Metasurface can be used to manipulate the polarization, amplitude, phase of electromagnetic waves which has been applied in various fields such as holography technology, imaging and sensing. However, traditional metas...
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ISBN:
(纸本)9798350386783;9798350386776
Metasurface can be used to manipulate the polarization, amplitude, phase of electromagnetic waves which has been applied in various fields such as holography technology, imaging and sensing. However, traditional metasurface optimization methods such as parameters scanning require a considerable amount of time and computing power and cannot cover every set of parameters due to the setting of step size, which may lead to the optimization result falling into a local optimal solution. To address the above-mentioned issues, in this paper, we propose a method that combines transfer learning optimization network with grey wolf optimization algorithm for structural parameters optimization. Using deep learning networks can significantly improve the speed of spectral prediction, while transfer learning algorithms can enhance the prediction accuracy of the networks. The grey wolf optimization algorithm belongs to a global optimization algorithm and its performance is superior to other traditional algorithms, thus enabling it to achieve a wider bandwidth. The results show that the bandwidth of the transmission spectrum obtained through the grey wolf optimization algorithm is 87.37 nm, which is wider than that achieved through traditional method of parameter scanning. At the same time, it only takes 45 minutes, which is one-seventieth of the time required by traditional optimization methods.
Cardiac image fusion is the combination of medical images of heart acquired by different modalities. A prerequisite for image fusion is the image registration process, that allows the spatial alignment of the volumetr...
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ISBN:
(纸本)9783642038815
Cardiac image fusion is the combination of medical images of heart acquired by different modalities. A prerequisite for image fusion is the image registration process, that allows the spatial alignment of the volumetric data sets involved in the fusion. In this paper we present a registration algorithm able to align positron emission tomography (PET) and computed tomography (CT) cardiac images. The registration method was developed by hypotizing a rigid transformation between data sets and using the mutual information (MI) as registration metric. The core of the study is the development of a new and robust optimization algorithm that allowed to determine the global maximum of MI and to define the correct spatial transformation. The optimization algorithm exploits a multi-resolution method. In the first step, a global optimization method (i.e. genetic algorithm) explores the entire space of possible transformations to find a solution near the global maximum of the MI. In the second step, a local optimizer (i.e. simplex method) refines the search finding the global maximum. Different data resolutions and interpolation algorithms were adopted in the two steps to achieve reasonable processing times. The developed algorithm was tested on two volumetric PET and CT cardiac software models and on PET and CT real data sets. The developed optimization algorithm allows to reach the global maximum of the similarity function and to determine the correct spatial alignment in both the synthetic and real data sets.
This passage is to put forward knapsack problem optimization algorithm based on complex network(KOABCN). Knapsack problem has extremely wide application in a great number of fields. For instance, knapsack problem can ...
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ISBN:
(纸本)9783038351153
This passage is to put forward knapsack problem optimization algorithm based on complex network(KOABCN). Knapsack problem has extremely wide application in a great number of fields. For instance, knapsack problem can be applied in information coding, budget control, project choosing, material cutting, cargo loading and unloading as well as Internet information safety. Since 1950's, knapsack problem has been one of the most heated topics in algorithm and complexity research. Therefore, knapsack will still be largely focused in the next period of research.
Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA...
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ISBN:
(纸本)9781509061266
Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA as starting location before running the FOA's procedure, and in the FOA's processing, the SVM parameters is modified by dynamically updating the location of each fruit-fly and the optimal feature subset is changed by evolutionary process of genetic algorithm (GA) at the same time. In the proposed method, a weighted objective function is designed to evaluate population and to take account the trade-off between sensitivity and specificity. The best individual is used to guide for evolutionary process of FOA and GA. To evaluate the classification performance of the proposed approach, this study designs several groups of comparative experiments using the proposed approach with the well-known methods, and the four binary classification datasets from the UCI Machine Learning data repository are used for the experiments. The empirical results show that the proposed approach achieves better results in terms of classification performance and a low computational cost on solving the binary classification problems.
In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray w...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
In order to improve the accuracy of wind speed prediction, a wind speed prediction model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), long short-term memory (LSTM) and gray wolf optimization (GWO) algorithm was proposed from the perspective of reducing wind speed nonstationarity and optimizing combination weight. First, CEEMDAN was used to decompose the observed wind speed into a series of sub-sequences reflecting the characteristics of the original wind speed. Then the subsequence is predicted by LSTM, and the predicted value of the subsequence is output. Finally, the combined weight of the sub-sequences was optimized by GWO, and the sub-sequences were combined to obtain the wind speed prediction results. The experimental results show that CEEMDAN-LSTM-GWO wind speed prediction model proposed in this study has better performance than the comparison model.
Throughout the last few decades, Nature-Inspired algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler Optimiz...
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In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor re...
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ISBN:
(纸本)9781728101057
In the actual industrial process, it is the key to recognize the fault variables accurately as soon as possible after the fault is detected. Recently, a fault variable recognition method based on k-nearest neighbor reconstruction (FVR-kNN) has been proposed. However, dealing with fault problem caused by multiple variables, the algorithm needs to exhaustive the arrangement of all variables, resulting in high complex computation. And the multivariate estimation in FVR-KNN is not accurate. Thus, this paper proposes a variable recognition optimization algorithm based on FVR-kNN (OFVR-kNN). It optimizes the estimation steps of FVR-kNN in reconstructing multivariate, guaranteeing that the estimations of these potential fault variables have no mutual influence. According to the fault magnitude in corresponding direction, the fault variables are selected in turn. OFVR-kNN does not need to exhaustive all the combinations, greatly reducing the number of reconstructions in fault sample. In this paper, the validity of the optimization method is proved in Tennessee Eastman process.
As global attention on renewable and clean energy grows, the research and implementation of microgrids become paramount. This paper delves into the methodology of exploring the relationship between the operational and...
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Diabetic Retinopathy is a severe health problem in the modern world that affects people of all ages and can result in blindness. Many of the affected persons won't lose their eyesight if Diabetic Retinopathy can b...
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This paper deals with one of the dire needs of wildlife conservation: effective decision-making frameworks. Growing global connectivity and economic development result in growing demand for certain wildlife products, ...
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