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Research on a cloud image forecasting approach for solar power forecasting

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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Modelling and performance analysis of IEEE 802.11e EDCA 30

Modelling and performance analysis of IEEE 802.11e EDCA

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30th European Simulation and Modelling Conference, ESM 2016

作者： Hu, Xiang Jiao, Li State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789077381953

IEEE 802.1 le can provide quality of service guarantees for different kinds of traffic such as voice and video. The access categories are defined with different priority levels and the contention-free bursting mechanism is introduced to improve the transmission efficiency. In this paper, a coloured Petri net model is built for the protocol, capturing all its crucial aspects. Some techniques can be used to facilitate the modeling process and reduce the model size, including hierarchical and modularized modeling, folding, modeling patterns, etc. The model can be simulated to compute some metrics for performance analysis. The accuracy of the model is validated through comparisons with, the simulator NS-2.

关键词： Petri nets

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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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Image quality assessment guided deep neural networks training

arXiv

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arXiv 2017年

作者： Chen, Zhuo Lin, Weisi Wang, Shiqi Xu, Long Li, Leida Interdisciplinary Graduate School Nanyang Technological University School of Computer Science and Engineering Nanyang Technological University Department of Computer Science City University of Hong Kong Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences School of Information and Control Engineering China University of Mining and Technology

For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i.e., artifact-free). However, digital images are subject to a wide range of distortions in real application scenarios, while the practical issues regarding image quality in high level visual information understanding have been largely ignored. In this paper, in view of the fact that most widely deployed deep learning models are susceptible to various image distortions, the distorted images are involved for data augmentation in the deep neural network training process to learn a reliable model for practical applications. In particular, an image quality assessment based label smoothing method, which aims at regularizing the label distribution of training images, is further proposed to tune the objective functions in learning the neural network. Experimental results show that the proposed method is effective in dealing with both low and high quality images in the typical image classification task. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

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Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

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Corrections to “Multi-Sensor Kalman Filtering With Intermittent Measurements” [Mar 18 797-804]

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IEEE Transactions on Automatic control 2018年第5期63卷 1545-1545页

作者： Chao Yang Jianying Zheng Xiaoqiang Ren Wen Yang Hongbo Shi Ling Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes Department of Automation East China University of Science and Technology Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

In the above technical note [ibid., vol. 63, no. 3, pp. 797–804, Mar. 2018], one of the author's names in the byline was misspelled and should have read as follows. Chao Yang, Jianying Zheng, Xiaoqiang Ren, Wen Y... 详细信息

关键词： Kalman filters Stochastic processes Sensor fusion Estimation error

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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Adaptive Islanding Detection and Diagnosis Using Wide Area Monitoring

Adaptive Islanding Detection and Diagnosis Using Wide Area M...

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IEEE PES Innovative Smart Grid Technologies Conference Europe

作者： Mark Rafferty Xueqin (Amy) Liu David Laverty Lei Xie Sean McLoone School of Electronics Electrical Engineering and Computer Science Queen's University Belfast State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou PR China

This paper proposes a method for the detection and diagnosis of power system islanding events using phase-angle monitoring. The method is based on multiblock principal component analysis (MB-PCA), an extension of PCA that allows the underlying relationship between data sets to be identified. MB-PCA divides the large-scale system data into several blocks thus enhancing the model's ability to explain and diagnose power system events. Also incorporated into the method is a moving window technique. This allows the method to update over time enhancing power system situational awareness, by using data that best represents the current condition of the time-varying power system. Using Hotelling's T~2 and Q statistics allows the method to detect specific power system events with the main emphasis placed on the detection of power system islands. Further diagnostic approaches, such as contribution plots, are used to locate and isolate the detected events. The reliability of the proposed method is demonstrated using simulated case studies showing the method's ability to detect a genuine islanding event and its avoidance of nuisance tripping.

关键词： Islanding detection and diagnosis Wide Area Monitoring Multiblock principal component analysis Moving window

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Localization of pallets in warehouses using passive RFID system

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Journal of Central South University 2015年第8期22卷 3017-3025页

作者：周俊儒张宏建周洪亮 State Key Laboratory of Industrial Control Technology Department of Control Science and EngineeringZhejiang University

Warehouse operation has become a critical activity in supply chain. Position information of pallets is important in warehouse management which can enhance the efficiency of pallets picking and sortation. Radio frequency identification(RFID) has been widely used in warehouse for item identifying. Meanwhile, RFID technology also has great potential for pallets localization which is underutilized in warehouse management. RFID-based checking-in and inventory systems have been applied in warehouse management by many enterprises. Localization approach is studied, which is compatible with existing RFID checking-in and inventory systems. A novel RFID localization approach is proposed for pallets checking-in. Phase variation of nearby tags was utilized to estimate the position of added pallets. A novel inventory localization approach combing angle of arrival(AOA) measurement and received signal strength(RSS) is also proposed for pallets inventory. Experiments were carried out using standard UHF passive RFID system. Experimental results show an acceptable localization accuracy which can satisfy the requirement of warehouse management.

关键词： warehouse management radio frequency identification （RFID） localization tag phase

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on Big Data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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