Top Read Articles

Published in last 1 year |  In last 2 years |  In last 3 years |  All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

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 Abstract （396）      PDF（pc） （2530KB）（231）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （300）      PDF（pc） （1322KB）（207）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

Stance Detection with LoRA-based Fine-tuning General Language Model HAN Xiaolong, ZENG Xi, LIU Kun, SHANG Yu Computer and Modernization    2025, 0 (01): 1-6.   DOI: 10.3969/j.issn.1006-2475.2025.01.001 Abstract （245）      PDF（pc） （2429KB）（196）       Knowledge map    Save  Stance detection is a key task in natural language processing， which determines the stance of an author based on text analysis. Text stance detection methods transition from early machine learning methods to BERT models， and then evolve to the latest large language models such as ChatGPT. Distinguishing from the closed-source feature of ChatGPT， this paper proposes a text stance detection model， ChatGLM3-LoRA-Stance， by using the domestic open-source ChatGLM3 model. In order to apply large models in professional vertical fields， this paper uses LoRA efficient fine-tuning method. Compared with P-Tuning V2 efficient fine-tuning method， LoRA is more suitable for zero-shot and few-shot text stance detection tasks in text. The paper uses the publicly available VAST dataset to fine-tune the ChatGLM3 model， evaluating the performance of existing models in zero-shot and few-shot scenarios. Experimental results indicate that ChatGLM3-LoRA-Stance model has significantly higher F1 scores than other models on zero-shot and few-shot detection tasks. Therefore， the results verify the potential of large language models on text stance detection tasks， and suggest that that the use of LoRA efficient fine-tuning technology can significantly improve the performance of ChatGLM3 large language model in text stance detection tasks. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （222）      PDF（pc） （5470KB）（163）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （199）      PDF（pc） （1434KB）（127）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （167）      PDF（pc） （1796KB）（139）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （165）      PDF（pc） （3845KB）（147）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （153）      PDF（pc） （1939KB）（139）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （150）      PDF（pc） （3358KB）（114）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （143）      PDF（pc） （3491KB）（115）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （138）      PDF（pc） （1856KB）（107）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （138）      PDF（pc） （1236KB）（88）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

Improved Underwater Target Detection Algorithm Based on YOLOv8 LIU Fei, YANG Degang, ZHANG Xin, QIN Jing Computer and Modernization    2025, 0 (01): 113-119.   DOI: 10.3969/j.issn.1006-2475.2025.01.018 Abstract （137）      PDF（pc） （3309KB）（93）       Knowledge map    Save Aiming at the problem of low detection accuracy caused by false detection and missing detection in underwater image target detection， this paper proposes a lightweight underwater image target detection algorithm based on improved YOLOv8n， aiming to improve the detection accuracy of underwater target images. Firstly， the backbone network in YOLOv8 is replaced by the residual network ResNet10 to enhance the feature extraction capability of the backbone network. Secondly， the large convolution kernel attention mechanism is used to improve the fast feature pyramid module to improve the model’s ability to fuse multi-scale features. Then， the C2f module of the original model is replaced by the generalized efficient layer aggregation network in the latest YOLOv9 algorithm， so that the model can maintain high accuracy while reducing computing costs. Finally， the new loss function Inner-SIoU is used to improve the generalization ability of the model and accelerate the convergence speed of the model. Through experiments， on the URPC2020 underwater image target detection dataset， the improved algorithm mAP50 has reached 86.2%， 2.6 percentage points higher accuracy than the original model. Compared with the advanced YOLOv8s and YOLOv7 tiny detectors， as well as the research work in the same field， the method proposed in this paper has achieved higher detection accuracy. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （136）      PDF（pc） （2681KB）（281）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （136）      PDF（pc） （916KB）（146）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （132）      PDF（pc） （1848KB）（104）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （132）      PDF（pc） （1594KB）（130）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （131）      PDF（pc） （2694KB）（127）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （131）      PDF（pc） （2405KB）（168）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

