Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Problems of optimal pole placement for linear time invariant systems via state feedback have been studied for several decades. The minimum gain pole exact placement problem involves obtaining a feedback matrix that will assign a certain desired set of closed-loop poles, while also minimizing the gain (matrix norm) of the feedback matrix. Numerous methodologies have appeared in the literature to address...
This paper deals with fair public service system design formulated as the weighted p-median problem minimizing the total disutility, like social costs. The social costs are often proportional to the total distance travelled by all users to the nearest located service center. The above objective denoted as min-sum criterion may cause such situation that the total social costs are minimal, but the disutility...
This paper presents an improved parameter extraction algorithm for photovoltaic (PV) panels based on only datasheet value, which is very useful in the development phase of power conditioning system (PCS). In order to increase the accuracy of PV circuit model especially in the maximum power point (MPP), optimization method with objective function incorporating only the MPP conditions was suggested...
Dynamic Traffic Assignment (DTA) has become a main component of modern traffic control centres. To calibrate a DTA model the observations from the field are required. There has been increasing number of sensors and technologies which can provide these data. In this paper we briefly describe these sensors and elaborate on the various traffic data types that are used in dynamic demand calibration. The...
In computer vision tasks such as action recognition and image classification, combining multiple visual feature sets is proven to be an effective strategy. However, simply combing these features may cause high dimensionality and lead to noises. Feature selection and fusion are common choices for multiple feature representation. In this paper, we propose a multi-view feature selection and fusion method...
Subspace segmentation is one of the hottest issues in computer vision and machine learning fields. Generally, data (e.g. face images) are lying in a union of multiple linear subspaces, therefore, it is the key to find a block diagonal affinity matrix, which would result in segmenting data into different clusters correctly. Recently, graph construction based segmentation methods attract lots of attention...
A new fuzzy c-means clustering with non-extensive entropy regularization is proposed in this paper. The purpose of entropy regularization is to form approximate solutions of singular problems in the maximum entropy framework. The non-extensive entropy with Gaussian gain is generally used for identifying non-uniform probability densities as in regular texture patterns. It is thus well suited for regularizing...
In the Smart Grid context, one of the most broadly investigated areas is the matter of flexibility. This term is still not defined in a unified way. As such, it is frequently encountered in both transmission and distribution system studies, nonetheless, with various perspectives. To some extent, the reason for this is that flexibility is a multidimensional commodity. It can be associated with a unit...
We proposed an approach to predict the availability of volunteer sensor networks (VSN) node. It is based on the Stronger Intelligent selection (SIS). First, the availability of VSN node is analyzed and predicted based on its location. The stronger model is defined and Studied on the Optimization Rules and Solution Tactics of availability. A simple and efficient stronger searching mechanism is presented...
Feature extraction is an essential step in pattern classification, which is normally divided into two tasks: transforming the input vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the subsequent classification process more effective and efficient. One of the most important feature extraction algorithms is linear discriminant analysis...
Many real-world networks are featured with dynamic changes, such as new nodes and edges, and modification of the node content. Because changes are continuously introduced to the network in a streaming fashion, we refer to such dynamic networks as streaming networks. In this paper, we propose a new classification method for streaming networks, namely streaming network node classification (SNOC). For...
Proper parameter settings of support vector machine (SVM) and feature selection are of great importance to its efficiency and accuracy. In this paper, we propose a parallel adaptive particle swarm optimization algorithm to simultaneously perform the parameter optimization and feature selection for SVM, termed PTVPSO-SVM. It is implemented in an efficient parallel environment using PVM (Parallel Virtual...
Transfer learning aims to address the problem where we lack the labeled data for training in one domain while utilizing the sufficient training data from other relevant domains. The problem becomes even more challenging when there are no labeled data in the target domain to build the association between two domains, which is more common in real-world scenarios. In this paper, we tackle with the challenge...
Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability...
Multiple kernel learning (MKL) usually searches for linear (nonlinear) combinations of predefined kernels by optimizing some performance measures. However, previous MKL algorithms cannot deal with Lq norm MKL if q
Conventional methods of determining Pareto dominance in multi-objective optimization evaluate and compare objective vectors of candidate solutions, but the computation and (or) experiment of evaluating objective vectors are overwhelmingly costly when computationally expensive multi-objective problems are involved. This study investigates a nearest neighbor prediction method of Pareto dominance using...
Although biometric recognition systems provide many advantages over traditional recognition methods, they can be vulnerable to specific attacks which may considerably decrease their security. In this paper we focus on the hill-climbing attack which is peculiar of biometric systems. Specifically, we evaluate the effectiveness of general approaches relying on parametric functions optimization for performing...
Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization...
In this paper we discuss the iteration complexity certification for solving constrained MPC problems for linear systems using an inexact augmented Lagrangian approach. We solve the augmented dual problem that arises from Lagrange relaxation of the linear constraints coming from the dynamics with the dual gradient method. Since the exact solution of the primal augmented Lagrange problem is usually...
For improve classification accuracy, this paper discusses the problem of feature selection for high-dimensional data and SVM parameter optimization. An SVM classification system based on simulated annealing(SA) is proposed to improve the performance of the SVM classifier. The experiments are conducted on the basis of benchmark dataset. The obtained results confirm the superiority of the SA-SVM approach...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.