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Reviews on Event Knowledge Graph Construction Techniques and Application XIANG Wei Computer and Modernization    2020, 0 (01): 10-.   DOI: 10.3969/j.issn.1006-2475.2020.01.003 Abstract （2315）      PDF（pc） （912KB）（1801）       Knowledge map    Save Given its rich and flexible semantics by the graph structure, knowledge graph which describes the things in the objective world and their relationships has received extensive attentions in many fields. In objective world, event knowledge graph focuses on various dynamic events, entities and their relationships in terms of structured graph for more efficient management of massive data. In particular, the mining of dynamic event information and event logic in the application field are of great significance for understanding the laws of world development and assisting various intelligent applications. The construction techniques and typical applications of event knowledge graph are reviewed in this article, including event knowledge representation, event knowledge extraction, and event relation extraction. The challenges and research perspectives are also discussed. Reference | Related Articles | Metrics | Comments（0）

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Mobile Robot Path Planning Based on Fusion of Improved A* and DWA Algorithms PANG Yong-xu, YUAN De-cheng Computer and Modernization    2022, 0 (01): 103-107.   Abstract （1968）      PDF（pc） （2046KB）（1323）       Knowledge map    Save Aiming at the path planning requirements of mobile robot to achieve global optimal path in complex environment and dynamic and real-time obstacle avoidance in unknown environment, the traditional A* (A-star) algorithm is improved, and Dynamic Window Approach (DWA) is integrated to achieve dynamic and real-time obstacle avoidance. Firstly, the obstacle proportion in the grid environment is analyzed. The obstacle proportion is introduced into the traditional A* algorithm to optimize the heuristic function h(n), so as to improve the evaluation function f(n) and improve its search efficiency in different environments. Secondly, in view of the intersection between the trajectory and the vertex of obstacles optimized by the traditional A* algorithm in the complex grid environment, the selection method of child nodes is optimized, and the redundant nodes in the path are deleted to improve the smoothness of the path. Finally, Dynamic Window Approach is integrated to realize dynamic and real-time obstacle avoidance of mobile robot in complex environment. The comparative simulation experiments under MATLAB show that the improved algorithm is optimized in the path length, path smoothness and elapsed time, meets the global optimal and can realize dynamic and real-time obstacle avoidance, and has better path planning effect. Reference | Related Articles | Metrics | Comments（0）

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Pulmonary Nodule Aided Diagnosis System Based on Target Detection Algorithm XI Xiao-qian, LIU Wei Computer and Modernization    2020, 0 (11): 1-7.   Abstract （1206）      PDF（pc） （1589KB）（1151）       Knowledge map    Save According to statistics, lung cancer is one of the diseases with the highest morbidity and mortality rate in the world. With the maturity of computer-aided diagnosis (CAD) and convolutional neural networks (CNN), the diagnosis and treatment in medical field are becoming more and more intelligent. In this paper, an automatic detection method of lung nodules based on target detection algorithm is presented, and a set of image processing flow of CT of lung parenchyma is presented, which combines threshold segmentation algorithm with digital morphological processing. After training and learning 1186 lung nodules in LUNA16 data set, we observe the evaluation results of YOLO V3 model in the data set to verify the model. The accuracy of the experimental results is 92.18%, the average detection time of each image is 0.035 seconds. In order to verify the validity of YOLO V3 model, this paper compares it with existing algorithms such as SSD, CNN, U-Net and so on. At the same time, this paper designs an auxiliary diagnosis system of pulmonary nodules based on CAD technology, realizes the human-computer interaction, and provides a simple and clear auxiliary diagnosis tool for doctors. Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （647）      PDF（pc） （1322KB）（1119）       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）

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Survey of Application of Knowledge Graph in Field of Intelligent Manufacturing JIANG Sulun1, 2, 3, YUAN Decheng1, GUO Qingda2, 3, LIU Jian3, YU Guangping2, 3 Computer and Modernization    2025, 0 (05): 48-59.   DOI: 10.3969/j.issn.1006-2475.2025.05.007 Abstract （124）      PDF（pc） （3678KB）（1073）       Knowledge map    Save  With the rapid promotion of new-goneration artifica  intelligence， computing， and  other technologies， the manufacturing field urgently needs to undergo intelligence and digitalization and upgrading. Through literature review and applied case studies， it is found that the construction of knowledge graph can promote the development of industrial intelligence， so the field of intelligent manufacturing has begun to apply knowledge graph to manage and optimize intelligent manufacturing equipment data and processes. At present， knowledge graph technology has been maturely applied in the direction of intelligent question answering， personalized recommendation， etc.， in order to explore the greater application potential of knowledge graph technology in the field of intelligent manufacturing， the current literature and application status is studied in detail and conduded. This paper firstly starts with the popular technologies such as knowledge acquisition， knowledge fusion， and knowledge reasoning involved in knowledge graphs， and then focuses on the research and analysis of several popular application directions such as industrial fault diagnosis， digital twins， human-computer collaborative interaction， and risk management based on knowledge graphs， and summarizes the general architecture， and discusses the future development trends and difficulties such as the combination with AIGC technology， and puts forward future prospects to provide reference for promoting intelligent manufacturing knowledge graphs. Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （735）      PDF（pc） （5470KB）（1001）       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）

