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    Review of Intent Detection Methods in Human-Machine Dialogue System
    LIU Jiao, LI Yanling, LIN Min
    Computer Engineering and Applications    2019, 55 (12): 1-7.   DOI: 10.3778/j.issn.1002-8331.1902-0129
    Abstract680)      PDF(pc) (700KB)(2688)       Save
    Spoken Language Understanding(SLU) is a vital part of the human-machine dialogue system, which includes an important sub-task called intent detection. The accuracy of intent detection is directly related to the performance of semantic slot filling, and it is helpful to the following research of the dialogue system. Considering the difficulty of intent detection in human-machine dialogue system, the traditional machine learning methods cannot understand the deep semantic information of user’s discourse. This paper mainly analyzes, compares and summarizes the deep learning methods applied in the research of intent detection in recent years, and further considers how to apply deep learning model to multi-intent detection task, so as to promote the research of multi-intent detection methods based on deep neural network.
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    Survey of Knowledge Reasoning Based on Neural Network
    ZHANG Zhongwei1,2, CAO Lei1, CHEN Xiliang1, KOU Dalei1,3, SONG Tianting2
    Computer Engineering and Applications    2019, 55 (12): 8-19.   DOI: 10.3778/j.issn.1002-8331.1901-0358
    Abstract554)      PDF(pc) (777KB)(2657)       Save
    Knowledge reasoning is an important means of knowledge graph completion and has always been one of the research hotspots in the field of knowledge graph. With the development of neural network, its applications in knowledge reasoning have been paid more and more attention in recent years. The knowledge reasoning methods based on neural network have not only stronger reasoning and generalization abilities, but also higher utilization rates of entities, attributes, relations and text information in the knowledge base. These methods are more effective in reasoning. The relevant concepts of knowledge graph and knowledge graph completion are introduced, the concepts and basic principles of knowledge reasoning are indicated, and then the latest research progresses of the technology of knowledge reasoning based on neural network are reviewed. The existing problems and development directions of knowledge reasoning in the aspect of theory, algorithm and application are summarized.
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    Attention Mechanism-Based CNN-LSTM Model and Its Application
    LI Mei1,2, NING Dejun1, GUO Jiacheng1,2
    Computer Engineering and Applications    2019, 55 (13): 20-27.   DOI: 10.3778/j.issn.1002-8331.1901-0246
    Abstract862)      PDF(pc) (914KB)(2219)       Save
    Time series have temporal property, and the characteristics of its short sequences are different in importance. Aiming at the characteristics of time series, a neural network prediction model based on Convolution Neural Network(CNN) and Long Short-Term Memory(LSTM) is proposed, which combines coarse and fine grain features to achieve accurate time series prediction. The model consists of two parts. CNN based on attention mechanism adds attention branch to standard CNN network to extract important fine-grained features. The back end is LSTM, which extracts the coarse-grained features of the hidden time series from fine-grained features. Experiments on real cogeneration heat load dataset demonstrate that the model is better than the autoregressive integrated moving average, support vector regression, CNN and LSTM models. Compared with the pre-determined method currently used by enterprises, the Mean Absolute Scaled Error(MASE) and Root Mean Square Error(RMSE) have been increased by 89.64% and 61.73% respectively.
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    Survey of Compressed Sensing Reconstruction Algorithms in Deep Learning Framework
    ZENG Chunyan, YE Jiaxiang, WANG Zhifeng, WU Minghu
    Computer Engineering and Applications    2019, 55 (17): 1-8.   DOI: 10.3778/j.issn.1002-8331.1903-0437
    Abstract472)      PDF(pc) (1041KB)(1873)       Save
    Compressed Sensing(CS) technology is a milestone in the field of signal processing, which samples signals far less than Nyquist frequency, and reconstructs the original signals with high probability. In recent years, the advantages of deep learning technology in feature extraction and pattern classification provide new ideas for CS. Data-driven method is adopted in deep learning-based compressed sensing reconstruction algorithm, which reduces the reconstruction time by an order of magnitude, and the reconstruction accuracy is comparable or higher. This paper focuses on the deep learning-based compressed sensing reconstruction methods, considering the traditional reconstruction methods, and divides them into three categories:prior knowledge-based, pure data-driven, mixed prior knowledge-driven and data-driven. The characteristics of typical algorithms, network structure and key steps are analyzed. Finally, three kinds of algorithms are analyzed and summarized, and the research prospects of deep learning technology applied to compressed sensing are prospected.
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    Computer Engineering and Applications    2019, 55 (21): 0-0.  
    Abstract245)      PDF(pc) (631KB)(1818)       Save
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    Overview of Human Pose Estimation Methods Based on Deep Learning
    DENG Yinong, LUO Jianxin, JIN Fenglin
    Computer Engineering and Applications    2019, 55 (19): 22-42.   DOI: 10.3778/j.issn.1002-8331.1906-0113
    Abstract625)      PDF(pc) (2837KB)(1590)       Save
    Human pose estimation is a research hot point in the field of computer vision. The human pose estimation methods based on deep learning get directly human pose information from two-dimensional image features through an appropriate neural network. This paper mainly follows the sequence from 2D to 3D human pose estimation, from the single-person detection to multi-person detection, from sparse node detection to dense model building, has systematically introduced the human post estimation methods in recent years based on deep learning to give a preliminary understanding of how to acquire the elements of human pose through deep learning, including the relative orientation and ratio scale of limb parts, the position coordinates and connection relations of joint points, and the information of the even more complex human skin model information. In the end, it summarizes the current research challenges and future hot point trends, which clearly present the development venation of this field for readers.
