Design for the competitiveness assessment index system of aviation &amp; aerospace manufacture industry on basis of GEM—Pearson—VC combination method

Zhang, Yongjun; Chen, Wei; Liu, Hongde

doi:10.1080/09720502.2016.1259777

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…de Jong and Beelaerts van Blokland (2015); Harik, El Hachem, Medini, and Bernard (2015); Sahu, Sahu, and Sahu (2016); Metaxas, Koulouriotis, and Spartalis (2016); Nerantzidis (2016); Paredes‐Gazquez, Rodriguez‐Fernandez, and de la Cuesta‐Gonzalez (2016); Garg (2017); Seo and Bae (2017); Mendola and Volo (2017); Tinggi, Hla, Jakpar, Cheuk, and Nichol (2017); Zhang, Chen, and Liu (2017); Beelaerts van Blokland, van de Koppel, Lodewijks, and Breen (2019); Azevedo, Godina, and Matias (2017); Rubio‐Romero, Pardo‐Ferreira, De la Varga‐Salto, and Galindo‐Reyes (2018).…”

Section: Review Of the Literaturementioning

confidence: 99%

See 1 more Smart Citation

Composite Indicators of Company Performance: A Literature Survey

Zeng

Blokland²,

Santema

et al. 2020

Perf Improvement Qrtly

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Section: Review Of the Literaturementioning

confidence: 99%

“…Newell (2009); Beelaerts van Blokland et al (2012); Liu, Liang, Wang, Liu, and Xie (2014); de Jong and Beelaerts van Blokland (2015); Paredes‐Gazquez, Rodriguez‐Fernandez, and de la Cuesta‐Gonzalez (2016); Zhang, Chen, and Liu (2017); Beelaerts van Blokland, van de Koppel, Lodewijks, and Breen (2019)…”

Section: Review Of the Literaturementioning

confidence: 99%

Composite Indicators of Company Performance: A Literature Survey

Zeng

Blokland²,

Santema

et al. 2020

Perf Improvement Qrtly

View full text Add to dashboard Cite

“…In order to be able to accurate classify vehicle types, pattern recognition methods were proposed by many researchers and engineers. Y Cai proposed a monocular and binocular vision method based on Deep Convolutional Neural Networks to search vehicle for the whole area of image [1,2,18,27]. Jie Yuan, Thanida Tangkocharoen, Zhaojin Zhang, Q Jiang and Hulin Kuang et al used multi-layer classifiers, deep learning classifiers or neural networks to recognize the vehicles [6,8,20,23,24].…”

mentioning

confidence: 99%

Research on regional clustering and two-stage SVM method for container truck recognition

Mi¹,

Wang²,

Mi³

et al. 2019

DCDS-S

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With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

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Design for the competitiveness assessment index system of aviation & aerospace manufacture industry on basis of GEM—Pearson—VC combination method

Cited by 2 publications

References 2 publications

Composite Indicators of Company Performance: A Literature Survey

Composite Indicators of Company Performance: A Literature Survey

Research on regional clustering and two-stage SVM method for container truck recognition

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