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Prediction of Debris Flow Based on Improved Extreme Learning Machine ZENG Ding, ZENG Yong Computer and Modernization    2020, 0 (09): 95-99.   DOI: 10.3969/j.issn.1006-2475.2020.09.017 Abstract （700）      PDF（pc） （1050KB）（916）       Knowledge map    Save In order to improve the accuracy of debris flow prediction, an improved Extreme Learning Machine(ELM) algorithm based on DBSCAN clustering is proposed in this paper. Firstly, DBSCAN algorithm is used to cluster the training data about debris flow. Secondly, the ELM classifier is trained by classifying the different training sets obtained by clustering. Finally, the ELM classifier is used to predict the data of the prediction sets. The experimental results show that the accuracy of the improved ELM algorithm in predicting the occurrence of debris flow is 91.6% on average. Compared with the traditional ELM algorithm, the stability of the improved ELM algorithm is significantly improved. Compared with the traditional ELM algorithm, BP neural network and Fisher prediction method, the improved ELM algorithm has higher prediction accuracy. Reference | Related Articles | Metrics | Comments（0）

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Review on Satellite Constellation Design and Simulation Software ZHOU Yan-xi, FENG Xu-zhe, DAI Jian-zhong Computer and Modernization    2019, 0 (08): 63-.   DOI: 10.3969/j.issn.1006-2475.2019.08.012 Abstract （1410）      PDF（pc） （1872KB）（838）       Knowledge map    Save Satellite constellation is developing into vaster number of satellites, multiple network features and complicated composition, which leads to higher requirement on the design and simulation of satellite constellations. Because determining the constellation topology and designing the constellation network are the key steps during constellation design, this article gives an in-depth introduction and comparison on the latest constellation design softwares and network simulation softwares. Software functions, software characteristics and their support on satellite constellation are focused. Finally, two simulation schemes are showed to be suitable. One of them is ns-3(Network Simulator 3) and SaVi(Satellite Constellation Visualization), which are based on Linux with high flexibility and openness. The other one is commercial softwares OPNET Modeler and STK(Satellite Tool Kit). They have poweful functions, complete graphical interface and are easy to employ. Under current sofware development conditions, the two schemes can be better adapted to satellite constellation. Reference | Related Articles | Metrics | Comments（0）

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Regformer： Hydraulic Prediction Model of Oil Pipeline Based on GS-XGBoost LI Ya-ping, WANG Jun-fang, YU Hong-mei, DOU Yi-min, XIAO Yuan, TIAN Ji-lin Computer and Modernization    2024, 0 (01): 59-66.   DOI: 10.3969/j.issn.1006-2475.2024.01.010 Abstract （209）      PDF（pc） （5692KB）（790）       Knowledge map    Save Abstract: Hydraulic pressure drop prediction is very important for production regulation of oil pipelines， and current machine learning methods regard pressure drop prediction as a regression problem， however， pipeline hydraulic calculation is affected by many factors， and the fixed weights obtained from the training set by traditional machine learning methods are difficult to generalize to more test samples or real engineering scenarios. This paper proposes a hydraulic pressure drop regression prediction method， Regformer， which introduces a sparse attention mechanism into the regression task， designs a smoothing probability method based on multi-headed attention， and incorporates a feature projection mechanism. In a comparative experimental analysis with seven mainstream methods on 10 public data sets， qualitative experiments show that Regformer has good fitting ability for local mutations; experiments on hydraulic pressure drop prediction show that the self-attentive method has significant advantages for regression tasks with multivariate uncertainty， especially for extreme cases reflecting the importance of adaptive regression parameters， and Regformer achieves better performance than Transformer with less computation， verifying the superiority of the proposed sparse attention and adaptive feature projection for the hydraulic pressure drop prediction task. Reference | Related Articles | Metrics | Comments（0）

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Multi-UAV Power Inspection Task Planning Technology Based on Deep Reinforcement Learning MA Rui, OUYANG Quan, WU Zhao-xiang, CONG Yu-hua, WANG Zhi-sheng Computer and Modernization    2022, 0 (01): 98-102.   Abstract （646）      PDF（pc） （1806KB）（779）       Knowledge map    Save UAVs have been widely used in the inspection tasks of power grid lines and electrical towers due to their advantages of flexibility, low cost and strong maneuverability. Because of the limited range of a single UAV, multiple UAVs are required to cooperate in a wide range grid inspection. However, the traditional planning methods cannot work well because of slow computing speed and unobvious collaborative effect. To remedy these deficits, a new mission planning algorithm is proposed in this work, which is based on multi-agent reinforcement learning algorithm QMIX. On the basis of the framework of intensive training and decentralized execution, this algorithm establishes RNN network for each UAV and gets the joint action value function guideline for training by mixing network. To simulate the collaboration capabilities of multi-agents, a reward function for collaboration task is designed, and it solves the problem of low collaboration efficiency in multi-UAV mission planning. The simulation results demonstrate that the proposed algorithm takes 350.4 seconds less than VDN algorithm. Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （328）      PDF（pc） （2573KB）（731）       Knowledge map    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. Reference | Related Articles | Metrics | Comments（0）