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    Deep Learning Approach and Its Application in Fault Diagnosis and Prognosis
    YU Ping, CAO Jie
    Computer Engineering and Applications    2020, 56 (3): 1-18.   DOI: 10.3778/j.issn.1002-8331.1910-0221
    Abstract723)      PDF(pc) (848KB)(1583)       Save
    In recent years, deep learning has been widely applied and has made remarkable progress in many fields because of its unique advantages and potential in feature extraction and pattern recognition. Its application in fault diagnosis and prognosis of complex industrial systems is an emerging field. This paper starts with an overview of deep learning, including deep learning methods-based application, platforms and useful tools. Five frequently-used deep learning models are introduced in this work, including Auto-Encoder(AE), Deep Belief Networks(DBN), Convolutional neural networks(CNN), Recurrent Neural Network(RNN) and Generative Adversarial Network(GAN). The application research based on deep learning in fault diagnosis and prognosis are systematically discussed in three aspects, research background, implementation process and research dynamics, and the current related literatures published in this field in recent years are reviewed. Problems, challenges and solutions of deep learning in the application of fault diagnosis and prognosis are discussed from the point view of research practice. The future research directions are also prospected at the end of this work.
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    Summary of Feature Selection Methods
    LI Zhiqin, DU Jianqiang, NIE Bin, XIONG Wangping, HUANG Canyi, LI Huan
    Computer Engineering and Applications    2019, 55 (24): 10-19.   DOI: 10.3778/j.issn.1002-8331.1909-0066
    Abstract452)      PDF(pc) (964KB)(1455)       Save
    As a data preprocessing process, feature selection plays an important role in data mining, pattern recognition and machine learning. Through feature selection, the complexity of the problem can be reduced, and the prediction accuracy, robustness and interpretability of the learning algorithm can be improved. This paper introduces the framework of feature selection methods, and focuses on the two processes of generating feature subsets and evaluation criteria. The feature selection algorithms are classified according to different combinations of feature selection and learning algorithms, and the advantages and disadvantages of various methods are analyzed. The existing problems of existing feature selection algorithms are discussed, and some research difficulties and research directions are proposed.
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    Application of Knowledge Graph in Full-Service Unified Data Center of National Grid
    WANG Yuan, PENG Chenhui, WANG Zhiqiang, FAN Qiang, YAO Yiyang, HUA Zhaoyun
    Computer Engineering and Applications    2019, 55 (15): 104-109.   DOI: 10.3778/j.issn.1002-8331.1810-0002
    Abstract1793)      PDF(pc) (629KB)(1443)       Save
    To solve the problems that the business data in state grid corporation cannot be crossed professionally, and the data resources cannot be intelligently analyzed and managed, this paper proposes a knowledge graph construction method based on the full-service unified data center. On the basis of multi-source data in the full-service unified data center collected by the big data technology, the semantic annotation method is used to extract the knowledge entities, attributions and relations from the structured, semi-structured and unstructured data. The knowledge graph is constructed through the knowledge fusion technology. The accurate result and related information can be returned intelligently according to the user’s search. Experiments show that this method improves the precision and recall rate, and has better intelligent search and analysis ability.
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    Survey on Vision-Based 3D Object Detection Methods
    LI Yujie, LI Xuanpeng, ZHANG Weigong
    Computer Engineering and Applications    2020, 56 (1): 11-24.   DOI: 10.3778/j.issn.1002-8331.1909-0024
    Abstract460)      PDF(pc) (825KB)(1390)       Save
    Vision-based object detection is an important component of environment perception systems. It has been a research hotspot in computer vision, robotics and other related fields. The 3D object detection is based on the 2D object detection, which involves the estimation of the object scale, localization and pose estimation in the camera coordinate. Compared to 2D object detection, there are still a big gap for 3D object detection in terms of accuracy and real-time performance. This paper systematically surveys the state-of-the-art vision-based 3D object detection methods based on monocular vision, stereo vision and RGB-D, and classifies them according to indoor and outdoor scenes. In addition, the paper compares and analyzes these methods on KITTI, SUN RGB-D and other datasets, and discusses on the future research direction.
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    Overview and Evaluation of Image Straight Line Segment Detection Algorithms
    ZHENG Hangjia, ZHONG Baojiang
    Computer Engineering and Applications    2019, 55 (17): 9-19.   DOI: 10.3778/j.issn.1002-8331.1905-0255
    Abstract284)      PDF(pc) (1419KB)(1342)       Save
    The existing line segment detectors are systematically reviewed and evaluated. In the overview part, the existing detectors are divided into two categories:the global Hough-transform-based method and the local perceptual grouping method. The technical basis and implementation mechanisms of these two kinds of algorithms are investigated, and their advantages and disadvantages are summarized. In the evaluation part, Cho et al. established the first objective evaluation system for line segment detectors in a recent work. Unfortunately, this evaluation system has serious bugs in both code implementation and practical application. In consequence, the results obtained in the previous work of Cho et al. have not given an accurate enough picture of the detection performance of the current algorithms. The bugs in the evaluation system are all corrected, and 8 state-of-the-art line segment detectors are evaluated and compared. Finally, the prospects of future trends in this field are discussed in order for facilitating the relevant research.
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    Computer Engineering and Applications    2020, 56 (24): 0-0.  
    Abstract197)      PDF(pc) (1126KB)(1322)       Save
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    Survey of Image Semantic Segmentation Based on Deep Learning
    KUANG Huiyu, WU Junjun
    Computer Engineering and Applications    2019, 55 (19): 12-21.   DOI: 10.3778/j.issn.1002-8331.1905-0325
