Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network

Cheng, Pengpeng; Chen, Daoling; Wang, Jianping

doi:10.1007/s00500-020-04735-9

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…It analyzes the covariance structure of multivariate statistical observation data in order to find the principal components (PCs) which can simply express the dependence relationship between these data. 38 The basic principle of PCA for a two-dimensional variable data set can be illustrated in Figure 2.…”

Section: Principal Component Analysis and Support Vector Machinementioning

confidence: 99%

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Deng

Zhang

Zhao

2022

Advances in Mechanical Engineering

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Rolling bearings are the key components of rotating machinery. Incipient fault diagnosis of bearing plays an increasingly important role in guaranteeing normal and safe operation of rotating machinery. However, because of the high complexity of the fault feature extraction, the incipient faults of rolling bearings are difficult to diagnose. To solve this problem, this paper presents a new incipient fault intelligent identification method of rolling bearings based on variational mode decomposition (VMD), principal component analysis (PCA), and support vector machines (SVM). In the proposed method, the bearing vibration signals are decomposed by using VMD, and a series of intrinsic mode functions (IMFs) with different frequencies are obtained. Then, the energy and kurtosis values of each IMF are calculated to reveal the intrinsic characteristics of the vibration signals in different scales. Finally, all energy and kurtosis values of IMFs are processed via PCA and subsequently fed into SVM to achieve the bearing fault identification automatically. The effectiveness of this method is verified through the experimental bearing data. The verification results indicate that the proposed method can effectively extract the bearing fault features and accurately identify the bearing incipient faults, and outperform the two compared methods obviously in identification accuracy and computation time.

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Section: Principal Component Analysis and Support Vector Machinementioning

confidence: 99%

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Deng

Zhang

Zhao

2022

Advances in Mechanical Engineering

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“…In other fields of ergonomics, GA was also a popular method. In order to improve the efficiency and accuracy of human shape prediction, Cheng et al designed a new prediction method by using GA and BP neural network [35]. e results showed that the prediction effect of this GA-BP model was better than that of BP, SVM, and K-means models, and it could accurately predict and cluster human body shapes.…”

Section: Complexitymentioning

confidence: 99%

Dimensional Evolution of Intelligent Cars Human-Machine Interface considering Take-Over Performance and Drivers’ Perception on Urban Roads

2020

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The study analyzed the drivers’ take-over behaviors in intelligent cars when driving on urban roads and tried to find reasonable dimensions of the human-machine interface. Firstly, the main driving assistance functions in the process of take-over were analyzed based on the entropy theory, and the weight values of each function for the consumer’s purchase intention were calculated. Secondly, we explored the perceived comfortable dimensions of the interactive components under typical interaction modes. By means of experiments using a within-subjects design, the initial population of the evolutionary computation was obtained. The evolutionary mechanism of dimensions driven by users’ perception was constructed with a genetic algorithm. After debugging the parameters of the model, we verified the rationality of the model and evolved appropriate dimensions. Finally, the validity of the evolved dimensions was proved by a controlled experiment and paired-sample t-test. The results indicated that the completion time of most take-over tasks under the HMI with the evolved dimensions was significantly shorter, which ensured the HMI could be more conducive to the take-over quality and traffic efficiency.

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“…In addition, BPANNs have been broadly used for prediction because of their good linear and nonlinear fitting ability, fault tolerance and high prediction accuracy. For example, the BPANN method has achieved more accurate prediction results in human body shape prediction [5], short-term photovoltaic power generation prediction [6], railway passenger traffic volume prediction [7], centrifugal pump performance prediction [8] and other prediction problems. However, because the error function is a multi-extremum function, if the gradient descent method is used to optimize the weights and thresholds of a BPANN, it is easy to fall into a local optimum, which will cause the fitting accuracy to be low.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

et al. 2022

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Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.

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Clustering of the body shape of the adult male by using principal component analysis and genetic algorithm–BP neural network

Cited by 20 publications

References 30 publications

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Dimensional Evolution of Intelligent Cars Human-Machine Interface considering Take-Over Performance and Drivers’ Perception on Urban Roads

Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network

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