Gesture Recognition Method Based on WiFi and Prototypical Network HUANG Tingpei1, MA Lubiao1, LI Shibao2, LIU Jianhang1 Computer and Modernization    2024, 0 (12): 34-39.   DOI: 10.3969/j.issn.1006-2475.2024.12.005 Abstract （130）      PDF（pc） （1535KB）（93）       Knowledge map    Save WiFi-based gesture recognition plays an important role in touchless human-computer interaction. However， existing WiFi-based gesture recognition systems faced the challenges of small data amount and poor cross-domain performance. In order to solve the above problems， the captured raw WiFi channel state information （CSI） is denoised by CSI Ratio， the extracted phase and converted into CSI images， which is transformed into an image classification problem. Then the transformed images are fed into the prototypical network （PN） for small sample cross-domain gesture recognition， and an enhanced Convolutional Block Attention Module （CSI-CBAM） is added to the PN feature extraction network to improve the gesture representation learning. Extensive experiments were conducted on the Widar3.0 dataset. The experimental results showed that when each class in support set reaches four labeled samples， the system average recognition accuracies are 93.54%， 91.28%， 91.99%， and 89.16% for cross-environment， cross-user， cross-location， and cross-orientation， respectively. Average cross-domain accuracy is higher than 90%，  the proposed method only required a small number of labeled samples to achieve high accuracy cross-domain recognition. Reference | Related Articles | Metrics | Comments（0）

Select

Academic Recommendation System Based on Knowledge Graph and Semantic Information ZHANG Yue, LI Huayu, ZHANG Zhikang, SHEN Xinyi Computer and Modernization    2025, 0 (01): 50-58.   DOI: 10.3969/j.issn.1006-2475.2025.01.009 Abstract （129）      PDF（pc） （1930KB）（115）       Knowledge map    Save In the diverse domains of the Internet， facing the ever-increasing volume of data， there is a growing need for recommendation systems to provide users with personalized information. Utilizing knowledge graphs can enhance the accuracy， diversity， and interpretability of these systems. Addressing the current methods’ limitations in accurately capturing genuine user preferences during propagation， and their lack of attention to the utility of semantic information， this paper proposes an advanced paper recommendation algorithm based on semantic features and knowledge graphs. This method employs the BERT model to extract semantic features from paper abstracts， and uses knowledge graphs for collaborative propagation to obtain entity representations of users and items. During propagation， user preferences are accurately transmitted through multi-head attention， and an attention aggregation network is differentiated between entity representation sets at each layer， the importance of initial information is emphasized. Performance evaluations on three public datasets demonstrate that the model proposed in this paper， compared to the selected optimal baseline models， achieves an increase of 0.010、0.018  and 0.007 in AUC， and 0.007 、0.008 and 0.008 in F1 score， respectively， thereby showing the effectiveness and the superiority of the algorithm proposed in this paper.  Reference | Related Articles | Metrics | Comments（0）

Select

YOLOLW: A Novel Lightweight Object Detection Model ZHANG Yu1, 2, LI Jing1, 2, MA Ming1, 2, WANG Zhongxiang1, 2, SUN Yan1, 2 Computer and Modernization    2024, 0 (11): 91-98.   DOI: 10.3969/j.issn.1006-2475.2024.11.014 Abstract （125）      PDF（pc） （2108KB）（97）       Knowledge map    Save  In response to the growing demand for real-time mobile object detection deployment， the current YOLO backbone network falls short. Hence， this paper proposes YOLOLW， a lightweight object detection model based on the anchor frame. Firstly， it incorporates a novel lightweight decoupling header to enhance focus on classification and regression tasks and improve model accuracy. Secondly， it designs a lightweight and reparameterized network structure that achieves superior detection accuracy while maintaining its lightweight nature. Thirdly， it enhances the feature pyramid structure （FPN） by effectively integrating shallow features through dynamic convolution and cross-hierarchy association. Lastly， spatial and channel attention mechanisms are introduced to significantly boost the model’s accuracy. Experimental results validate the effectiveness of the YOLOLW model. Reference | Related Articles | Metrics | Comments（0）

Select

Intent-based Lightweight Self-Attention Network for Sequential Recommendation HE Sida, CHEN Pinghua Computer and Modernization    2024, 0 (12): 1-9.   DOI: 10.3969/j.issn.1006-2475.2024.12.001 Abstract （125）      PDF（pc） （578KB）（143）       Knowledge map    Save  The parameters of the self-attention calculation mechanism in the existing sequence recommendation models are too large， and there is insufficient preference information in the user's shopping intention. This paper proposes an intent-based lightweight self-attention network for sequential recommendation. On the basis of the traditional product sequence embedding， the model introduces intention sequence embedding to further explore the conversion patterns between sequences. At the same time， in order to reduce the computational complexity of self-attention between pairwise products/intentions in the sequence， a convolutional segmentation sampling module is designed to divide the user behavior sequence and intention sequence into multiple segments， mapping user interests to multiple sequence segments. Comparative experiments are conducted on three public datasets， MovieLens-1M， Yelp， and Amazon-Books. Compared with baseline models， the self-attention mechanism is applied to capture the dependency between segments， effectively reducing the number of parameters. The results show that the hit rate， normalized discounted cumulative gain and mean reciprocal ranking are increased by 5.32%， 4.40% and 5.51% on the MovieLens-1M dataset， 30.93%， 22.73% and 28.84% on the Yelp dataset， and 7.78%， 11.55% and 13.98% on the Amazon-Books dataset， which verify the effectiveness of the model proposed in this paper. Reference | Related Articles | Metrics | Comments（0）