    Abstract405)      PDF(pc) (759KB)(1268)       Save
    Image semantic segmentation technology is one of the key technologies for intelligent systems to understand natural scenes. As an important research direction in the field of visual intelligence, this technology has broad application prospects in the fields of mobile robots, drones, intelligent driving and smart security. This paper gives a detailed review on the research and development of image semantic segmentation technology, including the traditional semantic segmentation method and the current mainstream image semantic segmentation theory based on deep learning, and the method of image semantic segmentation based on deep learning. It describes the framework and its implementation process, analyzes the effects, advantages and disadvantages of the typical representative algorithms, and then summarizes the algorithm evaluation indicators. Finally, the development of the technology is summarized and forecasted. The paper has a good reference for researchers and engineers who are engaged in image semantic segmentation technology.
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    Survey of Application of Deep Learning in Image Recognition
    ZHENG Yuanpan1,2, LI Guangyang1, LI Ye1
    Computer Engineering and Applications    2019, 55 (12): 20-36.   DOI: 10.3778/j.issn.1002-8331.1903-0031
    Abstract862)      PDF(pc) (1086KB)(1204)       Save
    As an important technical means in the field of image recognition, deep learning has broad application prospects. Carrying out image recognition technology research has important theoretical and practical significance for promoting the development of computer vision and artificial intelligence. The application of deep learning in image recognition gives a review. The origin of deep learning is introduced. Deep learning models such as deep belief network, convolutional neural network, cyclic neural network, generated confrontation network and capsule network are analyzed. The improved models of each deep learning model are compared and analyzed one by one. In this paper, the research results of deep learning in image recognition applications such as face recognition, medical image recognition and remote sensing image classification  are summarized. The existing researches are worth discussing. The development trend of deep learning in the field of image recognition is carried out. The discussion points out that the effective use of migration learning technology to identify small sample data, the use of unsupervised learning and semi-supervised learning to identify images, how to effectively identify video images and the theoretical significance of the model are further directions in this field.
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    Research on Progress of Image Semantic Segmentation Based on Deep Learning
    LIANG Xinyu, LUO Chen, QUAN Jichuan, XIAO Kaihong, GAO Weijia
    Computer Engineering and Applications    2020, 56 (2): 18-28.   DOI: 10.3778/j.issn.1002-8331.1910-0300
    Abstract531)      PDF(pc) (813KB)(1175)       Save
    Since the FCN network was proposed in 2014, a series of deep learning architectures for image semantic segmentation such as SegNet and DeepLab have been proposed. Compared with the traditional methods, these architectures are better and faster, and can be applied to the segmentation of natural images. This paper focuses on the image semantic segmentation technology. The commonly used data sets and typical network architectures are analyzed. And a comprehensive study is conducted about the new progress since 2017. The current evaluation indicators are used to compare and analyze the semantic segmentation effects of the main models. Finally, the challenges faced by semantic segmentation technology and the possible development trends are prospected.
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    Analysis of LEO Satellite Internet of Things Architecture
    JIN Cong1, HE Xin2, XIE Jidong1, ZHANG Gengxin1
    Computer Engineering and Applications    2019, 55 (14): 98-104.   DOI: 10.3778/j.issn.1002-8331.1806-0035
    Abstract170)      PDF(pc) (892KB)(1129)       Save
    The emergence of the Internet of Things(IoT) has a profound impact on people’s lives and working methods. However, it cannot achieve global coverage, LEO satellites can make up for its disadvantages properly and expand the coverage of the IoT greatly. The paper analyses the architecture of the satellite mobile communications network and the ground IoT. Then, it provides an overview of the architecture of the LEO satellite constellation-based IoT including the following topics:LEO satellite constellation structure, system components, transmission system and network architecture.
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    Application of Improved XGBoost Model in Stock Forecasting
    WANG Yan, GUO Yuankai
    Computer Engineering and Applications    2019, 55 (20): 202-207.   DOI: 10.3778/j.issn.1002-8331.1904-0007
    Abstract327)      PDF(pc) (1003KB)(1089)       Save
    With the continuous advancement of the times, people’s living standards have been increasing. In addition to solving the problem of food and clothing, there is surplus money available for investment. More and more people are turning their attention to stock market investment, which provides financial conditions for the development of the stock market. However, in the complicated stock market, how to find the optimal stock has become an urgent problem to be solved. This is not only a unilateral confusion for investors, but also a focus of scholars in the field of stock forecasting. In this paper, the grid prediction algorithm is used to optimize the XGBoost model to construct the financial forecasting model of GS-XGBoost, and the model is applied to short-term stock forecasting. The daily closing prices of China Ping An, China State Construction Engineering Corporation, CRRC Corporation Limited, IFLYTEK and SANY HEAVY INDUSTRY from April 2005 to December 28, 2018 are used as experimental data. Through experimental comparison, compared with the original XGBoost model, GBDT model and SVM model, the GS-XGBoost model shows good prediction results on the three evaluation indexes of MSE, RMSE and MAE. It is verified that the GS-XGBoost financial forecasting model has better fitting performance in short-term stock forecasting.
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    Survey of Data Fusion Based on Deep Learning
    ZHANG Hong, CHENG Chuanqi, XU Zhigang, LI Jianhua
    Computer Engineering and Applications    2020, 56 (24): 1-11.   DOI: 10.3778/j.issn.1002-8331.2007-0475
    Abstract724)      PDF(pc) (683KB)(1045)       Save

