The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
P300-speller which relies on P300-event related potential (ERP) is an important application of the BCI system. However, the accuracy and information transmission rate were relatively low for practical use. To solve the problem, researchers focused on two aspects of paradigms and classifiers. P300-speller with familiar face paradigm achieved a better performance. In addition, Bayesian linear discriminate...
P300-speller is a communication system based on brain-computer interface (BCI) which allows users to input characters by focused attention. Support vector machine (SVM) ensemble has been successfully applied to classify in the P300-speller. However, large scale of training data was needed for the sub-classifiers to ensure the performance of classification. Subjects gradually got fatigue during the...
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...
The fault identification of power system is of great significance in the event of failure This paper introduce a fault identification method based on multi-wavelet packet and artificial neural network. Firstly, through the simulation of a two-500Kv power source transmission line on PSCAD/EMTDC, the variety of fault signals is generated in different conditions. Then, these fault signals are decomposed...
Audio semantic analysis is an important issue for multimedia applications. In this paper, we propose a neural network based approach to analyze the high-level semantic content of audio event sequences for the action movies. According to the time interval between adjacent basic audio events, we first divide the given event sequence into some scene segments, and then discover the high-level semantic...
Testing is one of the most labor-intensive activities in software development life cycle and consumes between 30% and 50% of total development costs according to many studies. The communication gap between testers and developers that is caused by unclear or even invalid defect reporting usually makes the testing schedule delay, and contributes large amount of testing effort to rework and re-communication...
An empirical study is provided on teaching Verification & Validation (V&V) process practice in a real-client graduate level software engineering course which makes students and researchers mutual winners. From our observation and experiences during the course, on the education side, several reflection-in-action techniques are used to educate and train students. These include inspections, architecture...
Discriminative Training (DT) methods for acoustic modeling, such as MMI, MCE, and SVM, have been proved effective in speaker recognition. In this paper we propose a DT method for GMM using soft frame margin estimation. Unlike other DT methods such as MMI or MCE, the soft frame margin estimation attempts to enhance the generalization capability of GMM to unseen data in case the mismatch exists between...
Appearances of objects lie in high-dimensional spaces. For a given recognition task, feature selection aims to select most effective features in order to reduce the recognition cost and improve recognition accuracy. Feature selection can be achieved by a bottom-up scheme, e.g., using spatial information, or a top-down scheme, e.g., using class information. In this paper, we propose a model to integrate...
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...
Identification of non-native personnel is a critical piece of information for making crucial on-the-spot decisions for security purposes. Identification of a non-native speaker is often readily apparent in normal conversation with a native speaker through speech content and accent. Such identification which requires familiarity with language nuances may not be possible for a non-native interrogator...
Recently, Li et al. proposed a new auditory feature for robust speech recognition in noise environments. The new feature was derived by mimicking closely the function of human auditory process. Several filters were used to model the outer ear, middle ear, and cochlea, and the initial filter parameters and shapes were obtained from crude psychoacoustics results, experience, or experiments. Although...
Currently, almost all discriminative training algorithms for nonlinear classifier design are based on gradient-descent methods, such as the backpropagation and the generalized probabilistic descent algorithm. These algorithms are easy to derive and effective in applications. However, a drawback for the gradient-descent approaches is the slow training speed, which limits their applications in large...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.