Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time ...Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.展开更多
目的探索和优化以临床病理病生治疗学(clinical pathophysiology and therapy,CPPT)为基础的临床病例讨论课的教学内容,提高该课程的教学质量。方法自编带教教师评价表,对2016至2017学年参加该课程的全体学生进行调查,评价教学满意度及...目的探索和优化以临床病理病生治疗学(clinical pathophysiology and therapy,CPPT)为基础的临床病例讨论课的教学内容,提高该课程的教学质量。方法自编带教教师评价表,对2016至2017学年参加该课程的全体学生进行调查,评价教学满意度及教学特点。比较不同学制的学生或针对不同年资的教师评价结果的差异,分析评价结果与教师资历的相关性。结果学生对目前该课程整体有较高的评价,但不同学制的学生对启发思维、鼓励发言、引导提问、关注进程和有效反馈等方面的评价,以及掌握程度的自评差异具有统计学意义。针对不同资历的教师,除发言鼓励以外的各方面得分均存在统计学差异,但掌握程度差异无明显统计学意义。Spearman分析显示,得分与教师资历存在一定的相关性。结论CPPT病例讨论课整体情况值得肯定,但还需要在许多方面继续改进。展开更多
基金supported by the West Light Foundation of the Chinese Academy of Sciences
文摘Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.
文摘目的探索和优化以临床病理病生治疗学(clinical pathophysiology and therapy,CPPT)为基础的临床病例讨论课的教学内容,提高该课程的教学质量。方法自编带教教师评价表,对2016至2017学年参加该课程的全体学生进行调查,评价教学满意度及教学特点。比较不同学制的学生或针对不同年资的教师评价结果的差异,分析评价结果与教师资历的相关性。结果学生对目前该课程整体有较高的评价,但不同学制的学生对启发思维、鼓励发言、引导提问、关注进程和有效反馈等方面的评价,以及掌握程度的自评差异具有统计学意义。针对不同资历的教师,除发言鼓励以外的各方面得分均存在统计学差异,但掌握程度差异无明显统计学意义。Spearman分析显示,得分与教师资历存在一定的相关性。结论CPPT病例讨论课整体情况值得肯定,但还需要在许多方面继续改进。