    As data fusion is the key to maximize the value of big data, while deep learning is a technical tool for mining deep characteristic information of data, data fusion based on deep learning can fully tap the potential value of big data, thus expanding the exploration and understanding of the world to a new depth and breadth. And this paper learns the advantages of deep learning in data fusion by reviewing the literature related to data fusion based on deep learning in recent years. The common data-fusion methods are classified, the advantages and disadvantages of which are pointed out. Analysis is conducted on data fusion method based on deep learning from three perspectives, namely the data fusion method extracted based on features of deep learning, data fusion method based on deep learning fusion and data fusion method based on the whole process of deep learning, and corresponding comparisons and summaries are conducted as well. This paper summarizes the whole document, discusses the difficulties in the application of deep learning in data fusion and the problems which require further research in the future.

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    Research on Medical Anti-Counterfeiting Traceability System Based on Blockchain
    YU Zhong, GUO Chang, XIE Yongbin, XUE Dong
    Computer Engineering and Applications    2020, 56 (3): 35-41.   DOI: 10.3778/j.issn.1002-8331.1908-0113
    Abstract300)      PDF(pc) (919KB)(1044)       Save
    This paper presents a blockchain-based medical anti-counterfeiting traceability system which can solve the problems of centralization and effortlessness to falsify, incomplete information memory and information security in the current medical anti-counterfeiting traceability system. The system is created in the platform of the Hyperledger’s Fabric blockchain, and the system of computer environment is equipped with three organizations:pharmaceutical factory, dealer, and hospital. The chaincode is developed by using the Go language, combines with traceability function of the medicine in the chaincode, uses Node. js to write the client program and initiate the query request. Finally, the accounts of authenticated user can be used to query the drug information on the webpages, and the time of query response is 22?ms on average. The characters of unchangeable modification of data, time stamp and transaction traceability in blockchain can be well applied to the medical anti-counterfeiting traceability system which makes the system traceability function more complete, and consumers can get all traceability information, including production information, logistics information and use information of drugs.
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    Survey of Intelligent Question Answering Research Based on Knowledge Graph
    WANG Zhiyue, YU Qing, WANG Nan, WANG Yaoguo
    Computer Engineering and Applications    2020, 56 (23): 1-11.   DOI: 10.3778/j.issn.1002-8331.2004-0370
    Abstract785)      PDF(pc) (774KB)(1043)       Save