Select

Semi-supervised Image Generation Model Based on StyleGAN WANG Zhiqiang, ZHENG Shuang Computer and Modernization    2024, 0 (06): 14-18.   DOI: 10.3969/j.issn.1006-2475.2024.06.003 Abstract （124）      PDF（pc） （840KB）（93）       Knowledge map    Save Abstract： This paper introduces SG-GAN， a semi-supervised StyleGAN model that overcomes the limitations of traditional StyleGAN. The quality of generated images using StyleGAN is heavily dependent on the quality of the training data set. When the training image quality is low， StyleGAN often fails to generate high-quality images. To address this issue， SG-GAN generates and trains support vector machine（SVM）training samples based on the one-to-one correspondence between vectors w and images in StyleGAN. SVM and StyleGAN mapping network are then used to screen vectors w before generating each image to improve the quality of the resulting images. For batch image generation， gene vectors are generated by the gene vector generator and combined randomly while all permutations of style vectors are obtained using a dynamic cycle backtracking algorithm. Individuals are generated from the permutation results and screened for excellence using an evaluation function after multiple iterations. Experiments were carried out on open data sets and compared with other advanced methods， demonstrating that SG-GAN improves upon StyleGAN's accuracy significantly. The model achieves FID 2.74， an accuracy rate of 74.2%， and a recall rate of 51.2% on the lsun cat face data set， further validating the efficacy of the model. At the same time， the model achieved an accuracy of over 70% on the Cat Dataset， CIFAR-100， and ImageNet datasets， thereby verifying its good generalization ability. Reference | Related Articles | Metrics | Comments（0）

Select

News Long Text Classification Model Based on Improved TF-IDF and AGLCNN ZHOU Xianxi, MU Li Computer and Modernization    2024, 0 (08): 120-126.   DOI: 10.3969/j.issn.1006-2475.2024.08.019 Abstract （123）      PDF（pc） （1209KB）（110）       Knowledge map    Save  News long text classification is an important task in natural language processing， but traditional text representation methods have problems such as sparse features and insufficient semantics. In addition， long news texts contain a large amount of redundant information and may involve other topics， all of which can lead to incomplete text feature extraction. Therefore， this article proposes a news long text classification model based on improved TF-IDF algorithm and AGLCNN. This model firstly improves the TF-IDF algorithm by utilizing the distribution and position information of feature items between and within classes， and combines Word2Vec word vectors for text representation. Using attention mechanism to highlight keyword information， we input it into Bi-LSTM to capture text contextual features. Then we use CNN to highlight the prominent features of news topics. Considering that there may be sentences involving other topic information in long news texts， a gating mechanism is introduced to fuse the output features of Bi-LSTM and CNN to obtain the final text feature representation. Finally， we input the feature vectors into the Softmax layer for news classification. Comparative experiments are conducted on the THUCNews dataset and the Sohu News dataset， and the results show that the proposed model has recall rates of 0.985 and 0.976 on both datasets， respectively， which are superior to other classification models. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （122）      PDF（pc） （2245KB）（127）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （122）      PDF（pc） （472KB）（124）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （121）      PDF（pc） （1254KB）（95）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

