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2020 13th International Conference on Computer and Electrical Engineering, ICCEE 2020

作者： Xin, Yuanxia Zhang, Zhi Collage of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WuhanHubei430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology WuhanHubei430065 China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Strategy evolution on dynamic networks

arXiv

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arXiv 2023年

作者： Su, Qi McAvoy, Alex Plotkin, Joshua B. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Center for Mathematical Biology University of Pennsylvania PhiladelphiaPA19104 United States

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures. © 2023, CC BY.

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Change Point Reliability Modelling for Open Source Software with Masked data Using Expectation Maximization Algorithm

Change Point Reliability Modelling for Open Source Software ...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Jianfeng Yang Xibin Wang Yujia Huo Jing Cai Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guiyang China College of Data Science and Information Engineering Guizhou Minzu University Guiyang China

ISBN: (数字)9781728159461

ISBN: (纸本)9781728159478

Masked data is a common missing failure data in reliability engineering. In this paper, multiple change points (CPs) software reliability growth model (SRGM) based on nonhomogeneous Poisson process (NHPP) is proposed using masked data. The C-Chart technology is used to estimate the position of the change point during the software failure process. Moreover, the maximum likelihood estimation (MLE) process of the model parameters is derived in detail, and Expectation Maximization (EM) algorithm is used to solve the likelihood function complicated problem. Finally, using the Tomcat 5 software failure data to conduct a comparative analysis of model performance, the results show that the proposed reliability model is useful and powerful.

关键词： Analytical models Maximum likelihood estimation Software algorithms Reliability theory data models Software reliability Open source software

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Volume Measuring Method for Forming Additive Manufacturing Based on SGBM

Volume Measuring Method for Forming Additive Manufacturing B...

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Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Xiaolin Fan Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

For volume measurement problems in the processes of additive manufacturing, a volume measurement method is proposed through using the SGBM algorithm in this paper. In this method, the internal and external parameters of the camera are calibrated by the binocular vision system. And the image processes by SGBM are performed for the image acquired by binocular stereo vision system. The processes are including filtering, correction and stereo matching to obtain the disparity information of the object to be tested in the left and right cameras. The volume of the work-piece to be tested can be obtained by the calculation that measured the three-dimensional coordinate information through the disparity information. The experimental results illustrate that the method is of certain reliability and accuracy for volume measurement.

关键词： Cameras Calibration Volume measurement Three-dimensional printing Mathematical model Measurement by laser beam Three-dimensional displays

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The Laser Point Detecting Method Based on Faster R-CNN

The Laser Point Detecting Method Based on Faster R-CNN

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Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Chaoyang Zheng Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Aiming at issues of low accuracy and poor anti-interference ability in laser point detecting, a target detection method based on Faster R-CNN was proposed. The laser point position detecting method is realized by calibrating the laser point data set, initializing the weight coefficient and the training network model. Experiments illustrate that the proposed method is more accurate in cases of strong noise such as the light changing, the background changing and the smoke filling the air. The accuracy of the method is 98.1%, as a better detection effect and generalization ability.

关键词： Feature extraction Laser modes Training Proposals Laser noise Convolution Three-dimensional printing

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Design and Implementation of Log data Analysis Management System Based on Hadoop

引用

Journal of information Hiding and Privacy Protection 2020年第2期2卷 59-65页

作者： Dunhong Yao Yu Chen School of Computer Science and Engineering Huaihua UniversityHuaihua418000China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China School of Computer Science and Engineering Yulin Normal UniversityYulin537000China

With the rapid development of the Internet,many enterprises have launched their network *** users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log *** effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of ***,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the ***,the obtained results are displayed visually,and a log data analysis system is successfully *** has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by *** system is stable and effective.

关键词： Log data Hadoop data analysis data visualization

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A method of laser point temperature detection based on CNN

A method of laser point temperature detection based on CNN

引用

Chinese Automation Congress (CAC)

作者： Ruidong Xie Haichuan Yang Yingmin Yi Qimeng Shi keying Wang Jiabin Huang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

There has been a direct relationship between the temperature of the laser point and the quality of the casting in the process of the 3D printing. In the paper, a method based on convolutional neural network (CNN) was proposed to estimate the temperature of the laser point. The collected temperature data used were trained by the deep-learning method. A new structure of the model was proposed on the part of the CNN model, which improved from original LeNet. The process of the prediction for the testing set was carried out through the new model. The unknown temperature in the testing set can be estimated. The experimental result illustrates that the proposed method is satisfactory.

关键词： Laser modes Temperature measurement Training Temperature distribution Surface emitting lasers Testing Measurement by laser beam

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Extraction of transitional relations in healthcare processes from Chinese medical text based on deep learning 2019

Extraction of transitional relations in healthcare processes...

引用

4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： Ren, Fangwen Yuan, Song Gao, Feng School of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Institute of Big Data Science and Engineering Wuhan University of Science and Technology China

ISBN: (纸本)9781450362580

Knowledge extraction in healthcare has become a recent research hotspot due to the rapid development of AI technology as well as the urgent demand in the medicine research and practice. Traditional methods focus on extracting terms and relations using pattern matching and machine learning, but they depend on the construction of rules and the accuracy of feature extraction. In contrast, deep learning methods have a stronger ability to process data with high dimensions. Moreover, current study neglects the fact that the key knowledge in medicine is the process of diagnosing and treating patients, i.e., the relations that causes the status change in a healthcare workflow. This paper first identifies the relations relevant for medical processes, and adopts the method of relationship extraction based on Bidirection Gated Recurrent Unit (BiGRU) model and attention mechanism (BiGRU-ATT), in order to retrieve these relations from Chinese medical text. The experimental results show that regarding Chinese medical entity relationship extraction, we can achieve better accuracy rate and recall rate than using convolutional neural network(CNN). © 2019 Association for Computing Machinery.

关键词： Extraction

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Protection of Large Sparse Network Through Optimal Cropped Depth-First Traversal Tree

引用

IEEE Transactions on Network Science and Engineering 2025年

作者： Wei, Wei Hu, Qiuyuan Zhang, Qinghui Li, Peng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Zhengzhou450001 China Henan University of Technology Henan Grain Big Data Analysis and Application Engineering Research Center Zhengzhou450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing100086 China

The robustness of the network is its ability to withstand unexpected failures. There is an urgent need to enhance the robustness of sparse networks as they are prone to breakage. Among the various ways to enhance network robustness measured by the edge connectivity of the entire network, shielding candidate edges is a popular method when redundant edges are not affordable. However, existing tree-cutting favors dense graphs with exponential cropped-tree growth of search space, but the search space only scales linearly in sparse graphs. We explore sparse-graph limitations by extending cropping to maximum depth while maintaining sublinear overhead, leveraging ancestor-node constraints and cropping-chain theorems for optimal efficiency. Compared with the optimal algorithm, the results in representative network types show that the robustness measurement of small-scale graphs can be enhanced to 100%, and the ratio of acceleration can be greater than 105. In large-scale graphs with millions of nodes, robustness can be improved to over 99%, while the time can be as low as tens of seconds. © 2013 IEEE.

关键词： Trees (mathematics)

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Cloud detection from visual band of satellite image based on variance of fractal dimension

引用

Journal of Systems Engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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