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Research on Star/Galaxy Classification Based on Stacking Ensemble Learning

Chinese Astronomy and Astrophysics 2020年第3期44卷 345-355页

作者： Chao, L.I. Wen-hui, ZHANG Ran, L.I. Jun-yi, W.A.N.G. Ji-ming, L.I.N. College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Education Ministry for Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004 Guangxi Key Laboratory for Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Satellite Navigation and Position Sensing Guilin 541004

Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model;the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. © 2020

关键词： Galaxies: fundamental parameters methods: data analysis Stars: fundamental parameters techniques: photometric

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A Face Recognition Method Based on CNN

A Face Recognition Method Based on CNN

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Xie, Zhiming Li, Junjie Shi, Hui Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer;the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Cache Characterization of Workloads in a Microservice Environment

Cache Characterization of Workloads in a Microservice Enviro...

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IEEE International Conference on cloud computing in Emerging Markets (CCEM)

作者： Sanjith Athlur Nishant Sondhi Sachit Batra Subramaniam Kalambur Dinkar Sitaram Department of Computer Science Center for Cloud Computing and Big Data PES University Bengaluru

ISBN: (数字)9781728163345

ISBN: (纸本)9781728163352

The advent of Microservice based architecture has lead to the obsolescence of the tedious way of building and deploying applications in a monolithic way. The number of organizations taking advantage of cloud computation and microservice architecture is steadily increasing. From the cloud providers perspective, the purpose of cloud computation is to leverage economies of scale by co-locating several workloads dynamically on the same powerful many-core servers. In this paper, we attempt to characterize cache usage and its impact on performance when multiple workloads are collocated on multicore servers.

关键词： Servers Interference cloud computing Benchmark testing Hardware Multicore processing

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Small Evolution Antenna Design Method Based on Dynamic Hill Climbing Algorithm and Orthogonal Experiment

Small Evolution Antenna Design Method Based on Dynamic Hill ...

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Shi, Hui Li, Junjie Xie, Zhiming Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

Small-scale evolution antennas have the advantages of high gain, wide bandwidth and small size, and have broad application prospects in the aerospace field. Aiming at the complex high-dimensional and dynamic problems of aerial antenna modeling, a dynamic hill-climbing algorithm based on orthogonal design is designed and implemented to simulate and optimize the single-span antenna in high-frequency operation mode. In the optimization problem, this paper defines a new individual comparison mechanism, which can reflect the number of violations of constraints and the degree of violation of each constraint, making the comparison of individuals more reasonable. The antenna simulation results demonstrate the effectiveness and feasibility of using dynamic evolutionary algorithms for antenna design. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

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Multi-Scale Region-based Fully Convolutional Networks

Multi-Scale Region-based Fully Convolutional Networks

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IEEE International Conference on Power, intelligent computing and Systems (ICPICS)

作者： Chengqi Xu Xuehai Hong Yuanzhou Yao Hengheng Shen Qian Ma Hui Jiang Big data research institute Cloud Computing Center Chinese Academy of Sciences Shangrao China Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Engineering Sichuan Agricultural University Ya’ an City Sichuan State China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198750

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection.

关键词： Feature extraction Object detection Proposals Semantics Detection algorithms Convolution Entropy

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Holographic capture and projection system of real object based on tunable zoom lens

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PhotoniX 2020年第1期1卷 271-285页

作者： Di Wang Chao Liu Chuan Shen Yan Xing Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China.

In this paper, we propose a holographic capture and projection system of real objectsbased on tunable zoom lenses. Different from the traditional holographic system, aliquid lens-based zoom camera and a digital conical lens are used as key parts to reachthe functions of holographic capture and projection, respectively. The zoom camera isproduced by combing liquid lenses and solid lenses, which has the advantages of fastresponse and light weight. By electrically controlling the curvature of the liquid-liquidsurface, the focal length of the zoom camera can be changed easily. As anothertunable zoom lens, the digital conical lens has a large focal depth and the opticalproperty is perfectly used in the holographic system for adaptive projection, especiallyfor multilayer imaging. By loading the phase of the conical lens on the spatial lightmodulator, the reconstructed image can be projected with large depths. With theproposed system, holographic zoom capture and color reproduction of real objects canbe achieved based on a simple structure. Experimental results verify the feasibility ofthe proposed system. The proposed system is expected to be applied to microprojectionand three-dimensional display technology.

