In order to improve the effect of deep learning, this paper puts forward the method of deep learning of Japanese. Before classification, we need to preprocess the word segmentation and delete the termination word in the question, and then use the feature vector to represent it. By learning to store the information in the network, we use heuristic rules to classify the question intention, extract the feature vectors representing different types of questions, and then make statistical analysis on the corpus of actual marked questions, establish a classification system, and bind features based on word packets, Realization intention classification. The experimental results show that the classification accuracy and influence parameters are relatively higher after deep learning, and the automatic classification method of Japanese question intention should be unique.
Abstract. Efficient home energy consumption is a complex, dynamic activity, and householders need to monitor, plan and act with the support of home energy feedback system. This paper reviews the existing literatures of home energy feedback system design, and summarizes the characteristics of home contexts from the householder's perspective, including low level of attention, motivation, effort, as well as short of knowledge and skills. We then analyze the gaps of using energy feedback system to support the users' energy actives from perspective of data, information and user engagement, and we propose that using ambient display and natural interaction could improve the effectiveness of home energy feedback systems.
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