检索结果-内蒙古大学图书馆

作者： He, Ruisi Ai, Bo Stuber, Gordon L. Wang, Gongpu Zhong, Zhangdui State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

A geometric multiple-input multiple-output (MIMO) channel model is proposed for millimeter-wave (mmWave) mobile-to-mobile (M2M) applications based on the two-ring reference model, where cluster-based nonisotropic scattering at both ends of the radio link is considered. The proposed model employs a few clusters of scatterers located on two rings centered on the transmitter and receiver, and intracluster azimuth spread of scatterers is further characterized according to mmWave channel characteristics. From the model, the time-frequency correlation function, power delay profile (PDP), and the Doppler power spectrum are derived. By adjusting the cluster number, cluster center position, and the degree of intracluster nonisotropic scattering, the model is adaptable to a variety of mmWave M2M scenarios. Model validation is further conducted by comparing the simulated PDPs with mmWave outdoor measurements. Based on a detailed investigation of channel correlation functions, it is found that a small number of clusters leads to high channel correlation, and the degree of intracluster nonisotropic scattering has major impact on correlation only if cluster number is small. In addition, the Doppler power spectrum is similar to the U-shaped spectrum, and several factors (e.g., small cluster number, high intracluster azimuth spread, and large antenna spacing) introduce significant fluctuations in the Doppler power spectrum. Finally, the model is implemented with directional antennas for safety related M2M scenarios, which leads to high channel correlation compared with using omnidirectional antennas. These observations and conclusions can be considered as a guidance for the mmWave M2M MIMO system design. © 1967-2012 IEEE.

关键词： Millimeter waves

来源：评论

学校读者我要写书评

暂无评论

Deep differentiable random forests for age estimation

arXiv

引用

arXiv 2019年

作者： Shen, Wei Guo, Yilu Wang, Yan Zhao, Kai Wang, Bo Yuille, Alan Department of Computer Science Johns Hopkins University BaltimoreMD21218-2608 United States School of Communication and Information Engineering Shanghai University Shanghai200444 China College of Computer and Control Engineering Nankai University Tianjin300071 China Vector Institute and Peter Munk Cardiac Center of University Health Network TorontoON Canada

Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its main challenge is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging. In this paper, we propose two Deep Differentiable Random Forests methods, Deep Label Distribution Learning Forest (DLDLF) and Deep Regression Forest (DRF), for age estimation. Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes. This joint learning follows an alternating strategy: (1) Fixing the leaf nodes and optimizing the split nodes and the CNN parameters by Back-propagation;(2) Fixing the split nodes and optimizing the leaf nodes by Variational Bounding. Two Deterministic Annealing processes are introduced into the learning of the split and leaf nodes, respectively, to avoid poor local optima and obtain better estimates of tree parameters free of initial values. Experimental results show that DLDLF and DRF achieve state-of-the-art performance on three age estimation datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Bad Data Identification in Power System Based on Improved GSA Algorithm

Bad Data Identification in Power System Based on Improved GS...

引用

IEEE International Conference on computer and Communications

作者： Ce Liu Yinghua Han Kunkun Bao Jinku an Wang Qiang Zhao Northeastern University School of Computer and Communication Engineering Shenyang China Northeastern University at Qinhuangdao School of Computer and Communication Engineering Qinhuangdao China Northeastern University School of Control Engineering Shenyang China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao China

ISBN: (纸本)9781538683408

Based on the analysis of the improved GSA (gap statistical algorithm) data mining technology, it is applied to the identification of bad data in the power system. Aiming at the shortcomings of this method, a new data preprocessing method and clustering method are proposed. The improved GSA method is a technique to determine the most appropriate number of clusters. The original measurement data is tested by the improved RBF neural network, and the obtained results are clustered by the improved clustering method, and then the optimal number of clusters is judged by using the elbow criterion. Compared to the original algorithm, it can determine the number of clusters faster, greatly reduce the running time, and successfully identify the bad data in the power system.

关键词： Neural networks Clustering algorithms Power systems Dispersion Elbow Clustering methods Optimization

来源：评论

学校读者我要写书评

暂无评论

Two-timescale voltage control in distribution grids using deep reinforcement learning

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

arXiv

引用

arXiv 2020年

作者： Li, Jiashuo Zhou, Sili Wei, Wendong Qi, Jianchuan Li, Yumeng Chen, Bin Zhang, Ning Guan, Dabo Qian, Haoqi Wu, Xiaohui Miao, Jiawen Chen, Long Liang, Sai Feng, Kuishuang Institute of Blue and Green Development Shandong University Weihai264209 State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China School of International and Public Affairs Shanghai Jiao Tong University Shanghai200030 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China Laboratory of Anthropogenic Systems Ecology College of Engineering Peking University Beijing100871 China Department of Earth System Science Tsinghua University Beijing100871 China Institute for Global Public Policy Fudan University Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+. Copyright © 2020, The Authors. All rights reserved.

