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Hyperspectral imaging portrays materials through numerous and contiguous spectral bands. It is an application in various fields, including astronomy, medicine, food safety, forensics, and target detection. However, hyperspectral images include redundant measurements, and most classification studies in the hyperspectral image literature encountered the Hughes phenomenon. Generalized discriminant analysis...
The integrators are facing the problems of so many decision attributes and few data samples for decision-making analysis when evaluating the partners of collaboration and innovation in complex products and systems(CoPS). Firstly, this paper created a trust evaluation model of collaborative partners in CoPS. Secondly, followed by the application of RS attribute reduction as a data pre-processing removes...
Wind speed forecasting is very important to the utilization of wind energy in wind farm. In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and wavelet decomposition combine with least square support vector machine (LSSVM) is proposed in this paper. The wind speed time series was decomposed into several intrinsic mode functions (IMF) and the...
Wind speed and output power forecasting is very important to the utilization of wind energy. In order to improve the forecast precision, a forecasting method based on wavelet transform (WT) and least square support vector machine (LSSVM) is proposed in this paper. The wind speed time series was decomposed into different frequency components. The different LSSVM models to forecast the high frequency...
Wind speed is a kind of non-stationary time series, it is difficult to construct the model for accurate forecast. The way improving accuracy of the model for predicting wind speed up to one-month ahead has been investigated using measured data recorded by wind farm. A forecasting method based on empirical mode decomposition (EMD) and least square support vector machine (LSSVM) is proposed in this...
Facing the problems of so many decision attributes and few data samples for decision-making analysis when the integrators in complex products and systems evaluate the partners of collaboration and innovation, this paper creates a trust evaluation model of collaborative partners in CoPS based on rough set and (RS) Support Vector Machines (SVM). In this paper, firstly, trust evaluation system about...
In this paper a new method for diagnosing analog circuits fault based support vector machine (SVM) is presented. The fault features are extracted from the frequency domain response of circuit under test (CUT) and the SVM which trained by the fault features is used to recognize and classify the unknown faults. Support vector machine is simple in architecture and strong generalization ability. The experimental...
Screening for abnormalities through endoscopic images faces many challenges. The location, shape and size of the abnormal regions in the image are unknown and vary across images. It is very difficult to determine the appropriate patch-size to use for generally used patch-based approaches. So we propose to use multi-size patches simultaneously to represent the abnormal regions and construct a stacking...
Salicylate-induced rat model is one of the animal models for tinnitus study. In this study, a radial basis function neural network for automatic identification is firstly developed due to its features of easy training and learning. From the experimental results, the recognition rate is demonstrated to be as high as 98%. Not only the recognition rate is improved, but also it is very objective in analysis...
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