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Chinese Control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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Time-Dependent Pricing for On-Demand Bandwidth Slicing in Software Defined Networks

Time-Dependent Pricing for On-Demand Bandwidth Slicing in So...

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International Wireless Communications and Mobile Computing Conference

作者： Bo Gu Junhao Feng Zhenyu Zhou Mohsen Guizani Department of Information and Communications Engineering Kogakuin University Tokyo Japan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing China Electrical and Computer Engineering Department University of Idaho USA

In this paper, we propose a time-dependent pricing (TDP) scheme for bandwidth consumption scheduling of multimedia streaming applications. By decoupling the network control functions from data delivery, software defined network (SDN) enables multimedia streaming users to negotiate their QoS parameters in a on-demand basis. Our key idea is to employ TDP as an incentive mechanism in SDN to motivate users to shift their delay-tolerant traffic demand, and free up resources for delay-sensitive applications in peak-time. Then, a Stackelberg game is formulated to analyze the interactions between the ISP and users. Next, we proposed a greedy algorithm to obtain the optimal congestion price and bandwidth slicing in each time slot based on real-time traffic load as well as users' preferences for delay. Finally, simulation results confirm that the proposed method can significantly flatten out traffic fluctuation.

关键词： TDP Delay sensitivity Utility Congestion management Traffic demand QoS SDN Bandwidth slicing

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Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution systems

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INAE Letters 2017年第3期2卷 107-121页

作者： Mostafa Sedighizadeh Masoud Esmaili Amir Eisapour-Moarref Faculty of Electrical and Computer Engineering Shahid Beheshti University Tehran Iran Department of Electrical Engineering West Tehran Branch Islamic Azad University Tehran Iran Centre of Excellence for Power System Automation and Operation Iran University of Science and Technology Tehran Iran

Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multi-objective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

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Large Scale Integration of EVs into the Grid and its Potential Effects on power system Reliability

Large Scale Integration of EVs into the Grid and its Potenti...

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2017 International electrical and Energy Conference (CIEEC 2017)

作者： Mingzhi Zhang Chunlin Guo Lingfeng Wang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee

A distributed smart charging algorithm is introduced in this paper to coordinate the stochastic charging behaviors of large amounts of electric vehicles(EVs).Each EV has some level of intelligence and communication abilities to make charging decisions to achieve its own maximum ***,the EV aggregators use the smart pricing and sequential price update mechanisms to influence the charging behaviors of EVs.A binary hybrid GSA-PSO algorithm is used to solve each EV's charging optimization *** potential effects of large scale integration of EVs on power system reliability is also *** case studies show this distributed smart charging algorithm is able to effectively transfer the EV charging load from the peak hours to the valley hours,and therefore greatly reduce the system peak ***,it is beneficial to improving the power system's reliability level.

关键词： Electric vehicles grid integration Monte Carlo simulation reliability smart charging

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A modified DTC with capability of regenerative braking energy in BLDC driven electric vehicles using adaptive control theory

arXiv

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arXiv 2017年

作者： Geraee, Shiva Mohammadbagherpoor, Hamed Shafiei, Mehdi Valizadeh, Majid Montazeri, Farshad Feyzi, Mohammad Reza Faculty of Electrical and Computer Engineering University of Tabriz Faculty of Electrical and Computer Engineering North Carolina State University United States Department of Electrical and Electronic Engineering and Computer Science Queensland University of Technology Department of Electrical and Electronic Engineering University of Ilam Center of Excellence for Power System Automation and Operation Electronic Engineering Iran University of Science and Technology

This paper represents a novel regenerative braking approach for the Electric Vehicles. The proposed method solves the short-range problem which is corresponding to the charge of the battery pack. The DTC switching algorithm has been modified to recover the electrical energy from electrical Vehicle (EV), driven by Brushless DC motor, without using the additional power converter or the other electrical energy storage devices. During regenerative braking process, different switching pattern is applied to the inverter to convert the mechanical energy to the electrical energy through the reverse diodes. This switching pattern is different from the normal operation due to the special arrangement of voltage vectors which is considered to convert the mechanical energy to electrical energy. The state of charge of the battery is used as a performance indicator of the method. Simultaneously, a model reference adaptive system has been designed to tune the system's parameters. Several simulations are carried out to validate the performance and effectiveness of the proposed methods. The results show the high capability and performance of the designed method. Copyright © 2017, The Authors. All rights reserved.

关键词： Brushless DC motors

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Waveband Selection Based Feature Extraction Using Genetic Algorithm 4

Waveband Selection Based Feature Extraction Using Genetic Al...

