排序方式: 共有92条查询结果,搜索用时 244 毫秒
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对常规熟练曲线建模方法和支持向量机模型用于某型武器装备的批量生产经济性分析进行研究,着重分析了应用支持向量机进行批量生产经济性分析的具体形式以及方法特点,并给出了应用实例,结果令人满意。 相似文献
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Ibtissame Bentahar Mohammed Raji 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2021,67(3):945-963
The eastern part of the Rich area consists of the massive Paleozoic and Meso-Cenozoic cover formations that present the geodynamic development of the study area, where is characterized by various carbonate facies of Jurassic age. The geographical characteristic of the study area leaves the zone difficult to map by conventional methods. The objective of this work focuses on the mapping of the constituent lithological units of the study area using multispectral data of Landsat OLI, ASTER, and Sentinel 2A MSI. The processing of these data is based on a precise methodology that distinguishs and highlights the limits of the different lithological units that have an approximate similarity of spectral signature. Three techniques were used to enhance the image including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). Lithological mapping was performed using two types of supervised classification : Maximum likelihood classifier (MLC) and Support Vector Machine (SVM).The results of processing data show the effectiveness of Sentinel 2A data in mapping of lithological units than the ASTER and Landsat OLI data. The classification evaluation of two methods of the Sentinel 2A MSI image showed that the SVM method give a better classification with an overall accuracy of 93,93% and a Kappa coefficient of 0.93, while the MLC method present an overall accuracy of 82,86% and a Kappa coefficient of 0.80. The results of mapping obtained show a good correlation with the geological map of the study area as well as the efficiency of remote sensing in identification of different lithological units in the Central High Atlas. 相似文献
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Task allocation strategies for cooperative task planning of multi-autonomous satellite constellation
Feng Yao Jiting Li Yuning Chen Xiaogeng Chu Bang Zhao 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2019,63(2):1073-1084
In recent years, the application of imaging satellites has entered a completely new stage, with the new demands such as rapid response to emergency events, observation of large-scale regional targets and multivariate data fusion, the multi-autonomous satellite constellation has been proposed. This paper first designs the structure of multi-autonomous satellite constellation, and a centralized-distributed structure is proposed. This structure could improve the dynamic response capacity of the whole constellation. Then, this paper adds adapted filtering mechanism to single autonomous satellite online scheduling algorithm to enhance its performance. This article also pays more attention on task allocation strategies of the master satellite in constellation, and ten different task allocation strategies based on five dimensions are analyzed by simulation experiments. At last, this paper extracts several characteristic features of the regular observation targets and designs a selector based on support vector machine (SVM). This selector could select an appropriate strategy according to the features of each experiment scenario. 相似文献
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《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2020,65(4):1250-1262
The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model. 相似文献
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Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem 总被引:1,自引:0,他引:1
Mingmin Chi Rui Feng Lorenzo Bruzzone 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2008,41(11):1793-1799
With recent technological advances in remote sensing, very high-dimensional (hyperspectral) data are available for a better discrimination among different complex land-cover classes having similar spectral signatures. However, this large number of bands makes very complex the task of automatic data analysis. In the real application, it is difficult and expensive for the expert to acquire enough training samples to learn a classifier. This results in a classification problem with small-size training sample set. Recently, a regularization-based algorithm is usually proposed to handle such problem, such as Support Vector Machine (SVM), which usually are implemented in the dual form with Lagrange theory. However, it can be solved directly in primal formulation. In this paper, we introduces an alternative implementation technique for SVM to address the classification problem with small-size training sample set. It has been empirically proven that the effectiveness of the introduced implementation technique which has been evaluated by benchmark datasets. 相似文献
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DiverseAdaBoost-SVM分类方法及其在航空发动机故障诊断中的应用 总被引:1,自引:0,他引:1
提出采用考虑到精度/差异权衡的SVM作为弱分类器的一种新的组合分类诊断方法——Diverse AdaBoost-SVM。该方法通过在一组具有适当精度的弱分类器中进一步选择具有较大差异性的弱分类器,对这些具有较大差异性的弱分类器进行组合,从而较好解决AdaBoost算法中存在的精度/差异权衡的难题;同时该方法也较好地解决了现有的AdaBoost方法存在的弱分类器本身参数选取困难问题及训练轮数T的合理选取问题。通过对基准数据库的测试及航空发动机故障样本的诊断,结果表明和其他方法相比,Diverse AdaBoost -SVM方法具有更好的泛化性能,更适合对分散程度较大、聚类性较差的航空发动机故障样本进行分类,也更适合对非对称故障样本集进行分类。 相似文献
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