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Time Series Forecasting Model Based on LSTM-Prophet Nonlinear Combination ZHAO Ying, ZHAI Yuan-wei, CHEN Jun-jun, TENG Jian Computer and Modernization    2020, 0 (09): 6-11.   DOI: 10.3969/j.issn.1006-2475.2020.09.002 Abstract （1204）      PDF（pc） （2178KB）（725）       Knowledge map    Save At present, the single prediction model has low prediction accuracy for complex nonlinear time series and can not capture the composite characteristics of time series well. Therefore, this paper proposes a time series prediction model based on back propagation neural network combination of Long Short-Term Memory-Prophet (LSTM-Prophet). The prediction values obtained from the long short-term memory network and Prophet prediction model are combined by BP neural network to obtain the final prediction value. Then, a comparative experiment is designed and implemented between the proposed model and three individual models. The accuracy and validity of the proposed model are verified by data sets from three different fields. The experimental results show that the proposed prediction model has high prediction accuracy, good universality, and application prospect. Reference | Related Articles | Metrics | Comments（0）

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Microblog Rumor Detection Based on Sentiment Analysis and Transformer Model FENG Ru-jia, ZHANG Hai-jun, PAN Wei-min Computer and Modernization    2021, 0 (10): 1-7.   Abstract （1001）      PDF（pc） （1087KB）（710）       Knowledge map    Save Aiming at realizing the rumor detection on microblog, this paper deeply excavates the semantic information of the body content of microblog, and emphasizes the emotional tendency reflected by users in microblog comments, so as to improve the effect of rumor identification. In order to improve the rumor detection accuracy, based on XLNet word embedding method, the Transformer’s Encoder model is used to extract the semantic features of microblog body content. Combined with the BiLSTM+Attention network, the emotional feature extraction of microblog comments is realized. Two kinds of feature vectors are spliced and fused to further enrich the input features of neural network. Then, the microblog event classification results are output, and the microblog rumors detection is achieved. The experimental results show that the accuracy of the model in rumor recognition reaches 94.8%. Reference | Related Articles | Metrics | Comments（0）

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Research and Implementation of Real-time Analysis Technology of Moving Target Data HOU Bo, NIE Ying Computer and Modernization    2020, 0 (01): 17-.   DOI: 10.3969/j.issn.1006-2475.2020.01.004 Abstract （418）      PDF（pc） （1102KB）（710）       Knowledge map    Save Aiming at the real-time trajectory data of moving object， this paper analyzes the problems consisting in the existing research, and proposes two solutions for real-time analysis. The first one is trajectory prediction method based on five-point method, which can predict the next point’s position of moving object rapidly and has strong real-time performance. The second one is Storm-based historical frequency statistical analysis method, which analyzes historical track frequency with high accuracy. These two methods solve two important problems in real-time analysis: real-time, accurate, and have high practicability. Reference | Related Articles | Metrics | Comments（0）

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Attention and LSTM Based State Prediction of Equipment on Electric Power Communication Networks WU Hai-yang, CHEN Peng, GUO Bo, JIANG Chun-xia, LI Ji-xuan, ZHU Peng-yu Computer and Modernization    2020, 0 (10): 12-16.   Abstract （530）      PDF（pc） （713KB）（699）       Knowledge map    Save With the rapid growth of electric power communication networks, the importance of predicting the working state of online equipment is increasing as well. Since the running data of typical communication devices always come from heterogeneous resources, the prediction models have to be learned from features with high dimension, high sparsity as well as repetitive patterns. This problem severely restricts the performance of conventional machine learning approaches. This paper proposes a novel state prediction model based on the integration of attention mechanism and LSTM (Long Short-Term Memory). By a two-stage learning strategy, the attention mechanism can achieve both dimensionality reduction and feature extraction of original input. Meanwhile, most related features are extracted for final prediction from the end-to-end learning. Extensive experimental results on practical running data of electric power communication networks demonstrate that, the proposed method has high performance in the working state prediction problem. Reference | Related Articles | Metrics | Comments（0）

