Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,...Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.展开更多
针对传统算法无法很好地解决无线传感器网络(wireless sensor network,WSN)三维非均匀覆盖的问题,文章提出一种基于改进流向算法(improved flow direction algorithm,IFDA)的无线传感器网络三维覆盖优化算法。其首先引入Gauss映射对节...针对传统算法无法很好地解决无线传感器网络(wireless sensor network,WSN)三维非均匀覆盖的问题,文章提出一种基于改进流向算法(improved flow direction algorithm,IFDA)的无线传感器网络三维覆盖优化算法。其首先引入Gauss映射对节点初始化分布进行处理,使节点分布更为均匀,提高了传感器网络对事件的覆盖率;其次,将T分布扰动融入流向算法,使得算法的全局搜索能力进一步提高;最后提出了一种基于随机数的越界处理方法,以优化节点的越界重定位。将所提出的优化算法与虚拟力算法(virtual force algorithm,VFA)、未知目标精确覆盖算法(exact cover algorithm,ECA)和人工势场算法(artifical potential field algorithm,APFA)在事件呈T型不均匀部署和线型不均匀部署两种情况下进行对比实验,结果表明,在事件呈T型不均匀分布下,IFDA算法的覆盖效能较VFA算法、ECA算法、APFA算法的分别有3.0%、4.2%和6.3%的提高;在事件呈线型不均匀分布下,IFDA算法的覆盖效能较其他3种算法的分别有5.1%、6.2%和7.1%的提升,能够较好地解决无线传感器网络在三维非均匀覆盖情况下节点的分布问题。展开更多
基金Supported by the Anhui Province Sports Health Information Monitoring Technology Engineering Research Center Open Project (KF2023012)。
文摘Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.
文摘针对传统算法无法很好地解决无线传感器网络(wireless sensor network,WSN)三维非均匀覆盖的问题,文章提出一种基于改进流向算法(improved flow direction algorithm,IFDA)的无线传感器网络三维覆盖优化算法。其首先引入Gauss映射对节点初始化分布进行处理,使节点分布更为均匀,提高了传感器网络对事件的覆盖率;其次,将T分布扰动融入流向算法,使得算法的全局搜索能力进一步提高;最后提出了一种基于随机数的越界处理方法,以优化节点的越界重定位。将所提出的优化算法与虚拟力算法(virtual force algorithm,VFA)、未知目标精确覆盖算法(exact cover algorithm,ECA)和人工势场算法(artifical potential field algorithm,APFA)在事件呈T型不均匀部署和线型不均匀部署两种情况下进行对比实验,结果表明,在事件呈T型不均匀分布下,IFDA算法的覆盖效能较VFA算法、ECA算法、APFA算法的分别有3.0%、4.2%和6.3%的提高;在事件呈线型不均匀分布下,IFDA算法的覆盖效能较其他3种算法的分别有5.1%、6.2%和7.1%的提升,能够较好地解决无线传感器网络在三维非均匀覆盖情况下节点的分布问题。
