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Fuzzy integral is an aggregation tool for classification, which is used to improve the accuracy and robustness of the fusion of multiple systems. Multi-classifier fusion, based on Fuzzy Integrals measure system, will have a great impact on the performance of the fusion system. If well defined, the fuzzy measures could markedly improve the classification accuracy; conversely, it may even result in...
Remaining useful life (RUL) estimation is an important part in modern prognostic and health management (PHM) approach. In this paper, a particular prognostic named similarity-based RUL estimation is proposed. In some complex condition, typical methods will perform inefficient, and in engineering application background, some traditional methods may be hard implemented. On the other hand, similarity-based...
Short-term load forecast has a significant impact on the reliable and economic operation of power supplying system. Requirement for power system reserve and AGC capacity demand all relate to forecasting error. Due to lack of credibility assessment methods and tools for forecasting error, dispatching schedule must depend on the worst situation or experiences to conservative estimate the reserve capacity...
Phonocardiogram (PCG) is able to reflect the activities of the heart valve. The analysis of PCG has clinical importance in the diagnosis of heart disease. In this paper, the Wavelet Transform is used to extract the envelope of PCG involving normal and abnormal ones; the envelope is used to achieve the accurate position of S1 and S2. Support Vector Machines (SVM) is also used to calculate two eigen...
High-resolution remote sensing images can capture detailed geometrical and shape properties. Traditional classification accuracy assessments with overall accuracy or kappa coefficient based on pixels, cannot exhibit the geometrical properties of the objects that are present on the ground. Evaluation of object oriented classified maps based on geometrical and border information can provide more accurate...
Large scale forest mapping and change detection plays a significant role in the study of global change, particularly in the research of carbon source and sink. This paper presents results from forest/non-forest classification using ENVISAT-ASAR data. Both pixel-based and object-based classification method were developed for ASAR HH/HV images acquired on a single date. For the object-based classification,...
An adaptive dimensionality reduction method to conduct classification of hyper-spectral imagery using optimal segmentation of spectral signature is proposed. The method partitions the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error. To automatically obtain the best number of the segments, a quantitative indictor based...
Under the assumption of single bounce channel model, the position and velocity of a mobile station (MS) can be determined by time of arrival (TOA), angle of arrival (AOA) and doppler-shifted frequency (DSF) measurements at three base stations (BSs) when line of sight (LOS) paths between the three BSs and the MS are all blocked. The equations relating the measured TOAs, AOAs and DSFs to the location...
Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification...
Current approaches for generating wrappers for web page extraction suffer from the requirement of huge amount of labeled training pages to obtain satisfying results. On the other hand, the quality of data extracted by fully automatic methods is not reliable. In this paper, we propose a novel method to facilitate wrapper generation by combining wrapper induction and page analysis approaches. In addition...
MCS (minimal consistent set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative...
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