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A Survey of Research on Target Detection Algorithms Based on Deep Learning CAO Yan, LI Huan, WANG Tian-bao Computer and Modernization    2020, 0 (05): 63-.   DOI: 10.3969/j.issn.1006-2475.2020.05.011 Abstract （653）      PDF（pc） （1000KB）（696）       Knowledge map    Save Traditional target detection algorithms rely mainly on manually selecting features to detect objects. The artificially extracted feature pairs are mainly for certain specific objects, such as some features suitable for edge detection, and some suitable for texture detection, which is not universal. In recent years, deep learning has flourished, and significant research progress has been made in the field of computer vision such as image classification, target detection, and image semantic segmentation. As a feature learning method, deep learning can automatically learn the useful features of the target, avoiding the problem of manual extraction of features, and at the same time ensuring good detection results. Firstly, the research progress of target detection algorithm based on deep learning is introduced. Secondly, the common problems and solutions in target detection algorithm are summarized. Finally, the possible development direction of target detection algorithm is prospected. Reference | Related Articles | Metrics | Comments（0）

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Loop Closure Detection Algorithm Based on Mixed Global Pooling SONG Zhou-rui Computer and Modernization    2020, 0 (04): 115-.   DOI: 10.3969/j.issn.1006-2475.2020.04.019 Abstract （364）      PDF（pc） （1018KB）（666）       Knowledge map    Save The deep learning based loop closure detection algorithm has been verified to be superior to traditional methods. However, the computation burden of deep learning is heavy, so it is often difficult to deploy large convolutional neural networks on mobile robots, while small convolutional neural networks perform poorly in large scenes. Therefore, this paper proposes a scheme to deploy large convolutional neural networks on mobile robots. Firstly, the feature graph is transformed into the feature vector by using the mixed global pooling layer. Experiments show that the performance of this method is equivalent to that of other more complex methods and the calculation is simpler. Then, a block-based floating-point convolutional neural network acceleration engine is proposed, which significantly reduces the computational energy consumption and causes almost no performance loss without retraining. Reference | Related Articles | Metrics | Comments（0）

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Prediction of COVID-19 Based on Mixed SEIR-ARIMA Model DONG Zhang-gong, SONG Bo, MENG You-xin Computer and Modernization    2022, 0 (02): 1-6.   Abstract （838）      PDF（pc） （1176KB）（665）       Knowledge map    Save Novel coronavirus pneumonia, referred to as COVID-19, is an acute infectious pneumonia caused by novel coronavirus, which is of highly infectious and generally susceptible to the population. Therefore, the prediction of the number of novel coronavirus pneumonia infections is not only beneficial for the country to make scientific decisions in the face of the epidemic, but also facilitates the timely integration of epidemic prevention resources. In this paper, a hybrid model SEIR-ARIMA constructed by the model SEIR based on the traditional infectious disease dynamics and the differential integrated moving average autoregressive model ARIMA is proposed to make prediction and analysis of the novel coronavirus pneumonia epidemic in different time periods and locations. From the experimental results, the prediction based on the SEIR-ARIMA hybrid model has better prediction effect than the common logistic regression Logistic, long short-term memory artificial neural network LSTM, SEIR model, and ARIMA model used for COVID-19 prediction. In order to truly reflect whether the improvement of the experimental effect originates from the advantage of combining SEIR and ARIMA models, this paper also implements the SEIR-Logistic hybrid model and SEIR-LSTM hybrid model, and compares the analysis with SEIR-ARIMA to conclude that both SEIR-ARIMA predictions achieve better prediction results. Therefore, the analysis of the development trend of COVID-19 based on the SEIR-ARIMA hybrid model is relatively reliable, which is conducive to the scientific decision-making of the country in the face of the epidemic and has good application value for the prevention of other types of infectious diseases in China in the future. Reference | Related Articles | Metrics | Comments（0）

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A Fast Registration Method for Massive Point Clouds Based on 3D-SIFT and 4PCS LI Jia-le1, LI Zhe-run1, ZHAO Yong2, ZHANG Yang1 Computer and Modernization    2024, 0 (02): 1-6.   DOI: 10.3969/j.issn.1006-2475.2024.02.001 Abstract （387）      PDF（pc） （1952KB）（647）       Knowledge map    Save Abstract： The registration of measurement point cloud and model point cloud is the key of visual positioning. Aiming at the problems of poor visual positioning accuracy and low algorithm efficiency caused by large amount of measurement point cloud data and low overlap rate with CAD model point cloud， a registration method of measurement point cloud and model point cloud based on the fusion of 3D scale invariant feature transform （3D-SIFT） and four point fast robust matching algorithm （4PCS） is proposed. Firstly， the depth camera is used to extract the point cloud of the part， and the extracted measurement point cloud is denoised and filtered； Then 3D-SIFT feature point extraction algorithm is used to extract feature points from measurement point cloud and CAD model point cloud； Finally， the extracted feature points are used as the initial values of the 4PCS algorithm to achieve the registration of the two point cloud data. Compared with the commonly used 4PCS algorithm and Super-4PCS algorithm， the algorithm simulation and experimental results show that the proposed algorithm can improve the registration speed by more than 30% on the premise of ensuring the registration accuracy. Reference | Related Articles | Metrics | Comments（0）

