HER2)levels are needed to determine the aggressiveness of breast cancer in order to develop a treatment plan, this requires immunohistochemical(IHC)staining of the tissue sections. In order to solve the problem that IHC staining is expensive and time-consuming, firstly, a HER2 prediction network based on mixed attention residual module is proposed, and a CBAM module is added to the residual module, so that the network can focus on learning at the spatial and channel levels. The prediction network could directly predict HER2 level from HE stained sections, and the prediction accuracy reached more than 97.5%, which increased by more than 2.5 percentage points compared with other networks. Subsequently, a multi-scale generative adversarial network is proposed, which uses ResNet-9blocks with mixed attention residuals module as generator and PatchGan as discriminator and self-defines multi-scale loss function. This network can directly generate simulated IHC slices from HE stained slices. At low HER2 level, SSIM and PSNR between the generated image and the real image are 0.498 and 24.49 dB.

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    Federated Learning Aggregation Algorithm Based on AP Clustering Algorithm
    AO Bochao, FAN Bingbing
    Computer and Modernization    2024, 0 (04): 5-11.   DOI: 10.3969/j.issn.1006-2475.2024.04.002
    Abstract98)      PDF(pc) (1628KB)(124)       Save

    Abstract: In traditional federation learning, multiple clients’ local models are trained independently from their private data, and the central server generates a shared global model by aggregating the local models. However, due to statistical heterogeneity such as non-independent identically distributed (Non-IID) data, a global model often cannot be adapted to each client. To address this problem, this paper proposes an AP clustering algorithm-based federation learning aggregation algorithm (APFL) for Non-IID data. In APFL, the server calculates the similarity matrix between each client based on the data characteristics of the clients, and then uses the AP clustering algorithm to divide the clients into different clusters and construct a polycentric framework to calculate the suitable personalized model weights for each client. This algorithm is experimented on FMINST dataset and CIFAR10 dataset, and APFL improves 1.88 percentage points on FMNIST dataset and 6.08 percentage points on CIFAR10 dataset compared with traditional Federated Learning FedAvg. The results show that the proposed APFL improves the accuracy performance of Federated Learning on Non-IID data in this paper.
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    Improved Deciduous Tree Nest Detection Method Based on YOLOv5s
    CHENG Meng, LI Hao
    Computer and Modernization    2024, 0 (08): 24-29.   DOI: 10.3969/j.issn.1006-2475.2024.08.005
    Abstract98)      PDF(pc) (2245KB)(110)       Save
    To address the difficulty of detecting small bird nest targets in complex backgrounds, an improved YOLOv5s network architecture named YOLOv5s-nest is proposed. YOLOv5s-nest incorporates several enhancements: a refined attention mechanism called Bi-CBAM is inserted into the Backbone to effectively enhance the network’s perception of small targets; the SDI structure is introduced into the Neck to integrate more hierarchical feature maps and higher-level semantic information; the InceptionNeXt structure is inserted into the Neck to improve the model's performance and computational efficiency; and in the detection head, ordinary convolutions are replaced with PConv to efficiently extract spatial features and enhance detection efficiency. The experimental results show that the average precision of the improved model reached 89.1%, representing an increase of 6.8 percentage points compared to the original model.
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    Cryptographic Algorithm of IoV Communication Based on AES
    XU Xiaowei, CHENG Yu, QIAN Feng, ZHU Neng, DENG Mingxing
    Computer and Modernization    2024, 0 (09): 45-51.   DOI: 10.3969/j.issn.1006-2475.2024.09.008
    Abstract96)      PDF(pc) (2634KB)(71)       Save
    As V2X technology develops rapidly, the volume of communication between vehicles and other devices, as well as the importance of information are growing rapidly, and the risk of in-vehicle information being attacked, intercepted or leaked has also increased accordingly, so the security of information interaction has become an unavoidable topic. Addressing the issues of large data volume and frequent data encryption and decryption operations in vehicle networking, this paper analyzes classical encryption algorithms and improves the traditional AES-based encryption algorithm. By using the RC4 encryption algorithm to generate a pseudo-random key instead of the key generation module of the AES encryption algorithm, the encryption time is optimized, and security performance is enhanced. Experiments are conducted to verify encryption efficiency and security.
