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Wave propagation signals are commonly used as information carrier in structural health monitoring. To facilitate decision making, wave signals are often decomposed into multiple components to reveal its frequency or time-frequency content. In this research we investigate the use of time-frequency decomposition techniques for feature extraction. The method based on the adaptive harmonic wavelet transform...
Classification of text documents is commonly carried out using various models of bag-of-words that are generated using feature selection methods. In these models, selected features are used as input to well-known classifiers such as Support Vector Machines (SVM) and neural networks. In recent years, a technique called word embeddings has been developed for text mining and, deep learning models using...
The classification and visualization for surface objects receives a great deal of attention for high spectral dimensional data processing. A lot of methods were proposed and applied in this problem over the past decade. Whereas most of them still exist some challenge issues, include pre-treatment fussily, features extraction simplify, larger data processing difficultly and classification inaccurately...
Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS...
The paper proposed a model using real time driving front video recording to detect driver drowsiness. The video recordings were fed into the TRW's simulator to obtain the lane-related signals. Time domain features and frequency domain features were extracted from the lane-related signals to characterize the difference of alert state and drowsiness state. Both support vector machine and neural network...
Many existing car recognition algorithms are aiming at front/back and side view car detection. In this paper, an oblique car recognition algorithm is proposed. The algorithm is designed based on the common knowledge of the car, such as shape, contour, structure and so on. The experimental results of many oblique car images show that our method has fine accuracy in oblique car recognition.
This paper presents a multi-agent system(MAS_VFD&HM) developed for complex vehicle fault diagnosis and health monitoring. The MAS_VFD&HM consists of signal diagnostic agents, special case agents, and a vehicle diagnostic/monitoring agent. A signal agent is responsible for the fault diagnosis or monitoring of one particular signal using either a single signal or multiple signals depending on...
In this paper, a car recognition algorithm based on fuzzy clustering of multiple features of car image is proposes. Firstly main color model and image pyramid are used to get complete contour of car. Then round rate, Fourier descriptor, direction ratio and circumference ratio are extracted and analyzed in detail. Afterwards all features are combined into a feature vector and fuzzy clustering is used...
This paper proposes a car recognition algorithm based on multiple features of contour. Firstly, canny operator is used to get the edge of the image. Afterwards, image pyramid is used to shrink the image so that the contour will be single-edged and complete. Then, from a point in the contour, travel the whole contour to gain the Fourier descriptors and direction ratio from the traversal sequence. At...
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