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The feature vector is composed of multiple characteristics which can reflect fault information of the rolling bearing. In order to quantify the sensitivity of features for fault diagnosis, the quantitative problem is transformed into the sparse representation problem based on the sparse representation theory. Since the feature vector sparseness is unknown, a sparse dictionary is constructed based...
Currently, minimally invasive surgery (MIS) is applied in the diagnosis and surgery using an endoscope or a catheter for neurosurgery and for endovascular diseases, such as aneurysm, atrial septal defect (ASD), embolization, and cerebral aneurysm. This study proposes virtual-reality (VR) simulator system for double interventional cardiac catheterization (ICC) using fractional order vascular access...
Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction...
Big Data era is characterized by the explosive increase of image files on the Internet, massive image files bring great challenges to storage. It is required not only the storage efficiency of massive image files but also the accuracy and robustness of massive image file management and retrieval. To meet these requirements, distributed image file storage system based on cognition is proposed. According...
By introducing an extra dimension to the inputs, sigmoid function can simulate the behavior of traditional RBF units. This paper introduces a sigmoid based RBF neuron and compares it with traditional RBF neuron. Neural networks composed of these neurons are trained with ErrCor algorithm on two classic experiments. Comparison results are presented to show advantages of the sigmoid based RBF model.
Base on geometric flow of images and the second generation bandelet transform, a new feature extraction method was proposed, and it was used to detect human in still images. In the paper, bandelet coefficients and their statistical values were extracted as the feature of human images, combined with Adaboost classifier to classify and detect human in images. In the proposed algorithm, some general...
As generalization ability of neural network was restricted by overfitting problem in the network’s training. Early stopping algorithm based on fuzzy clustering was put forward to solve this problem in this paper. Subtractive clustering and Fuzzy C-Means clustering (FCM) were combined to realize optimal division of training set, validation set and test set. How to realize this algorithm in backpropagation...
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