Abstract: A wireless power transfer apparatus includes a first control system, a first energy transmission system, and a second energy transmission system. The first control system is coupled to the first energy transmission system, and inputs an energy transmission signal to the first energy transmission system. The first energy transmission system is coupled to the second energy transmission system, and inputs some of the energy transmission signals obtained from the first control system into the second energy transmission system. Both the energy transmission signal obtained by the first energy transmission signal from the first control system and an energy transmission signal obtained by the second energy transmission system from the first energy transmission system supply power to a target device.

Abstract: Disclosed is a cross-modality person re-identification method based on local information learning, the method comprising the following steps: acquiring a standard data set and performing data enhancement on the standard data set; dividing the enhanced standard data set into a training set and a test set; constructing a cross-modality person re-identification training network based on a dual-stream ResNet50 convolutional neural network architecture; inputting the training set into the cross-modality person re-identification training network to obtain a cross-modality person re-identification test network through training; randomly selecting an image to be queried from the test set, and inputting the image to be queried and a candidate database from the test set into the cross-modality person re-identification test network to obtain an identification accuracy value corresponding to the image to be queried.

Abstract: A person re-identification system and a person re-identification method integrating multi-scale GAN and label learning are provided. The occluded blocks with different sizes are added to an original image for data restoration and enhancement, multi-scale discrimination branches are introduced, multi-scale features are fused, and feature matching losses on different scales are calculated respectively to improve the quality of generative images. Further, an online label learning method based on semi-supervised learning is provided to label a generative image and reduce the interference of label noise on an identification model.

Abstract: A multi-scale deep supervision based reverse attention model is provided and includes an input end, a multi-scale feature learning module, an attention mechanism module, a reverse attention mechanism module, a deep supervision module, multiple loss functions, multiple average pool layers, multiple linear layers and multiple branches. The reverse attention mechanism module as provided can alleviate the problem of feature information loss caused by attention mechanisms, and part of the modules can be discarded in the testing phase, thereby improving the testing efficiency.

Abstract: Disclosed is a cross-modality person re-identification method based on local information learning, the method comprising the following steps: acquiring a standard data set and performing data enhancement on the standard data set; dividing the enhanced standard data set into a training set and a test set; constructing a cross-modality person re-identification training network based on a dual-stream ResNet50 convolutional neural network architecture; inputting the training set into the cross-modality person re-identification training network to obtain a cross-modality person re-identification test network through training; randomly selecting an image to be queried from the test set, and inputting the image to be queried and a candidate database from the test set into the cross-modality person re-identification test network to obtain an identification accuracy value corresponding to the image to be queried.

Abstract: The invention relates to a method for person re-identification based on deep model with multi-loss fusion training strategy. The method uses a deep learning technology to perform preprocessing operations such as flipping, clipping, random erasing and style transfer, and then feature extraction is performed through a backbone network model; joint training of a network is performed by fusing a plurality of loss functions. Compared with other deep learning-based person re-identification algorithms, the present invention greatly improves the performance of person re-identification by adopting a plurality of preprocessing modes, the fusion of three loss functions and effective training strategy.

Abstract: The present invention relates to a person re-identification method combining reverse attention and multi-scale deep supervision, including: constructing a person re-identification training network; training the person re-identification training network by using a training data set, to obtain a learning network, and discarding a reverse attention branch and a multi-scale deep supervision branch of a feature extraction module in the learning network, to obtain a test network; testing the test network by using a test data set, and after the test succeeds, inputting an actual data set into the learning network, to learn an image feature of the actual data set, and then discarding the reverse attention branch and the multi-scale deep supervision branch of the feature extraction module in the learning network, to obtain an application network; and inputting an actual query image into the application network, to obtain an identification result corresponding to the actual query image.

Abstract: The invention relates to a method for person re-identification based on deep model with multi-loss fusion training strategy. The method uses a deep learning technology to perform preprocessing operations such as flipping, clipping, random erasing and style transfer, and then feature extraction is performed through a backbone network model; joint training of a network is performed by fusing a plurality of loss functions. Compared with other deep learning-based person re-identification algorithms, the present invention greatly improves the performance of person re-identification by adopting a plurality of preprocessing modes, the fusion of three loss functions and effective training strategy.