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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
    Abstract1887)      PDF(pc) (912KB)(1535)       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.
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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.  
    Abstract1455)      PDF(pc) (2046KB)(1073)       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.
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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.  
    Abstract934)      PDF(pc) (1589KB)(909)       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.
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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
    Abstract594)      PDF(pc) (1050KB)(780)       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.
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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
    Abstract1119)      PDF(pc) (1872KB)(677)       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.
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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.  
    Abstract426)      PDF(pc) (1806KB)(615)       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.
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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
    Abstract285)      PDF(pc) (1102KB)(563)       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.
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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.  
    Abstract360)      PDF(pc) (713KB)(555)       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.
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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.  
    Abstract784)      PDF(pc) (1087KB)(551)       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%.
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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
    Abstract423)      PDF(pc) (1000KB)(541)       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.
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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
    Abstract921)      PDF(pc) (2178KB)(531)       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.
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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.  
    Abstract636)      PDF(pc) (1176KB)(530)       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.
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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
    Abstract798)            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.
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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
    Abstract990)            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.
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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
    Abstract2765)            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.
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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.  
    Abstract476)      PDF(pc) (3063KB)(447)       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.
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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.  
    Abstract452)      PDF(pc) (808KB)(446)       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.
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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.  
    Abstract599)      PDF(pc) (1152KB)(445)       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.
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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
    Abstract341)      PDF(pc) (3488KB)(445)       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.
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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
    Abstract215)      PDF(pc) (1018KB)(444)       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.
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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
    Abstract910)            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.

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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
    Abstract915)            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.

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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.  
    Abstract394)      PDF(pc) (1931KB)(430)       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.
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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.  
    Abstract506)      PDF(pc) (1427KB)(424)       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.
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    A Survey of Research on Mobile Grid
    CAI Zhi-ming;HU Chi;CHEN Chong-cheng
    Computer and Modernization    2012, 1 (1): 104-110.   DOI: 10.3969/j.issn.1006-2475.2012.01.028
    Abstract643)            Save
    The background of mobile grid and its development are introduced firstly. Then a survey of research on architecture of mobile grid and key technologies including service sustaining technology, service discovery, task scheduling, and utility computing is carried out and key problems needed to be solved are also illustrated. In the end, the prospect of application and development is given, and the position of mobile grid, namely as an extension of cloud computing in wireless area, is proposed.
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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
    Abstract272)            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.
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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
    Abstract1676)            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.
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    Design and Implementation of Task Scheduling Middleware for GPU Cluster
    CHEN Chun-lei
    Computer and Modernization    2013, 1 (2): 130-133.   DOI: 10.3969/j.issn.1006-2475.2013.02.032
    Abstract607)            Save
    In a GPU cluster, the static task scheduling policy may result in unbalanced allocation of computing resource, because GPUs work as co-processors. A weight-based dynamic scheduling policy is proposed and implemented as a middleware, so that it can be applied to the GPU cluster. Under this policy, local GPUs and remote GPUs are not explicitly distinguished, and no global information is required. Every cluster node decides whether to use local GPUs or remote GPUs, according to weights of GPUs. And these weights are locally maintained by each node, respectively. As a carrier of the policy, the middleware ensures that the policy is transparent to users. It is composed of three parts: API library, resource management daemon, and GPU execution daemon. The policy is validated on a two-node cluster. Experiments show that the weight-based dynamic scheduling policy can achieve a 16% higher GPU utilization rate than the static policy, and a 45% higher GPU utilization rate than another dynamic policy (global-queue-based policy).
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    Network Traffic Prediction Model Based on Mixed Kernels RVM Optimized by CS Algorithm
    CHEN Jing-zhu
    Computer and Modernization    2015, 0 (5): 94-97.   DOI: 10.3969/j.issn.1006-2475.2015.05.020
    Abstract215)            Save
    In order to improve the prediction accuracy of network traffic, a network traffic prediction model is proposed based on cuckoo searching algorithm optimizing the parameters of mixed kernel relevance vector machine (CS-RVM) to solve limitations of single kernel function for relevance vector machine. Firstly, the polynomial and Gaussian kernel functions are produced to mixed kernel function for the relevance vector machine, and then the cuckoo searching algorithm is introduced to optimize the parameters of hybrid kernel function, finally network traffic prediction model is established based on the relevance vector machine using the optimal parameters. The simulation results show that, CS-RVM model is of good effect and could improve the prediction accuracy of network traffic.
