We have calculated the ground-state energy of the symmetric quantum-dot pattern by the ab initio calculation method, i.e. unrestricted Hartree-Fock-Roothaan (UHFR) method based on the Gaussian basis, and studied their...We have calculated the ground-state energy of the symmetric quantum-dot pattern by the ab initio calculation method, i.e. unrestricted Hartree-Fock-Roothaan (UHFR) method based on the Gaussian basis, and studied their electric capacitance spectra, assuming each quantum dot of quantum-dot pattern to be confined in a three-dimensional spherical potential well of finite depth. For the systems in question, our results show that our method and theoretical model not only give the electric capacitance peaks similar to s-shell and p-shell atom-like quantum dot, but also show some new fine-structure of electric capacitance in the symmetric quantum-dot pattern system. This method might be a feasible tool to study few-electron problems on the symmetric quantum-dot pattern system.展开更多
The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB f...The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB filters are not sensitive to the slight color changes, which limits the performance of this kind of electronic nose. This paper demonstrates using quantum dot spec-troscopy technology to solve this problem. Multiple quantum dot filters are placed on the surface of image sensor. When capturing images, there are more response channels of the same incident light than RGB filters. Simulation and experiment both prove that quantum dot filters with appropriate processing are more sensitive to color changes than RGB filters.展开更多
In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to t...In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to transform vibration signals and simplify the defect classification process under stationary operating conditions.This work aims to enhance the SDP-CNN combination for detecting incipient defects in gear under variable working conditions.The vibration signals are filtered by Vold-Kalman Filter Multi-Order Tracking to highlight fault characteristics under variable working conditions.Subsequently,the signals are SDP-transformed and are then classified by optimized CNN.The new pipeline has been validated on an experimental dataset and compared with the classical one by developing both two-and multi-class CNNs.The results showed the applicability of the new pipeline in terms of percentage accuracy and ROC curve compared to the classical approach.Finally,the proposed pipeline was compared with other ML literature techniques using the same dataset.展开更多
基金the National Natural Seience Foundation of China(Grant No10074064)Youth Foundarion of Yantai Universiry(Grant No WL02Z7)
文摘We have calculated the ground-state energy of the symmetric quantum-dot pattern by the ab initio calculation method, i.e. unrestricted Hartree-Fock-Roothaan (UHFR) method based on the Gaussian basis, and studied their electric capacitance spectra, assuming each quantum dot of quantum-dot pattern to be confined in a three-dimensional spherical potential well of finite depth. For the systems in question, our results show that our method and theoretical model not only give the electric capacitance peaks similar to s-shell and p-shell atom-like quantum dot, but also show some new fine-structure of electric capacitance in the symmetric quantum-dot pattern system. This method might be a feasible tool to study few-electron problems on the symmetric quantum-dot pattern system.
文摘The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB filters are not sensitive to the slight color changes, which limits the performance of this kind of electronic nose. This paper demonstrates using quantum dot spec-troscopy technology to solve this problem. Multiple quantum dot filters are placed on the surface of image sensor. When capturing images, there are more response channels of the same incident light than RGB filters. Simulation and experiment both prove that quantum dot filters with appropriate processing are more sensitive to color changes than RGB filters.
文摘In the field of gear fault detection,the symmetrized dot pattern(SDP)technique,combined with a convolutional neural network(CNN),is widely used to classify various types of defects.The SDP-CNN combination is used to transform vibration signals and simplify the defect classification process under stationary operating conditions.This work aims to enhance the SDP-CNN combination for detecting incipient defects in gear under variable working conditions.The vibration signals are filtered by Vold-Kalman Filter Multi-Order Tracking to highlight fault characteristics under variable working conditions.Subsequently,the signals are SDP-transformed and are then classified by optimized CNN.The new pipeline has been validated on an experimental dataset and compared with the classical one by developing both two-and multi-class CNNs.The results showed the applicability of the new pipeline in terms of percentage accuracy and ROC curve compared to the classical approach.Finally,the proposed pipeline was compared with other ML literature techniques using the same dataset.
