【目的】为内蒙古地区宜机械粒收玉米品种评价提供数据支持及生产上宜机械粒收玉米品种选择和适时收获提供参考。【方法】以极早熟、早熟、中早熟、中熟、中晚熟、晚熟6个熟期组代表玉米品种为试验材料,在内蒙古7个玉米种植区域同步开...【目的】为内蒙古地区宜机械粒收玉米品种评价提供数据支持及生产上宜机械粒收玉米品种选择和适时收获提供参考。【方法】以极早熟、早熟、中早熟、中熟、中晚熟、晚熟6个熟期组代表玉米品种为试验材料,在内蒙古7个玉米种植区域同步开展机械粒收性状研究,于生理成熟,生理成熟后10、20、30 d 4个收获时间,测定植株倒伏倒折率、籽粒含水率以及籽粒破碎率、籽粒破损率、杂质率;利用内蒙古各区域鉴选的宜机械粒收品种进行60000、75000、90000、105000株/hm^(2)4个密度梯度下的产量比较。【结果】植株倒伏倒折率随生理成熟后天数的增加呈上升趋势;籽粒含水率和脱水速率随生理成熟后天数的增加呈下降趋势;籽粒破碎率、籽粒破损率、杂质率均随生理成熟后天数的增加而降低。籽粒破碎率、籽粒破损率、杂质率与籽粒含水率均呈一元二次曲线关系,当籽粒含水率依次为16.1%、17.0%和15.6%时,籽粒破碎率、籽粒破损率、杂质率最小,分别为1.3%、1.1%和0.6%。脱水速率与日平均气温呈一元二次曲线关系,当日平均气温在0~5℃时,脱水缓慢,日脱水0.197~0.211个百分点,从生理成熟到籽粒含水率降至25.0%时所需的积温为100~200℃。各区域宜机械粒收品种的适宜种植密度为90000株/hm^(2)。【结论】明确了宜机械粒收玉米评价指标:收获期植株倒伏倒折率≤5.0%、籽粒含水率≤25.0%、籽粒破碎率≤5.0%、籽粒破损率≤2.0%、杂质率≤1.1%、产量损失率≤5.0%,适宜收获期为生理成熟后10~20 d,适宜种植密度为90000株/hm^(2)。展开更多
A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, ...A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.展开更多
As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppr...As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.展开更多
文摘【目的】为内蒙古地区宜机械粒收玉米品种评价提供数据支持及生产上宜机械粒收玉米品种选择和适时收获提供参考。【方法】以极早熟、早熟、中早熟、中熟、中晚熟、晚熟6个熟期组代表玉米品种为试验材料,在内蒙古7个玉米种植区域同步开展机械粒收性状研究,于生理成熟,生理成熟后10、20、30 d 4个收获时间,测定植株倒伏倒折率、籽粒含水率以及籽粒破碎率、籽粒破损率、杂质率;利用内蒙古各区域鉴选的宜机械粒收品种进行60000、75000、90000、105000株/hm^(2)4个密度梯度下的产量比较。【结果】植株倒伏倒折率随生理成熟后天数的增加呈上升趋势;籽粒含水率和脱水速率随生理成熟后天数的增加呈下降趋势;籽粒破碎率、籽粒破损率、杂质率均随生理成熟后天数的增加而降低。籽粒破碎率、籽粒破损率、杂质率与籽粒含水率均呈一元二次曲线关系,当籽粒含水率依次为16.1%、17.0%和15.6%时,籽粒破碎率、籽粒破损率、杂质率最小,分别为1.3%、1.1%和0.6%。脱水速率与日平均气温呈一元二次曲线关系,当日平均气温在0~5℃时,脱水缓慢,日脱水0.197~0.211个百分点,从生理成熟到籽粒含水率降至25.0%时所需的积温为100~200℃。各区域宜机械粒收品种的适宜种植密度为90000株/hm^(2)。【结论】明确了宜机械粒收玉米评价指标:收获期植株倒伏倒折率≤5.0%、籽粒含水率≤25.0%、籽粒破碎率≤5.0%、籽粒破损率≤2.0%、杂质率≤1.1%、产量损失率≤5.0%,适宜收获期为生理成熟后10~20 d,适宜种植密度为90000株/hm^(2)。
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
基金Project(2018YFB2002100)supported by the National Key R&D Program of China。
文摘As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.