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    Visualization and Re-extraction Technology of 2D Radar Envelope Data
    FU Cheng, NIE Ying
    Computer and Modernization    2018, 0 (01): 69-73.   DOI: 10.3969/j.issn.1006-2475.2018.01.014
    Abstract414)            Save
    Radar information visualization is one of the most important aspects of modern battlefield visualization, it is a crucial role for the control of the overall situation of the battlefield. Aimed at the problem of data loss after visualization of two-dimensional radar envelope data, in this paper, the radar envelope detection algorithm is proposed to extract the multi-radar envelope, and the algorithm can accurately retrieve the original data corresponding to each boundary line. In addition, for the slow problem of radar envelope drawing, this paper uses bitmap technology to quickly maping the envelope. The experimental results show that the method can quickly update the radar envelope.
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    Implementation, Installation and Configuration of MPJ Parallel Programming Framework
    LIU Jun-li;LIN Xiao-rui;WANG Chu-bin;TAN Zi-yi;SITU Zhu-kun
    Computer and Modernization    2009, 8 (8): 164-168.   DOI: 10.3969/j.issn.1006-2475.2009.08.046
    Abstract2119)            Save
    The MPJ programming interface provides MPIlike message passing for Java applications. In this article, an overview and introduction on the design and implementation of the MPJ parallel programming framework, including architectures, implementation mechanisms and correlated technologies, are presented. Besides, the installation and configuration process of MPJ Express are also introduced. At last, a practical MPJ program demo is given. 
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    Image Edge Detection Algorithm Based on Local Expectation Threshold Segmentation
    LIU Zhan
    Computer and Modernization    2016, 0 (8): 52-55.   DOI: 10.3969/j.issn.1006-2475.2016.08.011
    Abstract248)            Save
    In classical image edge detection algorithms, gradient images are obtained by gradient operator convoluting, and contours are traced according to the gradient changing and gradient threshold. But the uneven changing of local gradient in image leads to not accurate edge information obtained through uniform threshold segmentation. To solve these problems, in this paper an adapted threshold method is introduced based on the expectation of local block in image. First, we get the image gradient matrix with the help of Sobel operator, then segment the matrix into a plurality of sub regions, and calculate the local expectations of each sub region as the threshold to each of them and filter out the edges of each sub regions finally. The experiment results show that the proposed method can enhance the recognition of primary object edge detection in image effectively, and the edge information obtained is accurate.
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    Loan Risk Prediction Method Based on SMOTE and XGBoost
    LIU Bin, CHEN Kai
    Computer and Modernization    2020, 0 (02): 26-.   DOI: 10.3969/j.issn.1006-2475.2020.02.006
    Abstract438)      PDF(pc) (769KB)(394)       Save
    In recent years, the rapid development of online credit loan results in both continuous growth of total amount of loan and the continuous rise of probability of default. Therefore, it is of great practical significance for online credit enterprises to prevent the risk of Internet finance by studying the risk of loan. Aiming at loan-related problems including the non-balanced distribution, a large number of noise, and high dimension, a loan risk prediction method based on SMOTE and XGBoost is proposed. Through the feature engineering, the dimensionality reduction and denoising of the data set are realized. For the non-equilibrium problem of the data, the SMOTE algorithm is used to oversample the number of positive and negative samples. Based on above-mentioned work, this paper builds an XGBoost classification model, compares it with some traditional classification algorithms, and conducts comparison of validity of the prediction results under different positive and negative sample proportions. The experiment shows that XGBoost algorithm has better effect in loan risk prediction model in comparison with traditional classification models, and the increase of the proportion of minority samples through the use of SMOTE algorithm can improve the effectiveness of prediction results.
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    Failure Prediction of Railway Scaffolding Structure Based on Kriging Model
    QIN Peng-xiao
    Computer and Modernization    2019, 0 (12): 6-.   DOI: 10.3969/j.issn.1006-2475.2019.12.002
    Abstract155)      PDF(pc) (1758KB)(391)       Save
    In order to study whether the railway scaffolding structure fails under the impact of the broken conductor, this paper proposes a Kriging model-based railway scaffolding structure failure prediction method. Firstly, the finite element model of the scaffolding structure is established to simulate the stress of the scaffold under the ultimate load. Then, the Kriging model is used to predict the damage area of the scaffolding structure under the ultimate load. Finally, the K-folding cross is used. The verification method verifies the accuracy of the established Kriging model and compares it with the radial basis function (RBF) model prediction accuracy. The results show that the proposed Kriging model-based railway scaffolding structure failure prediction method has higher prediction accuracy and can be applied to guide the structural design of railway scaffolding.
