检索结果-内蒙古大学图书馆

A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

来源：评论

学校读者我要写书评

暂无评论

A Green Paradigm for Internet of Things: Ambient Backscatter Communications

引用

China Communications 2019年第7期16卷 109-119页

作者： Wei Zhang Yao Qin Wenjing Zhao Minzheng Jia Qiang Liu Ruisi He Bo Ai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology and also with Institute of Telecommunication Satellites China Academy of Space Technology (CAST) Beijing Key Lab of Transportation Data Analysis and Mining Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Beijing Polytechnic College Beijing 100042 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.

关键词： ambient backscatter (AmBC) RF-powered device Internet of Things (IoT) battery-free tag wire-less sensor

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network applied in mime speech recognition using sEMG data

Convolutional Neural Network applied in mime speech recognit...

引用

Chinese Automation Congress (CAC)

作者： Qing Ai Wei Zhang Bixuan Zhang Guang Li Meng Yang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Computer Science and Technology China University of Mining and Technology Beijing China

Decoding speaking intention from the cervical and facial muscle activity enables speech recognition independent of acoustic signals, thus allowing silent communication between human and computing devices. This research aims to design and optimize the convolutional network classifier for sEMG mime speech signals. The muscles involved in the vocalization and timbre modulation are found according to the anatomical map. Six-channel signals have been collected from the corresponding position using the sEMG signal acquisition device. The original signals are subjected to pre-processing, including noise reduction, active segment detection and interpolation, to form training and testing sets. Convolutional network models are applied to Figure out the influence of structural parameters, such as convolution kernel size, the number of convolution kernels and the depth of network, on the recognition accuracy. Based on repeated trials, the optimal convolution network is provided, which provide above 80% accuracy rate.

关键词： Speech recognition Convolution Electromyography Task analysis Training Kernel Interference

来源：评论

学校读者我要写书评

暂无评论

Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

引用

International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

来源：评论

学校读者我要写书评

暂无评论

Silent Speech Recognition Based on Surface Electromyography

Silent Speech Recognition Based on Surface Electromyography

引用

Chinese Automation Congress (CAC)

作者： Siyuan Ma Dantong Jin Ming Zhang Bixuan Zhang You Wang Guang Li Meng Yang Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology China University of Mining and Technology Beijing China

Silent speech recognition (SSR) can decode the activities of electromyography (EMG) from surface of articulatory muscles to the according brain information. As the essentials in SSR, feature extraction and recognition are directly related to the final decoding effects, which requires careful research, especially on small datasets. This paper collected the surface EMG (sEMG) of 10 isolated Chinese words by 6 surface electrodes on face and around, and based on which sEMG features were extracted by wavelet packet transform, principal component analysis and XGBoost (an implementation of gradient boosted decision trees) respectively. Then Support Vector Machine, K-Nearest Neighbor and Random Forest were explored for recognition on the 10 words. The results indicated that the highest accuracy of 72% was achieved by the method of Random Forest based on XGBoost.

关键词： Feature extraction Principal component analysis Support vector machines Training Speech recognition Electromyography Forestry

来源：评论

学校读者我要写书评

暂无评论

Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

引用

IEEE Access 2019年 7卷 120315-120325页

作者： Ar Junejo Yin Shen Asif Ali Laghari Xiaobo Zhang Hao Luo School of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan School of Automation Guandong University of Technology Guangzhou China

Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI.

关键词： Heart Blood Diseases Arteries Mathematical model Artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Review of renewable energy curtailment in China: Current situation and solutions

引用

AIP Conference Proceedings 2022年第1期2681卷

作者： Yingai Jin Yiwen Zhang Nan Jiang Mingjie Lv Hang Yin Wenyuan Sun Liang Li 1State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025 China 2College of Automotive Engineering Jilin University Changchun 130025 China 3School of Physics Engineering and Computer Science University of Hertfordshire Hatfield AL10 9AB UK

In recent decades, China's renewable power generation has developed quickly. However, the randomness of output power causes wind and photovoltaic power curtailment. With the rapid development of renewable energy, renewable energy consumption has gradually become the focus of research. This article comprehensively reviews the current situation and practices of reducing the curtailment of renewable energy in China. From the perspective of methods used to stabilize the fluctuation characteristics of generation output, two modes are outlined: based on energy storage devices and complementary wind-solar system. Then, an Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) system simulation model is proposed as a crucial solution to reduce renewable energy waste. Finally, the application and development trend of renewable energy and energy storage technology has prospected.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

引用

arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

来源：评论

学校读者我要写书评

暂无评论

Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

引用

2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

来源：评论

学校读者我要写书评

暂无评论

LMI stability analysis for a micro RNA - Messenger RNA interaction mathematical model

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2019年第4期81卷 3-10页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

The micro RNA-messenger RNA dynamics is a topic of constant interest. In this paper we analyze the stability of equilibria of a mathematical model associated to this interaction. After proving the existence and uniqueness of an equilibrium point in the positive ortant, we provide numerically tractable conditions (by using Linear Matrix Inequalities techniques) to check the asymptotic stability of the equilibrium point. An illustrative numerical example is closing the paper along with some conclusions. © 2019, Politechnica University of Bucharest. All rights reserved.

关键词： RNA

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：