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Tongue Constitution Classification Method Based on Deep Learning XIE Haiqing, LING Jiaqi, YI Xinbo Computer and Modernization    2025, 0 (03): 99-105.   DOI: 10.3969/j.issn.1006-2475.2025.03.015 Abstract （126）      PDF（pc） （5379KB）（647）       Knowledge map    Save In response to the minimal inter-class differences in tongue images and the insufficient feature extraction by traditional networks， this paper constructs datasets for tongue image semantic segmentation and classification and conducts data preprocessing. Based on RepVGG network algorithm design and optimization， a multi-feature fusion tongue constitution classification network MTSNet based on convolutional neural network is proposed. MTSNet employs a multi-scale feature pyramid and combine high-level and low-level semantic information learned by the network to enhance the network’s representational capabilities. The addition of squeeze-excitation convolutional layers in the RepBlock module enables the network to focus more on information-rich features. The experimental results show that MTSNet significantly enhances classification performance across nine types of tongue constitutions， and its accuracy is 32.11 percentage points higher than that of AlexNet， 22.37 percentage points higher than that of SVM， and 17.68 percentage points higher than that of Resnet-18. Compared with the unoptimized RepVGG network， MTSNet achieves improvements of 9.90 percentage points in accuracy， 14.01 percentage points in macro-averaging， 9.90 percentage points in micro-averaging， and 11.09 percentage points in weighted-averaging. This tongue constitution， classification method provides scientific basis for users’ health management and has good reference application for traditional Chinese medicine’s adjunctive treatment and scientific research. Reference | Related Articles | Metrics | Comments（0）

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Breast Mass Recognition Based on Texture Features in Ultrasound Images LI Zi-long, LYU Yong, TAN Guo-ping, YAN Qin, Computer and Modernization    2021, 0 (02): 1-6.   Abstract （775）      PDF（pc） （1152KB）（646）       Knowledge map    Save Aiming at the breast ultrasound image, a method of breast mass recognition based on  texture feature extraction is proposed, which is helpful to use computer-aided identification method to judge whether breast mass is cancerous or not, and assist radiologists to predict the nature of images. Firstly, the max-response filter is applied to the breast ultrasound image to remove the main noise interference while ensuring the integrity of a certain edge tissue structure. On this basis, the first-order and second-order texture features of breast images are extracted, and then the features are identified and classified by artificial neural network. The accuracy of the method is verified on the real data set obtained from a hospital and the proposed method is compared with other methods from three aspects: preprocessing, feature extraction and classification. The results show that the proposed method improves the recognition rate of breast masses on the basis of reducing the complexity of the algorithm. Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （857）      PDF（pc） （2530KB）（644）       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）

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Application of MVC Design Pattern in PHP Development DAI Yi-ping Computer and Modernization    2011, 1 (3): 33-37,4.   DOI: 10.3969/j.issn.1006-2475.2011.03.010 Abstract （1162）            Knowledge map    Save The integrated development environment Zend Studio is introduced by examples to the MVC pattern in PHP development, through the understanding and use of MVC pattern, the software can be a good modular, separation system, said data control and data functions, in favor division of labor between development teams and cooperation, especially in the development of large complex projects, this mode helps speed up the progress of the project, shorten development cycles, enhance software maintainability and code reuse, increase development efficiency and project quality. Related Articles | Metrics | Comments（0）

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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 Abstract （222）      PDF（pc） （3880KB）（632）       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）

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Analysis and Design of Online Examination System Based on UML LIU Jun-li;YAN Jun-song Computer and Modernization    2009, 1 (7): 113-116.   DOI: 10.3969/j.issn.1006-2475.2009.07.032 Abstract （3745）            Knowledge map    Save In order to take scientifically and effectively the resources and technical advantages of information networks, the paper analyzes the online examination system and the flow of the entire structure of the system, designs based on UML case diagram, sequence diagram, activity diagram of the system so that the teacher can use the system to conduct online test for students, management and development of examination papers, and calculation and query scores and so on. Related Articles | Metrics | Comments（0）

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Web Application Research Based on WCF Services Framework and Silverlight TAN Qi Computer and Modernization    2011, 1 (1): 79-3.   DOI: 10.3969/j.issn.1006-2475.2011.01.023 Abstract （1047）            Knowledge map    Save For traditional B/S Web application’s poor interaction ability, low reuse degrees, display ability to unsatisfactory and many other issues, WCF service framework and Silverlight technology are combined together in order to solve the problems mentioned above. This solution makes full use of WCF integration itself with a variety of distributed technologies, and increases reuse levels to businesslevel serviceoriented programming capabilities. Finally Silverlight completes the frontend exhibit. And then discusses and implements an integrated framework for actual development. Related Articles | Metrics | Comments（0）

