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Density PSO-based software module clustering algorithm

The Journal of China Universities of Posts and Telecommunications 2018年第4期25卷 38-47页

作者： Sun Jiaze Ling Beilei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is *** shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

关键词： software module clustering complex network PSO MQ modularity density

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Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional network

Structured Information Extraction of Pathology Reports with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Kaiwen Tang Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiaotong University Xi’an Shaanxi China School of Computer Science and Technology Qian Xuesen College Xi’an Jiaotong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist's workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.

关键词： Pathology Cancer Task analysis Annotations Information retrieval Medical diagnostic imaging data mining

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Indicator & crowding Distance-Based Evolutionary Algorithm for Combined Heat and Power Economic Emission Dispatch

arXiv

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arXiv 2020年

作者： Sun, Jiaze Deng, Jiahui Li, Yang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Heat and power have become the most indispensable resources. However, the traditional ways of generating power and heat are inefficient and cause high pollution;a CHP (Combined Heat and Power) unit can solve these problems well. In recent years, more attention has been paid to energy conservation and environmental protection, and Combined Heat and Power Economic Emission Dispatch (CHPEED) has become an important multi-objective optimization problem. In this paper, an Indicator & crowding Distance-based Evolutionary Algorithm (IDBEA) is put forward for handling this non-convex and non-linear problem. With consideration of the valve-point effects and power transmission loss, IDBEA is tested on three standard test systems with different types, including four units, five units and seven units. In the experiment, IDBEA is compared with several evolutionary algorithms, the simulation results demonstrate that IDBEA has strong stability and superiority, while the solutions show better convergence and diversity than several typical algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Cogeneration plants

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A skeleton pattern representation method for anomaly detection in wireless sensor networks 21

A skeleton pattern representation method for anomaly detecti...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Yanping Yang, Ping Gao, Cong Wang, Zhongmin Yu, Zhong School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

Anomaly detection of time series in wireless sensor networks has gained significant attention. Researchers employ representation techniques to reduce the dimensionality of time series. The Piecewise Aggregate Pattern Representation (PAPR) method vertically divides the time series and represents as patterns. However, the computational cost of the PAPR method for anomaly detection is high. To overcome this issue, we propose a time series representation method for anomaly detection called Skeleton Pattern Representation for anomaly detection (SPR-AD). An improved Perceptually Important Point (iPIP) identification algorithm is devised to transform the subsequence into a skeleton with PIPs. Then the skeleton is divided into several equal-sized subspaces in the amplitude domain. Statistical information of the PIPs located in subspaces is constructed to represent the skeleton as a pattern. Based on the two-phase representation scheme, anomaly is determined by anomaly scores which are based on the of similarity among pattern. The experimental results demonstrate the performance of the SPR-AD method is better in terms of computational cost and detection accuracy than the PAPR method. © 2019 IEEE.

关键词： Anomaly detection

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On the Connectivity of Highly Dynamic Wireless Sensor networks in Smart Factory

On the Connectivity of Highly Dynamic Wireless Sensor Networ...

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International Conference on networking and network Applications (NaNA)

作者： Cong Gao Zhongmin Wang Yanping Chen School of Computer Science and Technology Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results.

关键词： Wireless networks Wireless sensor networks Euclidean distance Smart manufacturing Analytical models

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A recurrent Gaussian process regression model with composite kernel for industrial process quality prediction

A recurrent Gaussian process regression model with composite...

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2019 International Conference on networking and network Applications, NaNA 2019

作者： Chen, Yanping Gao, Cong Yang, Weihui Wang, Zhongmin Yu, Zhong Xia, Hong School of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'An University of Posts and Telecommunications Xi'an710121 China School of Communication and Information Engineering Xi'An University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728126296

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods. © 2019 IEEE.

