This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the co...This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the common equation the natural frequencies of an n th order network are correlated with the n port parameters. The equation is simple and dual in form and clear in its physical meaning. The procedure of finding the solution is simplified and standardized, and it will not cause the loss of roots. The common equation would find wide use and be systematized.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
We constructed a new set of diabatic poten-tial energy surfaces(PESs)for the two low-est states involved in Li+Li_(2)reaction by us-ing the fundamental-invariant neural net-work method.The Li_(3)system exhibits a coni...We constructed a new set of diabatic poten-tial energy surfaces(PESs)for the two low-est states involved in Li+Li_(2)reaction by us-ing the fundamental-invariant neural net-work method.The Li_(3)system exhibits a coni-cal intersection(CI)at the geometric D_(3)h symmetries with the energy of the CI point significantly lower than the ground-state en-ab initio ergy of the diatomic molecule.The diabaitc PESs accurately reproduce adiabatic en-ergies,derivative coupling,and energy gradient information,thereby providing a high-fideli-ty description of the CI between the two lowest electronic states.Quantum dynamical calcu-lations have revealed significant non-adiabatic effects in the Li+Li_(2)reaction.展开更多
A new two-state diabatic potential energy matrix(DPEM)for H3 has been constructed,based on the fun-damental invariant neural network(FI-NN)diabatization method pro-posed in our previous work[Phys.Chem.Chem.Phys.21,150...A new two-state diabatic potential energy matrix(DPEM)for H3 has been constructed,based on the fun-damental invariant neural network(FI-NN)diabatization method pro-posed in our previous work[Phys.Chem.Chem.Phys.21,15040(2019)].In that initial effort,a two-state DPEM was constructed only with a 10 eV energy threshold.The current work aims to expand the en-ergy range and improve the accura-cy of DPEM.This is achieved by the utilization of full configuration inter-action(FCI)with aug-cc-pVnZ ba-sis sets and complete basis set(CBS)extrapolation.The original dataset is augmented with additional points with higher adiabatic energies,which give rise to a total of 10985 data points.The DPEM constructed in this work now enables accurate representation of adiabatic energies up to 18 eV.Quantum dynamic calculations based on this DPEM are nearly identical to those obtained from benchmark surfaces,which makes it the most accurate DPEM for the H3 system to date,therefore facilitating detailed exploration of reaction mechanisms at higher collision energies.展开更多
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.展开更多
文摘This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the common equation the natural frequencies of an n th order network are correlated with the n port parameters. The equation is simple and dual in form and clear in its physical meaning. The procedure of finding the solution is simplified and standardized, and it will not cause the loss of roots. The common equation would find wide use and be systematized.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
基金supported by the National Natural Science Foundation of China(Nos.22103084 and 22233003 to Jiayu Huang,and No.22288201 to Dong H.Zhang)the Innovation Program for Quantum Science and Technology(No.2021ZD0303305)to Dong H.Zhangthe Dalian Innovation Support Program(No.2021RD05)to Dong H.Zhang.
文摘We constructed a new set of diabatic poten-tial energy surfaces(PESs)for the two low-est states involved in Li+Li_(2)reaction by us-ing the fundamental-invariant neural net-work method.The Li_(3)system exhibits a coni-cal intersection(CI)at the geometric D_(3)h symmetries with the energy of the CI point significantly lower than the ground-state en-ab initio ergy of the diatomic molecule.The diabaitc PESs accurately reproduce adiabatic en-ergies,derivative coupling,and energy gradient information,thereby providing a high-fideli-ty description of the CI between the two lowest electronic states.Quantum dynamical calcu-lations have revealed significant non-adiabatic effects in the Li+Li_(2)reaction.
基金supported by the National Natural Science Foundation of China(No.22288201)the Inno-vation Program for Quantum Science and Technology(No.2021ZD0303305)the Dalian Innovation Sup-port Program(No.2021RD05).
文摘A new two-state diabatic potential energy matrix(DPEM)for H3 has been constructed,based on the fun-damental invariant neural network(FI-NN)diabatization method pro-posed in our previous work[Phys.Chem.Chem.Phys.21,15040(2019)].In that initial effort,a two-state DPEM was constructed only with a 10 eV energy threshold.The current work aims to expand the en-ergy range and improve the accura-cy of DPEM.This is achieved by the utilization of full configuration inter-action(FCI)with aug-cc-pVnZ ba-sis sets and complete basis set(CBS)extrapolation.The original dataset is augmented with additional points with higher adiabatic energies,which give rise to a total of 10985 data points.The DPEM constructed in this work now enables accurate representation of adiabatic energies up to 18 eV.Quantum dynamic calculations based on this DPEM are nearly identical to those obtained from benchmark surfaces,which makes it the most accurate DPEM for the H3 system to date,therefore facilitating detailed exploration of reaction mechanisms at higher collision energies.
文摘For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