Automated Essay Scoring Method Based on GCN and Fine Tuned BERT MA Yu, YANG Yong, REN Ge, Palidan Tuerxun Computer and Modernization    2024, 0 (09): 33-37.   DOI: 10.3969/j.issn.1006-2475.2024.09.006 Abstract （121）      PDF（pc） （1152KB）（127）       Knowledge map    Save  Automatic scoring of essays is one of the important research directions in the field of smart education. It has the advantages of improving scoring efficiency， reducing labor costs， and ensuring the objectivity and consistency of scoring， so it has broad application prospects in the field of education. Although syntactic features play an important role in automatic scoring of compositions， there is still a lack of research on how to better utilize these features for automatic scoring of compositions. This paper proposes an automatic essay scoring method GFTB based on GCN and fine-tuned BERT. This model uses graph convolutional network to extract syntactic features of compositions， uses BERT and Adapter training methods to extract deep semantic features of compositions， and uses a gating mechanism to further capture the semantic features after the fusion of the two. The experimental results show that the proposed GFTB model achieves good average performance on 8 subsets of the public data set ASAP. Compared with baseline models such as Tongyi Qianwen， the proposed method can effectively improve the performance of automatic essay scoring. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （120）      PDF（pc） （2634KB）（87）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （119）      PDF（pc） （3880KB）（105）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （119）      PDF（pc） （1020KB）（112）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （119）      PDF（pc） （1564KB）（112）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

Hyperspectral Image Denoising Using Low Rank Tensor Decomposition and Weighted Group Sparse Regularization WANG Yefang1, JIA Xiaoning1, 2, CHENG Libo1, 2, LI Zhe1, 2 Computer and Modernization    2025, 0 (01): 30-36.   DOI: 10.3969/j.issn.1006-2475.2025.01.006 Abstract （118）      PDF（pc） （2260KB）（70）       Knowledge map    Save  Hyperspectral images have significant reference value in fields such as environmental monitoring， remote sensing science， and medical imaging. However， the imaging process is susceptible to contamination by mixed noise due to limitations in the imaging acquisition equipment and adverse weather conditions， leading to a significant decline in image quality. To tackle this problem， we propose a denoising model for hyperspectral images based on low rank tensor decomposition and weighted group sparsity-regularized. Specifically， to effectively retain the edge information of the hyperspectral image and extract sparse structural features， we propose a group sparse regularization method based on the [l2,1] norm， which aims to weight and constrain the differential images in the spatial and spectral directions. Then， a combined approach is proposed， which utilizes the [l1] norm and Frobenius norm， to effectively eliminate complex mixed noise in the images， thereby enhancing the overall image quality. Furthermore， we use ADMM algorithm to solve the model proposed in this paper. Experimental evaluations of the model are conducted using both simulated and real data， and the results demonstrate the superiority of the proposed model over the baseline model in terms of various evaluation metrics， particularly the proposed model has obvious advantages in hyperspectral image recovery. Reference | Related Articles | Metrics | Comments（0）

Select

A Task Scheduling Method for Biological Gene Multi Sequence Alignment Algorithm YANG Bo, WANG Hongjie, XU Shengchao, MAO Mingyang, JIANG Jinling, JIANG Darui Computer and Modernization    2024, 0 (07): 7-12.   DOI: 10.3969/j.issn.1006-2475.2024.07.002 Abstract （117）      PDF（pc） （1405KB）（72）       Knowledge map    Save Abstract： Aiming at the problem of slow alignment efficiency in current biological gene multi sequence alignment algorithms when facing large-scale data， a task scheduling method for biological gene multi sequence alignment algorithms is proposed to improve the efficiency of biological gene multi sequence alignment. Firstly， the Trie tree method is used to segment biological gene multi sequence data， thereby optimizing the efficiency of data search and matching in the subsequent gene multi sequence alignment process; Secondly a gene multi sequence BWT index is constructed and the BWT index method is used to complete biological gene multi sequence alignment; Finally， based on the multi sequence alignment method， a heterogeneous parallel system of CPU and GPU is used to complete the task scheduling of multi sequence alignment. The experimental results show that the proposed task scheduling method for biological gene multi sequence alignment algorithm is more efficient， performs better， and is more suitable for practical applications. Reference | Related Articles | Metrics | Comments（0）

Select

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 Abstract （116）      PDF（pc） （2884KB）（241）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

Select

OFDM Channel Estimation Based on Matrix Recovery ZHANG Jingjing, HUANG Xuejun Computer and Modernization    2024, 0 (05): 1-4.   DOI: 10.3969/j.issn.1006-2475.2024.05.001 Abstract （116）      PDF（pc） （1380KB）（131）       Knowledge map    Save Abstract： Orthogonal frequency division multiplexing （OFDM） is a crucial technology in channel estimation， this paper proposes an OFDM channel estimation method based on matrix recovery， multiple consecutive OFDM signal in the frequency domain channel is constructed to a channel matrix. Since this channel matrix is low rank， the channel estimation problem can be converted to the weighted truncated kernel norm minimization problem of the channel matrix and the improved Singular Value Thresholding algorithm is used for recovery. The simulation results show that compared with the traditional channel estimation algorithm， the proposed method can use fewer pilot signals when the same precision channel estimation is obtained. Compared with the channel estimation method based on compressed sensing， the proposed method consumes the same amount of pilot frequency but can directly obtain high precision frequency domain estimation of OFDM channel. Reference | Related Articles | Metrics | Comments（0）

