Multi-characteristics Based Data Scheduling Over the Smart Grid

In this paper, we propose multi-characteristics based data scheduling over smart grid. Three different pricing strategies are presented based on user priority and load rate. Then the corresponding novel scheduling algorithms are introduced by the proposed data priority and pricing strategies. The simulation experiments are carried out to evaluate the proposed algorithms based on trace data. And the results show that our methods can outperform the conventional method.

Fusion of urban remote image based on multi-characteristics

A fusion approach is proposed to refine the resolution of urban multi-spectral images using the corresponding high-resolution panchromatic （PAN） images. Firstly, the two images are decomposed by wavelet transformation, and five texture features are extracted from high-frequency detailed sub-images. Then a multi-characteristics fusion rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that, comparing with the non-characteristic methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the urban remote sensing images.

Terahertz composite imaging method

In order to improve the imaging quality of terahertz(THz) spectroscopy, Terahertz Composite Imaging Method(TCIM) is proposed. The traditional methods of improving THz spectroscopy image quality are mainly from the aspects of de-noising and image enhancement. TCIM breaks through this limitation. A set of images, reconstructed in a single data collection, can be utilized to construct two kinds of composite images. One algorithm, called Function Superposition Imaging Algorithm(FSIA), is to construct a new gray image utilizing multiple gray images through a certain function. The features of the Region Of Interest(ROI) are more obvious after operating, and it has capability of merging ROIs in multiple images. The other, called Multi-characteristics Pseudo-color Imaging Algorithm(Mc Pc IA), is to construct a pseudo-color image by combining multiple reconstructed gray images in a single data collection. The features of ROI are enhanced by color differences. Two algorithms can not only improve the contrast of ROIs, but also increase the amount of information resulting in analysis convenience. The experimental results show that TCIM is a simple and effective tool for THz spectroscopy image analysis.

Clonal variations in nutritional components of Pinus koreansis seeds collected from seed orchards in Northeastern China

From four Korean pine （Pinus koraiensis） orchards, 60 clones were selected and analyzed for the fatty acid and amino acid components of the seeds to reveal the variations and correlations of the seed characteristics among the clonal source orchards and clones. The nutri- tional components of the seeds of the P. koraiensis trees exhibited rich genetic variation; the variation coefficient of the fatty acids was 2.24-66.83 %, while the variation coefficient of the amino acids was 14.70-38.88 %. Rela- tively high genetic-improvement potential exists for the nutritional components of the seeds. The phenotypic dif- ferentiation of the fatty acid and amino acid components reveals that variation within the population （85.18 %） was the primary source for the variation of the fatty acid components; variation among the orchards （63.08 %） was the primary source of the variation of the amino acid components. Data drawn from various clonal source orchards all showed that the seed characteristics were highly controlled by heritability （h2 〉 80 %）, and the seed characteristics of the P. koraiensis trees exhibited a similar genetic gain trend. The principal components were ana- lyzed to obtain the comprehensive principal component value for each clonal seed orchard. Twelve clones were selected based on a clonal selection rate of 20 %. Corre- lation and multiple stepwise-regression analyses were conducted, considering different location conditions, to reveal the stable correlations between the seed character- istics to facilitate improvements of the seed yield of P. koraiensis trees and the clonal selection. Species of real characteristics in P. koraiensis were controlled by higher heritability. Genetic gain was obtained by selecting of superior clones.

MCA-TFP Model:A Short-Term Traffic Flow Prediction Model Based on Multi-characteristic Analysis

With the urbanization,urban transportation has become a key factor restricting the development of a city.In a big city,it is important to improve the efficiency of urban transportation.The key to realize short-term traffic flow prediction is to learn its complex spatial correlation,temporal correlation and randomness of traffic flow.In this paper,the convolution neural network(CNN)is proposed to deal with spatial correlation among different regions,considering that the large urban areas leads to a relatively deep Network layer.First three gated recurrent unit(GRU)were used to deal with recent time dependence,daily period dependence and weekly period dependence.Considering that each historical period data to forecast the influence degree of the time period is different,three attention mechanism was taken into GRU.Second,a twolayer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data.Besides,the prediction model was established by combining these three modules.Furthermore,in order to verify the influence of spatial correlation on prediction model,an urban functional area identification model was introduced to identify different functional regions.Finally,the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data.The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.