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Based on rod-plane electrodes in oil-paper insulation, the experiments for developing characteristics of partial discharge (PD) were performed using constant voltage method. Four statistical spectra and 29 characteristic values were extracted on the basis of the phase-resolved partial discharge (PRPD) pattern. Then 8 new parameters were extracted from the 29 characteristic values by using kernel principal...
Human iris can be used for detecting organ disorders based on iridology science. Nowadays, iridology diagnosis can be done automatically by computer using artificial intelligence approach. This research focused on cardiac diagnosis based on left iris map on clockwise direction around 2:00 to 3:00. The Principal Component Analysis (PCA) is used for feature extraction while the Support Vector Machine...
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features...
In this paper we propose a video aesthetic quality assessment method that combines the representation of each video according to a set of photographic and cinematographic rules, with the use of a learning method that takes the video representation's uncertainty into consideration. Specifically, our method exploits the information derived from both low- and high-level analysis of video layout, leading...
Signature verification is an important part of digital forensics. In order to solve the shortcomings of manual identification in technical accuracy and subjectivity, this paper proposed an off-line signature identification method based on Support Vector Machine (SVM). A powerful feature set is collected by extracting grid features and global features of a signature picture. The method is applied for...
Dynamic ranking learning problem is considered when the training sample is a data stream, consisting of a sequence of a series of objects characterized by a set of features and relative ranks within each series. The problem is reduced to preference learning to rank on clusters in the feature space of ranked objects, while aggregated training dataset is formed from the centers of clusters and estimates...
In order to make transformer potential fault diagnose effectively, Support Vector Machine (SVM) is introduced as an effective algorithm. Firstly, the above SVM algorithm is formed by four common kernel functions: linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function, Secondly, Differential Evolution Algorithm (DEA) based on new fitness function is introduced...
Software logging is an essential programming practice that saves important runtime information that can be used later by software developers for troubleshooting, debugging and monitoring the software. Even though software logging has numerous benefits this practice is underutilized because of lack of any formal guiding principles to developers for making strategic and efficient logging decisions....
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
Tree species identification is a crucial matter in forest managing and supervision. In recent years, tree species identification has been studying a lot using high-resolution satellite imagery data of 0.5m spatial resolution Worldview-3. This has because of some of the tree species have very high market value type of wood. Besides that, Tropical forest stored a large stock of carbon that contributes...
The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel...
Many existing issues pertaining to power sector such as-demand response management, theft detection, outage management etc. can be solved efficiently with grid modernization. Out of these, demand response is one such issue which affects the overall grid stability. One way of managing demand response is to balance the load in smart grid (SG). In this paper, a novel scheme for handling the demand response...
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and...
A classifier based on the Least Square Support Vector Machine (LS-SVM) with Fruit Fly Optimization Algorithm (FOA) for polarimetirc Synthetic Aperture Radar (SAR) image classification is proposed in this paper. This method uses pixel-based information and region-based information as the features of land cover. The former one comes from the integration of multiple polarimetric parameters obtained by...
To address the multi-classification problems of hyperspectral dataset, a new method with weighted kernel function based on Chernoff distance is proposed. Chernoff distance utilizes the information between categories and strengthens the separability of original dataset. The adjustable parameter in Chernoff distance can fit the hyperspectral dataset well compared with other least upper bounds. Pairwise...
Near infrared spectroscopy with support vector machine (NIR-SVM) to predict the crude protein (CP) in Alfalfa samples. The R2 of the predicted CP versus the experimental CP of the training data set is 0.983. The R2 of the independent test Data Set is 0.9823. The result suggested that it is feasible to rapidly determine the CP of Alfalfa by NIR data based on SVR.
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
Handwritten character recognition systems suffers from different training and testing sets distributions. In this paper, we propose a two-step domain adaptive multiple kernel learning algorithm, which learns a kernel function based on several kernels in the first step, and trains a target classifier by applying the learned kernel in the second step. Our method can be employed both in semi-supervised...
Quality monitoring and prediction plays a key role in improving product quality and achieving automated quality control in manufacturing processes such as the abrasion-resistant material manufacturing process. Traditional methods that rely on the use of first-principle models are difficult to formulate due to the increasing complexity and high dimensionality of manufacturing processes. Data-driven...
This paper studies support vector machine (SVM) for the parametric identification of ship coupled heave and pitch motions with real oceanic conditions. The simulation results are based on a mathematical model of a ship model in the simulated marine environment. By analyzing the identification precision of the equation parameters of coupled heave-pitch motions, this paper presents the proper numbers...
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