关键词： Fossil fuel power plants

来源：评论

学校读者我要写书评

暂无评论

Smart Meter Data Analytics for Distribution Network

Smart Meter Data Analytics for Distribution Network

引用

第37届中国控制会议

作者： Guojing Tang Yinghua Han Jinkuan Wang Qiang Zhao School of Computer and Communication Engineering Northeastern University School of Computer and Communication Engineering Northeastern University at Qinhuangdao School of Information Science and Engineering Northeastern University School of Control Engineering Northeastern University at Qinhuangdao

With the rapid development of data analysis technology, the implementation of smart grid construction, the mass installation and use of smart sensing devices, the combination of data analysis technology and smart grid is an inevitable trend. The power data will bring great benefits to power companies. Among them, the topology estimation of distribution network is the cornerstone of smart grid data analysis technology. This paper firstly introduces the voltage deviation analysis of the users on the distribution network, and uses the relationship to conduct a series of topology analysis of the distribution network, such as topology verification, phase identification and topology estimation. The data used in this paper is the actual electricity data of a city, and the result of topology verification is validated through actual investigation.

关键词： Data analysis Topology verification Phase identification Topology estimation

来源：评论

学校读者我要写书评

暂无评论

A statistical learning approach to reactive power control in distribution systems

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Observation of nonreciprocal magneto-optical scattering in nonencapsulated few-layered CrI3

arXiv

引用

arXiv 2020年

作者： Guo, Kai Liu, Zhen Hu, Guangwei Shi, Zhongtai Li, Yue Zhang, Linbo Chen, Haiyan Zhang, Li Zhou, Peiheng Lu, Haipeng Lin, Miao-Ling Liu, Sizhao Cheng, Yingchun Liu, Xue Lu Xie, Jianliang Bi, Lei Tan, Ping-Heng Deng, Long jiang Qiu, Cheng-Wei Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing211816 China State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences P. O. Box 912 Beijing100083 China

"Magneto-optical" effect refers to a rotation of polarization plane, which has been widely studied in traditional ferromagnetic metal and insulator films and scarcely in two-dimensional layered materials. Here we uncover a new nonreciprocal magneto-inelastic light scattering effect in ferromagnetic few-layer CrI3. We observed a rotation of the polarization plane of inelastic light scattering between -20o and +60o that are tunable by an out-of-plane magnetic field from -2.5 to 2.5 T. It is experimentally observed that the degree of polarization can be magnetically manipulated between -20% and 85%. This work raises a new magneto-optical phenomenon and could create opportunities of applying 2D ferromagnetic materials in Raman lasing, topological photonics, and magneto-optical modulator for information transport and storage. © 2020, CC BY-NC-SA.

关键词： Ferromagnetic materials

来源：评论

学校读者我要写书评

暂无评论

Wireless Service Pricing Competition under Network Effect, Congestion Effect, and Bounded Rationality

Wireless Service Pricing Competition under Network Effect, C...

引用

作者： Zhang, Mengyuan Yang, Lei Gong, Xiaowen He, Shibo Zhang, Junshan State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Mobile social networks have greatly strengthened people's online social interactions, generating massive volume of mobile data traffic, and bringing remarkable revenues to the wireless service providers. Meanwhile, the potential revenue growth is restrained by the network capacity of physical communication infrastructure and also challenged by the competition among service providers. In this paper, we study the pricing strategies of multiple service providers in a competitive data service market, where mobile users' data consumption behaviors are influenced by two effects: the positive network effect and the congestion effect. To analyze the strategic interactions between mobile users and service providers, we devise a two-stage Stackelberg game consisting a pricing game in Stage I and a usage game in Stage II, respectively. In particular, for the usage game, we characterize the equilibrium solution and establish its uniqueness. For the pricing game, our analysis indicates that a mixed-strategy equilibrium solution is guaranteed for the scenario with rational providers as well as the scenario with providers of bounded rationality. We further develop a distributed learning algorithm for finding a mixed-strategy equilibrium solution in the second scenario. Our numerical results provide insights into how positive the network effect and the congestion effect would impact the system performance, and demonstrate that the bounded rational behavior incurs degradation to service providers' revenues. © 1967-2012 IEEE.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Fast online reinforcement learning control using state-space dimensionality reduction

arXiv

引用

arXiv 2019年

作者： Sadamoto, Tomonori Chakrabortty, Aranya Imura, Jun-Ichi Department of Mechanical and Intelligent Systems Engineering Graduate School of Informatics and Engineering University of Electro Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Department of Systems and Control Engineering Graduate School of Engineering Tokyo Institute of Technology Oh-Okayama Meguro Tokyo2-12-1 Japan

Reinforcement Learning (RL) is an effective way of designing model-free linear quadratic regulator (LQR) controller for linear time-invariant (LTI) networks with unknown state-space models. However, when the network size is large, conventional RL can result in unacceptably long learning time. In this paper we resolve this problem by developing an alternative approach for RL-based LQR that combines dimensionality reduction with RL theory. The approach is to construct a compressed state vector by projecting the measured state through a projective matrix. This matrix is constructed from the state measurements, and can be viewed as an empirical controllability gramian that captures the level of redundancy in the controllability of the open-loop network model. Next, a RL-controller is learned using the reduced-dimensional state instead of the original state such that the resultant cost is close to the optimal LQR cost. Numerical benefits as well as the cyber-physical implementation benefits of the proposed approach are verified using illustrative examples including an example of wide-area control of the IEEE benchmark power system. Copyright © 2019, The Authors. All rights reserved.

关键词： controllers

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：