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18th UK Workshop on Computational Intelligence (UKCI)

作者： Jamil, Waqas Bouchachia, Abdelhamid Bournemouth Univ Machine Intelligence Grp Poole Dorset England

ISBN: (纸本)9783319979823;9783319979816

This paper discusses the problem of selecting model parameters in time series forecasting using aggregation. It proposes a new algorithm that relies on the paradigm of prediction with expert advice, where online and offline autoregressive models are regarded as experts. The desired goal of the proposed aggregation-based algorithm is to perform not worse than the best expert in the hindsight. The theoretical analysis shows that the algorithm has a guarantee that holds for any data sequence. Moreover, the empirical evaluation shows that the algorithm outperforms other popular model selection criteria such as Akaike and Bayesian information criteria on cyclic behaving time series.

关键词： Model selection Online learning aggregation algorithm Time series

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Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination

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SOLAR ENERGY 2018年 166卷 226-241页

作者： Raza, Muhammad Qamar Mithulananthan, N. Summerfield, Alex Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Univ Queensland Sch Architecture Brisbane Qld 4072 Australia

An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.

关键词： Neural network ensemble aggregation algorithm Bayesian model averaging (BMA) Wavelet transform (WT) Feedforward neural network (FNN) Elman backpropagation network (ELM) Cascade-forward backpropagation network (NewCF) One day ahead forecast

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A Movement Control Strategy Based on virtual repulsion and aggregation algorithm in multi-tier mobile sensor network

A Movement Control Strategy Based on virtual repulsion and a...

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8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

作者： Gong, Jianliang Yu, Min Zou, Chengwu Li, Qiannan Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang Peoples R China

ISBN: (纸本)9781612846835;9781612846828

Wireless Sensor Network (WSN) is the hot research topic. In order to adapt to the future development direction of WSN, this paper presented a movement control strategy based on virtual repulsion in Multi-tier Mobile Sensor Network. The strategy first used a hierarchical node aggregation algorithm to divide all sensor nodes into many communication clusters in the monitoring area. It combines virtual repulsive force to distribute nodes uniformity in every cluster. Finally, using aggregation algorithm again based on the nodes uniform distribution to design fusion nodes' optimal movement route for data collection. The simulation results show that the strategy can ensure the efficiency of network data collection, reduce data loss rate, and nodes power consumption.

关键词： mobile sensor network movement control virtual repulaio force aggregation algorithm

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Examining performance of aggregation algorithms for neural network-based electricity demand forecasting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 64卷 1098-1105页

作者： Hassan, Saima Khosravi, Abbas Jaafar, Jafreezal Univ Teknol PETRONAS Dept Comp & Informat Sci Tronoh 31750 Perak Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia Kohat Univ Sci & Technol Inst Informat Technol Kohat KPK Pakistan

The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Neural network ensemble aggregation algorithm Forecasting Electricity load demand

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A Novel Wireless Sensor Network Frame for Urban Transportation

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IEEE INTERNET OF THINGS JOURNAL 2015年第6期2卷 586-595页

作者： Hu, Xiaoya Yang, Liuqing Xiong, Wei Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.

关键词： aggregation algorithm application scenarios intelligent transportation systems (ITSs) protocol framework wireless sensor network (WSN)

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Marked Point Process Model for Curvilinear Structures Extraction 10

Marked Point Process Model for Curvilinear Structures Extrac...

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10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)

作者： Jeong, Seong-Gyun Tarabalka, Yuliya Zerubia, Josiane Inria Ayin Team F-06902 Sophia Antipolis France

ISBN: (纸本)9783319146126;9783319146119

In this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets.

关键词： curvilinear structure extraction marked point process Monte Carlo sampling with delayed rejection aggregation algorithm

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Web Based Spatio -Temporal data Modeling and Application Development for Solar Power Plant Siting using GIS for India

Web Based Spatio -Temporal data Modeling and Application Dev...

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Conference on IT in Business, Industry and Government (CSIBIG)

作者： Surve, B. C. Mukesh Patel Sch Technol Management & Engn Shirpur India

ISBN: (纸本)9781479930630;9781479930647

Renewable energy is the answer to energy crises which can bring rays of hopes for a country which is bless by nature with abundant natural resources. Solar energy is one of them. There is a need of accurate, reliable and easily accessible solar GIS. This paper works on sitting algorithm for Solar power generation;also explore Spatio-Temporal data modeling and method to develop web based application for Solar Power plant siting in India using GIS. Application incorporate Open Geo suit with Postgres Spatial database as back-end and Jave as front-end as well as Web Map services from Google Map.

关键词： Concentrated solar plants (CSPs) Photo voltaic (PV) cells Sitting criteria aggregation algorithm

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Preconditioning Based on aggregation with Inexact Solvers

Preconditioning Based on Aggregation with Inexact Solvers

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7th International Conference on Engineering Computational Technology

作者： Kruis, J. Mayer, P. Czech Tech Univ Fac Civil Engn Dept Mech Prague Czech Republic Czech Tech Univ Fac Civil Engn Dept Math Prague Czech Republic

ISBN: (纸本)9781905088416

Preconditioned iterative methods represent very efficient tool for solution of large systems of algebraic equations obtained after discretization of real world engineering problems by a numerical method. This contribution is devoted to the conjugate gradient method preconditioned by the aggregation algorithm of Schwarz type equipped with inexact solver. The method is used especially for solution of second and forth order problems of mechanics or heat transfer. The solution is based on the finite element method. The key idea is in approximation of the inverse matrix by the sum of inverse matrices of overlapping aggregates. Solution of local problems on particular aggregates based on a matrix factorization is replaced by a suitable iterative method.

关键词： inexact solver aggregation algorithm BOSS method preconditioning

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Top-K results for E-commerce Web Database

Top-K results for E-commerce Web Database

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Fangjiao Jiang Yukun Li Nan Yang Institute of Intelligence Information Processing Xuzhou Normal University Xuzhou China School of Information Renmin University Beijing China

With the size of the E-commerce Web database rapidly growing, a common problem arises that there are too many query results returned for a user query. In this paper, we propose a Top-k results approach which ranks the returned tuples taking attribute general weight, user preference and value desirableness into consideration and select the Top-k tuples based on the aggregation scores. Moreover, we develop early termination techniques based on aggregation algorithms in our approach. Experimental results show that our approach can effectively and efficiently select the Top-k results.

关键词： E-commerce Web database attribute weight aggregation algorithm

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时间序列数据流上历史数据的聚集算法

时间序列数据流上历史数据的聚集算法

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第二十届全国数据库学术会议

作者：张冬冬李建中王伟平郭龙江哈尔滨工业大学计算机科学与技术学院黑龙江大学计算机科学与技术学院

1问题的提出近年来,数据流数据在应用系统中频繁地出现,它不同于传统的存储在关系表中相对静止的数据,而是以一种流的形式源源不断地进入应用系统。典型的数据流数据包括股票交易所的股票价格信息数据,网络与道路交通监测系统的监测信... 详细信息

1问题的提出近年来,数据流数据在应用系统中频繁地出现,它不同于传统的存储在关系表中相对静止的数据,而是以一种流的形式源源不断地进入应用系统。典型的数据流数据包括股票交易所的股票价格信息数据,网络与道路交通监测系统的监测信息数据,环境

关键词： Data stream Historical data aggregation algorithm HDS-tree

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