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The paper deals with the reduction of the channel outage in a smart grid scenario since it plays a crucial role on the control performance of the Demand/Response Management. A study on a two-way cognitive-based switching procedure is carried out and tools for the optimum sensing-time evaluation are provided. Such evaluation considers a cost function that takes into account both the sensing-accuracy...
As integrated circuit process technology progresses into the deep sub-micron region, the phenomenon of process variation has a growing impact on the design and analysis of digital circuits and more specifically in the accuracy and integrity of timing analysis methods. The assumptions made by the analytical models, impose excessive and unwanted pessimism in timing analysis. Thus, the necessity of removing...
We present an evolutionary multi-objective optimization method for sensor deployment applied to an indoor positioning system with range-difference measurements. Stationary sensors at known locations are used to obtain the position of a moving emitter. Coverage and accuracy of the positioning system depend on the number and location of the sensors for a given indoor space (floor plan) and on the properties...
To make faster and more complete Kazakh syntactic analysis, the improved algorithm analysis Chart analysis method is presented. First introduced the tradition of bottom-up and top-down chart analysis, focusing on bottom-up analysis algorithms applications statement and found that the algorithm increases the length of the sentence lower efficiency of the algorithm. For a long sentence Kazakh left recursive...
Mixed Fruit Fly Optimization Algorithm LGM-FOA (Logistic Mapping-FOA) is an improved mixed fruit fly algorithm on the basis of the Logistic map, but the algorithm was showing an ideal state about convergence precision and stability in the optimization process, because there are three discontinuous points from the Logistic map. To solve this problem, the author proposed a new mixed fruit fly algorithm...
Regulating the power consumption to avoid peaks in demand is a common practice. Demand Response(DR) is being used by utility providers to minimize costs or ensure system reliability. Although it has been used extensively there is a shortage of solutions dealing with dynamic DR. Past attempts focus on minimizing the load demand without considering the sustainability of the reduced energy. In this paper...
Quantitative financial analysis requires repeated computations of the same functions with the same arguments when prototyping trading strategies; many of these functions involve resource intensive operations on large matrices. Reducing the number of repeated computations either within a program or across runs of the same program would allow analysts to build and debug trading strategies more quickly...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising paths toward the understanding of fundamental questions in biology and medicine. High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Feature selection (FS) and clustering are among the methods used in...
Data classification is an important branch of data mining and there are different methods for its implementation. Neural networks are one of the best ways for classification in machine learning. Structure and weights of neural network are most important in their precision. In recent years, due to the defects in gradient-based search algorithms in neural network training algorithms, metahuristic algorithms...
Nowadays, extremely wide and facilitated access to the internet has made the plagiarism and text reuse more common. Many studies have been conducted on automatic plagiarism detection. But there are few studies on automatic Persian plagiarism detection methods due to lack of a suitable Persian corpus. In this paper, an external Persian plagiarism detection method based on the vector space model (VSM)...
In recent years, progress in the field of artificial neural networks provides a very important tool for complex problems in pattern recognition, data mining and medical diagnosis. The training algorithms of neural networks play an important role for adjustment the network parameters. Different algorithms have been presented for training neural networks; the most common one is the use of gradient descent...
In this study, we propose a localization method based on the fusion of the laser range sensor (LRS) measurements and the odometry information of a vehicle using moving horizon estimation (MHE). LRS measurement includes outliers and suffers from the intermittent observation; alleviation of their effect is required in order to localize a vehicle position with high accuracy. Proposed localization method...
Intrusion Detection Systems(IDS) have become an essential part of every security framework. These systems rely on monitoring and detection of intrusions, thus composing an additional line of defense. Several paradigms have been applied for implementing IDS. In this paper we propose a NIDS model based on Information Gain for feature selection and Support Vector Machines(SVM) for the detection component...
A force fields-based multi-scale docking method is proposed in this paper. Molecular docking problem has been divided into three sub problems: rigid-rigid phase, flexible-flexible phase and flexible-rigid phase. Residue groups of protein have been adopted to describe the conformation of protein. K-mean clustering algorithm and genetic algorithm have been developed to solve the optimization problem...
We consider a joint scheme of antenna subset selection (ASS) and optimal power allocation (OPA) for localization inmultiple-input multiple-output (MIMO) radar sensors networks. The sensor management is accomplished by solving a constrained optimization problem that is formulated to minimize the error in estimating target position, while conserving transmitter number and power budget. We propose a...
In this paper, we present a supervoxel generation algorithm based on partially absorbing random walks to get more accurate supervoxels in these regions. A novel spatial-temporal framework is introduced by making full use of the appearance features and motion cues, which effectively exploits the temporal consistency in the video sequence. Moreover, we build a new Laplacian optimization structure with...
Generalized Fourier series with orthogonal polynomial bases have useful applications in several fields, including pattern recognition and image and signal processing. However, computing the generalized Fourier series can be a challenging problem, even for relatively well behaved functions. In this paper, a method for approximating a sparse collection of Fourierlike coefficients is presented that uses...
In evolutionary computation, crowding is a popular technique to handle multi-modal optimization problems, which include many possible local or global optimal solutions. In our previous publication, we proposed a new evolutionary algorithm, called DEAL (Direction-guided Evolutionary Algorithm). It works effectively on non-linear optimization problems. In this paper, we extend further DEAL towards the...
In this paper, we study and propose an efficient method to accurately estimate the position and the transmit power of a primary user for spatial resource utilization in cognitive radio networks (CRNs). Not similar to conventional methods where positions of at least four secondary users are assumed to be known and then, complex techniques such as least square and constrained optimization algorithms...
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