针对雷达装备测试性评估这一不确定性、多属性决策问题,依据装备特点和测试性要求,构建了雷达装备测试性评估指标体系。采用改进的模糊层次分析法(fuzzy analytical hierarchy process,FAHP)和熵权法对评估指标进行主客观赋权。在评估...针对雷达装备测试性评估这一不确定性、多属性决策问题,依据装备特点和测试性要求,构建了雷达装备测试性评估指标体系。采用改进的模糊层次分析法(fuzzy analytical hierarchy process,FAHP)和熵权法对评估指标进行主客观赋权。在评估模型中使用相对熵替换逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)的欧式距离,同时引入秩和比法(rank-sum ratio,RSR),利用改进的TOPSIS-RSR法对备选方案进行测试性优劣等级分档并排序,通过实例分析验证了该模型的有效性。展开更多
目的建立超高效液相色谱法(ultra performance liquid chromatography,UPLC)快速测定蜂胶提取物中的14种化学成分,结合多元统计分析方法对不同厂家的蜂胶提取物质量进行综合评价。方法收集来自不同厂家的17批蜂胶提取物样品,采用UPLC采...目的建立超高效液相色谱法(ultra performance liquid chromatography,UPLC)快速测定蜂胶提取物中的14种化学成分,结合多元统计分析方法对不同厂家的蜂胶提取物质量进行综合评价。方法收集来自不同厂家的17批蜂胶提取物样品,采用UPLC采集色谱图,甲醇-0.2%磷酸水溶液为流动相,梯度洗脱,同时测定咖啡酸、p-香豆酸、阿魏酸、异阿魏酸、3,4-二甲氧基肉桂酸、咖啡酸苯乙酯、阿替匹林C、槲皮素、山奈素、芹菜素、异鼠李素、乔松素、白杨素、高良姜素的含量,运用统计学软件进行主成分分析(principal component analysis,PCA)、聚类分析(clustering analysis,CA)、偏最小二乘-判别分析(partial least squares-discriminant analysis,PLS-DA),筛选分析质量差异标志物。通过熵权法计算各指标权重,将结果应用于优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)和秩和比法(rank sum ratio,RSR)构建综合评价模型,评价不同批次的蜂胶提取物质量优劣。结果14个指标成分在各自的浓度范围内线性关系良好(r≥0.9992),平均加样回收率是96.37%~102.21%,相对标准偏差小于2%。化学计量学结果表明17批样品聚为4类,同一个厂家的样品聚为一类,不同厂家的样品存在明显差异,3,4-二甲氧基肉桂酸、异阿魏酸、槲皮素、高良姜素、阿替匹林C、咖啡酸苯乙酯可能是影响厂家质量差异的潜在标志物。通过熵权-TOPSIS、熵权-RSR以及两者相结合的方式构建的综合质量评价模型,对不同批次蜂胶提取物的质量优劣排序结果较为一致。结论基于UPLC的多指标测定方法准确便捷,结合PCA、CA、PLS-DA和TOPSIS-RSR建立的评价模式能够有效分析不同厂家的差异性,为蜂胶提取物的整体质量评价提供参考。展开更多
Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial featur...Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.展开更多
Ideal point method is one of the methods to solve multi-objective problem. It is applied to forest harvest regu-lation, and showed very good results by analyzing changes of quantitative indexes of forest resource stru...Ideal point method is one of the methods to solve multi-objective problem. It is applied to forest harvest regu-lation, and showed very good results by analyzing changes of quantitative indexes of forest resource structure before andafter the regulation. This method can be applied as one of the mathematical tools in forest harvest regulation.展开更多
文摘针对雷达装备测试性评估这一不确定性、多属性决策问题,依据装备特点和测试性要求,构建了雷达装备测试性评估指标体系。采用改进的模糊层次分析法(fuzzy analytical hierarchy process,FAHP)和熵权法对评估指标进行主客观赋权。在评估模型中使用相对熵替换逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)的欧式距离,同时引入秩和比法(rank-sum ratio,RSR),利用改进的TOPSIS-RSR法对备选方案进行测试性优劣等级分档并排序,通过实例分析验证了该模型的有效性。
文摘目的建立超高效液相色谱法(ultra performance liquid chromatography,UPLC)快速测定蜂胶提取物中的14种化学成分,结合多元统计分析方法对不同厂家的蜂胶提取物质量进行综合评价。方法收集来自不同厂家的17批蜂胶提取物样品,采用UPLC采集色谱图,甲醇-0.2%磷酸水溶液为流动相,梯度洗脱,同时测定咖啡酸、p-香豆酸、阿魏酸、异阿魏酸、3,4-二甲氧基肉桂酸、咖啡酸苯乙酯、阿替匹林C、槲皮素、山奈素、芹菜素、异鼠李素、乔松素、白杨素、高良姜素的含量,运用统计学软件进行主成分分析(principal component analysis,PCA)、聚类分析(clustering analysis,CA)、偏最小二乘-判别分析(partial least squares-discriminant analysis,PLS-DA),筛选分析质量差异标志物。通过熵权法计算各指标权重,将结果应用于优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)和秩和比法(rank sum ratio,RSR)构建综合评价模型,评价不同批次的蜂胶提取物质量优劣。结果14个指标成分在各自的浓度范围内线性关系良好(r≥0.9992),平均加样回收率是96.37%~102.21%,相对标准偏差小于2%。化学计量学结果表明17批样品聚为4类,同一个厂家的样品聚为一类,不同厂家的样品存在明显差异,3,4-二甲氧基肉桂酸、异阿魏酸、槲皮素、高良姜素、阿替匹林C、咖啡酸苯乙酯可能是影响厂家质量差异的潜在标志物。通过熵权-TOPSIS、熵权-RSR以及两者相结合的方式构建的综合质量评价模型,对不同批次蜂胶提取物的质量优劣排序结果较为一致。结论基于UPLC的多指标测定方法准确便捷,结合PCA、CA、PLS-DA和TOPSIS-RSR建立的评价模式能够有效分析不同厂家的差异性,为蜂胶提取物的整体质量评价提供参考。
基金This work is supported by the National Nature Science Foundation of China(NSFC)under Grant Nos.61571106,61501169,41706103the Fundamental Research Funds for the Central Universities under Grant No.2242013K30010.
文摘Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.
文摘Ideal point method is one of the methods to solve multi-objective problem. It is applied to forest harvest regu-lation, and showed very good results by analyzing changes of quantitative indexes of forest resource structure before andafter the regulation. This method can be applied as one of the mathematical tools in forest harvest regulation.
