• The Korean Journal of Nuclear Medicine
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• v.36 no.1
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• pp.28-33
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• 2002

Lung cancer has become a loading cause of cancer death in Korea. Positron emission tomography was introduced in clinical nuclear medicine in the early 1990s and many studios using this functional imaging technology were performed for evaluation of its clinical utility. I review the current role of positron emission tomography in the diagnosis, staging, and therapy monitoring of lung cancer.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.3
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• pp.256-263
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• 2009

After several PWSCCs were found in Bugey(France), Ringhals(Sweden), Tihange(Belgium), Oconee, Arkansas, Crystal Fever, Davis-Basse, VC Summer(U.S.A.), Thuruga(Japan), USNRC and PNNL started the research on PWSCC, that is, the PINC project. USNRC required KINS to participate in the PINC project in May 2005. KINS organized the Korean consortium at March 2006 and Pre-RRT for RVHP were performed for the preparation of PINC RRT. Through these preliminary RRT, Korea NDE teams can learn and develop the detection and sizing technique for RVHP dissimilar metal weld. These techniques are now being prepared in Korea and need to be utilized for the In-service inspection of the RVHP and BMI of Korea Nuclear Power Plants. PINC RRT mock-ups will be helpful to training.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1230-1244
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• 2022

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.

• The Korean Journal of Nuclear Medicine
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• v.4 no.2
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• pp.1-4
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• 1970

• The Korean Journal of Nuclear Medicine
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• v.18 no.2
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• pp.47-49
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• 1984

• The Korean Journal of Nuclear Medicine
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• v.18 no.2
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• pp.1-16
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• 1984

• The Korean Journal of Nuclear Medicine
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• v.9 no.1
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• pp.1-2
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• 1975

• The Korean Journal of Nuclear Medicine
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• v.9 no.1
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• pp.15-19
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• 1975

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2803-2815
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• 2021

This paper addresses the uncertainty quantification and sensitivity analysis of a depleted light-water fuel assembly of the Turkey Point-3 benchmark. The uncertainty of the fuel assembly decay heat and isotopic densities is quantified with respect to three different groups of diverse parameters: nuclear data, assembly design, and reactor core operation. The uncertainty propagation is conducted using a two-step analysis code system comprising the lattice code STREAM, nodal code RAST-K, and spent nuclear fuel module SNF through the random sampling of microscopic cross-sections, fuel rod sizes, number densities, reactor core total power, and temperature distributions. Overall, the statistical analysis of the calculated samples demonstrates that the decay heat uncertainty decreases with the cooling time. The nuclear data and assembly design parameters are proven to be the largest contributors to the decay heat uncertainty, whereas the reactor core power and inlet coolant temperature have a minor effect. The majority of the decay heat uncertainties are delivered by a small number of isotopes such as ²⁴¹Am, ¹³⁷Ba, ²⁴⁴Cm, ²³⁸Pu, and ⁹⁰Y.

• Mycobiology
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• v.50 no.5
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• pp.259-268
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• 2022

The nuclear import of proteins is a fundamental process in the eukaryotes including plant. It has become evident that such basic process is exploited by nuclear effectors that contain nuclear localization signal (NLS) and are secreted into host cells by fungal pathogens of plants. However, only a handful of nuclear effectors have been known and characterized to date. Here, we first summarize the types of NLSs and prediction tools available, and then delineate examples of fungal nuclear effectors and their roles in pathogenesis. Based on the knowledge on NLSs and what has been gleaned from the known nuclear effectors, we point out the gaps in our understanding of fungal nuclear effectors that need to be filled in the future researches.