关键词： Spatial light modulator Holographic system Zoom lenses Display technology

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

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IEEE International Conference on Services computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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Lane Marking Detection Algorithm Based on High-Precision Map 6th

Lane Marking Detection Algorithm Based on High-Precision Map

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6th International Conference on data Science, ICDS 2019

作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Wang, Ruchuan School of Computer and Software Nanjing Institute of Industry Technology Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China

ISBN: (纸本)9789811528095

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © Springer Nature Singapore Pte Ltd 2020.

关键词： Road and street markings

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Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach 15th

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underl...

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15th International Conference on intelligent computing, ICIC 2019

作者： Li, Li-Ting Qiu, Hai-Yan Liu, Mi-Mi Cai, Yong-Ming Guangdong Pharmaceutical University GuangzhouGuangdong510000 China Guangdong Provincial TCM Precision Medicine Big Data Engineering Technology Research Center GuangzhouGuangdong510000 China Guangdong University College of Precision Medicine Big Data Engineering Research Center Based on Cloud Computing GuangzhouGuangdong510000 China GuangzhouGuangdong510000 China

ISBN: (纸本)9783030269685

Epidemiological studies have shown that primary dysmenorrhea is the most common disease in gynecology, and its incidence rate is between 20% and 90%, which is a common cause of affecting women’s normal work and quality of life. Its high incidence rate, wide spread and economic losses and social harm have caused widespread concern worldwide. The present work adopted a network pharmacology-based approach to provide new insights into the active compounds and therapeutic targets of Danggui-Baishao herb pair for the treatment of primary dysmenorrhea. Fifteen active compounds of the herb pair possessing favorable pharmacokinetic profiles and biological activities were selected, interacting 17 dysmenorrhea-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as ABCB1, ESR1, PGR, AKR1C3, PTGS2, CYP2C8, PTGS1 were mainly involved in Steroid hormone biosynthesis, Arachidonic acid metabolism, serotonergic synapse and Ovarian steroidogenesis through steroid metabolic process, steroid hormone mediated signaling pathway, cyclooxygenase pathway, response to estradiol and response to oxidative stress. © 2019, Springer Nature Switzerland AG.

关键词： Metabolism

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Extracting hierarchical relationships of aspects from reviews

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Applied Soft computing 2025年 178卷

作者： Jiangtao Qiu Ling Lin Siyu Wang Chongqing Engineering Research Center for Intelligent Applications of Financial Big Data Chongqing Institute of Engineering Chongqing China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China College of Artificial Intelligence (CUIT Shuangliu Industrial College) Chengdu University of Information Technology Chengdu China Gusu Laboratory of Materials Suzhou China

Aspect Based Sentiment Analysis (ABSA) attracts significant attention in recent years. Three elements of ABSA including aspect term, aspect, and domain (or entity) present a hierarchical relationships in e-commerce reviews. Extracting the hierarchical relationships can significantly enhance various applications, such as creating user profiles, identifying hierarchical topics, and visualizing review data. In this study, we proposed a framework to tackle this task, consisting of two primary components: a text adversarial autoencoder that efficiently encodes review content, and a deep network that extracts the clusters of aspect terms from review dataset and organizes them to a hierarchical structure using the Student-Teacher paradigm. Our framework also addresses the challenge of acquiring labeled training data by utilizing self-supervised learning. We evaluated the proposed framework on three public datasets and observed that it outperforms baseline models, indicating the feasibility and effectiveness of our approach.

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