农作物区域试验精确度分析是品种和试验环境科学评价的基础。本研究分析了2000―2014年期间长江流域、黄河流域和西北内陆棉区国家棉花区试的试验精确度分布与演变动态,并分析比较了棉花12个主要性状的精确度差异,旨在全面分析和评价我...农作物区域试验精确度分析是品种和试验环境科学评价的基础。本研究分析了2000―2014年期间长江流域、黄河流域和西北内陆棉区国家棉花区试的试验精确度分布与演变动态,并分析比较了棉花12个主要性状的精确度差异,旨在全面分析和评价我国棉花品种区域试验精确度的发展水平和演变规律,为全国棉花品种区域试验的合理布局和优化设计提供参考依据。研究结果表明:(1)单年单点棉花区域试验能鉴别出5%、8%、10%、12%、15%和20%品种间差异的比率分别约为20%、50%、70%、83%、92%和98%。(2)单年多点棉花区域试验精确度呈逐年提高的演变趋势,RLSD0.05(RLSD,relative least significant difference)由2000年的11.43%下降到2014年的7.1%,其中长江流域的RLSD0.05已经连续9年、黄河流域棉区的RLSD0.05连续4年稳定在5%以下,可以稳定地鉴别品种间5%的差异;西北内陆棉区的RLSD0.05也呈逐年下降趋势,目前可以可靠鉴别品种间10%的差异。(3)棉花12个主要性状的试验精确度差异显著,其中皮棉产量和结铃数的精确度较低,霜前花率、衣分、纤维长度和生育期的精确度较高,其余性状精确度中等。展开更多
A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms (CLONALG), and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.展开更多
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kapp...Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.展开更多
文摘农作物区域试验精确度分析是品种和试验环境科学评价的基础。本研究分析了2000―2014年期间长江流域、黄河流域和西北内陆棉区国家棉花区试的试验精确度分布与演变动态,并分析比较了棉花12个主要性状的精确度差异,旨在全面分析和评价我国棉花品种区域试验精确度的发展水平和演变规律,为全国棉花品种区域试验的合理布局和优化设计提供参考依据。研究结果表明:(1)单年单点棉花区域试验能鉴别出5%、8%、10%、12%、15%和20%品种间差异的比率分别约为20%、50%、70%、83%、92%和98%。(2)单年多点棉花区域试验精确度呈逐年提高的演变趋势,RLSD0.05(RLSD,relative least significant difference)由2000年的11.43%下降到2014年的7.1%,其中长江流域的RLSD0.05已经连续9年、黄河流域棉区的RLSD0.05连续4年稳定在5%以下,可以稳定地鉴别品种间5%的差异;西北内陆棉区的RLSD0.05也呈逐年下降趋势,目前可以可靠鉴别品种间10%的差异。(3)棉花12个主要性状的试验精确度差异显著,其中皮棉产量和结铃数的精确度较低,霜前花率、衣分、纤维长度和生育期的精确度较高,其余性状精确度中等。
基金Project(2010ZC13012) supported by the Aviation Science Funds of China
文摘A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms (CLONALG), and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
基金Supported by the National 973 Program of China (No. 2006CB701302)the Hubei Department of Science and Technology (No. 2007ABA276)
文摘Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.