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Li T.,Xuancheng Vocational and Technical College | Gao Y.,Guangdong University of Technology
2010 International Conference on E-Product E-Service and E-Entertainment, ICEEE2010 | Year: 2010

There are a large number of traditional dwellings which keep better temperature environment in Meizhou, ventilation under the subtropical climate in south of the Five Ridges affects architecture inner temperature environment generally, through computer ventilation analogue test of Weilong mold dwelling and Gang mold dwelling, the rational number analyze different styles of dwellings' ventilating effect, digs out traditional dwellings' ventilation architecture technology to make the architecture inner temperature better in the Five Ridges as the technical base. ©2010 IEEE. Source


Liu Y.,Nanjing University of Aeronautics and Astronautics | Liu Y.,Xuancheng Vocational and Technical College | Pi D.,Nanjing University of Aeronautics and Astronautics
Journal of Computational and Theoretical Nanoscience | Year: 2016

At present, Gray model (GM) prediction is on the basis of the original sequence, the original sequence exits the randomness, abnormal and volatility. So the GM bring down forecasting accuracy. This paper proposes a new prediction algorithm: Moving Average Gray Algorithm based on information entropy-MAGA. Firstly, the algorithm can eliminate abnormal data; Secondly, using mutual information method to find moving steps, then by moving average to get a new series, in order to avoiding to select the unreasonable steps; Finally, in the new series on the basis of using the new algorithm, to predict sequence; Experimental results show that the MAGM algorithm can effectively improve the accuracy of forecast. © 2016 American Scientific Publishers All rights reserved. Source


Liu Y.Z.,Nanjing University of Aeronautics and Astronautics | Liu Y.Z.,Xuancheng Vocational and Technical College | Jia X.P.,Xuancheng Vocational and Technical College
Applied Mechanics and Materials | Year: 2014

Association rules has played a significant role in mining classification clear affairs, but the performance is poor for the continuous time series data . Firstly, this paper presents the trend of time series, including the rise, decline and steady trend, and the time series trend method is proposed; Secondly, define the trend of association rules, including the trend of association rules’ support degree, trend of association rule’s confidence; Finally, gives an application example, show the effectiveness of the method in classification and association analysis of time series. © (2014) Trans Tech Publications, Switzerland. Source


Liu Y.,Nanjing University of Aeronautics and Astronautics | Liu Y.,Xuancheng Vocational and Technical College | Jia X.,Xuancheng Vocational and Technical College
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2014

This paper proposes a novel similarity algorithm of time series based on the two points. First, it analyzes the time series form of two points. Secondly, it presents the similarity theorem. And proved them; Finally, it gives the algorithm and experiment analysis. The experimental results show that the algorithm can determine the similarity in a certain threshold, it provide a reference for the municipal water supply. © Sila Science. All Rights Reserved. Source


Liu Y.,Nanjing University of Aeronautics and Astronautics | Liu Y.,Xuancheng Vocational and Technical College | Jia X.,Xuancheng Vocational and Technical College | Pi D.,Nanjing University of Aeronautics and Astronautics
Journal of Applied Science and Engineering | Year: 2016

In order to reduce the power consumption of the sensor, the key points of the algorithm are proposed, which can greatly reduce the transmission data and reduce the power consumption; The Sink receives the key point sequence, and uses the piece-wise linear algorithm to fit the data, for the user to query, statistics and graphics and other operation; The empirical evidence of this algorithm fits the raw data well, Less computation, less transmission of data, is conducive to reduce the power consumption in the wireless sensor. Source

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