Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree he...Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.展开更多
Nowadays,the city’s focus on education makes the improvement of learning environment become a new demand for development.From the the perspective of ecology,the theory of"resilience"is introduced to break t...Nowadays,the city’s focus on education makes the improvement of learning environment become a new demand for development.From the the perspective of ecology,the theory of"resilience"is introduced to break the stereotype of the existing learning environment and create a space for dialogue with nature from inside to outside.At the same time,the learning environment is divided into observation learning space,exploration learning space and communication learning space by theory and method of multiple intelligence integrating teaching and learning.Through the analysis on resilience characteristics of three types of space,eight basic unit forms of learning environment design are obtained.Arrangement and combination of its form can improve the campus environment,increase children’s learning efficiency and the risk response ability of campus environment,and give children the opportunity to interact with and understand nature in the learning process,so as to provide reference for the diversified development of education and learning space.展开更多
宽带跳频与深度强化学习结合的智能跳频通信模式能有效提高通信抗干扰能力。针对同时调整信号频点和功率的双动作空间智能决策由于频点离散但功率非离散使得决策依赖的深度强化学习算法难以设计的问题,基于离散型深度确定性策略梯度算法...宽带跳频与深度强化学习结合的智能跳频通信模式能有效提高通信抗干扰能力。针对同时调整信号频点和功率的双动作空间智能决策由于频点离散但功率非离散使得决策依赖的深度强化学习算法难以设计的问题,基于离散型深度确定性策略梯度算法(Wolpertinger Deep Deterministic Policy Gradient,W-DDPG),提出了一种适于宽带跳频通信且具有发射频率和功率组成的双动作空间智能抗干扰决策方法。该决策方法面向频率/功率双动作空间,在频率空间中使用Wolpertinger架构处理频率动作,并与功率动作组成联合动作,然后使用DDPG算法进行训练,使该算法能够适用于宽带跳频双动作空间的抗干扰场景,在复杂的电磁环境下能够快速作出决策。仿真结果表明,该方法在宽带跳频双动作空间干扰模式下的收敛速度及抗干扰性能较传统抗干扰算法提升了大约25%。展开更多
文摘Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.
文摘Nowadays,the city’s focus on education makes the improvement of learning environment become a new demand for development.From the the perspective of ecology,the theory of"resilience"is introduced to break the stereotype of the existing learning environment and create a space for dialogue with nature from inside to outside.At the same time,the learning environment is divided into observation learning space,exploration learning space and communication learning space by theory and method of multiple intelligence integrating teaching and learning.Through the analysis on resilience characteristics of three types of space,eight basic unit forms of learning environment design are obtained.Arrangement and combination of its form can improve the campus environment,increase children’s learning efficiency and the risk response ability of campus environment,and give children the opportunity to interact with and understand nature in the learning process,so as to provide reference for the diversified development of education and learning space.
文摘宽带跳频与深度强化学习结合的智能跳频通信模式能有效提高通信抗干扰能力。针对同时调整信号频点和功率的双动作空间智能决策由于频点离散但功率非离散使得决策依赖的深度强化学习算法难以设计的问题,基于离散型深度确定性策略梯度算法(Wolpertinger Deep Deterministic Policy Gradient,W-DDPG),提出了一种适于宽带跳频通信且具有发射频率和功率组成的双动作空间智能抗干扰决策方法。该决策方法面向频率/功率双动作空间,在频率空间中使用Wolpertinger架构处理频率动作,并与功率动作组成联合动作,然后使用DDPG算法进行训练,使该算法能够适用于宽带跳频双动作空间的抗干扰场景,在复杂的电磁环境下能够快速作出决策。仿真结果表明,该方法在宽带跳频双动作空间干扰模式下的收敛速度及抗干扰性能较传统抗干扰算法提升了大约25%。
