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
A real-time workbench able to reproduce the same behavior as an indoor network is in order. Such a tool will help researchers and industrials extend the smart grid technology indoors and have a better acknowledgment o...A real-time workbench able to reproduce the same behavior as an indoor network is in order. Such a tool will help researchers and industrials extend the smart grid technology indoors and have a better acknowledgment of Smart Grid systems interoperability. In order to develop such a tool many approaches should be studied to choose the most suitable one by comparing the models and the technologies. The considered hardware are the embedded systems and the selected approach is the statistical one. It appears that the FlR filter is the most pertinent approach.展开更多
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.
文摘A real-time workbench able to reproduce the same behavior as an indoor network is in order. Such a tool will help researchers and industrials extend the smart grid technology indoors and have a better acknowledgment of Smart Grid systems interoperability. In order to develop such a tool many approaches should be studied to choose the most suitable one by comparing the models and the technologies. The considered hardware are the embedded systems and the selected approach is the statistical one. It appears that the FlR filter is the most pertinent approach.
