The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output devices.These devices may complicate communication with compu...The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output devices.These devices may complicate communication with computers especially for people with disabilities.Hand gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer interaction.Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy.Thiswork aims to develop a powerful hand gesture recognition model with a 100%recognition rate.We proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve accuracy.The majority voting method was used to aggregate accuracies produced by each classifier and get the final classification result.Our model was trained using a self-constructed dataset containing 1600 images of ten different hand gestures.The employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition rate.The experimental results had shown the robustness of our proposed model.Logistic Regression and Support Vector Machine have achieved 100%accuracy.The developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.展开更多
The n-γ discrimination performance of two experimental arrangements based on the rise-time method and the zero-crossing method was compared for a 50.8 mm-diametered and 50.8 mm-high BC501A liquid scintillator coupled...The n-γ discrimination performance of two experimental arrangements based on the rise-time method and the zero-crossing method was compared for a 50.8 mm-diametered and 50.8 mm-high BC501A liquid scintillator coupled to a 50.8 mm-diametered 9807B photomultiplier in this work. The low energy limitation of the detected neutron with different detector high voltages and the figure of merit of the n-γ discrimination in four neutron energy regions (1–2 MeV, 0.75–1 MeV, 0.5–0.75 MeV and below 0.5 MeV) were studied by using the Am-Be neutron source. Under a time statistical model of the photoelectron emission process in scintillation counters, the intrinsic capability of the n-γ discrimination performance under the optimal condition was evaluated. The experimental results of the zero-crossing method demonstrate a better n-γ. discrimination performance than those of the rise-time method, which is consistent with the calculated results.展开更多
A digital pulse shape discrimination system based on a programmable module NI-5772 has been established and tested with an EJ-301 liquid scintillation detector. The module was operated by running programs developed in...A digital pulse shape discrimination system based on a programmable module NI-5772 has been established and tested with an EJ-301 liquid scintillation detector. The module was operated by running programs developed in Lab VIEW, with a sampling frequency up to 1.6 GS/s. Standard gamma sources ^22 Na,^137Cs and ^60 Co were used to calibrate the EJ-301 liquid scintillation detector, and the gamma response function was obtained. Digital algorithms for the charge comparison method and zero-crossing method have been developed. The experimental results show that both digital signal processing（DSP） algorithms can discriminate neutrons from γ-rays. Moreover,the zero-crossing method shows better n-γ discrimination at 80 ke Vee and lower, whereas the charge comparison method gives better results at higher thresholds. In addition, the figure-of-merit（FOM） for detectors of two different dimensions were extracted at 9 energy thresholds, and it was found that the smaller detector presented better n-γseparation for fission neutrons.展开更多
文摘The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output devices.These devices may complicate communication with computers especially for people with disabilities.Hand gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer interaction.Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy.Thiswork aims to develop a powerful hand gesture recognition model with a 100%recognition rate.We proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve accuracy.The majority voting method was used to aggregate accuracies produced by each classifier and get the final classification result.Our model was trained using a self-constructed dataset containing 1600 images of ten different hand gestures.The employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition rate.The experimental results had shown the robustness of our proposed model.Logistic Regression and Support Vector Machine have achieved 100%accuracy.The developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.
文摘The n-γ discrimination performance of two experimental arrangements based on the rise-time method and the zero-crossing method was compared for a 50.8 mm-diametered and 50.8 mm-high BC501A liquid scintillator coupled to a 50.8 mm-diametered 9807B photomultiplier in this work. The low energy limitation of the detected neutron with different detector high voltages and the figure of merit of the n-γ discrimination in four neutron energy regions (1–2 MeV, 0.75–1 MeV, 0.5–0.75 MeV and below 0.5 MeV) were studied by using the Am-Be neutron source. Under a time statistical model of the photoelectron emission process in scintillation counters, the intrinsic capability of the n-γ discrimination performance under the optimal condition was evaluated. The experimental results of the zero-crossing method demonstrate a better n-γ. discrimination performance than those of the rise-time method, which is consistent with the calculated results.
基金Supported by National Natural Science Foundation of China(91226107,11305229)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA03030300)
文摘A digital pulse shape discrimination system based on a programmable module NI-5772 has been established and tested with an EJ-301 liquid scintillation detector. The module was operated by running programs developed in Lab VIEW, with a sampling frequency up to 1.6 GS/s. Standard gamma sources ^22 Na,^137Cs and ^60 Co were used to calibrate the EJ-301 liquid scintillation detector, and the gamma response function was obtained. Digital algorithms for the charge comparison method and zero-crossing method have been developed. The experimental results show that both digital signal processing（DSP） algorithms can discriminate neutrons from γ-rays. Moreover,the zero-crossing method shows better n-γ discrimination at 80 ke Vee and lower, whereas the charge comparison method gives better results at higher thresholds. In addition, the figure-of-merit（FOM） for detectors of two different dimensions were extracted at 9 energy thresholds, and it was found that the smaller detector presented better n-γseparation for fission neutrons.
