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In order to improve the accuracy of SOC estimation for vehicle battery pack and optimize the management of the battery system, a new method based on deep belief network for remote correction of SOC accuracy is proposed in this paper. The monitoring data obtained by the remote monitoring center of the electric vehicle should be pre-processed before the establishment of deep belief network, which is...
Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a benchmark dataset for natural language person retrieval. To do so, we generate bounding...
Person detection in complex real-world scenes is a challenging problem. State-of-the-art methods typically use supervised learning relying on significant amounts of training data to achieve good detection results. However, labeling training data is tedious, expensive, and error-prone. This paper presents a novel method to improve detection performance by supplementing real-world data with synthetically...
Supervised classification of fully polarimetric SAR image using neural network is a common method nowadays. As an effective learning method of neural network, BP algorithm is the most widespread one in the neural network algorithms. However, BP network is easy to fall into local extremum and exists shortcomings such as the slow training process. To this end, this paper presents a method of supervised...
Making a computer generate its own emotion is an important part of the affective computing, and this would have wide applications in human-computer interaction and artificial intelligence. In this paper, we will describe an emotion generation model for a multimodal virtual human. The relationship among the emotion, mood and personality are discussed firstly, and the PAD (pleasure-arousal-dominance)...
In recent years, back-propagation (BP) neural network has been widely applied to the remote sensing image classification. However, the BP method based on the gradient descent principle suffers from the problem of getting stuck at local minimum. In addition, only using spectral information for multispectral remote sensing image classification could not get the ideal result. In this paper, a new method...
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