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    A Moving Object Detection Algorithm Aiming at Jittery Drone Videos
    LIU Yaoxin1, CHEN Renxi2, YANG Weihong1
    Computer and Modernization    2024, 0 (05): 99-103.   DOI: 10.3969/j.issn.1006-2475.2024.05.017
    Abstract96)      PDF(pc) (2681KB)(237)       Save
    Abstract: To solve the problem that moving object detection is susceptible to jitter in hovering drones, leading to the generation of a significant amount of background noise and lower accuracy, a multiscale EA-KDE (MEA-KDE) background difference algorithm is proposed. This algorithm initially achieves a multiscale decomposition of image sequences to obtain a multiscale image sequence. Subsequently, before performing detection, the segmentation threshold for detection is calculated and updated by considering the area threshold and the current image frame, thereby incorporating information from the current frame. Background difference operations using high and low dual segmentation thresholds are performed on images at different scales to enhance detection robustness. Finally, a top-down fusion strategy is employed to merge the detection results from various scales, preserving the clear contours of the targets while eliminating noise. Furthermore, a proposed boundary expansion fusion post-processing algorithm helps alleviate the fragmented targets caused by detection breaks. Experimental results demonstrate that the proposed algorithm effectively suppresses background noise caused by jitter. On two real drone datasets, average F1 scores of 0.951 and 0.952 were obtained, representing improvements of 0.144 and 0.276, respectively, compared to the original algorithm.

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    Retinal Vessel Segmentation Based on Improved U-Net with Multi-feature Fusion
    FU Lingli, QIU Yu, ZHANG Xinchen
    Computer and Modernization    2024, 0 (06): 76-82.   DOI: 10.3969/j.issn.1006-2475.2024.06.013
    Abstract95)      PDF(pc) (1564KB)(91)       Save
    Abstract: Due to some problems such as uneven distribution of blood vessel structure, inconsistent thickness, and poor contrast of blood vessel boundary, the image segmentation effect is not good, which cannot meet the needs of practical clinical assistance. To address the problem of breakage of small vessels and poor segmentation of low-contrast vessels, a CA module was integrated into the down-sampling process based on U-Net. Additiondly, to solve the problem of insufficient feature fusion in the original model, Res2NetBlock module was introduced into the model. Finally, a cascade void convolution module is added at the bottom of the model to enhance the receptive field, thereby improving the network’s spatial scale information and the contextual feature perception ability. So the segmentation task achieves better performance. Experiments on DRIVE, CHASEDB1 and self-made Dataset100 datasets show that the accuracy rates are 96.90%, 97.83% and 94.24%, respectively. The AUC is 98.84%, 98.98%, and 97.41%. Compared with U-Net and other mainstream methods, the sensitivity and accuracy are improved, indicating that the vessel segmentation method in this paper has the ability to capture complex features and has higher superiority.
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    Knowledge Prompt Fine-tuning for Event Extraction
    LI Lu, ZHU Yan
    Computer and Modernization    2024, 0 (07): 36-40.   DOI: 10.3969/j.issn.1006-2475.2024.07.006
    Abstract94)      PDF(pc) (1020KB)(89)       Save
     Event extraction is an important research focus in information extraction, which aims to extract event structured information from text by identifying and classifying event triggers and arguments. Traditional methods rely on complex downstream networks, require sufficient training data, and perform poorly in situations where data is scarce. Existing research has achieved certain results in event extraction using prompt learning, but it relies on manually constructed prompts and only relies on the existing knowledge of pre-trained language models, lacking event specific knowledge. Therefore, a knowledge based fine-tuning event extraction method is proposed. This method adopts a conditional generation approach, injecting event information to provide argument relationship constraints based on existing pre-trained language model knowledge, and optimizing prompts using a fine-tuning strategy. Numerous experiment results show that compared to traditional baseline methods, this method outperforms the baseline method in terms of trigger word extraction and achieves the best results in small samples.
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    Influenza-like Illness Prediction Based on LSTM-SIR-EAKF
    LI Jin1, WEI Yanlong1, XUE Hongxin2, LIANG Haijian2
    Computer and Modernization    2024, 0 (09): 38-44.   DOI: 10.3969/j.issn.1006-2475.2024.09.007
    Abstract94)      PDF(pc) (1856KB)(83)       Save
    The paper explores the combination method based on machine learning model and infectious disease model to predict influenza trend, and provides advice for medical institutions to take preventive measures. To precisely capture the temporal features of influenza-like illness (ILI), this paper proposes a combined prediction model (LSTM-SIR-EAKF) based on long and short-term memory(LSTM)neural networks, Suceptible-Infected-Recovered(SIR)model, and Ensemble Adjustment Kalman Filter(EAKF). Firstly, the model of LSTM is employed to learn the temporal relationship between ILI. Then, SIR model is used to simulate the transmission process of ILI. Finally, EAKF correctes the anticipated values of ILI from SIR model to obtain the final prediction values of ILI. The experimental results show that through the prediction of ILI in three time periods, the goodness of fit(R2)proposed by the LSTM-SIR-EAKF model are 0.996, 0.991 and 0.995, respectively, and the evaluation indicators of the prediction results are better than the comparison model. LSTM-SIR-EAKF model makes long-term prediction of influenza in time through long and short term memory network, and the infectious disease model simulates the changes of influenza population in space, effectively improving the prediction effect.