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Application of DCT Transform in Image Compression WU Ying Computer and Modernization    2013, 1 (4): 103-106.   DOI: 10.3969/j.issn.1006-2475.2013.04.026 Abstract （960）            Knowledge map    Save In the age of information explosion nowadays, image compression is one of the key technologies to promote the further development of multimedia technology. Due to the unique advantage of discrete cosine transform (DCT) in image compression and coding, it is widely extended and applied. This paper describes a DCT-based JPEG system, and analyzes the distribution of one-dimensional DCT and two-dimensional DCT pros and cons before and after transformation coefficients band from a mathematical point of view. And then using Matlab simulation software to simulate the fast-FFT algorithm and the DCT matrix transform algorithm, simulation results show that the DCT transform in image compression has the advantages of easy to implement and flexible, easy to operate and high compression quality. Related Articles | Metrics | Comments（0）

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Classification of Motor EEG Signals Based on PCA and PSO-SVM HUANG Xu-bin, LIANG Shu-jie Computer and Modernization    2021, 0 (03): 70-76.   Abstract （864）      PDF（pc） （4542KB）（598）       Knowledge map    Save The feature extraction, classification and recognition of electroencephalogram signals of motor imagination are the difficult problems faced by the current Brain Computer Interface (BCI) technology. Aiming at this problem, this paper proposes a classification method of motor imaging EEG signals combining Principal Component Analysis (PCA) and Particle Swarm Optimization optimized-Support Vector Machine (PSO-SVM). Firstly, PCA is used to reduce the dimension of the collected high-dimensional electroencephalogram signal, eliminating the noise components and extracting the feature vectors reflecting the different characteristics of three-dimensional EEG signals. Then SVM is used to classify the feature vectors. In view of the problem that the SVM classification performance is greatly affected by the kernel parameters, the global optimization ability of PSO algorithm is used to optimize the SVM classification performance so as to improve the SVM classification performance. Finally, the Graz data used in the BCI competition is used for experiments. The results show that the proposed PCA fusion PSO-SVM method can obtain 95.3% classification performance, and has a high application prospect. Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （241）      PDF（pc） （1848KB）（591）       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）

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A SAR Ship Detection Method Based on Improved Faster R-CNN YUE Bang-zheng1，2, HAN Song2 Computer and Modernization    2019, 0 (09): 90-.   DOI: 10.3969/j.issn.1006-2475.2019.09.016 Abstract （604）      PDF（pc） （3488KB）（585）       Knowledge map    Save SAR ship detection plays an important role in marine traffic monitoring. Traditional SAR target detection algorithms are mostly based on contrast difference between target and background clutter, whose performance is limited especially in complex scenes, for instance coastal areas. In order to improve the detection performance in complex scenes, a SAR ship detection algorithm based on Faster R-CNN is proposed in this paper. After analyzing the influence of feature resolution on detection performance, a feature extraction network suitable for SAR ship target detection is designed based on the idea of VGG and dilated convolution to improve the detection capability of small ship targets. In addition, a small size anchor is selected according to the target size distribution in the sentinel-1A dataset. And by removing the redundant anchor, the detection speed is improved. Experiments on the sentinel-1A dataset demonstrate that the proposed algorithm can detect ship targets in SAR images of complex scenes with high speed and accuracy. Reference | Related Articles | Metrics | Comments（0）

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A Recommendation Algorithm Based on Text Convolutional Neural Network YANG Hui, WANG Yue-hai, DOU Zhen-ze Computer and Modernization    2020, 0 (10): 7-11.   Abstract （607）      PDF（pc） （808KB）（581）       Knowledge map    Save The traditional matrix factorization model can not effectively extract the features of users and items, while the deep learning model can extract the feature information well. At present, the mainstream recommendation algorithm based on deep learning only uses the output of neural network or the product of item features and user features to make recommendation prediction, which can not fully mine the relationship between users and items. Based on this, this paper proposes a recommendation algorithm based on the combination of text convolutional neural network and bias singular value decomposition (BiasSVD). Text convolutional neural network (TextCNN) is used to fully extract the feature information of users and items, and then singular value decomposition method is used to make recommendations, which can deeply understand the document context information and further improve the accuracy of recommendation. After extensive evaluation and analysis on two real datasets of MovieLens, the recommendation accuracy of this algorithm is obviously better than that of ConvMF algorithm and mainstream deep learning recommendation algorithm. Reference | Related Articles | Metrics | Comments（0）

