A numerical method for determining the five-parameter model of photovoltaic cells is presented in the paper. Explicit equations are applied to analyze the relations between parameters which are solved by an optimizati...
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A numerical method for determining the five-parameter model of photovoltaic cells is presented in the paper. Explicit equations are applied to analyze the relations between parameters which are solved by an optimization algorithm. Lambert W function is implemented to convert the I-V characteristic implicit equation to an explicit one, so the output current and voltage of photovoltaic cells can be obtained by substituting the five parameters into the explicit I-V equation. Several cells are used to verify the accuracy of the proposed method from different aspects. It is found that the proposed method gives precise results and can be applicable to various types of photovoltaic cells. (C) 2014 Elsevier Ltd. All rights reserved.
The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. optimization can be applied to explore the design space, however gradient-based...
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ISBN:
(数字)9781624106101
ISBN:
(纸本)9781624106101
The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multiobjective system architecture optimization problems. Performance is compared to the NSGAII multi-objective evolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published.
Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identif...
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Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis;the best results contrasted with several
To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building ...
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To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.
Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previou...
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Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. (C) 2014 Elsevier Ltd. All rights reserved.
An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the ...
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An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the multi-microgrid (MMG) system is investigated. Each microgrid (MG) contains some smaller microgrids (SMGs), which are connected by energy routers (ERs) of the system and can monitor energy in real-time with each other. In addition, a time-varying multi-energy management optimization model (MEMOM) is proposed in this paper in order to minimize the total cost of the MEMP which considers environmental cost, renewable energy cost and fuel cost. Furthermore, time-varying distributed neurodynamic optimization algorithms are proposed for solving the above MEMP based on consensus theory and sliding mode control technique. Compared with the optimization algorithms which consist of symbolic functions proposed in traditional energy management problems, algorithms consisting of hyperbolic tangent functions proposed in this paper can effectively reduce the oscillation of the algorithms and improve the stability of algorithms. Furthermore, the algorithm can converge the optimal trajectory of optimization problems with time-varying external disturbances and communication time delays. Meanwhile, the stability and convergence of the algorithms are proved theoretically by constructing appropriate Lyapunov functions. Finally, the performance evaluation re-sults of numerical simulations show that the proposed algorithms can efficiently handle energy trading under time-varying load and maintain excellent stability with time-varying external disturbances and communication time delays.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.
Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization alg...
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Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization algorithm (FOA), has been developed. This paper presents a variation on original FOA technique, named multi-swarm fruit fly optimization algorithm (MFOA), employing multi-swarm behavior to significantly improve the performance. In the MFOA approach, several sub-swarms moving independently in the search space with the aim of simultaneously exploring global optimal at the same time, and local behavior between sub-swarms are also considered. In addition, several other improvements for original FOA technique is also considered, such as: shrunk exploring radius using osphresis, and a new distance function. Application of the proposed MFOA approach on several benchmark functions and parameter identification of synchronous generator shows an effective improvement in its performance over original FOA technique. (C) 2014 Elsevier Inc. All rights reserved.
The Mars Oxygen ISRU Experiment (MOXIE) is an instrument onboard NASA's Perseverance rover. On April 20th, 2021, MOXIE generated oxygen on Mars from the carbon dioxide present in the Martian atmosphere, demonstrat...
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The Mars Oxygen ISRU Experiment (MOXIE) is an instrument onboard NASA's Perseverance rover. On April 20th, 2021, MOXIE generated oxygen on Mars from the carbon dioxide present in the Martian atmosphere, demonstrating, for the first time, in-situ resource utilization (ISRU) on the surface of another celestial body. Learnings from MOXIE on Mars have aided in the design of a scaled-up version of MOXIE. Oxygen generated from this scaled-up system would be used as propellant in a Mars Ascent Vehicle that would enable the crew to return to Earth once their mission was complete, as well as in life support systems. Failure of any of its subsystems would result in a loss of mission due to the inability of the crew to return to Earth. Accordingly, risk analysis is one of the most crucial steps in the design of the scaled-up MOXIE that must be completed and understood before building and launching the system to Mars. The intent of this paper is to present a comprehensive, quantitative analysis of the operational risks associated with this Mars ISRU plant. We then present an approach to optimize the reliability of each subsystem using a modified probabilistic risk assessment and heuristics-based optimization algorithm.
Market analyzers use different parameters as features in the market data to analyze the market trends. The feature's values act as a signal to market fluctuations. Many studies have examined these features to pred...
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Market analyzers use different parameters as features in the market data to analyze the market trends. The feature's values act as a signal to market fluctuations. Many studies have examined these features to predict market movement more effectively. However, the method to minimize the uncertainties associated with the features is not available in the literature. This exploratory study introduces the uncertainty optimization based feature selection method for stock marketing. We introduce a notion of certainty region of the feature as the set of feature values, which signify particular happening with certainty. We use rough set theory to find the feature's certainty region and uncertainty region and measure each feature's significance. The feature whose certainty region is the maximum is the most significant in the feature space. Hence we group the features by minimizing the uncertainty region of the most informative features to get feature subsets for feature selection. We propose an algorithm based on uncertainty optimization to find subsets of the feature set for effectiveness and performance enhancement in the feature selection. We obtain the decision rules with comprehensive coverage and excellent support using the selected features. The accuracy of classification using the chosen parameters is up to 85.91%, which is higher than 79.54% of the complete feature set. The study provides an uncertainty optimization model for more efficient market movement prediction.
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