We propose a parallel stochastic Newton method （PSN） for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions under which acceleration can be expec...
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We propose a parallel stochastic Newton method （PSN） for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions under which acceleration can be expected when compared to its serial counterpart. We show how PSN can be applied to the large quadratic function minimization in general, and empirical risk minimization problems. We demonstrate the practical efficiency of the method through numerical experiments and models of simple matrix classes.
Radio maps are important enablers for many applications in wireless networks, ranging from network planning and optimization to fingerprint based localization. Sampling the complete map is prohibitively expensive in p...
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ISBN:
(纸本)9781538665282
Radio maps are important enablers for many applications in wireless networks, ranging from network planning and optimization to fingerprint based localization. Sampling the complete map is prohibitively expensive in practice, so methods for reconstructing the complete map from a subset of measurements are increasingly gaining attention in the literature. In this paper, we propose two algorithms for this purpose, which build on existing approaches that aim at minimizing the tensor rank while additionally enforcing smoothness of the radio map. Experimental results with synthetic measurements derived via ray tracing show that our algorithms outperform state of the art techniques.
Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matc...
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ISBN:
(纸本)9781479968923
Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matching is used to extract depth information from images. The main aim of our project is to use stereo matching algorithms to plot the disparity map of segmented images which gives the depth information. Particle Swarm optimization (PSO) algorithms are used for image segmentation. Our objective is to implement stereo matching algorithms on the segmented images and perform subjective analysis of reconstructed 3-D images. For some applications, such as image recognition or stereo vision, whole images cannot be processed, as it not only increases the computational complexity, but it also requires more memory. Thus, segmentation-based stereo matching algorithm should be used. This paper presents two novel methods for segmentation of images based on the Particle Swarm optimization (PSO) and Darwinian Particle Swarm optimization (DPSO).
The artificial bee colony algorithm is a swarm intelligence optimization algorithm inspired by the intelligent foraging behavior of honeybees. In this paper, modified ABC algorithms are proposed for numerical optimiza...
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This conference proceedings contains 21 papers, which are organized into six sessions: implementation and optimization;language design and applications;integrity constraints and derived data;rule processing I;rule pro...
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ISBN:
(纸本)0818653604
This conference proceedings contains 21 papers, which are organized into six sessions: implementation and optimization;language design and applications;integrity constraints and derived data;rule processing I;rule processing II;and design and debugging. Some topics discussed are cited as examples: semantic change computation optimization in active databases;modeling database applications using generalized production rules;achieving consistency in active databases;execution ordering for multilevel secure rules;retroactive and proactive database processing;a framework for handling errors during the execution of trigger rules for an active object-oriented DBMS;transaction optimization in rule databases;conditional term rewriting as a formal basis for analysis of active database rules;and simulation-based debugging of active databases.
This paper introduces a technique for hybrid BIST time optimization for testing core-based systems that use test pattern broadcasting for both pseudorandom and deterministic patterns. First we formulate the test time ...
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ISBN:
(纸本)0769520812
This paper introduces a technique for hybrid BIST time optimization for testing core-based systems that use test pattern broadcasting for both pseudorandom and deterministic patterns. First we formulate the test time minimization problem for such an architecture. Thereafter we present algorithms for finding an efficient combination of pseudorandom and deterministic test sets under given memory constraints, so that the system testing time can be shortened. We also analyze the significance of the pseudorandom sequence quality for the final results. The results are illustrated and the efficiency of the approach is demonstrated by experimental results.
Single-Phase multilevel converters are suitable for medium power applications as photovoltaic systems and switched reluctance machines. An overview of possible modulation methods including carrier-based Pulse Width Mo...
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ISBN:
(纸本)9781424407545
Single-Phase multilevel converters are suitable for medium power applications as photovoltaic systems and switched reluctance machines. An overview of possible modulation methods including carrier-based Pulse Width Modulation and Space Vector Modulation techniques for multilevel single-phase converters is presented. A new space vector modulation for this type of converters is proposed. This space vector modulation method is very simple presenting low computational cost. Different solutions for the space vector modulation are presented achieving similar output results but imposing restrictions on the power converter topology. optimizationalgorithms balancing the DC-Link voltage or minimizing the commutation losses are presented. Experimental results using a 150 kVA five-level diode-clamped converter are shown to validate the proposed modulation and optimization methods.
Optimized task scheduling is in general an NP-hard problem, even if the tasks are prioritized like surgeries in hospitals. Better pruning algorithms for the constraints within Such constraint optimization problems, in...
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ISBN:
(纸本)9783642006746
Optimized task scheduling is in general an NP-hard problem, even if the tasks are prioritized like surgeries in hospitals. Better pruning algorithms for the constraints within Such constraint optimization problems, in particular for the constraints representing the objectives to be optimized, will result in faster convergence of branch & bound algorithms. This paper presents new pruning rules for linear weighted (task) sums where the summands are the start times of tasks to be scheduled on an exclusively available resource and weighted by the tasks' priorities. The presented pruning rules are proven to be correct and the speed-up of the optimization is shown in comparison with well-known general-purpose pruning rules for weighted sums.
This work presents the development of a multilevel power inverter prototype with the use of optimized modulation which manages to significantly minimize the harmonic content of output voltages using Genetics Algorithm...
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ISBN:
(纸本)9783030867027;9783030867010
This work presents the development of a multilevel power inverter prototype with the use of optimized modulation which manages to significantly minimize the harmonic content of output voltages using Genetics algorithms. Additionally, it integrates a DC/DC converter that allows to regulate the RMS value of the inverter output voltage through the control of the DC bus voltage. A control loop is implemented that allows obtaining the optimal power quality by verifying compliance with the IEEE 1159 (1995) and IEEE 519 (1992) standards. Through this it is possible to avoid most of the related power quality phenomena such as sag, swell, flicker, undervoltage, etc. Finally, the prototype was successfully implemented and verified.
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local...
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ISBN:
(纸本)9780769535630
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, "Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.
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