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Accurate forecasting and scientific analysis of building electrical load can improve the level of building energy management to meet the requirements of energy saving. To further strengthen the forecasting accuracy, this study presents a hybrid model for building electrical load forecasting. The proposed method combines the fuzzy inference system and the periodicity knowledge together to generate accurate forecasting results. In this method, in order to better reflect the actual characteristic of the electrical load, the wavelet transform method is firstly utilized to filter the original building electrical load data. Then, the daily periodic pattern is extracted from such filtered electrical load data, and the residual data are obtained through removing the daily periodic pattern. Further, the residual data-driven forecasting model is constructed by the functionally weighted single-input-rule-modules connected fuzzy inference system (FWSIRM-FIS). This FWSIRM-FIS model is used to provide the compensation to the periodic component. In other words, the daily periodic component and the residual forecasting are combined to achieve the final forecasting result. Specifically, in order to assure the forecasting performance of the FWSIRM-FIS model, the subtraction clustering method is employed to construct the SIRMs while the least square estimation is utilized to optimize the parameters in the functional weights of the FWSIRM-FIS. Finally, in this paper, two real-world experiments are made and detailed comparisons with four traditional models are given. Experimental and comparison results demonstrate that the proposed hybrid model has the smallest forecasting errors and can achieve the best performance. [ABSTRACT FROM AUTHOR]

Abstract In this work, we investigate the pilot contamination problem in multi-cell massive multi-input multi-output (MIMO) systems. The conventional approaches to address pilot contamination for multi-cell MIMO systems impose orthogonal pilots constraint across multiple users and cells, which leads to bandwidth inefficiency under large number of active users. We propose an uplink (UL) transmission protocol in which the pilot segments of users partially overlap across cells and even overlap with data from other cells, which provides more freedom in system designing. We also apply semi-blind algorithms at base station (BS) in order to separate the data streams of each user. Our analysis shows that semi-blind receiver removes pilot contamination effectively under large UL data length. The simulation results demonstrate that the performance of the proposed protocol with semi-blind receiver employing Constant Modulus Algorithm (CMA) is superior as compared to the existing schemes. [ABSTRACT FROM AUTHOR]

Forecasting fuzzy time series (FTS) methods are generally divided into two categories, one is based on intervals of universal set and the other is based on clustering algorithms. Since there are some challenging problems with the interval based algorithms such as the ideal interval length, clustering based FTS algorithms are preferred. Fuzzy Logical Relationships (FLRs) are usually used to establish relationships between input and output data in both interval based and clustering based FTS algorithms. Modeling complicated systems demands high number of FLRs that incurs high runtime to train FTS algorithms. In this study, a fast and efficient clustering based fuzzy time series algorithm (FEFTS) is introduced to handle the regression, and classification problems. Superiority of FEFTS algorithm over other FTS algorithms in terms of runtime and training and testing errors is confirmed by applying the algorithm to various benchmark datasets available on the web. It is shown that FEFTS reduces testing RMSE for regression data up to 40% with the least runtime. Also, FEFTS with the same accuracy as compared to Fuzzy-Firefly classification method, diminishes runtime moderately from 324.33 s to 0.0055 s. [ABSTRACT FROM AUTHOR]

A new generalization of the Lindley distribution is recently proposed by Ghitany et al. [1] , called as the power Lindley distribution. Another generalization of the Lindley distribution was introduced by Nadarajah et al. [2] , named as the generalized Lindley distribution. This paper proposes a more generalization of the Lindley distribution which generalizes the two. We refer to this new generalization as the exponentiated power Lindley distribution. The new distribution is important since it contains as special sub-models some widely well-known distributions in addition to the above two models, such as the Lindley distribution among many others. It also provides more flexibility to analyze complex real data sets. We study some statistical properties for the new distribution. We discuss maximum likelihood estimation of the distribution parameters. Least square estimation is used to evaluate the parameters. Three algorithms are proposed for generating random data from the proposed distribution. An application of the model to a real data set is analyzed using the new distribution, which shows that the exponentiated power Lindley distribution can be used quite effectively in analyzing real lifetime data. [ABSTRACT FROM AUTHOR]

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises. [ABSTRACT FROM AUTHOR]

In recent years, statisticians have grown increasingly engaged in research of mixture models, particularly in the previous decade, without adequate consideration of challenge of estimating the parameters of mixture models from a frequentist perspective. Except for maximum likelihood estimation, this study addresses this vacuum by discussing the two other classical methods of estimation for mixture model. We commence by briefly describing the three frequentist approaches, namely maximum likelihood, ordinary, and weighted least squares, and then comparing them through extensive numerical simulations. The model's applicability is illustrated by its application to simulated and real-world data, which yields promising results in terms of enhanced estimation. • Frequentist approaches, namely MLE, ordinary, and WLSE for mixture of exponential distributions are considered. • The existence of MLEs is shown. • Different methods of estimations are compared through extensive numerical simulations. • The log-likelihood function has global maximum roots. [ABSTRACT FROM AUTHOR]

Abstract: under a set of assumptions, Maximum likelihood estimation method is analyzed and simulated to make wireless sensor network''s localization more accurate. The result shows that, the range error between the anchor node (we call it the reference node) and the unknown node contained in the nth equation (we call it the reference equation) and the position of the reference node has significant impact on the node localization. The result of the simulation shows that if the range error contained in the reference equation can be reduced more, and the position of the reference node is appropriate, the localization error will be smaller. [Copyright &y& Elsevier]

Abstract: This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets. [Copyright &y& Elsevier]