Artikel Jurnal :: Kembali

Artikel Jurnal :: Kembali

Judul Performance analysis of an automatic green pellet nuclear fuel quality classification using modified radial basis function neural networks / Benyamin Kusumoputro, Dede Sutarya, Akhmad Faqih
Nomor Panggil PDF
Pengarang
Pengarang/kontributor lain
Subjek
Penerbitan 2016
Kata Kunci Green pellet quality classification · Nuclear fuel cell · Orthogonal least squared method · RBF NN · Weight initialization ·
 Info Lainnya
ISSN20872100
Deskripsi Fisiknone
Catatan Umumnone
VolumeVol 7, No 4 (2016) 709-719
Akses Elektronik http://www.ijtech.eng.ui.ac.id/index.php/journal/article/view/3138
Institusi Pemilik Universitas Indonesia
Lokasi
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Nomor Panggil No. Barkod Ketersediaan
PDF 03-17-355520186 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20449856
Cylindrical uranium
dioxide pellets, which are the main components for nuclear fuel elements in
light water reactors, should have a high density profile, a uniform shape, and
a minimum standard quality for their safe use as a reactor fuel component. The
quality of green pellets is conventionally monitored by laboratory measurement
of the physical pellet characteristics; however, this conventional
classification method shows some drawbacks, such as difficult usage, low
accuracy, and high time consumption. In addition, the method does not address
the non-linearity and complexity of the relationship between pellet quality
variables and pellet quality. This paper presents the development and
application of a modified Radial Basis Function neural network (RBF NN) as an
automatic classification system for green pellet quality. The weight
initialization of the neural networks in this modified RBF NN is calculated
through an orthogonal least squared method, and in conjunction with the use of
a sigmoid activation function on its output neurons. Experimental data confirm
that the developed modified RBF NN shows higher recognition capability when
compared with that of the conventional RBF NNs. Further experimental results
show that optimizing the quality classification problem space through eigen
decomposition method provides a higher recognition rate with up to 98% accuracy.
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041eng
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100Benyamin Kusumoputro, author
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245|a Performance analysis of an automatic green pellet nuclear fuel quality classification using modified radial basis function neural networks / Benyamin Kusumoputro, Dede Sutarya, Akhmad Faqih |c
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338online resource (rdacarrier)
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520Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy.
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650Technology
653Green pellet quality classification; Nuclear fuel cell; Orthogonal least squared method; RBF NN; Weight initialization
700Dede Sutarya, author Akhmad Faqih, author
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850Universitas Indonesia
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856http://www.ijtech.eng.ui.ac.id/index.php/journal/article/view/3138
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