• Journal of Applied Biological Chemistry
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• 제52권1호
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• pp.38-40
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• 2009

• Bulletin of the Korean Chemical Society
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• 제31권3호
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• pp.739-741
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• 2010

• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2007년도 추계학술발표회
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• pp.296.2-296.2
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• 2007

• Bulletin of the Korean Chemical Society
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• 제30권5호
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• pp.1167-1169
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• 2009

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2348-2356
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• 2021

As a relatively new radiation imaging method, the cosmic-ray muon scattering imaging technology can be used to prevent nuclear smuggling and is of considerable significance to nuclear safety. Proposed in this paper is a new reconstruction algorithm based on density clustering, aiming to improve inspection quality with better performance. Firstly, this new algorithm is introduced in detail. Then in order to eliminate the inequity of the density threshold caused by the heterogeneity of the muon flux in different positions, a new flux correction method is proposed. Finally, three groups of simulation experiments are carried out with the help of Geant4 toolkit to optimize the algorithm parameters, verify the correction method and test the inspection quality under shielded condition, and compare this algorithm with another common inspection algorithm under different conditions. The results show that this algorithm can effectively identify and locate nuclear material with low misjudging and missing rates even when there is shielding and momentum precision is low, and the threshold correcting method is universally effective for density clustering algorithms.

• Journal of Applied Biological Chemistry
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• 제52권2호
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• pp.84-87
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• 2009

• Asia Marketing Journal
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• 제6권2호
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• pp.69-87
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• 2004

본 사례는 신규 정보통신 서비스인 KT 네스팟의 태동과 첨단기술 기반의 틈새적 서비스로부터 대중적 서비스로 변화하려는 2003년 초까지의 약 1년 6개월에 걸친 마케팅 활동을 소개함으로써 첨단 정보통신 기술상품의 마케팅 전략을 살펴보았다. 첨단 정보통신 기술상품은 제품의 특성상 시장 진입기에서 성장기로 넘어가는 전략이 특히 중요하며, 이를 성공적으로 수행하기 위해 실시한 실제 마케팅 사례를 살펴보았다.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.208-215
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• 2021

Cosmic-ray muon scattering tomography (MST) technology is a new radiation imaging technology with unique advantages. As the performance of its image reconstruction algorithm has a crucial influence on the imaging quality, researches on this algorithm are of great significance to the development and application of this technology. In this paper, a fast inspection algorithm based on clustering analysis for the identification of the existence of nuclear materials is studied and optimized. Firstly, the principles of MST technology and a binned clustering algorithm were introduced, and then several simulation experiments were carried out using Geant4 toolkit to test the effects of exposure time, algorithm parameter, the size and structure of object on the performance of the algorithm. Based on these, we proposed two optimization methods for the clustering algorithm: the optimization of vertical distance coefficient and the displacement of sub-volumes. Finally, several sets of experiments were designed to validate the optimization effect, and the results showed that these two optimization methods could significantly enhance the distinguishing ability of the algorithm for different materials, help to obtain more details in practical applications, and was therefore of great importance to the development and application of the MST technology.

• 생약학회지
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• 제37권4호
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• pp.235-240
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• 2006

We investigated the antioxidant activities such as DPPH radical scavenging capacity, xanthine oxidase inhibitory activity, and superoxide radical scavenging capacity of the aqueous EtOH extract and its solvent fractions of Artemisia scoparia. The ethyl acetate fraction showed high antioxidant activity, compared to positive controls such as ascorbic acid, butylated hydroxy anisole (BHA), trolox, and allopurinol in these assay systems. Moreover, we examined the inhibitory effect of solvent fractions of A. scoparia on the production of pro-inflammatory factors that the nitric oxide (NO), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2) and prostaglandin E2 $(PGE_2)$ production activated with LPS $(1{\mu}g/ml)$ in murine macrophage cell line RAW264.7. The amounts of protein levels were determined by immunoblottting. Tn the sequential fractions of hexane and dichloromethane inhibited the NO and $PGE_2$ production and the protein level of iNOS and COX-2. These results suggest that A. scoparia may have anti-inflammatory activity through the antioxidant activity and inhibition of pro-inflammatory factors.