This paper presents a rigorous formulation of the multi-alternative routing problem. The memetic algorithm yielding a global transportation plan is developed. We propose an algorithm constructing the optimal path amon...
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This paper presents a rigorous formulation of the multi-alternative routing problem. The memetic algorithm yielding a global transportation plan is developed. We propose an algorithm constructing the optimal path among nodes of a road graph. Finally, the modular structure of the routing subsystem is designed and implemented.
Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensiona...
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Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensional projection of the data which is well-clustered. This yields a one-dimensional projection in the simplest situation with two clusters, and extends naturally to a multi-label scenario for more than two clusters. In this paper, (a) we first show that this joint clustering and dimension reduction formulation is equivalent to previously proposed discriminative clustering frameworks, thus leading to convex relaxations of the problem;(b) we propose a novel sparse extension, which is still cast as a convex relaxation and allows estimation in higher dimensions;(c) we propose a natural extension for the multi-label scenario;(d) we provide a new theoretical analysis of the performance of these formulations with a simple probabilistic model, leading to scalings over the form d = O (root n) for the affine invariant case and d = O (n) for the sparse case, where n is the number of examples and d the ambient dimension;and finally, (e) we propose an efficient iterative algorithm with running-time complexity proportional to O (nd(2)), improving on earlier algorithms for discriminative clustering with the square loss, which had quadratic complexity in the number of examples.
We propose a robust static control algorithm for linear objects under parametric and structural uncertainty and an external uncontrollable disturbance. The resulting algorithm ensures that the object output tracks the...
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We propose a robust static control algorithm for linear objects under parametric and structural uncertainty and an external uncontrollable disturbance. The resulting algorithm ensures that the object output tracks the reference signal with the necessary precision. We give modeling results that illustrate that the algorithm operates correctly.
Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detect...
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Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD) problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM) algorithm. This method converts transients hidden in the noisy signal into sparse coefficients;thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR) shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA) in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.
In literature, chaotic economic systems have got much attention because of their complex dynamic behaviors such as bifurcation and chaos. Recently, a few researches on the usage of these systems in cryptographic algor...
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In literature, chaotic economic systems have got much attention because of their complex dynamic behaviors such as bifurcation and chaos. Recently, a few researches on the usage of these systems in cryptographic algorithms have been conducted. In this paper, a new image encryption algorithm based on a chaotic economic map is proposed. An implementation of the proposed algorithm on a plain image based on the chaotic map is performed. The obtained results show that the proposed algorithm can successfully encrypt and decrypt the images with the same security keys. The security analysis is encouraging and shows that the encrypted images have good information entropy and very low correlation coefficients and the distribution of the gray values of the encrypted image has random-like behavior.
The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme...
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The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
In this paper, we propose an image-based system for Arabic Sign Language (ArSL) recognition. The algorithm starts by detecting the face of the signer using a Gaussian skin color model. The centroid of the detected fac...
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In this paper, we propose an image-based system for Arabic Sign Language (ArSL) recognition. The algorithm starts by detecting the face of the signer using a Gaussian skin color model. The centroid of the detected face is then used as a reference point for tracking the hands' movements. The hands regions are segmented using a region growing algorithm assuming the signer wears a yellow and an orange colored gloves. From the segmented hands regions. an optimal set of features is extracted. To represent the time varying feature patterns, a Hidden Markov Model (HMM) is then used. Before using HMM in testing, the number of states and the number of Gaussian mixtures are optimized. The proposed system was implemented for both signer dependent and signer independent conditions. The experimental results show that an accuracy of more than 95% can be achieved with a large database of 300 signs. The results outperform previous work on ArSL mainly restricted to small vocabulary size. (C) 2011 Elsevier Ltd. All rights reserved.
Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43-based algorithms for 50 patie...
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Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43-based algorithms for 50 patients treated at our institution with either a Strut-Adjusted Volume Implant (SAVI) or Contura device. Methods and Materials: We recalculated target coverage, volume of highly dosed normal tissue, and dose to organs at risk (ribs, skin, and lung) with each algorithm. For 1 case an artificial air pocket was added to simulate 10% nonconformance. We performed a Wilcoxon signed rank test to determine the median differences in the clinical indices V90, V95, V100, V150, V200, and highest-dosed 0.1 cm(3) and 1.0 cm(3) of rib, skin, and lung between the two algorithms. Results: The CCC algorithm calculated lower values on average for all dose-volume histogram parameters. Across the entire patient cohort, the median difference in the clinical indices calculated by the 2 algorithms was < 10% for dose to organs at risk, < 5% for target volume coverage (V90, V95, and V100), and < 4 cm(3) for dose to normal breast tissue (V150 and V200). No discernable difference was seen in the nonconformance case. Conclusions: We found that on average over our patient population CCC calculated (<10%) lower doses than TG-43. These results should inform clinicians as they prepare for the transition to heterogeneous dose calculation algorithms and determine whether clinical tolerance limits warrant modification. (C) 2016 Elsevier Inc. All rights reserved.
Program obfuscation is a semantic-preserving transformation aimed at bringing a program into a form that impedes understanding of its algorithm and data structures or prevents extracting certain valuable information f...
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Program obfuscation is a semantic-preserving transformation aimed at bringing a program into a form that impedes understanding of its algorithm and data structures or prevents extracting certain valuable information from the text of the program. Since obfuscation may find wide use in computer security, information hiding and cryptography, security requirements to program obfuscators have become a major focus of interest in the theory of software obfuscation starting from the pioneering works in this field. In this paper we give a survey of various definitions of obfuscation security and basic results that establish possibility or impossibility of secure program obfuscation under certain cryptographic assumptions.
As compared to the two-fluid single-pressure model, the two-fluid seven-equation two-pressure model has been proved to be unconditionally well-posed in all situations, thus existing with a wide range of industrial app...
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As compared to the two-fluid single-pressure model, the two-fluid seven-equation two-pressure model has been proved to be unconditionally well-posed in all situations, thus existing with a wide range of industrial applications. The classical 1st-order upwind scheme is widely used in existing nuclear system analysis codes such as RELAP5, CATHARE, and TRACE. However, the 1st-order upwind scheme possesses issues of serious numerical diffusion and high truncation error, thus giving rise to the challenge of accurately modeling many nuclear thermal-hydraulics problems such as long term transients. In this paper, a semiimplicit algorithm based on the finite volume method with staggered grids is developed to solve such advanced well-posed two-pressure model. To overcome the challenge from 1st-order upwind scheme, eight high-resolution total variation diminishing (TVD) schemes are implemented in such algorithm to improve spatial accuracy. Then the semi-implicit algorithm with high-resolution TVD schemes is validated on the water faucet test. The numerical results show that the high-resolution semi-implicit algorithm is robust in solving the two-pressure two-fluid two-phase flow model;Superbee scheme and Koren scheme give two highest levels of accuracy while Minmod scheme is the worst one among the eight TVD schemes.
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