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
A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online....A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.展开更多
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。
基金Supported by the National Key Fundamental Research & Development Programs of P. R. China (2001CB309403)
文摘A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.
