检索结果-南通市图书馆

Minimum Data Set for Assessing Soil Quality in Farmland of Northeast China

Pedosphere 2013年第5期23卷 564-576页

作者： CHEN Yu-Dong WANG Huo-Yan ZHOU Jian-Min XING Lu ZHU Bai-Shu ZHAO Yong-Cun CHEN Xiao-Qin Institute of Soil Science Chinese Academy of Science University of Chinese Academy of Sciences Southwest University College of Resources and Environment

Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soybean （Glycine max L. Merill） growing area located in Northeast China, was evaluated using soil quality index （SQI） methods. Each SQI was computed using a minimum data set （MDS） selected using principal components analysis （PCA） as a data reduction technique. Eight MDS indicators were selected from 20 physical and chemical soil measurements. The MDS accounted for 74.9% of the total variance in the total data set （TDS）. The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods. The results showed that SQI values correlated well with soybean yield （r = 0.658＊＊） when indicators in MDS were weighted by the regression coefficient computed for each yield and index. Stepwise regression between yield and principal components （PCs） indicated that available boron （AvB）, available phosphorus （AvP）, available potassium （AvK）, available iron （AvFe） and texture were the main factors limiting soybean yield. The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.

关键词： norm value principal component analysis soil quality index stepwise regression

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维普期刊数据库

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A Face Quality Evaluation Method Based on DCNN

A Face Quality Evaluation Method Based on DCNN

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第32届中国控制与决策会议

作者： Chen ShuangYe Zhang HongLu Yang JianMin Beijing University of Technology

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In response to the problem of low recognition rate caused by low quality face image in face recognition, a face quality evaluation method based on deep convolutional neural network is proposed. Firstly, the depthwise separable convolution method is adopted to establish a depth-convolution model which contain 8 blocks to achieve face feature extraction. Secondly, the last layer of the convolution module connects 8 output branches, and uses regression and classification methods to predict the probability of 8 attributes, namely yaw, pitch, norm, opening mouth, occlusion, blur, dim, and closed eyes. The proposed method also proves that norm value can help analyze the quality of face image. Finally, the weight optimization is realized by taking the video pass rate as the optimal objective function. the quality score of face image can be calculated by weighting each branch The higher the score, the better the quality of the face image. In this paper, an end-to-end face quality evaluation model is implemented by using depthwise separable convolution method. The parameters of the model are less than 60000, the operation speed is fast, and the evaluation results are accurate;and it can filter out low-quality face image in real time, and recommend high-quality face image to face recognition model.

关键词： Deep Learning Multi-task Network norm value Face Quality Evaluation

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