A novel principal components analysis (PCA) method for energy absorbing structural design enhanced by data mining

Du, Xianping; Zhu, Feng

doi:10.1016/j.advengsoft.2018.10.005

Cited by 35 publications

(9 citation statements)

References 28 publications

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“…The research carried out by Subasi and Gursoy [ 70 ] demonstrated that applying PCA achieves much higher classification accuracy than considering all features. Based on the findings of the research, PCA was adopted for dimension reduction in diverse applications [ 71 , 72 , 73 ].…”

Section: Proposed Attention Recovery Systemmentioning

confidence: 99%

A BCI Based Alerting System for Attention Recovery of UAV Operators

Park

Shin

et al. 2021

Sensors

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As unmanned aerial vehicles have become popular, the number of accidents caused by an operator’s inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator’s attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator’s electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.

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Section: Proposed Attention Recovery Systemmentioning

confidence: 99%

A BCI Based Alerting System for Attention Recovery of UAV Operators

Park

Shin

et al. 2021

Sensors

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“…PCA is a statistical signal processing technique used to decorrelate original sensed data. It can usefully be applied to reduce data dimensionality because it converts a possibly correlated set of data into an uncorrelated data set [17]. The amount of correlated data is less than or equal to the amount of original data.…”

Section: Pca and Feature Extraction-based Algorithm For Identifying Imentioning

confidence: 99%

“…be higher than a particular accuracy threshold t; if not, reset the feature number n (Step 6 in Section 3.1) and repeat Steps 1-6. The accuracy threshold t is described as follows:= (17)where the threshold values T i in(17) are set in the range 0.90-0.99 sequentially; { } = {0.90, 0.91, 0.92, . .…”

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confidence: 99%

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Lin

2019

Journal of Sensors

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This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

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“…De maneira mais precisa, o referido avanço disserta sobre uma estratégia que visa melhorar a robustez ao ruído no que diz respeito à tarefa de identificação de sistemas dinâmicos não lineares tanto sob uma perspectiva de classificação quanto sob uma perspectiva de regressão. Desse modo, a fim de realizar tal avanço, a estrutura denominada Feature Engineering to Deal with Noisy Data in Sparse Identification (SINDyFE) foi desenvolvida, onde esta é baseada na hibridização de uma coleção de técnicas de aprendizado de máquina, tais como aumento de dados [25,26], regressão esparsa [27,28], seleção de características [20,29], extração de características [13,30], critério de informação [31,32], pesquisa em grade [20,33] e validação cruzada [20,34].…”

Section: Objetivosunclassified

“…Em linhas gerais, no que concerne às perspectivas de classificação e regressão, a metodologia intitulada Feature Engineering to Deal with Noisy Data in Sparse Identification (SINDyFE) avança a abordagem original denominada Sparse Identification of Nonlinear Dynamics (SINDy) no que diz respeito à inferência de sistemas dinâmicos a partir de dados corrompidos pelo ruído. Precisamente, a estratégia proposta consiste em uma hibridização de técnicas de aprendizado de máquina, tais como aumento de dados [25], regressão esparsa [28], seleção de características [29], extração de características [30], critério de informação [32], pesquisa em grade [33] e validação cruzada [87]. Pontua-se ainda que os dados associados aos estados do sistema a ser identificado são coletados por intermédio de sensores.…”

Section: Metodologia Sindyfeunclassified

Engenharia De Recursos Para Lidar Com Dados Ruidosos Na Identificação Esparsa Sob as Perspectivas De Classificação E Regressão

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A novel principal components analysis (PCA) method for energy absorbing structural design enhanced by data mining

Cited by 35 publications

References 28 publications

A BCI Based Alerting System for Attention Recovery of UAV Operators

A BCI Based Alerting System for Attention Recovery of UAV Operators

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Engenharia De Recursos Para Lidar Com Dados Ruidosos Na Identificação Esparsa Sob as Perspectivas De Classificação E Regressão

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