• The Korean Journal of Nuclear Medicine
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• v.38 no.1
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• pp.74-84
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• 2004

Purpose: Since I-125 emits low energy (27-35 keV) radiation, thinner crystal and collimator could be employed and, hence, it is favorable to obtain high quality images. The purpose of this study was to derive the optimized parameters of I-125 SPECT using a new simulation tool, GATE (Geant4 Application for Tomographic Emission). Materials and Methods: To validate the simulation method, gamma camera developed by Weisenberger et al. was modeled. Nal(T1) plate crystal was used and its thickness was determined by calculating detection efficiency. Spatial resolution and sensitivity curves were estimated by changing variable parameters for parallel-hole and pinhole collimator. Peformances of I-125 SPECT equipped with the optimal collimator were also estimated. Results: in the validation study, simulations were found to agree well with experimental measurements in spatial resolution (4%) and sensitivity (3%). In order to acquire 98% gamma ray detection efficiency, Nal(T1) thickness was determined to be 1 mm. Hole diameter (mm), length (mm) and shape were chosen to be 0.2:5:square and 0.5:10:hexagonal for high resolution (HR) and general purpose (GP) parallel-hole collimator, respectively. Hole diameter, channel height and acceptance angle of pinhole (PH) collimator were determined to be 0.25 mm, 0.1 mm and 90 degree. The spatial resolutions of reconstructed image of the I-125 SPECT employing HR:GP:PH were 1.2:1.7:0.8 mm. The sensitivities of HR:GP:PH were 39.7:71.9:5.5 cps/MBq. Conclusion: The optimal crystal and collimator parameters for I-125 Imaging were derived by simulation using GATE. The results indicate that excellent resolution and sensitivity imaging is feasible using I-125 SPECT.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.