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      Study on Structural Characteristics of Composite Smart Grille Based on Principal Component Analysis

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      1 , 2 , 3 , 1 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          In recent years, many scholars have conducted in-depth and extensive research on the mechanical properties, preparation methods, and structural optimization of grid structural materials. In this paper, the structural characteristics of composite intelligent grid are studied by combining theoretical analysis with experiments. According to the existing conditions in the laboratory, the equilateral triangular grid structure experimental pieces were prepared. In this paper, principal component analysis combined with nearest neighbor method was used to detect the damage of composite plates. On this basis, the multiobjective robustness optimization of the structure is carried out based on artificial intelligence algorithm, which makes the structure quality and its sensitivity to uncertain parameters lower. Particle swarm optimization (PSO) is used in neural network training. The damage characteristics of different grid structures, different impact positions, and different impact energies were studied. The results show that the structural damage types, areas, and propagation characteristics are very different when the structure is impacted at different positions, which verifies that the grid structure has a good ability to limit the damage diffusion and shows that the grid structure has a good ability to resist damage.

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          Intelligent energy management based on SCADA system in a real Microgrid for smart building applications

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            Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning †

            Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.—the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.
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              Structural characteristics of a highly branched and acetylated pectin from Portulaca oleracea L.

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                5 January 2022
                : 2022
                : 4712041
                Affiliations
                1College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
                2School of Mechanical and Transportation Engineering of Guangxi University of Science, Guangxi, China
                3Research Institute of Science and Technology of Chinalco, Beijing, China
                Author notes

                Academic Editor: Guobin Chen

                Author information
                https://orcid.org/0000-0003-3745-9387
                Article
                10.1155/2022/4712041
                8754626
                35035459
                e1078ce0-d94b-4e78-b294-cbc9bb5c5b72
                Copyright © 2022 Kong Fanxiao et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 November 2021
                : 9 December 2021
                : 16 December 2021
                Funding
                Funded by: Fundamental Research Funds for the Central Universities
                Award ID: 2021CDJXDJH003
                Funded by: Chongqing Research Program of Basic Research and Frontier Technology
                Award ID: cstc2019jcjy-msxmX0539
                Funded by: Natural Science Foundation of Guangxi Province
                Award ID: 2018GXNSFAA281273
                Funded by: Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi
                Award ID: 2019KY0370
                Categories
                Research Article

                Neurosciences
                Neurosciences

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