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    Stock Price Prediction Based on Business Content to Construct Stock Association Relationships
    YANG Jiang1, SUN Xiaomei1, XU Tao2
    Computer and Modernization    2024, 0 (07): 21-25.   DOI: 10.3969/j.issn.1006-2475.2024.07.004
    Abstract93)      PDF(pc) (1254KB)(76)       Save
    Traditional stock price prediction methods are mostly based on the time series of a single stock, ignoring the complex interrelationships between stocks. In response to this issue, the article proposes a stock price prediction method based on business content to construct stock correlation relationships from the perspective of building a more effective stock portfolio. The model consists of three components: the association relationship construction component, the temporal feature extraction component and the association capture component. The association relationship construction component uses improved TF-IDF to extract the similarity of business content keywords in the annual reports of listed companies to construct stock correlation relationships. The temporal feature extraction component uses LSTM to extract temporal features of stock trading data. The association capture component utilizes GCN to capture high-dimensional features of stock interactions, and finally outputs the predicted stock price through the fully connected layer. The experimental results in the Chinese A-share market indicate that this model has the smallest error, the better fit, and can more effectively predict stock prices compared to single stocks and industry relationship based prediction methods. It is a stock price prediction model that captures the mutual influence between stocks more fully.
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    Network Intrusion Detection Based on Improved XGBoost Model
    SU Kaixuan
    Computer and Modernization    2024, 0 (06): 109-114.   DOI: 10.3969/j.issn.1006-2475.2024.06.018
    Abstract93)      PDF(pc) (472KB)(97)       Save
    Abstract: In order to enhance the accuracy and practicability of the traditional network intrusion detection model, this paper proposes a network intrusion detection based on an improved gradient lift tree (XGBoost) model. Firstly, the random forest algorithm is used to predict the key feature points, and the feature with the highest importance weight is effectively selected and the feature set is constructed in the data pre-processing stage. Secondly, the prediction method of XGBoost model is improved by using card equation. Finally, the cost sensitive function is introduced into the XGBoost optimization algorithm to improve the detection rate of small sample data, and the mesh method is used to reduce the complexity of the model. Experimental simulation results show that compared with other artificial intelligence algorithms, the proposed model can reduce the waiting time by more than 50% with higher inspection accuracy, and has strong scalability and adaptability under noisy environment. Combined with other models, the experimental results show that the tree depth has the greatest impact on the model performance.
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    An Improved YOLOv5-based Method for Dense Pedestrian Detection Under Complex Road Conditions
    SUN Ruiqi1, DOU Xiuchao2, LI Zhihua1, JIANG Xuemei2, SUN Yuhao1
    Computer and Modernization    2024, 0 (05): 85-91.   DOI: 10.3969/j.issn.1006-2475.2024.05.015
    Abstract93)      PDF(pc) (2884KB)(212)       Save
    Abstract: Aiming at the problem of low pedestrian detection accuracy in complex street scene environment, a new network YOLO-BEN is proposed based on the improvement of YOLOv5 network. The network uses a residual connection module Res2Net with hierarchical system to integrate with C3 module,enhancing fine-grained multi-scale feature representation. The paper adopts the Bi-level routing attention module to construct and prune a region level directed graph, and applies fine-grained attention in the union of routing regions, enabling the network to have dynamic query aware sparsity and improving the feature extraction ability of fuzzy images. We incorporate the EVC module to preserve local corner area information and compensate for the problem of information loss caused by occluded pedestrians. In this paper, NWD metric and original IoU metric are used to form a joint loss function, and a small target detection head is added to improve the effect of long-distance pedestrian detection. In the experiment, the method has achieved good results on self-made data sets and some WiderPerson data sets. Compared with the original network, the accuracy, recall and average accuracy of the improved network are increased by 2.8, 4.3 and 3.9 percentage points respectively.

