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In the distillation process, many important process variables are often difficult to be measured online. For example, the aviation kerosene is an important index of operation quality, but current methods cannot obtain the real-time value of the aviation kerosene efficiently. To solve this problem, a method of selecting the input variable based on partial least squares regression (PLS) is proposed...
In order to effectively detect malware in Android, dynamic analysis techniques with Android emulators are widely adopted. Emulators can be deployed for large-scale malware detection and restored to an ensured clean state in a short period after each app analysis process such that dynamic analysis upon emulators can effectively detect malware. Moreover, emulators significantly reduce the detection...
A critical task in corner detection in 2D images is on the distinction between a corner pixel and a pixel with a large gradient (i.e., an edge pixel). Imbalanced point detection was proposed to address this problem, where a corner pixel is characterized as a pixel with an imbalanced appearance, while an edge pixel has the opposite property. With extensive experiments, an imbalanced point detector...
Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
Learning based hashing is gaining traction in large-scale retrieval systems. It aims to learn compact binary codes that can preserve semantic similarity in the hamming space. This paper presents a supervised topology hashing (SPTH) algorithm to learn compact binary codes that can exploit both the supervisory information as well as the local topology structure of datasets. To build a connection between...
Least squares support vector machine (LS-SVM) has been successfully applied in many classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack of sparseness. Combing the primal least squares twin support vector machine (LS-TSVM) and the sparse LS-SVM with L0-norm minimization, a new sparse least squares support vector regression algorithm with L0-norm in primal space(L...
In recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. The paper proposes short-term traffic forecasting model based on support vector regression, the traffic volumes at preceding periods of time and upstream and downstream are considered as input, traffic volumes at current period of time are considered as output. The...
Due to the exponentially growing bioinformatics databases and rapidly popular of GPU for general purpose computing, it is promising to employ GPU techniques to accelerate the sequence search process. Hmmsearch from HMMER bioinformatics software package is a wildly used software tool for sensitive profile HMM (Hidden Markov Model) searches of biological sequence databases. In this paper, we implement...
Calculating Euclidean distance matrix is a data intensive operation and becomes computationally prohibitive for large datasets. Recent development of Graphics Processing Units (GPUs) has produced superb performance on scientific computing problems using massive parallel processing cores. However, due to the limited size of device memory, many GPU based algorithms have low capability in solving problems...
Support Vector Machine (SVM) is one of the most popular tools for solving general classification and regression problems because of its high predicting accuracy. However, the training phase of nonlinear kernel based SVM algorithm is a computationally expensive task, especially for large datasets. In this paper, we propose an intelligent system to solve large classification problems based on parallel...
In this paper, we propose a method for automatic classifying fragments in computer-aided restoration of ceramic cultural relics, using surface texture clustering. Firstly, center of clustering will be initialized by re-division on multi-variant finite model. Then kernel-based fuzzy clustering algorithm is applied and parameters of difference degree are used to control iterations of clustering. The...
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