Based on the characteristics of strata movement of solid backfilling mining technology, the surface subsidence prediction method based on the equivalent mining height theory was proposed, and the parameters selection ...Based on the characteristics of strata movement of solid backfilling mining technology, the surface subsidence prediction method based on the equivalent mining height theory was proposed, and the parameters selection guideline of this method was also described. While comparing the parameters of caving mining with equivalent height, the subsidence efficient can be calculated according to the mining height and bulk factor of sagging zone and fracture zone, the tangent of main influence angle of solid backfilling mining is reduced by 0.2-0.5（while it cannot be less than 1.0）. For sake of safety, offset of the inflection point is set to zero, and other parameters, such as horizontal movement coefficient and main propagation angle are equal to the corresponding parameters of caving mining with equivalent height. In the last part, a case study of solid backfilling mining subsidence prediction was described. The results show the applicability of this method and the difference of the maximum subsidence point between the prediction and the observation is less than 5%.展开更多
Plant height(PH)is an essential trait in maize(Zea mays)that is tightly associated with planting density,biomass,lodging resistance,and grain yield in the field.Dissecting the dynamics of maize plant architecture will...Plant height(PH)is an essential trait in maize(Zea mays)that is tightly associated with planting density,biomass,lodging resistance,and grain yield in the field.Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction,as the genetic basis controlling PH in maize remains largely unknown.In this study,we developed an automated high-throughput phenotyping platform(HTP)to systematically and noninvasively quantify 77 image-based traits(i-traits)and 20 field traits(f-traits)for 228 maize inbred lines across all developmental stages.Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth.An i-trait-based genome-wide association study identified 4945 traitassociated SNPs,2603 genetic loci,and 1974 corresponding candidate genes.We found that rapid growth of maize plants occurs mainly at two developmental stages,stage 2(S2)to S3 and S5 to S6,accounting for the final PH indicators.By integrating the PH-association network with the transcriptome profiles of specific internodes,we revealed 13 hub genes that may play vital roles during rapid growth.The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize.One candidate gene,ZmVATE,was functionally validated and shown to regulate PH-related traits in maize using genetic mutation.Furthermore,machine learning was used to build predictive models for final PH based on i-traits,and their performancewas assessed across developmental stages.Moderate,strong,and very strong correlations between predictions and experimental datasets were achieved from the early S4(tenth-leaf)stage.Colletively,our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH,as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with i展开更多
为解决在刑侦领域需要通过脚印信息预测身高的问题,文中提出一种基于深度学习的回归预测算法。该算法首先对原始数据进行预处理来得到适用于深度学习回归模型的数据集,然后根据足迹数据的特性提出了一种由边缘提取和回归预测两个部分组...为解决在刑侦领域需要通过脚印信息预测身高的问题,文中提出一种基于深度学习的回归预测算法。该算法首先对原始数据进行预处理来得到适用于深度学习回归模型的数据集,然后根据足迹数据的特性提出了一种由边缘提取和回归预测两个部分组成的新型网络架构FtH-Net(Foot to Height-Net),最后基于预处理得到的数据集和FtH-Net训练得到一个性能良好的预测模型。实验结果表明,相比于传统方法,该方法在保证模型泛化能力的同时大幅度提升了预测的准确率,预测身高2 cm以内的准确率达到了67%。展开更多
基金Project(2012BAB13B03)supported by the National Scientific and Technical Supporting Programs Funded of ChinaProject(41104011)supported by the National Natural Science Foundation of China+1 种基金Project(2013QNB07)supported by the Natural Science Funds for Young Scholar of China University of Mining and TechnologyProject(2012LWB32)supported by the Fundamental Research Funds for the Central Universities,China
文摘Based on the characteristics of strata movement of solid backfilling mining technology, the surface subsidence prediction method based on the equivalent mining height theory was proposed, and the parameters selection guideline of this method was also described. While comparing the parameters of caving mining with equivalent height, the subsidence efficient can be calculated according to the mining height and bulk factor of sagging zone and fracture zone, the tangent of main influence angle of solid backfilling mining is reduced by 0.2-0.5（while it cannot be less than 1.0）. For sake of safety, offset of the inflection point is set to zero, and other parameters, such as horizontal movement coefficient and main propagation angle are equal to the corresponding parameters of caving mining with equivalent height. In the last part, a case study of solid backfilling mining subsidence prediction was described. The results show the applicability of this method and the difference of the maximum subsidence point between the prediction and the observation is less than 5%.
文摘将增材制造技术和弧焊机器人技术相结合,对复杂薄壁件进行电弧增材制造技术研究.首先在传统的分层方法的基础上,对成形过程中高度变化的几何模型进行预测并分析,再通过机器人的离线编程与轨迹规划技术,实现成形轨迹的自动提取.进一步利用圆弧离散局部逼近算法,对复杂薄壁件的切片截面进行微分,计算出四元数矩阵,实现焊枪位姿自动调整;并对焊接工艺进行改良,保证成形质量.最后通过焊制部分薄壁件进行试验验证.结果表明,薄壁件成形质量良好,预测尺寸与实际成形尺寸误差不超过1 mm.
基金supported by the National Key Research and Development Program of China(2021YFF1000301 and 2021YFF1000304)the National Natural Science Foundation of China(32172091)+4 种基金the National Key Research and Development Program of China(2016YFD0100103)the Fundamental Research Funds for Central Non-Profit of Chinese Academy of Agricultural Sciences(CAAS-ZDRW202109 and Y2020PT06)the Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-ZDRW202004)the 2020 Research Program of Sanya Yazhou Bay Science and Technology City(SKJC-2020-02-005)the Nanfan special project of the Chinese Academy of Agricultural Sciences(YBXM15).
文摘Plant height(PH)is an essential trait in maize(Zea mays)that is tightly associated with planting density,biomass,lodging resistance,and grain yield in the field.Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction,as the genetic basis controlling PH in maize remains largely unknown.In this study,we developed an automated high-throughput phenotyping platform(HTP)to systematically and noninvasively quantify 77 image-based traits(i-traits)and 20 field traits(f-traits)for 228 maize inbred lines across all developmental stages.Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth.An i-trait-based genome-wide association study identified 4945 traitassociated SNPs,2603 genetic loci,and 1974 corresponding candidate genes.We found that rapid growth of maize plants occurs mainly at two developmental stages,stage 2(S2)to S3 and S5 to S6,accounting for the final PH indicators.By integrating the PH-association network with the transcriptome profiles of specific internodes,we revealed 13 hub genes that may play vital roles during rapid growth.The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize.One candidate gene,ZmVATE,was functionally validated and shown to regulate PH-related traits in maize using genetic mutation.Furthermore,machine learning was used to build predictive models for final PH based on i-traits,and their performancewas assessed across developmental stages.Moderate,strong,and very strong correlations between predictions and experimental datasets were achieved from the early S4(tenth-leaf)stage.Colletively,our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH,as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with i
文摘为解决在刑侦领域需要通过脚印信息预测身高的问题,文中提出一种基于深度学习的回归预测算法。该算法首先对原始数据进行预处理来得到适用于深度学习回归模型的数据集,然后根据足迹数据的特性提出了一种由边缘提取和回归预测两个部分组成的新型网络架构FtH-Net(Foot to Height-Net),最后基于预处理得到的数据集和FtH-Net训练得到一个性能良好的预测模型。实验结果表明,相比于传统方法,该方法在保证模型泛化能力的同时大幅度提升了预测的准确率,预测身高2 cm以内的准确率达到了67%。
