The present study highlights an attempt of integrating the geothermal power plant (GTPP) in automatic generation control of an interconnected system comprising of dish-Stirling solar-thermal system (DSTS) and the conv...
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The present study highlights an attempt of integrating the geothermal power plant (GTPP) in automatic generation control of an interconnected system comprising of dish-Stirling solar-thermal system (DSTS) and the conventional thermal system (TS). Generation rate constraints of 3%/min are considered for the TSs. A new fractional-order (FO) cascade controller named as FO proportional (P)-integral (I)-FOP-derivative (D) (FOPI-FOPD) is proposed as secondary controller and performance is compared with commonly used classical controllers. Controller gains and other parameters are optimised using a novel stochastic algorithm called sine-cosine algorithm. The analysis reveals the superiority of FOPI-FOPD over others. The effect of inclusion of GTPP and DSTS is also analysed on the conventional TS, both in a combined manner and separately. Sensitivity analysis reflects the robustness of optimum FOPI-FOPD controller gains and other parameters obtained at nominal and recommend that the optimised parameters do not suffer much deviations and are able to withstand wide fluctuations in system operating conditions, system loading and inertia constant. The dynamic behaviour of the system is studied with 1% step load perturbation in area1.
Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quan...
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This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fus...
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This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The aim of this thesis is to construct nonparametric estimators of distribution, density and regression functions using stochastic approximation methods in order to correct the edge effect created by kernels estimator...
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The aim of this thesis is to construct nonparametric estimators of distribution, density and regression functions using stochastic approximation methods in order to correct the edge effect created by kernels estimators. In the first chapter, we give some asymptotic properties of kernel estimators. Then, we introduce the Robbins- Monro stochastic algorithm which creates the recursive estimators. Finally, we recall the methods used by Vitale, Leblanc and Kakizawa to define estimators of distribu- tion and density functions based on Bernstein polynomials. In the second chapter, we introduced a recursive estimator of a distribution function based on Vitale's ap- proach. We studied the properties of this estimator: bias, variance, mean integrated squared error (MISE) and we established a weak pointwise convergence. We com- pared the performance of our estimator with that of Vitale and we showed that, with the right choice of the stepsize and its corresponding order, our estimator dominates in terms of MISE. These theoretical results were confirmed using simulations. We used the cross-validation method to search the optimal order. Finally, we applied our estimator to interpret real dataset. In the third chapter, we introduced a recur- sive estimator of a density function using Bernstein polynomials. We established the characteristics of this estimator and we compared them with those of the estimators of Vitale, Leblanc and Kakizawa. To highlight our proposed estimator, we used real dataset. In the fourth chapter, we introduced a recursive and non-recursive estimator of a regression function using Bernstein polynomials. We studied the characteristics of these estimators. Then, we compared our proposed estimators with the classical kernel estimators using real dataset.
Parallel processing is considered effective in order to solve problems with significant computational complexity. The development of graphics processing units (GPU) in recent years has led to eneral purpose GPU (GPGPU...
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ISBN:
(纸本)9781538606216
Parallel processing is considered effective in order to solve problems with significant computational complexity. The development of graphics processing units (GPU) in recent years has led to eneral purpose GPU (GPGPU) and brought significant benefits to the AI research field. In conventional parallel processing, processes must be frequently synchronized;thus, parallel computing does not necessarily improve the efficiency of computation except for special algorithms designed for parallel computation. In this study, we investigate the effect of applying MultiStart based speculative computation on GPGPU. This method incurs little synchronization overhead. Although the effect of this method is stochastic, an expected value is theoretically calculable. We analyze theoretically about the effects of the speculative method, and provide the results of applying the method to combinatorial optimization problems.
Prediction of the electric emissions in space missions is critical due to the sensitivity of their payload. A reliable method to predict such emissions is the accurate electric source identification. In this work, eve...
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Prediction of the electric emissions in space missions is critical due to the sensitivity of their payload. A reliable method to predict such emissions is the accurate electric source identification. In this work, every space component is modeled by a small number of electric dipoles based on the measurement of the magnitude of the electric field at novel set of near-field positions. The location (x(i), y(i), z(i)) and electric moment (p(x i), p(y i), p(z i),) are accurately predicted via stochastic algorithms resulting in the correct reconstruction of the source's electric field both at measurement and extrapolation points.
Orthogonal array-based Latin hypercubes, also called U-designs, have popularly been adopted for designing a computer experiment. The relationship between the averaged discrepancy of all U-designs generated from a give...
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Orthogonal array-based Latin hypercubes, also called U-designs, have popularly been adopted for designing a computer experiment. The relationship between the averaged discrepancy of all U-designs generated from a given orthogonal array and the generalized wordtype pattern of the orthogonal array is established. Motivated by the relationship, we define a weighted wordtype pattern and a minimum weighted aberration criterion to compare orthogonal arrays of the same parameters. U-designs generated from an orthogonal array with less weighted aberration are shown to have low discrepancies on average. Then, an algorithm to construct uniform U-designs is proposed. It begins with a minimum weighted aberration orthogonal array and its advantage is illustrated by comparing with the threshold-accepting algorithm. (C) 2016 Elsevier B.V. All rights
This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic va...
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ISBN:
(纸本)9781538627150
This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves topics, word sentiments and user preferences altogether and has been shown to achieve good performance, but for large dataset it can only learn from a relatively small sample. We develop the variational models vTSPRA and svTSPRA to improve the time use, and our new approach is capable of processing millions of reviews. We rebuild the generative process, improve the rating regression, solve and present the coordinate-ascent updates of variational parameters, and show the time complexity of each iteration is theoretically linear to the corpus size, and the experiments on Amazon datasets show it converges faster than TSPRA and attains better results given the same amount of time. In addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor oscillations of sentiment and preference overtime. Some interesting fluctuations are captured and possible explanations are provided. The results give strong visual evidence that user preference is better treated as an independent factor from sentiment.
Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of gr...
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Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China's national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.
A novel stochastic algorithm using pre-processing technique is proposed in this paper to deal with the problem of underwater target tracking using passive Sonar. Pre-processing is a concept of reducing the variance of...
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A novel stochastic algorithm using pre-processing technique is proposed in this paper to deal with the problem of underwater target tracking using passive Sonar. Pre-processing is a concept of reducing the variance of noise present in the measurements given by sensors. This key step is performed ahead of conventional estimation algorithms. Pre-processed measurements are obtained by taking weighted average of present measurements and projected previous measurements. The method is expected to bring down the variance of noise to a great deal based on the fact that the sensor errors are unbiased by nature. The most attractive feature of this algorithm is the capability to track long range targets in heavy noise environments. The algorithm is tested by running Monte Carlo simulations in Matlab R2009a environment. There, it is shown that the estimation error and the time of convergence of the pre-processing technique based algorithms like pre-processed Unscented Kalman Filter (PP-UKF) and Integrated Unscented Kalman filter (PP-IUKF) are much less compared to their non-pre-processing counterparts namely UKF and IUKF, thus indicating the importance of the proposed novel method. (C) 2016 Elsevier GmbH. All rights reserved.
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