    The answer selection model based on knowledge graph has become one of the hottest directions at present. This paper introduces the implementation of answer selection model based on knowledge graph from four aspects of template method, semantic parsing, deep learning and knowledge graph embedding, sums up their advantages, disadvantages and unsolved problem. Combined with the development of artificial intelligence technology, this paper introduces intelligent question-answer system based on deep learning. This research is helpful for more researchers to devote themselves to the intelligent question-answer system and develops different kinds of intelligent question-answer system to improve the social intelligent information service.

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    Review of Application Prospect of Deep Reinforcement Learning in Intelligent Manufacturing
    KONG Songtao, LIU Chichi, SHI Yong, XIE Yi, WANG Kun
    Computer Engineering and Applications    2021, 57 (2): 49-59.   DOI: 10.3778/j.issn.1002-8331.2008-0431
    Abstract601)      PDF(pc) (982KB)(1030)       Save

    As the latest development of machine learning, deep reinforcement learning has been shown in many application fields. The algorithm research and application research of deep reinforcement learning have produced many classical algorithms and typical application fields. The application of deep reinforcement learning in industrial manufacturing can realize high level control in complex environment. First of all, the research on deep reinforcement learning is summarized, and the basic principles of deep reinforcement learning are introduced, including deep learning and reinforcement learning. Then, the paper introduces the theoretical methods of the application of deep reinforcement learning algorithm. On this basis, it classifies the algorithms of deep reinforcement learning, respectively introduces the reinforcement learning algorithm based on value function and the reinforcement learning algorithm based on strategy gradient, and lists the main development results of these two kinds of algorithms, as well as other related research results. Then, the typical applications of deep reinforcement learning in industrial manufacturing are classified and analyzed. Finally, the existing problems and future development direction of deep reinforcement learning are discussed.

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    Research Status of Deep Learning in Agriculture of China
    LV Shengping, LI Denghui, XIAN Rongheng
    Computer Engineering and Applications    2019, 55 (20): 24-33.   DOI: 10.3778/j.issn.1002-8331.1907-0089
    Abstract495)      PDF(pc) (741KB)(1009)       Save
    Deep Learning(DL) has been widely used in intelligent agriculture for plant disease detection, plant and fruit recognition, crop and weed detection and classification and so on. 65 articles from 2014 to 2019 on the application of DL in agriculture of China are presented. At first, the basic concept and development history of DL are briefly introduced, and the article retrieval and distribution of the reviewed articles are given. Subsequently, the articles are reviewed from various points of view such as research object, data source, inter-class differences, preprocessing, data-augmentation, framework and performance comparison. Eventually, the advantages and disadvantages of DL are analyzed, and its development trends in agriculture are demonstrated.
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    Review of Research on Generative Adversarial Networks and Its Application
    WEI Fuqiang, Gulanbaier Tuerhong, Mairidan Wushouer
    Computer Engineering and Applications    2021, 57 (19): 18-31.   DOI: 10.3778/j.issn.1002-8331.2104-0248
    Abstract386)      PDF(pc) (1078KB)(994)       Save

    The theoretical research and applications of generative adversarial networks have been continuously successful and have become one of the current hot spots of research in the field of deep learning. This paper provides a systematic review of the theory of generative adversarial networks and their applications in terms of types of models, evaluation criteria and theoretical research progress; analyzing the strengths and weaknesses of generative models with explicit and implicit density-based, respectively; summarizing the evaluation criteria of generative adversarial networks, interpreting the relationship between the criteria, and introduces the research progress of the generative adversarial network in image generation from the application level, that is, through the image conversion, image generation, image restoration, video generation, text generation and image super-resolution applications; analyzing the theoretical research progress of generative adversarial networks from the perspectives of interpretability, controllability, stability and model evaluation methods. Finally, the paper discusses the challenges of studying generative adversarial networks and looks forward to the possible future directions of development.

