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Precise precipitation forecasting can better reflect the changing trend of climate and also provide timely and efficient environmental information for management decision, as well as prevent the occurrence of floods or droughts. In the era of big data, this paper proposes a novel approach for precipitation forecasting based on deep belief nets, called DBNPF (Deep Belief Network for Precipitation Forecast)...
This paper deals with the issue on air conditioning energy consumption and system monitoring of different data in building. Various environmental parameters inside the building are changed in real time, while the conventional air conditioning energy consumption forecasting with the load simulation software cannot adapt to these variations. Therefore, the air conditioning energy consumption forecasting...
According to the wind speed prediction appeared in data acquisition difficulties, so as to the poor forecast accuracy, this paper proposed the history data of near BP-ANN wind short-term forecast model, with emphasis on BP model of input layer and hidden layer parameters are estimated. In the certain scope, enumerated input layer and hidden layer parameters, and use a large number of data simulation,...
Traditional marketing approach mainly adopts advertising and telemarketing method to attract potential consumers. It will need a lot of manpower and resources, but cannot position the targeted consumers accurately. In this paper, a novel precision automotive marketing model based on telecom big data mining is proposed to predict the potential high-end luxury car buyers. Initially, both logistic regression...
In the opportunistic networks, nodes carry and store the data and forward it until they encounter each other. How to choose an appropriate opportunity to forward data is pivotal for nodes' routing in this type of networks. Since nodes currently will keep a regular movement state in the scene of this paper discussed, forecasting a node's moving track in the near future would be very helpful. Through...
Today's storage systems and database systems are highly complex and configurable, which makes storage management intricate and costly. One critical aspect of storage management, particularly in large storage infrastructures (e.g. cloud storage), is to determine which application data sets to store on which devices. With a mechanism which has the ability to predict the performance of the storage device...
Constructing effective and efficient indexes for explosive growing multimedia data is a very challenging problem. To solve the problem, Haghani et al. provide a distributed similarity search method in high dimensions using Locality Sensitive Hashing. However, their method needs to estimate a global parameter on the whole dataset beforehand. It is impractical for a large-scale dynamical dataset. This...
Simulating the outdoor thermal environment of Exhibition Center in Taizhou with the improved lumped parameter model to quantitatively analyze the contribution ratio of underlying surface materials, green and shading types for lowering the heat island intensity. The results show that tree shading improving the thermal environment is the most obvious, the second is water.
Storage device performance prediction is a critical element of self-managed storage systems and application planning tasks, such as data assignment and configuration. We proposed a new hybrid method (RT-RBF), which combines regression tree (RT) and radial-based functions network(RBF), to model storage device performance. In our proposed algorithm, the RT is firstly used to split the large space of...
The aim of this study is to analisis the method of estabishing a model of AmOn integrative nutrients removal reactor, and based on the results of experiments, to optimize the operating conditions as well. The ASM mechanisms and WEST software were unilized in the model. Results showed that the relatively errors of COD, NH3-N, TN and TP between value of experiment and simulated are 7.2%, 9.1%, 12.5%...
Doppler radar reflectivity and radial velocity data are added directly each hour to the mesoscale model ARPS (The Advanced Regional Prediction System) in numerical simulation by its data processing system ADAS (ARPS Data Analysis System). By simulating the Typhoon Chanthu (1003) process, we analyzed the impact of improving the initial field and forecasting results which brought about by radar data...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
Based on the statistical data during the period from 2003 to 2008 released by National Bureau of Statistics of China, this paper focused on forecasting the amount of the scientists and technicians in China, using GM (1, 1) model and regression model. The result of this empirical study is that the grey prediction theory can fit the scientists and technicians amount development precisely in China. The...
Various attribute and relation information is used in social recommendation systems. However, previous approaches fail to use them in a unified way. In this paper, we propose a unified framework for social recommendation. Entities like users and items are described by their tags. We model each entity using topic models like Latent Dirichlet Allocation(LDA) and then connect these topic models to form...
MAD (Mean Absolute Difference) is a widely adopted expression method of image complexity. By analyzing the relationship between average MAD of some previously encoded P frames in the GOP and the actual MAD of the last previously encoded P frame, a more accurate MAD prediction model is proposed to substitute linear model for MAD prediction. The experiment results show that proposed model performs better...
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