Increasing attention is being paid to the classification of ground objects using hyperspectral spectrometer images. A key challenge of most hyperspectral classifications is the cost of training samples. It is difficult to acquire enough effective marked label sets using classification model frameworks. In this paper, a semi-supervised classification framework of hyperspectral images is proposed to better solve problems associated with hyperspectral image classification. The proposed method is based on an iteration process, making full use of the small amount of labeled data in a sample set. In addition, a new unlabeled data trainer in the self-training semi-supervised learning framework is explored and implemented by estimating the fusion evidence entropy of unlabeled samples using the minimum trust evaluation and maximum uncertainty. Finally, we employ different machine learning classification methods to compare the classification performance of different hyperspectral images. The experimental results indicate that the proposed approach outperforms traditional state-of-the-art methods in terms of low classification errors and better classification charts using few labeled samples.

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Mingrui ZHU †a) , Student Member, Yangjian JI †b) , Wenjun JU † †c) , Xinjian GU †d) , Chao LIU † †e) , and Zhifang XU † †f) , Nonmembers SUMMARY With the development of power market demand response capability, load aggregators play a more important role in the coordination between power grid and users. They have a wealth of user side business data resources related to user demand, load management and equipment operation. By building a business model of business data resource utilization and innovating the content and mode of intelligent power service, it can guide the friendly interaction between power supply, power grid and load, effectively improve the flexibility of power grid regulation, speed up demand response and refine load management. In view of the current situation of insufficient utilization of business resources, low user participation and imperfect business model, this paper analyzes the process of home appliance enterprises participating in peak shaving and valley filling (PSVF) as load aggregators, and expounds the relationship between the participants in the power market; a business service model of smart home appliance participating in PSVF based on cloud platform is put forward; the market value created by home appliance business resources for each participant under the joint action of market-oriented means, information technology and power consumption technology is discussed, and typical business scenarios are listed; taking Haier business resource analysis as an example, the feasibility of the proposed business model in innovating the content and value realization of intelligent power consumption services is proved.

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Under the background of the rapid development of Internet technology and the popularity of smart grids, the analysis and prediction of short-term time series data of users’ power consumption has important guiding significance for grid planning, management decision of economic sector and optimization and allocation of power resources. Considering that the traditional statistical-based time series analysis method is weak in generality and can not handle the complex linear problem in prediction, the long-term dependence of the ordinary cyclic neural network model is insufficient, and the time series data has multidimensional problems, a deep neural network is proposed. The PCA-LSTM model is used for time series data prediction. The model firstly uses the PCA (principal component analysis) method to reduce the dimensionality of the electricity consumption time series data, optimizes the number of input variables, and inputs the data into the long- and short-term memory network LSTM for training prediction. The experimental results show that the LSTM network prediction based on PCA improves the accuracy of short-term time series data prediction, and also improves the convergence speed of LSTM network. It proves that the method has better prediction performance and versatility.