Uncertainties in the plant model parameters and perturbations in the controller gains imposed by implementation errors represent a challenge to ensure robust stability and controller non-fragility simultaneously. Opti...
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Uncertainties in the plant model parameters and perturbations in the controller gains imposed by implementation errors represent a challenge to ensure robust stability and controller non-fragility simultaneously. Optimal design of robust non-fragile proportional-integral-derivative (PID) controller is presented for an automatic voltage regulator (AVR). The PID design relies basically on Kharitonov theorem and optimization by future search algorithm (FSA). The proposed algorithm has low computational complexity and fast convergence rate because it utilizes both local and global search methods. Further, FSA can improve the exploration characteristic and prevent trapping in local optima by updating its random initial. The PID controller is optimized by FSA to cope with expected parametric uncertainties of the plant model and tolerate its gain perturbations such that robust stability and controller non-fragility are simultaneously met. An interval plant model is suggested to account for model uncertainties where only eight extreme plants derived by Kharitonov theorem are considered in design. FSA-based PID optimization is constrained by the stability conditions of Kharitonov's plants derived using Routh-Hurwitz. A new figure-of-demerit (FoD) based performance index is suggested to enforce simultaneous minimization of the time domain specifications. The suggested objective function is represented by a weighted sum of FoD of nominal response and the sum of reciprocals of the perturbation radii of PID gains. The output results of the recommended design are compared to that of artificial bee colony (ABC) algorithm and teaching-learning based optimization (TLBO) algorithm, multi-objective extremal optimization (MOEO), and non-dominated sorting genetic algorithm II (NSGA II). The results can confirm better response of the suggested technique measured up to other techniques where robustness and non-fragility are simultaneously ensured. (c) 2020 ISA. Published by Elsevier Ltd.
In this paper a method for data reduction is introduced. Aspects of Lyapunov, Entropy and Variance (ALEV) provide an approach for mining large stocks of time series data. Methods of Artificial Intelligence (AI) offer ...
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ISBN:
(纸本)9781424416424
In this paper a method for data reduction is introduced. Aspects of Lyapunov, Entropy and Variance (ALEV) provide an approach for mining large stocks of time series data. Methods of Artificial Intelligence (AI) offer two different ways for modeling observation data: The recall times of Expert Systems (XPS) depend on the size of a knowledge base. Connectionist approaches like the Multi-Layer Perceptron (MLP) have to be trained with a representative data set for mapping system behavior. While the duration of this learning process also depends on the amount of representative data the recall times are very short. On basis of the Mackey-Glass function a technique for Visual Data Mining (VDM) is proposed. Performance tests on basis of real world traffic speed patterns from different observation time periods show that ALEV thins out large pattern stocks. Viability of Data Mining methods is increased and generalization quality remains the same.
In this paper, innovative indoor navigation methods have been proposed to meet the challenges in robotic navigation systems. The general positioning methods for robotic navigation include vision-based approaches, WIFI...
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ISBN:
(纸本)9781510824652
In this paper, innovative indoor navigation methods have been proposed to meet the challenges in robotic navigation systems. The general positioning methods for robotic navigation include vision-based approaches, WIFI beacons, infrared beacons, ultrasonic beacons, etc. However, the common problem with these methods is their inaccuracy. Especially, improving the precision of robotic positioning mechanisms is the key to indoor navigation systems. This paper proposes an approach that combines the external rotating beacon with an internal rotation of position sensitive devices ( PSD) which are installed on the robot. While two infrared beams from an external beacon source are equally projected to both sides of the PSD, the robot's position can be calculated precisely. The high performance and accurate results can be achieved by optimizing the rotation aligning time, dividing the working area, and compensating errors with information fusion. In comparison with other generic approaches, this proposed innovative approach requires less computing resources and is easier to implement due to its much lower complexity for the computing algorithms.
The purpose of this study is to present guidelines in selection of statistical and computing algorithms for variance components estimation when computing involves software packages. For this purpose two major methods ...
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The purpose of this study is to present guidelines in selection of statistical and computing algorithms for variance components estimation when computing involves software packages. For this purpose two major methods are to be considered: residual maximal likelihood (REML) and Bayesian via Gibbs sampling. Expectation-Maximization (EM) REML is regarded as a very stable algorithm that is able to converge when covariance matrices are close to singular, however it is slow. However, convergence problems can occur with random regression models, especially if the starting values are much lower than those at convergence. Average Information (AI) REML is much faster for common problems but it relies on heuristics for convergence, and it may be very slow or even diverge for complex models. REML algorithms for general models become unstable with larger number of traits. REML by canonical transformation is stable in such cases but can support only a limited class of models. In general, REML algorithms are difficult to program. Bayesian methods via Gibbs sampling are much easier to program than REML, especially for complex models, and they can support much larger datasets;however, the termination criterion can be hard to determine, and the quality of estimates depends on a number of details. computing speed varies with computing optimizations, with which some large data sets and complex models can be supported in a reasonable time;however, optimizations increase complexity of programming and restrict the types of models applicable. Several examples from past research are discussed to illustrate the fact that different problems required different methods.
For aircraft, the contemporary approach to Structural Health Monitoring (SHM), known as Prognostics and Health Management (PHM), is driven by condition-based maintenance and structural health prognosis, where pre-empt...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
For aircraft, the contemporary approach to Structural Health Monitoring (SHM), known as Prognostics and Health Management (PHM), is driven by condition-based maintenance and structural health prognosis, where pre-emptive planning of maintenance and procurement of parts will allow for maintenance and sustainment practices to be optimized, offering major beneficial fiscal and safety implications for both the civilian and defense aerospace sectors. Despite some current reluctances in industry, AI-based technologies, such as, deep learning, may have a major role to play in the contemporary aircraft PHM system. With this in mind, this paper aims to serve as an important contribution to their broader adoption at this critical time in aircraft SHM, showing how the relatively simplistic implementation of the state-of-the-art in deep learning can change the entire potential capability of the aircraft SHM system. The Bidirectional Long Short-Term Memory (BiLSTM) deep neural architecture is introduced as an indirect Multi-Input Single-Output (MISO) modeling strategy for poorly conditioned (weak MISO coherence) load predictions. It is shown that a single fixed-architecture BiLSTM strategy can be used as a general purpose strategy for MISO modeling of the loads, with a prediction accuracy that is of an unprecedented levels of fidelity and robustness/consistency. More specifically, the previous use of more traditional strategies, such as, regression models and even standard artificial neural networks, has only been viable in terms of yielding a statistically reasonable distribution of extrema (from generally very poor MISO time-series models). Hence, in this research campaign (ongoing for the past four years), the introduction of the deep learning strategy has changed the paradigm by which the weakly coherent MISO airframe loads are modeled, where the high-fidelity point-to-point time-series predictions has introduced the viability of contemporary fatigue assessment algorithms.
Galerkin projection is a commonly used reduced order modeling approach;however, stability and accuracy of the resulting reduced order models are highly dependent on the modal decomposition technique used. In particula...
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Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of...
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Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760.
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