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Moving Target Tracking Based on Improved Optical Flow Characteristics LIU Hong-fei, YANG Yao-quan, YANG Yu-hang Computer and Modernization    2021, 0 (03): 115-121.   Abstract （942）      PDF（pc） （3616KB）（580）       Knowledge map    Save In the city intelligent video monitoring, it is necessary to track the moving object in real time. Aiming at the problems of the traditional moving object detection, for example, the target is easy to lose, low tracking rate and poor real-time performance, a moving object tracking detection method based on the improved optical flow characteristics is proposed to track the moving pedestrian object. Firstly, the improved Vibe moving background modeling method is used to detect the moving pedestrian in the video. Then the Shi-Tomasi corner detection and LK optical flow method are combined. The corner detection results are integrated into LK optical flow method, and the moving optical flow features of the detected corner points are extracted. Finally, Kalman filter is used to predict and track the pedestrians, and the Hungarian optimal matching algorithm is used to achieve continuous matching of moving objects and tracking of moving objects. The simulation results show that the proposed method can detect and track the moving objects in the video, and it has better recognition effect, and the detection efficiency is improved. Reference | Related Articles | Metrics | Comments（0）

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Multi-objective Optimization Algorithm for Flexible Job Shop Scheduling Problem XU Ming, ZHANG Jian-ming, CHEN Song-hang, CHEN Hao Computer and Modernization    2021, 0 (12): 1-6.   Abstract （745）      PDF（pc） （1427KB）（572）       Knowledge map    Save Flexible job shop scheduling problems have the characteristics of diversified solution sets and complex solution spaces. Traditional multi-objective optimization algorithms may fall into local optimality and lose the diversity of solutions when solving those problems. In the case of establishing a flexible job shop scheduling model with the maximum completion time, maximum energy consumption and total machine load as the optimization goals, an improved non-dominated sorting genetic algorithm (INSGA-II) was proposed to solve this problem. Firstly, the INSGA-II algorithm  combines random initialization and heuristic initialization methods to improve population diversity. Then it adopts a targeted crossover and mutation strategies for the process part and the machine part to improve the algorithm’s global searching capabilities. Finally,  adaptive crossover and mutation operators are designed to take into account the global convergence and local optimization capabilities of the algorithm. The experimental results on the mk01～mk07 standard data set show that the INSGA-II algorithm has better algorithm convergence and solution set diversity. Reference | Related Articles | Metrics | Comments（0）

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Multi-UAV Hunting Based on Improved Whale Optimization Algorithm LING Wen-tong, NI Jian-jun, CHEN Yan, TANG Guang-yi Computer and Modernization    2021, 0 (06): 1-5.   Abstract （577）      PDF（pc） （1931KB）（568）       Knowledge map    Save UAV hunting is a challenging and realistic task. In order to enable UAVs to hun moving targets successfully and effectively, a multi-UAV hunting algorithm based on dynamic prediction of hunting points and improved whale optimization algorithm is proposed. When the environment is unknown and the target motion trajectory is unknown, this paper first uses polynomial fitting to predict the target motion trajectory, obtains the prediction point by dynamically predicting the number of steps, sets up hunting points around it, and then uses the two-way negotiation method to make reasonable assign each target point. Aiming at the shortcomings of the whale optimization algorithm that it is easy to fall into the local optimum, a method based on adaptive weights and changing the position of the spiral is proposed to improve the development ability and search ability of the algorithm. Finally, several experimental simulations were carried out in different experimental environments, and the experimental results showed the effectiveness of the proposed algorithm. Reference | Related Articles | Metrics | Comments（0）

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Simulation and Visualization of Discrete Particle Systems Based on CA ZHONG Wan;YU Du;JIANG Shun-liang Computer and Modernization    2011, 1 (1): 1-5.   DOI: 10.3969/j.issn.1006-2475.2011.01.001 Abstract （1043）            Knowledge map    Save Considering the efficiency of calculation, according to the characteristics of the real discrete particle systems, the cellular automata model is designed and built, and an example in a threedimensional spiral space is given and analyzed. Examples show that the method can be run in threedimensional complex space, and the cellular automata can qualitatively reflect the phenomena of the contact and friction between particles. The method can be used to display the evolution of threedimensional discrete particle system dynamically, and the computational performance of CA is highly over one of the discrete element method. Related Articles | Metrics | Comments（0）

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Group Activity Recognition Algorithm Based on Interaction Relationship Grouping Modeling Fusion WANG Chuan-xu, LIU Ran Computer and Modernization    2022, 0 (01): 1-9.   Abstract （599）      PDF（pc） （3063KB）（561）       Knowledge map    Save The modeling of interaction relationship between group members is the core technology of group activity recognition. High complexity and information redundancy in relational reasoning are tough problems in complex scenarios when modeling its group interactions. In order to solve these problems, we propose a model of grouping interactive relation. Firstly, CNN and RoIAlign are used to extract the scene information and personal information as initial features in each frame, and the whole group is divided into two subgroups by the personal spatial coordinates (For example, in the Volleyball data set, the X coordinates of participants’bounding boxes are used to rank, then, everyone set is set up an ordinal ID and 12 people are divided into two group from left to right). Secondly, the two local groups and the global scene groups are divided, the Graph Convolutional Network （GCN） is used to deduce their interaction relationship respectively, and the key persons in each group are determined. Then, we can regard global relationship features as the real value, and merge the characteristics of local relation of two groups as predicted value. In order to match the key figures of two groups with key figures from the whole group successfully, the cross-entropy loss function is built between the two and feedback to optimize the upper-level group GCN interaction relationship network. Next, with the information of key figures in the global interaction relationship as a guide, the key figures in the two subgroups are matched respectively. After successful matching, the matched key figures in the two subgroups are taken as the target nodes to establish a relationship graph between these two subgroups, and then it is deduced by GCN. Finally, the initial features are fused with intergroup and global interaction characteristics respectively to obtain two group behavior branches, and the final recognition result is obtained through decision fusion. The experiment shows that the accuracy is 93.1% on Volleyball data set and the accuracy is 48.1% on NBA data set. Reference | Related Articles | Metrics | Comments（0）