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    Summary of Rapidly-Exploring Random Tree Algorithm in Robot Path Planning
    CHEN Qiulian, JIANG Huanyu, ZHENG Yijun
    Computer Engineering and Applications    2019, 55 (16): 10-17.   DOI: 10.3778/j.issn.1002-8331.1905-0061
    Abstract305)      PDF(pc) (867KB)(968)       Save
    Path planning is a vital research content of mobile robot technology. Rapidly-Exploring Random Tree(RRT) algorithm has been studied and developed since it was proposed because of its successful application in robot path planning. As a novel random node sampling algorithm, compared with traditional algorithms, the rapidly-exploring random tree has the characteristics of short modeling time, robust search ability and convenience to add nonholonomic constraints. This paper introduces the basic principle and properties of the rapidly-exploring random tree algorithm, summarizes the research status of the algorithm from the aspects of single random tree extension, multiple random tree extension and other improvements. Finally, the future research directions and challenges of the algorithm are prospected.
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    EEG-Based Emotion Recognition Using Deep Convolutional Neural Network
    CHEN Jingxia, WANG Liyan, JIA Xiaoyun, ZHANG Pengwei
    Computer Engineering and Applications    2019, 55 (18): 103-110.   DOI: 10.3778/j.issn.1002-8331.1901-0400
    Abstract296)      PDF(pc) (854KB)(954)       Save
    In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram(EEG), an EEG emotional feature learning and classification method using deep Convolution Neural Network(CNN) models is proposed based on temporal features, frequential features and their combination features of EEG signals in DEAP dataset. The shallow machine learning models including Bagging Tree(BT), Support Vector Machine(SVM), Linear Discriminant Analysis(LDA) and Bayesian Linear Discriminant Analysis(BLDA) models and deep CNN models are used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results show that the deep CNN models achieve the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.
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    Research and Implementation of Efficient Connection Middleware Between Flink and MongoDB
    HU Cheng, YE Feng
    Computer Engineering and Applications    2019, 55 (23): 64-69.   DOI: 10.3778/j.issn.1002-8331.1808-0342
    Abstract1354)      PDF(pc) (758KB)(941)       Save
    In order to improve the reading and writing rate between big data processing platform Flink and MongoDB, this paper proposes and implements an efficient connection middleware of Flink and MongoDB. Based on Flink’s parallelization idea, by logically fragmenting the data, the interface in the Mongo-Java package is called to realize parallel reading and writing of data. With different scale of hydrological sensor datasets as experimental data, the reading and writing speeds of the data in Java single-threaded operation, Hadoop and MongoDB connector and the Flink and MongoDB connection middleware proposed in this paper are tested. The results show that the efficiency of using Flink to read and write data is 1.5 times higher than the single-threaded operation, which validates that the connection middleware can effectively improve the reading and writing speed of massive data.
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    Overview of 3D Tracking Registration Technology in Augmented Reality
    HAN Yuren, LI Tiejun, YANG Dong
    Computer Engineering and Applications    2019, 55 (21): 26-35.   DOI: 10.3778/j.issn.1002-8331.1907-0283
    Abstract760)      PDF(pc) (908KB)(940)       Save
    The 3D tracking registration technology is an important key technology in the field of augmented reality. By tracking and locating images or objects in a real scene, the virtual objects are superimposed into the real scene according to the correct spatial perspective relationship. This paper comprehensively introduces the 3D tracking registration technology in augmented reality, elaborates the advantages and disadvantages of different tracking registration methods, introduces the current application status of different tracking registration methods, and then discusses the development trend and existence of tracking registration technology. The problem is further explored by the further study of 3D tracking registration technology.
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    Survey of Image Compression Algorithm Based on Deep Learning
    YU Heng, MEI Hongyan, XU Xiaoming, JIA Huiping
    Computer Engineering and Applications    2020, 56 (15): 15-23.   DOI: 10.3778/j.issn.1002-8331.2003-0294
    Abstract481)      PDF(pc) (923KB)(939)       Save

    With the continuous development of deep learning and the explosive growth of image data, how to use deep learning to obtain higher compression ratio and higher quality images has gradually become one of the hot research issues. Through the analysis of the related literatures in recent years, the image compression method based on the deep learning is summarized and analyzed according to the Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Generative Adversarial Network(GAN). This paper enumerates the typical examples, and the image compression algorithm based on depth study of the training data set, commonly used evaluation indexes are introduced, according to the deep learning advantages in the field of image compression for its future development trend are summarized and discussed.

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    Survey of Research on Network Security Situation Awareness
    SHI Leyi, LIU Jia, LIU Yihao, ZHU Hongqiang, DUAN Pengfei
    Computer Engineering and Applications    2019, 55 (24): 1-9.   DOI: 10.3778/j.issn.1002-8331.1906-0349
    Abstract509)      PDF(pc) (995KB)(936)       Save
    Different from traditional security measures, network security situation awareness can identify the behavior of various activities in the network and conduct intent understanding and impact assessment from a macro perspective so as to provide reasonable decision support. It has great significance in improving network monitoring capabilities, emergency response capabilities, and predicting the development trend of network security. This paper first separately generalizes the definitions of situation awareness and network security situation awareness, and then sorts out the classical and newly developed system models. It introduces the key technologies of network security situation awareness, which is mainly divided into hierarchical analysis, machine learning, immune system and game theory. Then the latest application of network security situation awareness in Internet, industrial control network and Internet of Things are explained. It summarizes and forecasts the future development trends and problems that need to be solved.
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    Survey of Small Object Detection Algorithms Based on Deep Learning
    LIU Yang, ZHAN Yinwei
    Computer Engineering and Applications    2021, 57 (2): 37-48.   DOI: 10.3778/j.issn.1002-8331.2009-0047
    Abstract915)      PDF(pc) (959KB)(917)       Save

    With the development of artificial intelligence technology, deep learning technology has been widely used in face recognition, pedestrian detection, unmanned driving and other fields. As one of the most basic and challenging problems in machine vision, object detection has attracted extensive attention in recent years. Aiming at the problem of object detection, especially small object detection, this paper summarizes the common data sets and performance evaluation metrics, and compares the characteristics, advantages and difficulties of various common data sets. At the same time, this paper systematically summarizes the common object detection methods and the challenges faced by small object detection. In addition, combing the latest work based on deep learning, this paper introduces the multi-scale and super-resolution small object detection methods in the highlight and presents the lightweight strategy and the performance of some lightweight models based on the object detection. Finally, this paper summarizes the characteristics, advantages and limitations of various methods, and looks at the future development direction of small object detection method based on deep learning.

