In the context of big data age, the energy conservation and emission reduction of transportation is a natural big data industry. The planning, management, decision-making of energy conservation and emission reduction of transportation and other aspects should be supported by the analysis and forecasting of large amounts of data. Now, with the development of information technology, such as intelligent city, sensor road and so on, information collection technology in the direction of the Internet of things gradually become popular. The 3G/4G network transmission technology develop rapidly, and a large number of energy conservation and emission reduction of transportation data is growing into a series with different ways. The government not only should be able to make good use of big data to solve the problem of energy conservation and emission reduction of transportation, but also to explore and use a large amount of data behind the hidden value. Based on the analysis of the basic characteristics and application technology of energy conservation and emission reduction of transportation data, this paper carries out its application research in energy conservation and emission reduction of transportation industry, so as to provide theoretical basis and reference value for low carbon management.

http://iopscience.iop.org/article/10.1088/1755-1315/69/1/012029/pdf

Application research on big data in energy conservation and emission reduction of transportation industry

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

The static decoupling control method is tested on a dual-chamber electric furnace. Experiments have shown that the method enables dual-chamber body electric furnace-the coupling system operate well, and also reduces the changes of the controlled parameter's amplitude to a certain extent. Typically, for multivariable control system, it can basically meet the specific requirements in the project after achieving these indicator requirements.

http://ieeexplore.ieee.org/iel7/6552458/6560882/06561065.pdf

Research and practice of decoupling control based on the dual-chamber electric furnace

Based on the study of the tridiagonal matrix and the block tridiagonal matrix calculation,a new algorithm of diagonal decoupling is proposed for a typical class of multi-variable coupled tridiagonal industrial systems.The key of the algorithm is to construct two compensation matrices,the former series compensation matrix L(s)and after series compensation matrix R(s).Thus the multi-variable coupled system is transferred to a diagonal system and decoupling purposes are achieved.Considering zero components may exist on sub-diagonal,two construction algorithms of L(s)and R(s)are discussed.The simulation not only confirms the validity of the method,but obtains the L(s)and R(s)have the feature of many same components,which will greatly reduce the workload in industry.

Tridiagonal decoupling control of a kind of special system

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Tridiagonal decoupling control of a kind of special system