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.
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone),...
A Support Vector Machine (SVM) based approach for microgrid islanding decision and control is investigated. The IEEE 13-feeder system is modified and serves as the microgrid model connected to Kundur four-machine two-area system that models the main transmission grid. A representative data set is obtained through simulations in MATLAB/Simulink considering multiple typical scenarios with or without...
This paper presents novel means for estimating the polynomial static nonlinearity coefficients of a Wiener system in absence of a priori information about the linear block. To capture the system structure, the identification is performed with respect to a Volterra series model, whose kernels are parameterized in terms of Laguerre functions. A property of the resulting Volterra-Laguerre model is exploited...
In this paper, an approach for speeding up a kernel based nonlinear state estimator is proposed. The kernel based observer, which we are going to speed up in this paper, is one of the state estimator which employs a non-parametric structure. Although it shows high precision for nonlinear estimation due to its nonlinear nature, large amount of calculation makes it rather slow. In this paper, we propose...
Attempting to understand and characterize trends in the stock market has been the goal of numerous market analysts, but these patterns are often difficult to detect until after they have been firmly established. Recently, attempts have been made by both large companies and individual investors to utilize intelligent analysis and trading algorithms to identify potential trends before they occur in...
This paper presents an efficient Parkinson disease diagnosis system using Least Squares Twin Support Vector Machine (LSTSVM) and Particle Swarm Optimization (PSO). LSTSVM is a promising binary classifier and has shown better generalization ability and faster computational speed. PSO is used for feature selection and parameter optimization. Parkinson disease dataset is taken from UCI repository. The...
Human machine interaction is one of the most burgeoning area of research in the field of information technology. To date a majority of research in this field has been conducted using unimodal and multimodal systems with asynchronous data. Because of the above, the improper synchronization, which has become a common problem, due to that, the system complexity increases and the system response time...
Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance...
In embedded systems, many numerical algorithms are implemented with fixed-point arithmetic to meet area cost and power constraints. Fixed-point encoding decisions can significantly affect cost and performance. To evaluate their impact on accuracy, designers resort to simulations. Their high running-time prevents thorough exploration of the design-space. To address this issue, analytical modeling techniques...
This paper proposes a novel naïve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a distance...
Oncogene is a kind of inherent genes exists in humans' cells. It has been recognized as a genetic disease, if the cells activated, it can make a person carcinogenesis. So, the research of digging out the useful information from gene chip is very hot in modern society. The sample size is small, high dimension, nonlinear which causes the 'dimension disaster', so dimensionality reduction becomes the...
The One-Class Classifier (OCC) has been widely used for solving the one-class and multi-class classification problems. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. However, extending the OCC to the multi-class classification achieves less accuracy comparatively to other multi-class classifiers. Hence, in order...
It is an important issue to assess the state-of-health (SOH) of battery, including aircraft. There are several methods on the assessment of SOH of battery having been reported in recent years. Herein, we proposed a hybrid PSO+SVM model, which employed particle swarm optimization (PSO) algorithm to search the best parameters of support vector machine (SVM) to estimate SOH of aviation lead-acid battery...
The accuracy and the computational complexity of a Gaussian mixture model depends upon the number of components. In a stochastic dynamical system, the number of these components must change over time to account for the change in the uncertainty over time. A new splitting technique is provided based on the minimization of Fokker Planck Kolmogorov Equation. The effect of the splitting on the other components...
Worst-case time (WCET) analyses for single tasks are well established and their results ultimately serve the purpose of providing execution time parameters for schedulability analyses. Besides WCET analysis, an important problem is maximum blocking time (MBT) analysis which is essential in deferred preemption schedules for the selection of preemption points. Among the most pressing problems in this...
We present a design scheme for SVM decision function based on the hardware-friendly kernel on FPGA device. This scheme is suitable for classification and regression problems. We adopt ModelSim simulation platform for SVM classification and regression experiments. The hardware implementation obtains the same classification accuracy as the LIBSVM package by using the appropriate fixed-point number precision...
Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation,...
Advanced machine learning techniques offer an unprecedented opportunity to increase the accuracy of board-level functional fault diagnosis based on the historical data of successfully repaired boards. However, the training complexity increases significantly in diagnosis systems due to the increasing amount of the historical data. We propose a smart learning method in the diagnosis system using incremental...
Graph Laplacian based framework is a powerful image modeling technology. In this paper, we present a new graph-based method. The key idea is to perform manifold regularization on data graph via local kernel regression. The graph Laplacian nature of our method is theoretically illustrated. Furthermore, based on the proposed framework, we apply our method to several image editing tasks, including remote...
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.