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    3D Path Planning Algorithm Based on Deep Reinforcement Learning
    HUANG Dongjin, JIANG Chenfeng, HAN Kaili
    Computer Engineering and Applications    2020, 56 (15): 30-36.   DOI: 10.3778/j.issn.1002-8331.2001-0347
    Abstract422)      PDF(pc) (1152KB)(899)       Save

    Reasonable path selection is a difficulty in the field of 3D path planning. The existing 3D path planning methods can not adapt to the unknown terrain, and the obstacle avoidance form is single. In order to solve these problems, a 3D path planning algorithm for agents based on LSTM-PPO is proposed. Virtual ray is designed to detect simulation environment, and the collected state space and action states are introduced into Long Short-Term Memory Networks(LSTM). Through the extra reward function and intrinsic curiosity module, the agent can learn to jump through low obstacles and avoid large obstacles. Using the PPO’s clipped surrogate objective to optimize the update range of planning strategy. The results show that the algorithm is feasible, more intelligent and more reasonable for path planning, and can adapt well to the unknown environment with many obstacles.

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    Research on Lightweight Convolutional Neural Network Technology
    BI Pengcheng, LUO Jianxin, CHEN Weiwei
    Computer Engineering and Applications    2019, 55 (16): 25-35.   DOI: 10.3778/j.issn.1002-8331.1903-0340
    Abstract568)      PDF(pc) (753KB)(884)       Save
    In order to better apply the convolutional neural network model to mobile and embedded devices, it is necessary to reduce the amount of model parameters and reduce computational complexity. Firstly, several popular solutions are briefly introduced. Next, six lightweight convolutional neural network models are elaborated, showing the computational complexity and parameter quantities of different network computing methods. The core building blocks of the model, the overall network structure and innovations are discussed. The classification accuracy of each network and conventional convolutional network on the ImageNet dataset is analyzed. Furthermore, comparing the techniques of lightening the weight of each network, the conclusion is drawn that the direct index is used instead of the indirect index when designing the model. At the same time, the importance of the residual structure to ensure the accuracy of the lightweight model is found. Finally, the development prospect of lightweight convolutional neural network is prospected.
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    Survey of Data Integrity Verification Technology Based on Provable Data Possession
    YUAN Ying, ZHU Hongliang, CHEN Yuling, OUYANG Zhi, XIN Yang, YANG Yixian
    Computer Engineering and Applications    2019, 55 (18): 1-7.   DOI: 10.3778/j.issn.1002-8331.1905-0073
    Abstract510)      PDF(pc) (620KB)(883)       Save
    In the cloud storage environment, to ensure the integrity and availability of user’s data, users need to verify the integrity of data stored in the cloud server. There are two main data integrity verification mechanisms: Provable Data Possession(PDP) and Proof of Retrievability(POR). This paper focuses on the PDP-based cloud storage data integrity verification mechanism. The characteristics of the PDP verification mechanism are combined to classify the PDP scheme and the techniques used by each category are summarized. According to the classification, the research status of PDP scheme is described, and the typical schemes are compared and analyzed in terms of dynamic verification, batch auditing and computational overhead. The future development direction of cloud storage data integrity verification mechanism based on PDP is discussed.
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    Review of Machine Learning for Predictive Maintenance
    LI Jieqi, HU Liangbing
    Computer Engineering and Applications    2020, 56 (21): 11-19.   DOI: 10.3778/j.issn.1002-8331.2006-0016
    Abstract338)      PDF(pc) (660KB)(883)       Save

    Machine learning algorithms can process high-dimensional and multi-variable data, and extract hidden relationships in the data in complex and dynamic environments, and have good application prospects in predictive maintenance technology. However, the performance of predictive maintenance system depends on the choice of machine learning algorithms. This paper reviews the current machine learning algorithms used in predictive maintenance system, compares the advantages and disadvantages of several machine learning algorithms characteristic in detail. The application of the machine learning in predictive maintenance is prospected in the future.

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    Survey of Breast Cancer Histopathology Image Classification Based on Deep Learning
    LI Hua, YANG Jianeng, LIU Feng, NAN Fangzhe, QIAN Yurong
    Computer Engineering and Applications    2020, 56 (13): 1-11.   DOI: 10.3778/j.issn.1002-8331.2001-0220
    Abstract532)      PDF(pc) (919KB)(879)       Save

    Accurate and efficient histopathological image classification of breast cancer is one of the important contents of computer-aided diagnosis. With the development of machine learning technology, deep learning has gradually become an effective method to classify breast cancer histopathological images. Firstly, the classification methods of breast cancer histopathological image and the existing problems are analyzed. Secondly, four relevant deep learning models are introduced, and the classification methods of breast cancer histopathological image based on deep learning are combed, and the performance of the existing models is compared and analyzed through experiments. Finally, the key issues of histopathological image classification of breast cancer are summarized and the future research trends are discussed.

