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Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A fusion RUL prediction approach based on Deep Belief Network (DBN) and Relevance Vector Machine (RVM) is proposed in this paper. In the fusion prediction approach, DBN...
In this paper a new vulnerability detecting method is proposed to detect buffer boundary violations. The main idea is to use the metric of array index manipulation rather than using any heuristic method. We employ a SVM-based classifier to classify the vulnerable functions and innocent functions. Then the vulnerable functions are fed to function call graph guided symbolic execution to precisely determine...
In this paper, the performance of Convolution Neural Network (CNN) in image recognition and emotion recognition in speech will be compared and presented. Feature extraction and selection in pattern recognition is an important issue and have been frequently discussed. Moreover, two-dimensional signals such as image and voice are hard to be modelled well by traditional models like SVM. The ability of...
OCSVM (one-class support vector machine) is a variant of SVM which only use positive class sample set in training. Since only positive samples can be used in OCSVM, Fully exploiting and using the features of the training samples is of great significance to improve its classification performance. Thus, two aspects of study on kernels have been done in this paper: first, we propose a kernel constructing...
In contrast to tags with no inter-relation, web users are more familiar with concept systems with hierarchies, which could also be leveraged in various information retrieval applications. The construction of tag hierarchies relies on the analysis of tag semantic characteristics, while such characteristics are evidenced by semantic case objects which are co-related to tags. Based on such an observation,...
Support vector regression optimized by genetic algorithm (G-SVR) is proposed to forecast tourism demand. Genetic algorithm (GA) is used to search for SVR's optimal parameters, and adopt the optimal parameters to construct the SVR models. This study examines the feasibility of SVR in tourism demand forecasting by comparing it with back-propagation neural networks (BPNN).The experimental results indicate...
Forecasting the tax gross exactly is significant to carry on the macroscopic regulation efficiently under the market economy. Conventional linear macroscopic economic model is very difficult to hold non-linear phenomena in economic system, thus the tax forecasting error will increase. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small...
Forecasting agriculture water consumption is significant to optimize confiration of water resources. In the paper, we have combined particle swarm optimization (PSO) and support vector machines (SVM) for agriculture water consumption forecasting. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. Thus, PSO is very suitable to determine training...
This article utilizes near infrared reflectance spectroscopy (NIRS) technology to quantificationally analyze protein content of chocolate, using genetic support vector regression (GSVR) to build spectrum calibration model. GSVR first adopts genetic algorithm to select the efficiency wavelength regions, and then applies linear support vector regression (SVR) to establish a calibration model with the...
Due to the information of test data is incomplete and deviated in the power transformer fault diagnosis, and the Bayesian network can deal with uncertainty well. The article discusses the NB (naive Bayesian classifier), SB (selective Bayesian classifier), TAN (tree augmented naive Bayesian), BAN (BN augmented naive Bayesian classifier) and GBN (general Bayesian network), the five Bayesian classifier...
In this paper we present the use of non-contact near infrared spectroscopy (NIRS) technology employing a diffuse reflection fiber optic probe for discrimination of chocolate varieties. 120 samples of 8 typical varieties of chocolate are selected randomly, and the samples are scanned in diffuse reflectance mode by a cooled InGaAs array spectrometer (950-1700 nm). Partial least squares (PLS) method...
In this paper, we propose a new method of designing and constructing ldquogoodrdquo mappings defined by kernel functions for classification task, called Optimal Successive Mappings (OSM). Kernel methods, such as Support Vector Machines (SVM), could not provide satisfactory classification accuracy on some complicated data sets, which are still not linearly separable in feature space. It means kernels...
Blind steganalysis detects hidden message without any knowledge about steganographic method. To implement blind steganalysis, statistical model based on high-order wavelet decomposition is built to capture statistical difference between cover images and stego images. However, not all wavelet statistics are able to reflect well statistical changes due to hidden message embedded. Analysis of variance...
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