A method of spectrometry analysis based on approximation coefficients and deep belief networks was developed. Detection rate and accurate radionuclide identification distance were used to evaluate the performance of t...A method of spectrometry analysis based on approximation coefficients and deep belief networks was developed. Detection rate and accurate radionuclide identification distance were used to evaluate the performance of the proposed method in identifying radionuclides. Experimental results show that identification performance was not affected by detection time, number of radionuclides, or detection distance when the minimum detectable activity of a single radionuclide was satisfied. Moreover, the proposed method could accurately predict isotopic compositions from the spectra of moving radionuclides. Thus, the designed method can be used for radiation monitoring instruments that identify radionuclides.展开更多
Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed un...Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.展开更多
Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据...Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据并行加速策略,其中包含基于标签集的范围分区(Label Set based on Range Partition,LSRP)算法和基于权重的缓存替换(Cache Replacement based on Weight,CRW)算法.通过LSRP算法解决数据倾斜问题,采用CRW算法解决RDD(Resilient Distributed Datasets)重复利用以及缓存数据过多造成内存空间不足问题.结果表明:与传统DBN相比,DDBN训练速度提高约2.3倍,通过LSRP和CRW大幅提高了DDBN分布式并行度.展开更多
基金supported by the National Natural Science Foundation of China(No.11675078)the Foundation of Graduate Innovation Center in NUAA(No.kfjj20160606,kfjj20170613,and kfjj20170617)+3 种基金the Primary Research and Development Plan of Jiangsu Province(No.BE2017729)the Fundamental Research Funds for the Central Universities(No.NJ20160034)the Funding of Jiangsu Innovation Program for Graduate Education(No.KYLX16_0353)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘A method of spectrometry analysis based on approximation coefficients and deep belief networks was developed. Detection rate and accurate radionuclide identification distance were used to evaluate the performance of the proposed method in identifying radionuclides. Experimental results show that identification performance was not affected by detection time, number of radionuclides, or detection distance when the minimum detectable activity of a single radionuclide was satisfied. Moreover, the proposed method could accurately predict isotopic compositions from the spectra of moving radionuclides. Thus, the designed method can be used for radiation monitoring instruments that identify radionuclides.
基金supported in part by the National Natural Science Foundation of China(No.51606213)the National Major Science and Technology Projects(No.J2019-III-0010-0054)。
文摘Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.
文摘Spark下分布式深度信念网络(Distributed Deep Belief Network,DDBN)存在数据倾斜、缺乏细粒度数据置换、无法自动缓存重用度高的数据等问题,导致了DDBN计算复杂高、运行时效性低的缺陷.为了提高DDBN的时效性,提出一种Spark下DDBN数据并行加速策略,其中包含基于标签集的范围分区(Label Set based on Range Partition,LSRP)算法和基于权重的缓存替换(Cache Replacement based on Weight,CRW)算法.通过LSRP算法解决数据倾斜问题,采用CRW算法解决RDD(Resilient Distributed Datasets)重复利用以及缓存数据过多造成内存空间不足问题.结果表明:与传统DBN相比,DDBN训练速度提高约2.3倍,通过LSRP和CRW大幅提高了DDBN分布式并行度.