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    Efficient Multi-Object Efficient Object Detection Method Based on Improved SSD
    WANG Wenguang, LI Qiang, LIN Maosong, HE Xianzhen
    Computer Engineering and Applications    2019, 55 (13): 28-35.   DOI: 10.3778/j.issn.1002-8331.1811-0157
    Abstract305)      PDF(pc) (865KB)(865)       Save
    In order to improve the defect of poor detection accuracy of the one-stage object detection algorithm, an efficient multi-target location detection algorithm FSD based on SSD is proposed. The algorithm mainly improves the one-stage object detection algorithm from two aspects: on the one hand, it designs a more efficient dense residual network, namely R-DenseNet, by adopting a narrower dense network structure form to maintain feature extraction. The capacity reduces the computational complexity, which improves the detection and convergence performance of the algorithm. On the other hand, the loss function is improved. By suppressing the weight of the easily-divided samples in the loss function, the robustness of the algorithm is improved, and the phenomenon of sample imbalance in object detection is improved. The Tensorflow deep learning framework is used to deploy the network, and experiments are carried out on Ubuntu equipped with Nvidia Titan X. Experiments show that FSD achieves the highest detection accuracy on both COCO and PASCAL VOC object detection data sets, among which FSD300 detection accuracy compared with the SSD300, there is a 3.7% improvement, and the detection phase rate is 10.87% higher than that of the SSD.
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    Review of Fully Convolutional Neural Network
    ZHANG Lin, YUAN Feiniu, ZHANG Wenrui, ZENG Xialing
    Computer Engineering and Applications    2020, 56 (1): 25-37.   DOI: 10.3778/j.issn.1002-8331.1910-0164
    Abstract474)      PDF(pc) (845KB)(860)       Save
    In recent years, the fully convolutional neural network has been developing rapidly, and has shown very bright results in many visual research fields. This paper focuses on collecting a large amount of recent high-quality literatures, analyzing and summarizing the fully convolutional methods proposed, and trying to make the reader has a more comprehensive understanding of key technologies, research status and latest developments of the fully convolutional neural network through the study of the paper. The collected literatures are classified and summarized according to the different fields of study, it focuses on extracting several areas where research is very active, and introduces in detail some of the most representative and state-of-the-art classic algorithms, and highlights the quintessence of the various methods. It also provides an overview of the latest research progress in the past year. Through the research on a large number of literatures, the achievements of the fully convolutional neural network are summarized, and the advantages and defects of these methods are analyzed. Based on the existing problems in the fully convolutional neural network, the possible future development direction is given.
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    Survey of Medical Image Segmentation Algorithm in Deep Learning
    PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie
    Computer Engineering and Applications    2021, 57 (3): 44-57.   DOI: 10.3778/j.issn.1002-8331.2010-0335
    Abstract436)      PDF(pc) (1397KB)(835)       Save

    Medical image segmentation is an important application area of computer vision in the medical image processing, its goal is to segment the target area from medical images and provide effective help for subsequent diagnosis and treatment of diseases. Since deep learning technology has made great progress in the image processing, medical image segmentation algorithm based on deep learning has gradually become the focus and hotspot of research in this field. This paper gives a description on the tasks and difficulties of medical image segmentation. Then, it details the deep learning-based medical image segmentation algorithm, classifies and summarizes the current representative methods. Moreover, this paper presents the frequently-used algorithm evaluation indicators and datasets in the field of medical image segmentation. The development of medical image segmentation technology is summarized and forecasted.

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    Analysis on Application of Machine Learning in Stock Forecasting
    XU Haoran, XU Bo, XU Kewen
    Computer Engineering and Applications    2020, 56 (12): 19-24.   DOI: 10.3778/j.issn.1002-8331.2001-0353
    Abstract586)      PDF(pc) (892KB)(829)       Save

    It has always been regarded as the emphasis of research to reveal the operation law of stock market. In recent years, machine learning method has made good progress in stock forecasting, and it has shown unique advantages over traditional methods such as fundamental analysis and technical analysis. This paper focuses on collecting the key references in the field of stock prediction that uses machine learning methods in recent years, and analyzing as well as summarizing feature engineering, the application of machine learning algorithms and the main problems in stock prediction research. The characteristics and shortcomings of each algorithm in application are reviewed, and future development direction of this field is made a thorough analysis and forecasted from the aspects of transfer learning, feature engineering and deep learning model fusion.

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    Detection Approach of Malicious JavaScript Code Based on Convolutional Neural Network
    LONG Tingyan, WAN Liang, DENG Xunkun
    Computer Engineering and Applications    2019, 55 (18): 89-94.   DOI: 10.3778/j.issn.1002-8331.1808-0005
    Abstract174)      PDF(pc) (854KB)(828)       Save
    Time and manpower have been wasted largely in the process of features extraction when JavaScript malicious code detection methods of machine learning are used, and these frequently-used methods have failed to meet the actual needs in the current information explosion. A JavaScript malicious code detecting method based on convolution neural network have been proposed in this paper. The sample data are collected through the crawler tool to obtain the benign and malicious JavaScript script code. The JavaScript samples are converted into the corresponding gray scale images, simultaneously, the image dataset is established. The image data set is trained when the convolution neural network model is constructed, so the model has obtained the ability to detect JavaScript malicious code. The experimental results show that the accuracy of the method is 98.9% for the 5, 800 JavaScript labeled images collected.
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