Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data....Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.展开更多
In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural...In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.展开更多
EMS诱变育种是作物种质创新的重要手段,利用反向遗传学手段TILLING(targeting induced local lesions in genomes)筛选EMS突变体,是研究基因功能与获得优良种质的有效手段之一。本研究采用基于HRM,即高分辨率熔解曲线分析技术的TILLING...EMS诱变育种是作物种质创新的重要手段,利用反向遗传学手段TILLING(targeting induced local lesions in genomes)筛选EMS突变体,是研究基因功能与获得优良种质的有效手段之一。本研究采用基于HRM,即高分辨率熔解曲线分析技术的TILLING筛选方法(HRM-TILLING)进行突变体筛选技术体系的探索,通过设计不同大小扩增片段引物及Mg^(2+)浓度梯度,比较了不同条件下的HRM筛选效果,结果表明当扩增片段长度为150 bp,Mg^(2+)浓度为3.0 mmol/L时,可以有效区分DNA 16倍混合池中ARF7A基因存在碱基差异的两种茄子(Solanum melongena L.)材料,建立了一套基于HRM的茄子EMS突变体TILLING技术的筛选方法。以含有2000个M2株系的茄子(EP26)EMS突变体库为材料,筛选出1个ARF7A基因和2个Pad-1基因突变体株系。本研究建立的筛选技术体系可以快速、高效地筛选茄子EMS突变体,所筛选的突变体为进一步验证、获取茄子单性结实种质及功能基因组学的研究提供研究材料。展开更多
为创制更丰富的豌豆变异材料,获取优异突变体豌豆种质,该研究以‘青建1号’豌豆为试验材料,以甲基磺酸乙酯(EMS)作为诱变剂,以EMS浓度1%、诱变时间8 h为半致死诱变条件,分析该诱变条件下豌豆植株突变类型,获得突变体重要表型性状数据,...为创制更丰富的豌豆变异材料,获取优异突变体豌豆种质,该研究以‘青建1号’豌豆为试验材料,以甲基磺酸乙酯(EMS)作为诱变剂,以EMS浓度1%、诱变时间8 h为半致死诱变条件,分析该诱变条件下豌豆植株突变类型,获得突变体重要表型性状数据,建立豌豆表型突变体库,并结合田间表型数据,筛选优异突变体材料。结果表明:(1)通过1%、8 h EMS诱变10000粒豌豆种子,M_(1)群体有4682株成苗,M_(2)群体筛选到342份豌豆突变体。(2)突变体豌豆表型性状突变类型比较丰富,其中单株籽粒干重变异系数最大,达到0.965。(3)通过对田间表型数据进行综合分析,筛选到10份优异的豌豆突变体种质。该研究结果丰富了豌豆种质资源,可为豌豆相关功能基因挖掘和研究及优良品种选育提供参考依据。展开更多
基金This work is supported by the NationalNatural Science Foundation of China(No.52075350)the Major Science and Technology Projects of Sichuan Province(No.2022ZDZX0001)the Special City-University Strategic Cooperation Project of Sichuan University and Zigong Municipality(No.2021CDZG-3).
文摘Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.
文摘In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.
文摘为创制更丰富的豌豆变异材料,获取优异突变体豌豆种质,该研究以‘青建1号’豌豆为试验材料,以甲基磺酸乙酯(EMS)作为诱变剂,以EMS浓度1%、诱变时间8 h为半致死诱变条件,分析该诱变条件下豌豆植株突变类型,获得突变体重要表型性状数据,建立豌豆表型突变体库,并结合田间表型数据,筛选优异突变体材料。结果表明:(1)通过1%、8 h EMS诱变10000粒豌豆种子,M_(1)群体有4682株成苗,M_(2)群体筛选到342份豌豆突变体。(2)突变体豌豆表型性状突变类型比较丰富,其中单株籽粒干重变异系数最大,达到0.965。(3)通过对田间表型数据进行综合分析,筛选到10份优异的豌豆突变体种质。该研究结果丰富了豌豆种质资源,可为豌豆相关功能基因挖掘和研究及优良品种选育提供参考依据。
