检索结果-内蒙古大学图书馆

Chinese Intelligent Systems Conference, CISC 2017

作者： Cai, Qiang Li, Qiang Li, Haisheng Mao, Dianhui School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (纸本)9789811064982

Food safety supervision and management departments have accumulated a large number of food sampling data, and the data shows hierarchical and high-dimensional characteristics. Because of the lack of appropriate visualization methods, it is difficult to carry out efficient analysis. In this paper, we extracted a hierarchical and high-dimensional dataset from the food inspection data for visualization, and proposed a method based on the force-directed model. Besides, we applied an optimized method to design the interactive mode. Through the visual interaction, the information can be displayed much more efficiently and thus facilitate the understanding of information. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated vision inspection of rail surface cracks: A double-layer data driven framework 48

Automated vision inspection of rail surface cracks: A double...

引用

48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Zhuang, Li Wang, Long Zhang, Zijun School of Data Science City University of Hong Kong Hong Kong Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. © 2018, Curran Associates Inc. All rights reserved.

关键词： Crack detection

来源：评论

学校读者我要写书评

暂无评论

Supplementary material for CDC submission no. 1461

arXiv

引用

arXiv 2020年

作者： Ju, Yue Chen, Tianshi Mu, Biqiang Ljung, Lennart Chinese University of Hong Kong Shenzhen518172 Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518172 Hong Kong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

In this paper, we study the influence of ill-conditioned regression matrix on two hyper-parameter estimation methods for the kernel-based regularization method: the empirical Bayes (EB) and the Stein’s unbiased risk estimator (SURE). First, we consider the convergence rate of the cost functions of EB and SURE, and we find that they have the same convergence rate but the influence of the ill-conditioned regression matrix on the scale factor are different: the scale factor for SURE contains one more factor cond(ΦTΦ) than that of EB, where Φ is the regression matrix and cond(·) denotes the condition number of a matrix. This finding indicates that when Φ is ill-conditioned, i.e., cond(ΦTΦ) is large, the cost function of SURE converges slower than that of EB. Then we consider the convergence rate of the optimal hyper-parameters of EB and SURE, and we find that they are both asymptotically normally distributed and have the same convergence rate, but the influence of the ill-conditioned regression matrix on the scale factor are different. In particular, for the ridge regression case, we show that the optimal hyper-parameter of SURE converges slower than that of EB with a factor of 1/n2, as cond(ΦTΦ) goes to ∞, where n is the FIR model order. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Wheat yellow rust monitoring method based on Sentinel-2 image and BPNN model

引用

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural engineering 2019年第17期35卷 178-185页

作者： Huang, Linsheng Jiang, Jing Huang, Wenjiang Ye, Huichun Zhao, Jinling Ma, Huiqin Ruan, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University 230601 China Sanya Institute of Remote Sensing Sanya572029 China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China

Wheat yellow rust is a deadly disease of winter wheat and its timely and accurate detection at regional scale is critical to ameliorate infectious yield loss and safeguard wheat production. With the development in remote sensing technology, satellite imagery with high spatial resolution and high revisiting frequency has become increasingly accessible. Remote sensing data has a unique advantage over traditional method in detecting crop diseases and controlling their spreading, including simple operation, real-time detection, high spatiotemporal resolution and targeting specific-disease, especially the multispectral satellite imagery which covers a wide range of wave bands and provides rich information related to crop diseases at regional scale. Compared to conventional broad band satellite imagery, the Sentinel-2 is a sensor with three wave bands within the edge of the red light which are sensitive to crop diseases. In this study, a Sentinel-2 image acquired in May 12, 2018 was used to extract 26 characteristic variables related to wheat yellow rust, including 3 visible bands (blue, green and red) reflectance variables, one near infrared band, 3 red-edge bands, 14 broad-bands and 5 red-edge vegetation indices. An approach combining K-means and ReliefF algorithm is proposed to filter these features. We first used the RelieF algorithm to calculate the weight of each feature and filter out 10 features most relevant to the disease. The feature with maximum weight was then taken as the initial center of the K-Means algorithm, and other features were added to form a cluster in descending order of their weight. The combination of features with the highest clustering accuracy was taken as the final input variable to the model. The optimal features, including enhanced vegetation index (EVI), structure intensive pigment index (SIPI), simple ratio index (SR), normalized red-edge2 index(NREDI2), normalized red-edge3 index (NREDI3), three wide-band vegetation indices and 2 red ed

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms 12

Gestational Diabetes Mellitus Prediction Based on Two Classi...

