Sponsor(s): Northeastern University
12 issues per year
Current Issue: Issue 07, 2020
Established in 1986, the Chinese journal of Control and Decision is sponsored by Northeastern University and under the administration of the Ministry of Education of China. The journal is published monthly with a total of 256 pages in A4 size, and is distributed at home and abroad. For more than 30 years, "Control and Decision" has adhered to its mission and purpose, and has committed to gathering and disseminating outstanding academic achievements, inspiring scientific and technological innovations, and advancing the development of China's academic disciplines. The journal has published a great number of high-standard, original, and outstanding research achievements, both in theory and in practical applications, in the field of control and decision-making. With a good academic reputation in both academia and industry, high academic influence, and excellent level of publications, "Control and Decision" has attained top rankings in both core impact factors and comprehensive evaluation indicators. The journal has received many prestigious awards including "Top 100 Outstanding Academic Journals of China", "China's Most Influential Academic Journals”, and so on, and has been included in key national and international databases. In November 2019, the journal was selected for the “China Science and Technology Journal Excellence Action Plan”, a national project jointly implemented by the China Association for Science and Technology, the Ministry of Finance, the Ministry of Education, the Ministry of Science and Technology, the General Administration of Press and Publication, the Chinese Academy of Sciences and the Chinese Academy of Engineering. This will be yet another great motivation for “Control and Decision” to contribute relentlessly to the construction of the Chinese science and technology journals system of open innovation, cooperation, and world-class.
Editor in Chief: Filiwang, Guang-Hong Yang
Control and Decision,2020,Vol 35,No. 07
It is well known that the parameter K of variational mode decomposition (VMD) needs to be preset according to prior knowledge without theoretical support for optimal setting. Thus, for the existing fault diagnosis methods of bearings based on VMD, the correctness of characteristic extraction and accuracy of fault diagnosis are extremely difficult to be guaranteed. A novel collaborative diagnosis approach of petrochemical equipment for bearing based on optimal VMD and multiscale entropy (MSE) is proposed to solve this problem. Firstly, because of the difficulty in optimizing the decomposition parameter K of VMD, an effective estimation model of K is constructed according to the frequency distribution characteristics of the decomposition components of local mean decomposition (LMD). Then, a novel characteristic extraction technique combining MSE with linear discriminant analysis (LDA) is proposed to establish characteristic samples. Furthermore, aimed at the fault features of small samples for bearing, support vector machine (SVM) is introduced to identify the fault characteristics. Finally, the bearing fault data collected from the simulation platform of the petrochemical equipment laboratory is used to verify the effectiveness and engineering practicability of the proposed approach. The comparative analyses show that the proposed algorithm can effectively diagnose the faults of the bearing with good engineering operability and scalability.
SAR image segmentation algorithm of regionalized fuzzy clustering based on the Gamma mixture model with variable shape parameters
Control and Decision,2020,Vol 35,No. 07
With regard to the problem that the traditional fuzzy clustering algorithm cannot precisely describe the distribution characteristics of synthetic aperture radar (SAR) intensity image and overcome the inherent speckle noises, the SAR image segmentation algorithm of regionalized fuzzy clustering based on the Gamma mixture model (GaMM) with variable shape parameters is proposed. First, the image domain is completely divided into several Voronoi polygons by Voronoi tessellation. Assuming that the pixel intensities follow the GaMM with variable shape parameters, the dissimilarity between the intensities of pixels in Voronoi polygons and clusters is described by the negative logarithmic function of the GaMM. Then, the regionalized fuzzy objective function is defined with the combination of the GaMM and regularization term with spatial constraint between neighbor Voronoi polygons. In the parameter estimation procedure, the moving-updating operation is designed to solve the implicit parameters according to the criterion of minimizing the objective function. The qualitative and quantitative analyses for the segmentation results of real and simulated SAR images effectively prove the fitting ability of the regionalized GaMM with variable shape parameters to SAR data and the noise immunity of the proposed algorithm.