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4th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017

作者： Li, Yujun Liang, Kun Tang, Xiaojun Gai, Keke School of Automation and Information Engineering Xian University of Technology Xian Shaanxi710048 China School of Computer Science and Engineering Xian University of Technology Xian Shaanxi710048 China State Key Laboratory of Electrical Insulation and Power Equipment Xian Jiaotong University Xian Shaanxi710049 China Department of Computer Science Pace University New YorkNY10038 United States

ISBN: (纸本)9781509066438

In order to explain the geological structure accurately and quickly, we analyze the gas mixture gathered from the well by Infrared Spectroscopy Fourier Transform Spectrometer instead of gas chromatograph. In the process of the spectrum analysis, the reduction of the spectrum data dimention is very neccessary to perform. In this paper, we propose a feature extraction method is based on waveband selections using genetic algorithm, which is named FEWSGA. This approach can directly selecte eigenvalues from the limited waveband spectrum data instead of using mathematical transformation, such as the PCA (principal component analysis) and PLS (partial least squares) algorithm. Experiments results show that our method can reduce the spectrum data dimention from 1866 to 317, and the mean relative error (MRE) of the analysis model decrease from 34.68% to 26.59%. Moreover, the feature extraction from the whole waveband spectrum data using GA only reduce the data dimention from 1866 to 937. The MRE of the analysis model only reduces from 34.68% to 32.97%. Our approach has a better performance. © 2017 IEEE.

关键词： Genetic algorithms

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A Baseline Load Estimation Approach for Residential Customer based on Load Pattern Clustering

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Energy Procedia 2017年 142卷 2042-2049页

作者： Kangping Li Bo Wang Zheng Wang Fei Wang Zengqiang Mi Zhao Zhen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Baoding 071003 China State Key Laboratory of Operation and Control of Renewable Energy & Storage System (China Electric Power Research Institute) Haidian District Beijing 100192 China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA

Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.

关键词： Demand response Customer baseline load Load pattern clustering

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Research on a cloud image forecasting approach for solar power forecasting

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Energy Procedia 2017年 142卷 362-368页

作者： Zhao Zhen Zheng Wang Fei Wang Zengqiang Mi Kangping Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Baoding 071003 China State Key Laboratory of Operation and Control of Renewable Energy & Storage System (China Electric Power Research Institute) Haidian District Beijing 100192 China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA

Accurate cloud image forecasting is very necessary for sky image based solar power forecasting. To provide a means that can track the cloud deformation process and then forecast the cloud shape and position in a future sky image, a cloud image forecasting approach is proposed in this paper. Firstly, the cloud pixels in sky images are identified using Otsu’s method. Secondly, a mathematical description method of cloud location, shape, and deformation is suggested, so that the key information of cloud and its deformation process can be extracted from original images and then digitized. Thirdly, genetic algorithm (GA) is applied to optimize the cloud deformation process according to digitized historical cloud information. In the end, the cloud shape and position in a future image is forecasted using linear extrapolation. The feasibility and effectiveness of the proposed method are validated by simulation.

关键词： sky image solar power forecasting optimization cloud image forecasting

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Model-driven deep learning for physical layer communications

arXiv

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arXiv 2018年

作者： He, Hengtao Jin, Shi Wen, Chao-Kai Gao, Feifei Li, Geoffrey Ye Xu, Zongben National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Institute for Information and System Sciences Xian Jiaotong University Xi'an710049 China

Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and demonstrated an impressive performance improvement in recent years. However, most of the existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, modeldriven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Eigenvalue Analysis of Subsynchronous Oscillation in Grid-connected PV power Stations

Eigenvalue Analysis of Subsynchronous Oscillation in Grid-co...

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2017 International electrical and Energy Conference (CIEEC 2017)

作者： Lin Yang Zhao Yu Tao Xu Jingbo He Chao Wang Chengzong Pang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) State Grid Corporation of China Department of Electrical Engineering and Computer Science Wichita State University WSU

Along with the development of modern society,the fossil fuel increasingly exhausted,and living environment and energy problem have become the top challenges in our *** is of great significance to research and make full use of renewable *** development of photovoltaic(PV) technology in recent years strongly indicates that in medium to long term,PV power stations may become more commercially attractive that there will be large-scale implementation in many countries in the *** the increasing capacity and penetration rate of photovoltaic(PV) power stations,subsynchronous oscillation caused by grid-connected PV power stations begin to receive the attention of *** oscillation will have a great influence on the security and stability of power system's *** the basis of overall considering detailed models of important components in grid-connected PV power stations,a whole system small signal model is *** and participation factor analysis are used to study subsynchronous oscillation *** eigenvalues show that a subsynchronous oscillation mode may appear in the power system with PV power *** participation factor analysis shows that state 1 and state 6(voltage outer-loop controller's states) are strongly correlated in subsynchronous oscillation *** effects of parameters in current inter-loop controller and voltage outer-loop controller of PV inverter on synchronous oscillation have been fully *** is found that the subsynchronous oscillation mode is sensitive to the parameters of voltage outer-loop controller.

关键词： photovoltaic power stations subsynchronous oscillation eigenvalue participation factor voltage outer-loop controller

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