• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.453-459
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• 2010

In the study of traffic safety, it is utmost important to obtain more reliable estimates of the expected crashes for a site (or a segment). The observed crashes have been mainly used as the estimate of the expected crashes in Korea, while the empirical Bayes (EB) estimates based on the Poisson-gamma mixture model have been used in the USA and several European countries. Although numerous studies have used the EB method for estimating the expected crashes and/or the effectiveness of the safety countermeasures, no past studies examine the difference in the estimation errors between the two estimates. Thus, this study compares the estimation errors of the two estimates using a Monte Carlo simulation study. By analyzing the crash dataset at 3,000,000 simulated sites, this study reveals that the estimation errors of the EB estimates are always less than those of the observed crashes. Hence, it is imperative to incorporate the EB method into the traffic safety research guideline in Korea. However, the results show that the differences in the estimation errors between the two estimates decrease as the uncertainty of the prior distribution increases. Consequently, it is recommended that the EB method be used with reliable hyper-parameter estimates after conducting a comprehensive examination on the estimated negative binomial model.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.85-92
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• 2023

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars. However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs. Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes. In this research, I propose a visualization system which helps people to understand the accumulated weight changes. The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space. This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance. These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Chest Surgery
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• v.39 no.9 s.266
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• pp.692-698
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• 2006

Background: Owing to the fact that the average life span has increased and the progress in medical science has been made, the number of patients with chronic renal failure (CRF) who have to take hemodialysis (HD) has been going up gradually. Accordingly, it is considered to be as a significant issue to obtain blood vessels which can be used repetitively and supply enough blood flows. Therefore, there have been various kinds of study on an inosculation rate andfactors influencing it following an arteriovenous fistula (AV fistula) and lots of studies are ongoing for the purpose of escalating the inosculation rate. The authors analyzed the effects of short-term result, age, sex, diabetes and hypertension on arteriovenous inosculations in 134 anatomical snuffbox operated subjects among the patients who have taken an AV fistula at this center. Material and Method: Based on 134 patients who underwent an AV fistula at the department of thoracic surgery of this center from July, 2000 to May, 2004, the difference in arteriovenous inosculation rate was compared and analyzed depending or age (discriminated by 65-year-old), sex and the condition of the presence or absence of diabetes and hypertension. Correlation analyses were conducted for each parameter and statistical tests were performed by using SPSS for windows Release 11.0.1, which were determined to be statistically significant if p value was below 0.05. Result: The total number of operations was 169 including 35 of re-operations. The male/female rate was 70 : 64 (52% : 48%). The average age was $56.3{\pm}12.26$ years and there were 33 (24%) old aged patients above 65-year-old; there were 103 (71%) patients with hypertension and 90 (67%) patients with diabetes. Overall arteriovenous inosculation rate was $93{\pm}2.4%,\;91{\pm}2.7%,\;89{\pm}3.0%$ at 6, 12, 24 months, respectively. The arteriovenous inosculation rate of above 65-year-old patient group was $85{\pm}4.8%,\;80{\pm}5.8%,\;80{\pm}5.8%$ and below 64-year-old patient group's was $85{\pm}4.8%,\;80{\pm}5.8%,\;80{\pm}5.8%$ at given time points, respectively, which showed higher inosculation rate in below 64-year-old patient group with a statistical significance (p=0.0034). However, no statistical significance was found between the patients with hypertension and diabetes and the patients with no complication. In addition, there was no statistical significance in inosculation rate between male and female. Conclusion: The arteriovenous inosculation ratewas higher in the treated patient below 64-year-old than in the treated patient above 65-year-old. Thus it is advantageous for increase in long-term inosculation rate to obtain hemodialysis routes at an early age. The conditions of sex and the presence or absence of diabetes and hyper- tension do not make statistically significant effect on the arteriovenous inosculation rate.