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A Review of Deep Neural Networks Combined with Attention Mechanism HUANGFU Xiao-ying, QIAN Hui-min, HUANG Min Computer and Modernization    2023, 0 (02): 40-49.   Abstract （1521）      PDF（pc） （2408KB）（544）       Knowledge map    Save Attention mechanism has become one of the research hotspots in improving the learning ability of deep neural network. In view of the wide attention paid to the attention mechanism， this paper aims to give a comprehensive analysis and elaboration of attention mechanism in deep neural network from three aspects: the classification of attention mechanism， the way of combining with deep neural network， and the specific applications in natural language processing and computer vision. Specifically， attention mechanism has been divided into soft attention mechanism， hard attention mechanism and self-attention mechanism， and their advantages and disadvantages are compared. Then， the common ways of combining attention mechanism in recursive neural network and convolutional neural network are discussed respectively， and the representative model structures of each way are given. After that， the applications of attention mechanism in natural language processing and computer vision are illustrated. Finally， several future developments of attention mechanism are illustrated expecting to provide clues and directions for subsequent researches. Reference | Related Articles | Metrics | Comments（0）

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Multi-robot Scheduling Method in Intelligent Warehouse WANG Zhen-ting, CHEN Yong-fu, LIU Tian Computer and Modernization    2020, 0 (07): 65-70.   DOI: 10.3969/j.issn.1006-2475.2020.07.013 Abstract （710）      PDF（pc） （1051KB）（543）       Knowledge map    Save In recent years, the traditional storage system has been unable to meet the increasing demand and has gradually turned to intelligent storage. Aiming at the scheduling problem of robots in intelligent warehouse and optimizing the turning times, distance cost and the maximum task waiting time, a scheduling algorithm for both task assignment and path planning is proposed. To ensure that the tasks assigned to each robot are not repeated, tasks are assigned with genetic algorithm and tasks are assigned for multiple mobile robots. Then Q-learning algorithm is used to carry out path planning for tasks assigned by the robot. The path is constrained according to the account of turns and the cost of the distance, and the penalty value is set for the turning of the path and the feasible action in each step. Finally, a path with less turning times and shorter travel is formed. The effectiveness of the proposed algorithm is verified by comparing it with other algorithms. Reference | Related Articles | Metrics | Comments（0）

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A Path Automatic Generation Method for Dynamic Taint Analysis DONG Guo-Liang1,2, ZANG Lie1, LI Hang1, GAN Lu1 Computer and Modernization    2017, 0 (7): 32-37+41.   DOI: 10.3969/j.issn.1006-2475.2017.07.006 Abstract （375）            Knowledge map    Save Based on the research and analysis of the existing dynamic taint analysis platform， a path automatic generation method is proposed. The sequence of instructions can be obtained by using binary static analysis technique and the binary code coverage rate is calculated with the base block as the granularity. The execution path of the target program is captured in the dynamic execution of the target program and the new path constraint conditions are constructed by the collected path constraint conditions, new test cases which will cover other paths can be generated by constraint solving. The parallel implementation of dynamic taint analysis by using virtualization technology can greatly improve the efficiency and code coverage of the taint analysis. Reference | Related Articles | Metrics | Comments（0）

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Design of Agriculture Information Integrated Service Platform Based on .NET LI Dong-yuan;ZHOU Wen-hui;MO Jian-bin Computer and Modernization    2011, 1 (4): 88-91,9.   DOI: 10.3969/j.issn.1006-2475.2011.04.026 Abstract （1827）            Knowledge map    Save A agriculture information integrated service platform is developed according to the rural informatization development, with the purpose of being prosperous and unhindered management. Most functions can be implemented with SMS, WAP, Web, voicein, etc., including information publish, business subscription, clients management, statistical analysis, etc.. This platform is developed based on. NET, including portals, business undertaking management subsystem, ICP(Internet Content Provider) management subsystem and so on. Most key technologies are discussed emphatically, touching upon multiple roles information channel net, information super matching access technology, twodimension information retrieval technology, SMS/MMS gateway interface technology. Application and dissemination of the platform makes clear the informatization that are achieved in agriculture productionsupplymarketing and production are increased. Related Articles | Metrics | Comments（0）