关键词： Forecasting

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PSO with reverse edge for multi-objective software module clustering

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International Journal of Performability Engineering 2018年第10期14卷 2423-2431页

作者： Sun, Jiaze Xu, Yang Wang, Shuyan School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China

The multi-objective software module clustering problem (MOSMCP) divides the complex software system into subsystems to obtain a perfect structure, which is based on the relations between modules to meet the conflicting software refactor objectives as much as possible. The modularization quality (MQ) and reverse edges number between clusters are considered as evaluation objectives, and a novel particle swarm optimization (PSO) with reverse edge, called REPSO, is proposed. First, the module dependency graph (MDG) in software system under clustering is constructed, and then the multi-objective particle swarm optimization (MOPSO) is improved to cluster the MDG. The exploring approach is used to update the particle locations. Four typical open source projects for module clustering are selected to verify the effectiveness of the REPSO. The laboratorial results prove that the REPSO algorithm is very effective and stable, and the diversity of the optimal solution is good. The REPSO algorithm provides an efficient engineering method for MOSMCP, which enhances the software structure and maintainability. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Modular construction

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Multi-objective test case prioritization based on epistatic particle swarm optimization

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International Journal of Performability Engineering 2018年第10期14卷 2441-2448页

作者： Sun, Jiaze Chen, Jingmin Wang, Gang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China

To address the Multi-Objective Test Case Prioritization (MOTCP) problem, an Epistatic Particle Swarm Optimization (EPSO) algorithm is presented. The epistasis in biology is introduced into the new algorithm, and the particles are updated based on the crossover of Epistatic Test Case Segment (ETS) in the test case sequence. The average coverage percentage of program entity and effective execution time of the test case sequence are set as two objective fitness functions in EPSO. The experiment selects four typical open12 source projects as benchmark programs. We adopted Average Percentage of Branch Coverage (APBC) and Effective Execution Time (EET) as objective fitness. The four classical Java testing projects results show that the EPSO is more effective and more diverse than single-point PSO and order PSO. The EPSO algorithm efficiently solves the MOTCP problem by promoting early detection of software defects and reducing software testing costs in regression testing. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Software testing

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Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU

Object Detection Algorithm for Improving Non-Maximum Suppres...

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作者： Zhiqiang Hou Xiaoyi Liu Lilin Chen College of Computer Science and Technology Xian University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications

To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm,this paper proposed a GDT-NMS(Generalized Intersection over Union Dual Threshold NMS,GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union).Using the GIoU indicator computing the similarity between objects,can better describe the relative position and overlap between the *** we proposed the dual-threshold NMS algorithm,which can balance the relationship between the object missed detection problem and the object false detection problem,reduce "false positive example" *** nonlinearly processing the weight function,the object is better *** algorithm uses Faster R-CNN as the *** experimental results show that the improved algorithm has outstanding performance.

关键词： GDT NMS Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU

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Effects of annotation granularity in deep learning models for histopathological images

arXiv

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arXiv 2020年

作者： Shi, Jiangbo Gao, Zeyu Zhang, Haichuan Puttapirat, Pargorn Wang, Chunbao Zhang, Xiangrong Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiao tong University Xi'an Shaanxi710049 China Shaan xi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Xi'an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi710061 China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi710071 China

Pathological examination is an important step in cancer diagnosis. Pathologists make diagnosis and pathology report based on observed cell and tissue structure on pathological slides. With the development of statistical machine learning, especially deep learning, automated classifiers are being used to analyze histopathological slides and assist pathologists in diagnosis. Currently, commonly used annotation methods in histopathological slides include image-wise, bounding box, ellipse-wise, pixel-wise. In order to verify the influence of annotation in pathological slide on deep learning model, we design corresponding experiments to test the performance based on annotations with different granularity annotation. In classification, all state-of-the-art deep learning based classifiers perform better when they are trained by the dataset with pixel-wise annotation. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows that finer-grained annotation can not only improve the performance of deep learning models, but help deep learning model extract more accurate phenotypic information from histopathological slides. An accurate acquisitions of phenotypic information can help pathologists to enquire the model based on which regions and features in the slide were mainly used to calculate the prediction, improve the reliability of the model prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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