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    Survey of Digital Twin Modeling and Applications in Power System
    LIU Ruoying1, ZOU Weiyu1, HU Shaoqian2, JI Shunhui3
    Computer and Modernization    2024, 0 (09): 61-68.   DOI: 10.3969/j.issn.1006-2475.2024.09.011
    Abstract92)      PDF(pc) (1236KB)(65)       Save
    The power system is closely related to the production activities of various industries and people’s life. The digital twin technology can be used to effectively monitor the operation of the power system, respond in time, and reduce unnecessary time and labor costs. Based on the introduction of the concepts of power system and digital twin, this paper summarizes the research on the modeling and application of digital twin in power system in recent years. A systematic review is conducted on the relevant achievments of digital twin modeling in power systems from five perspectives: geometry, physics, behavior, rule, and multi-scale. The application of power system based on digital twin is summarized from five perspectives: fault detection, fault diagnosis, scheduling, state evaluation and multi-purpose. Finally, the challenges of digital twin modeling and application in power system are summarized, and the future development direction is explored.
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    Camera Module Defect Detection Based on Improved YOLOv8s
    ZHANG Ze1, ZHANG Jianquan2, 3, ZHOU Guopeng2, 3
    Computer and Modernization    2024, 0 (09): 107-113.   DOI: 10.3969/j.issn.1006-2475.2024.09.018
    Abstract90)      PDF(pc) (3880KB)(92)       Save
     Aiming at the problems of the great change of defect size, unclear contour and high missed detection rate of small target defects in camera module defect detection, an improved YOLOv8s algorithm is proposed. Firstly, the small target detection layer is added to improve the detection performance of small targets. Secondly, BiFormer is introduced to improve the C2f module in the backbone network, and the C2f-Bif module is proposed to enhance the ability of the network to extract image features. Then, the H-SPPF (Hybrid Fast Space Pyramid Pooling) module is proposed to enhance the ability of the network to capture local and global information. Finally, the parameter-free SimAM attention mechanism is added to suppress the non-target background interference information and improve the attention of the target. The experimental results show that the average accuracy of the improved YOLOv8s algorithm for camera module defect detection reaches 87.2% under the condition of reducing the number of model parameters, which is 3.2 percentage points higher than that of the YOLOv8s algorithm. The detection speed reaches 55 FPS, which meets the factory’s real-time detection requirements for camera module defects.
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    Regional Enterprise Association Visualization and Relationship Mining Based on#br# Knowledge Graph
    WANG Xianshun, XIONG Qingzhi, WAN Lei, LI Xiang, LIN Chongshan, JIN An’an
    Computer and Modernization    2024, 0 (08): 11-16.   DOI: 10.3969/j.issn.1006-2475.2024.08.003
    Abstract90)      PDF(pc) (2609KB)(60)       Save
    Given the complex network structure of existing regional enterprise association analysis results, which is difficult to comprehend, and the dynamic nature of regional enterprise associations in time and space. In response to the challenges in interpreting results in current regional enterprise analysis, this paper adopts a knowledge graph-based model for regional enterprise association analysis. It utilizes diverse and heterogeneous data for knowledge extraction and storage, coupled with the Neo4j graph database to realize knowledge storage of regional enterprise relationships. In terms of force-directed layout, the utilization of repulsive force optimization and node-edge processing successfully achieves the visualization of enterprise relationships. Through in-depth exploration and analysis of inter-enterprise associations, the aim is to reveal cooperation and competition relationships among regional enterprises, providing decision support for government industrial policy formulation, enterprise investment attraction, and inter-enterprise collaboration. Experimental results demonstrate that the model accurately reveals inter-enterprise relationships, offering robust support for regional economic development.
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    Proportional Dominance Logistic Regression Optimized Voice Disorder Index Algorithm
    HE Ruonan1, FAN Xiang2, CHEN Yi1, JIANG Yufei1, CAO Hui1
    Computer and Modernization    2024, 0 (08): 1-4.   DOI: 10.3969/j.issn.1006-2475.2024.08.001
    Abstract89)      PDF(pc) (1000KB)(88)       Save
    To address the problem that the voice impairment index lacks the analysis and optimization of non-traditional acoustic feature parameters when extracting traditional acoustic feature parameters, this paper proposes an algorithm to optimize the voice impairment index based on ordered proportional dominance logistic regression. Firstly, the spectral flatness is extracted and correlated with the voice impairment index. Secondly, the new equation of voice disorder index is obtained by applying the proportional odds logistic regression method. Finally, a comparison is made between the DSI and the traditional voice disorder index for the samples taken from the database. This paper optimizes the algorithm to broaden the range of values for DSI. The algorithm in this paper is applied to the classification of voice disorders. The experimental results show that the algorithm can effectively determine the values of DSI and obtain good classification results quickly.
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