引用

12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhong, Weiyang Jiang, Shanxi Wu, Wei Peng, Danhong Xu, Tian Wang, Jun Wang, Gongdao Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China China Tower Corporation Limited Nantong Branch China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing210009 China Department of Obstetrics and Gynecology Zhongda Hospital Southeast University Nanjing School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9781728148526

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network. © 2019 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

EV105/#457 Risk factors associated with overall survival of cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional-hazards models

引用

International Journal of Gynecological Cancer 2024年 34卷 A140-A140页

作者： Zejia Mao Ling Long Yuan Li Hongji Wu Qiaoling Li Qianjie Xu Misi He Cong Zhang Haike Lei Dongling Zou Chongqing University Chongqing Shapingba China Department of Gynecologic Oncology Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital Chongqing Chongqing Shapingba China Chongqing Specialized Medical Research Center of Ovarian Cancer Chongqing China； Chongqing China Chongqing Medical University Chongqing China Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center Chongqing University Cancer Hospital Chongqing Chongqing China Organoid Transformational Research Center Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer Hospital Chongqing Chongqing China

来源：评论

学校读者我要写书评

暂无评论

Web Intelligence (WI) 3.0: in search of a better-connected world to create a future intelligent society

引用

Artificial Intelligence Review 2025年第9期58卷

作者： Kuai, Hongzhi Huang, Jimmy X. Tao, Xiaohui Pasi, Gabriella Yao, Yiyu Liu, Jiming Zhong, Ning School of Computer Science and Technology/School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China School of Information Technology York University North YorkON Canada School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD Australia Department of Informatics Systems and Communication University of Milano-Bicocca Milano Italy Department of Computer Science University of Regina Regina Canada Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Faculty of Engineering Maebashi Institute of Technology Gunma Maebashi Japan

Over the past two decades, Web Intelligence (WI) has emerged as a key field driving the evolution of AI in the connected world, addressing the demands of a future intelligent society. This paper provides a comprehensive review of WI’s contributions since its inception in 2000, spanning three distinct phases: Wisdom World Wide Web (WI 1.0, 2000–2009), Wisdom Web of Things (WI 2.0, 2010–2017), and Wisdom Web of Everything (WI 3.0, since 2018). For each phase, we examine key advancements, challenges, and future directions from the perspectives of both intelligent machines and human experts, highlighting significant societal impacts. To advance WI research, we propose a large language model-based learning framework for topic analysis and trend prediction. Moving beyond single-perspective approaches, we emphasize the Connected Intelligence Ecosystem defined by the HIGH5 scheme comprising one goal, two twins, three fundamentals, four functions, and five services that are realized through WI 3.0. This vision serves as a bridge from localized models to a global reference framework for addressing sustainability challenges in future societies. To illustrate the real-world implications of WI 3.0, we present case studies focusing on brain-inspired research, particularly in the intersection of brain intelligence, brain health, and brainternet-fostering interdisciplinary collaboration across diverse research communities. © The Author(s) 2025.

关键词： Intelligent computing

来源：评论

学校读者我要写书评

暂无评论

Multi-view SVM Classification with Feature Selection

引用

Procedia Computer Science 2019年 162卷 405-412页

作者： Yuting Niu Yuan Shang Yingjie Tian School of Information Engineering Zhengzhou University Zhengzhou 450001 China Smart City Institute Zhengzhou University Zhengzhou 450001 China Supercomputer Center Smart City Institute Zhengzhou University Henan 450001 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.

关键词： multi-view learning classification feature selection sparsity regularization linear programming

来源：评论

学校读者我要写书评

暂无评论

Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：