Good Point!是香港理工大学开发的一个全英在线交流平台,其教学目标是培养学习者的英语在线学习能力,因此将其应用于大学英语教学。通过对比分析前期和后期的在线交流记录、写作记录以及学生反馈探讨该平台的教学效果。结果发现:该平台...Good Point!是香港理工大学开发的一个全英在线交流平台,其教学目标是培养学习者的英语在线学习能力,因此将其应用于大学英语教学。通过对比分析前期和后期的在线交流记录、写作记录以及学生反馈探讨该平台的教学效果。结果发现:该平台能有效提高受试的英语在线交流能力、写作能力和英语思辨能力。可见该平台有利于培养大学生的英语在线探究能力,是一个值得推广的教学平台。展开更多
Good Point是香港理工大学开发的在线系统,使用者主要是旨在提高写作技能、发展批判性思维的教师和学生群体。结合Good Point系统的主要特点、功能,分析该系统在英语写作教学中的应用优势,指出该系统符合网络外语学习对教和学的方式、...Good Point是香港理工大学开发的在线系统,使用者主要是旨在提高写作技能、发展批判性思维的教师和学生群体。结合Good Point系统的主要特点、功能,分析该系统在英语写作教学中的应用优势,指出该系统符合网络外语学习对教和学的方式、教学资源、教学评价、激励等方面的要求。能够突破传统英语写作教学的种种局限,引导学生自信地走向有效进行独立写作的良性循环。恰当应用该系统可在写作课程的教学结构、教学资源、教学模式等方面有所创新,实现教学绩效的良性提升。展开更多
With the integral-level approach to global optimization, a class of discon-tinuous penalty functions is proposed to solve constrained minimization problems. Inthis paper we propose an implementable algorithm by means ...With the integral-level approach to global optimization, a class of discon-tinuous penalty functions is proposed to solve constrained minimization problems. Inthis paper we propose an implementable algorithm by means of the good point set ofuniform distribution which conquers the default of Monte-Carlo method. At last weprove the convergence of the implementable algorithm.展开更多
锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,L...锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)的组合模型超参数的超超临界锅炉NO_(x)排放预测的方法。首先通过Pearson相关性判定与NO_(x)排放相关的特征参数;其次建立CNN-LSTM预测模型,利用卷积神经网络CNN提取分层数据结构,长短期记忆网络挖掘长期依赖关系,然后结合佳点集、t分布变异策略对蜣螂算法进行改进,用改进后的算法对LSTM超参数进行优化得到最终预测模型;最后与其他神经网络模型进行对比验证。以某660 MW机组锅炉深度调峰实际数据进行预测,结果得到NO_(x)排放浓度实际值与预测值的平均绝对误差为3.3516,平均相对误差为2.4667,数据结果表明该预测模型具有更准确的预测效果。展开更多
A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low opti...A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.展开更多
基金This work is supported by the National Natural Science Foundation of China(grants 19871053)and by the Science and Technology Development Foundation of Shanghai
文摘With the integral-level approach to global optimization, a class of discon-tinuous penalty functions is proposed to solve constrained minimization problems. Inthis paper we propose an implementable algorithm by means of the good point set ofuniform distribution which conquers the default of Monte-Carlo method. At last weprove the convergence of the implementable algorithm.
文摘锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)的组合模型超参数的超超临界锅炉NO_(x)排放预测的方法。首先通过Pearson相关性判定与NO_(x)排放相关的特征参数;其次建立CNN-LSTM预测模型,利用卷积神经网络CNN提取分层数据结构,长短期记忆网络挖掘长期依赖关系,然后结合佳点集、t分布变异策略对蜣螂算法进行改进,用改进后的算法对LSTM超参数进行优化得到最终预测模型;最后与其他神经网络模型进行对比验证。以某660 MW机组锅炉深度调峰实际数据进行预测,结果得到NO_(x)排放浓度实际值与预测值的平均绝对误差为3.3516,平均相对误差为2.4667,数据结果表明该预测模型具有更准确的预测效果。
基金the National Natural Science Foundation of China(No.62176146)。
文摘A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.