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    Design and Implementation of Network Virtual Experiment System Based on B/S Mode
    CUI Yang;CHEN Guang;SHEN Jia
    Computer and Modernization    2013, 1 (5): 117-120.   DOI: 10.3969/j.issn.1006-2475.2013.05.027
    Abstract382)            Save
    Using .NET controls technology and C# program language, this paper designs and achieves a virtual experiment system of network server based on B/S mode. Students don’t need to enter the real lab., they can finish the online autonomous server configuration experiments only through the Web browser. This paper introduces the design and application process of the virtual experiment system.
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    Android Malware Application Detection Method Based on BPSO-NB
    HAN Jing-dan, SUN Lei, WANG Shuai-li, WANG Ze-wu
    Computer and Modernization    2017, 0 (4): 109-113.   DOI: 10.3969/j.issn.1006-2475.2017.04.022
    Abstract174)            Save
     In order to improve the efficiency of Android malware application detection, the binary particle swarm optimization (BPSO) is used for optimal selection of complete ensemble of original features, combined with the Nave Bayesian (NB) classification algorithm,an Android malware detection method based on BPSO-NB algorithm is proposed. First, this method uses static analysis for unknown applications to extract the permission information in an AndroidManifest.XML file as a feature. Then, it uses the BPSO algorithm to optimize selected classification feature,  and uses the classification accuracy of  NB algorithm as the evaluation function. Finally, NB classification algorithm is used to construct classifier for Android malicious applications. Through cross experiment, BPSO-NB classification equipment has higher classification accuracy, and the optimal selection of BPSO algorithm classification characteristics under the condition of the security classification accuracy can effectively improve the efficiency of detection.
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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.  
    Abstract722)      PDF(pc) (3616KB)(384)       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.
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    WAPM: A Web Accessibility Evaluation Metric Based on Improved AHP
    LI Fei1, LI Han-jing1,2, YAO Deng-feng2, LYU Hui-hua2
    Computer and Modernization    2018, 0 (01): 78-83.   DOI: 10.3969/j.issn.1006-2475.2018.01.016
    Abstract195)            Save
    In the Web accessibility evaluation, the comprehensive value of the accessibility measurement reflects the accessibility of the Web. Existing research has shown that finding the right weight for different checkpoints is a challenging problem. Although some indicators derive checkpoint weights from WCAG 2.0 priorities, previous surveys have shown that WCAG 2.0 priorities are not significant related to the accessibility of the site’s accessibility, operability, comprehensibility, and robustness. In addition, the results of our website’s accessibility assessment also confirm that sites of using existing metrics do not match the four principles. In order to overcome this limitation, we propose a Web accessibility principle metric, which is called the Web Accessibility Principle Metric(WAPM), so as to better match the evaluation results of the auxiliary function with the four principles, that is, using the analytic hierarchy process to analyze the four principles, because the association mapping between the detection point and the four principles is not exactly the case, we improve the analytic hierarchy process to derive the optimal detection point weights from the four principles. The actual website accessibility assessment data experiment verifies the effectiveness of WAPM.
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    Search Engine Access Control Based on Index Filter
    ZHANG Wei-cheng, AI Li-rong
    Computer and Modernization    2014, 0 (1): 161-163.  
    Abstract267)            Save
    With the development of Internet, search engine has been widely applied. Now the application of search engine in the inline network is also gradually increasing. Inline network has the characteristics of separation of access, so it is necessary to realize security access in search process, and role permissions also conform to the security level of information searched. This paper proposes a kind of search strategy to realize security based on index filter, this strategy can ensure the accuracy and security of information search.
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    Infrared Dim Small Target Detection Based on Wavelet Packet Transform
    FENG Yang
    Computer and Modernization    2020, 0 (12): 112-115.  
    Abstract225)      PDF(pc) (1889KB)(382)       Save
    For the detection of infrared dim small targets under complex background, an infrared dim small targets detection algorithm based on wavelet packet transform is proposed. Firstly, the wavelet packet transform is used to decompose the infrared image with small and weak targets to obtain the high and low frequency node coefficients at different scales. Secondly, according to the different contributions to the target energy during the reconstruction of different node coefficients, the frequency band node coefficients in the middle of the energy distribution in the high frequency band are selected to reconstruct the image to complete the background suppression. Finally, the target image after reconstruction is segmented by adaptive threshold segmentation method, and the result of target detection is obtained. In the experiment, multiple infrared sequence images were used to verify the results. The simulation results show that the algorithm can suppress the background and cloud edge well, detect the target signal accurately, and improve the target’s signal-to-clutter ratio and contrast.
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