Select

Optimization and Deployment of Object Detection Algorithm Based on Domestic AI Chips CHEN Siyun1, MA Huaibo2, ZHANG Huajun2, LAN Zining2, CHEN Wenxin2 , HU Jie1, CHANG Sheng1 Computer and Modernization    2025, 0 (01): 25-29.   DOI: 10.3969/j.issn.1006-2475.2025.01.005 Abstract （116）      PDF（pc） （2281KB）（79）       Knowledge map    Save  At present， various types of neural networks have gradually been widely applied in all aspects of society. The performance of neural network models largely depends on the quality of their training strategies， and their deployment cannot be separated from the support of corresponding hardware platforms. In order to ensure the information security and development of the electronic information industry in China under the current situation， it is urgent to replace relevant domestic AI chips. Taking the replacement of domestic AI chips as the starting point， this article explores the deployment process of neural network algorithms on domestic platforms based on the Quanai QA-200RC development kit. The improvement of YOLOv6 neural network training and host program optimization are carried out according to specific task requirements. With real-time detection through cameras， target detection of rocket debris is achieved， the frame rate is 30 FPS， the mAP_0.5 is 90.1%， and the power consumption is 8.1 W， which meets the requirements for completing object detection tasks on edge platforms and is helpful for promoting the application of domestic chips in related fields. Reference | Related Articles | Metrics | Comments（0）

Select

SAR Ship Detection Algorithm Based on Improved YOLOv8 GU Yue, DENG Songfeng, SHEN Ji, MU Wentao, ZHAO Enqi Computer and Modernization    2024, 0 (12): 78-83.   DOI: 10.3969/j.issn.1006-2475.2024.12.012 Abstract （116）      PDF（pc） （1264KB）（99）       Knowledge map    Save To enhance the accuracy of ship target detection in SAR images， especially when facing challenges such as uneven target sizes， dense distributions， and complex backgrounds， an improved YOLO-3M ship target detection algorithm based on YOLOv8 is proposed. Firstly， the algorithm introduces a Multi-Scale Dilated Convolution Block （MSDB） into the backbone network， which uses convolutions with different dilation rates to extract multi-scale features， thereby enlarging the receptive field without increasing computational costs. Secondly， a Multidimensional Collaborative Attention （MCA） mechanism is incorporated into the neck network to capture key features across the channel， height， and width dimensions， facilitating interaction between different dimensional information and helping the network to effectively focus on key parts within complex backgrounds. Finally， an MPDIoU loss function is introduced in the detection head to address issues with existing loss functions that struggle to effectively detect when the predicted bounding box and the actual bounding box have the same aspect ratio but completely different widths and heights. Experimental results on the SSDD dataset show that the YOLO-3M algorithm achieves higher accuracy and average precision while effectively reducing the number of parameters and computational requirements， making the model more lightweight and suitable for resource-constrained environments. Additionally， there is a significant improvement in reducing false positives and false negatives in complex ship detection scenarios. Reference | Related Articles | Metrics | Comments（0）

Select

STRL: Testing Algorithm Based on Reinforcement Learning#br# #br# ZHAO Huarui Computer and Modernization    2024, 0 (08): 5-10.   DOI: 10.3969/j.issn.1006-2475.2024.08.002 Abstract （115）      PDF（pc） （1805KB）（66）       Knowledge map    Save  Reinforcement learning has become research focus in the field of machine learning in recent years due to its characteristic of generating dynamic data through interaction with the environment without requiring a large number of samples for training. This paper proposes a new software testing framework STRL based on reinforcement learning， which can effectively solve the problem of long time consuming and low state coverage of regression testing. STRL utilizes reinforcement learning algorithm PPO to achieve efficient adaptive exploration. Experiments results show that the STRL algorithm outperforms manual testing and automated script testing in terms of state coverage and testing time. Reference | Related Articles | Metrics | Comments（0）