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Based on a vivid simulation training environment, substation secondary circuit simulation system can improve the training effect for substation operation. During practical training, some virtual measurement instruments have to be used. It is necessary to discuss the technology implementation of virtual measurement system of substation secondary simulation circuit. The model of virtual digital multimeter...
This paper presents a novel method based on sparse representation classification (SRC) and random dimensionality reduction projection (RDRP) to classify electric power system fault types in real time. Each testing fault sample is firstly represented as an overcomplete sparse linear combination of training fault samples. Then RDRP is applied to extract fault features with reduced dimensionality and...
For many large sample size one-class classification problems, most existing methods fail due to the requirement lengthy execution time and large memory space. To solve these problems, a novel method referred to as Morphology domain description (MDD) is proposed by employing the concepts of Mathematical Morphology. First, the sample space is divided into blocks. Then, training samples are put into...
Based on the requirement of real-time on road vehicle detection from a moving camera, a feature based rear of vehicle detection algorithm is developed. A Haar-like feature classifier is trained to capture the rear of vehicles from the video stream in real-time and a premier test is performed. Aiming at some issues found in the test, which include duplicated detection, false alarms, undetected objects...
Locally weighted learning (LWL), which is an effectual and flexible method for prediction problems, is widely used in many regression scenarios. The training data samples, referring to the history experience knowledge base, are required to help do regression by new queries. However, sometimes, the knowledge base tends to be helpless due to the lake of information, such as inadequate training data...
Internet security is seriously threatened by spam spreading, and content-based spam filtering has become one of effective spam-filtering methods. Aiming at the practical problems, we propose an active learning based method which takes naive Bayesian means as basic classifiers. This method randomly initialize a small training set to generate basic classifiers, and then use them to classify mails, which...
AdaBoost is known as an effective method to improve the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost is always prone to overfitting especially in noisy case. In addition, most current works on Boosting assume that the loss function is fixed and therefore do not take the distinction between noisy case and noise-free case into...
This paper presents a new concept of building classification-type loss for regression sample based on conversion between regression and classification problems used in Support Vector Regression (SVR). By introducing the classification-type loss to calculate example's error, AdaBoost algorithm can be generalized from classification to regression. A new Boosting algorithm for regression, called AdaBoost...
In view of the difficulty of modeling for complex nonlinear system, a novel one step iterative identification algorithm for the linear part of the system is proposed in this paper, based on Taylor series expansion. The effect of sample on the precision of the model was analyzed by utilizing rigorous mathematical theory, and neuro-fuzzy model was used to identify the Taylor remainder and noise. To...
Conditional Random Fields (CRFs) are widely used in machine learning and natural language processing fields. A number of methods have been developed for CRF training. However, even with state-of-the-art algorithms, the training of CRF is still very time and space consuming. This make it infeasible to use CRFs in large-scale data analysis tasks. This paper proposes an efficient algorithm, HOCT, for...
Although an improvement of hierarchical text classification can be achieved by using hierarchical structure information, existing hierarchical text classification methods suffer from two problems: data skew (especially in large-scale hierarchy) and error propagation. In this paper, we first define the concept of path-based semantic vector for the presentation of categories. Then a set of additional...
Aiming at the problem on determining breakage rate of corn seeds in real time, a method for identifying broken corn seeds was presented based on contour curvature. The broken corn seeds were classified into three classes according to damage position and level. Firstly, the seed contour was rebuilt with Fourier descriptors to achieve smooth contour. Then, contour curvature was calculated and the point...
Although an improvement of hierarchical text classification can be achieved by using hierarchical structure information, existing hierarchical text classification methods suffer from a problem, namely error propagation (especially in large-scale deep hierarchy). In this paper, we define the concept of path-based semantic vector for the presentation of categories based on which prior information provided...
In this paper, a novel type of neural networks called grey radial basis function network (GRBFN), is proposed. The reasons why grey theory is introduced into the RBF neural network are based on two facts. First, the modeling performance will be affected by the randomness inherent in the data when neural network approach is used to the model. That is, poor performance results from large randomness...
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