• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Information Systems Review
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• v.22 no.1
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• pp.147-165
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• 2020

The sharing economy service is now spreading in various fields such as accommodation, cars and bicycles. In particular, bicycle-sharing service have become very popular around the world, and since September 2015, Seoul has been providing a bicycle-sharing service called 'Ttareungi'. However, the number of bicycles is unbalanced among rental stations continuously according to the user's bicycle use. In order to solve these problems, we employed social network analysis using Ttareungi data in Seoul, Korea. We analyzed degree centrality, closeness centrality, betweenness centrality and k-core. As a result, the degree centrality was found to be closely linked with bus or subway transfer center. Closeness centrality was found to be in an unbalanced departure and arrival frequency or poor public transport proximity. Betweenness centrality means where the frequency of departure and arrival occurs frequently. Finally, the k-core analysis showed that Mapo-gu was the most important group by time zone. Therefore, the results of this study may contribute to the planning of relocation and additional installation of bike rental station in Seoul.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.199-205
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• 2024

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.61-74
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• 2024

Daemadeung, located in the estuary of the Nakdong River, is formed by sand dunes and possesses well-developed intertidal flats. This study aimed to investigate the habitat of benthic microalgae, photosynthetic pigments, and photosynthetic efficiency in the intertidal flats of Daemadeung from January to December 2011. The inorganic nitrogen content in the sediment pore water was primarily composed of ammonium, while nitrate + nitrite was dominant in the upper layer water. The concentration of chlorophyll a and fucoxanthin in the sediment surface was significantly higher than the mean of all the sediment layer. The average Fv/Fm of benthic microalgae during the entire survey period was 0.52±0.03, with the highest value (0.61±0.08) observed in February. The rETR_max showed a seasonal trend, being high from spring to early autumn (April to October) and low from winter to early spring (January to March, November, December), with the highest value (153.05±2.30 µmol electrons m^-2 s^-1) in July and the lowest (38.49±5.17 µmol electrons m^-2 s^-1) in January. The average Fv/Fm of diurnal microalgae was 0.48±0.03, with the highest value (0.61±0.08) observed at noon. The rETR_max showed a highest peak at noon (54.24±11.35 µmol electrons m^-2 s^-1) and reached its lowest point at 16:00 (26.17±4.75 µmol electrons m^-2 s^-1). These findings suggest that the productivity of benthic microalgae varies significantly depending on the survey time and sediment depth. Therefore, to quantify the productivity of benthic microalgae using Diving-PAM, surveys should be conducted based on tidal conditions, and simultaneous pigment analysis of sediment layers should also be performed.

• Korean Journal of Microbiology
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• v.51 no.3
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• pp.256-262
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• 2015

In order to study the effect of the receptor protein (SeaR), which is isolated from Saccharopolyspora erythraea, we introduced the SeaR gene to Streptomyces virginiae as host strains. An effective transformation procedure for S. virginiae was established based on transconjugation by Escherichia coli ET12567/pUZ8002 with a ${\varphi}C31$-derived integration vector, pSET152, which contained int, oriT, attP, and $ermEp^{\ast}$ (erythromycin promotor). Therefore, the pEV615 was introduced into S. virginiae by conjugation and integrated at the attB locus in the chromosome of the recipients by the ${\varphi}C31$ integrase (int) function. Transformants of S. virginiae containing the SeaR gene were confirmed by PCR and transcriptional expression of the SeaR gene in the transformants was analyzed by RT-PCR, respectively. And, we examined the production time of virginiamycins in the culture media of both the transformants and the wild type. The production time of virginiamycins in the wild type and transformants was the same. When 100 ng/ml of synthetic $VB-C_6$ was added to the state of 6 or 8 hour cultivation of wild type and transformants, respectively, the virginiamycins production was induced, meaning that the virginiamycins production in the wild type was detected 2 h early than transformants. From these results, SeaR expression was also affected to virginiamycins production in transformants derived from S. virginiae. In this study, we showed that the SeaR protein worked as a repressor in transformants.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.7-18
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• 2012

This study suggested a method of forecasting market-share of each mode after introducing new urban rail transit lines. The study reflected the observed market share of presently operating urban rail transit into forecasting process in order to improve accuracy in predicting market share of each modes. For more realistic representation of the forecasting model, we categorized O/D pairs according to attributes of trip distance, access time and number of transfers. The analysis results of traveler's mode choice behavior with observed data showed that the trip distances are longer, the share of urban rail tends to be higher, and that the number of transfers is fewer and the access times are lesser, the share of urban rail also tends to be higher. Then, incremental logit model was used in estimating mode choice probabilities for O/D pairs along with rail transit lines while utilizing observed market shares of each modes and differences in transit service level. As the next step, the market share of rail transit after introducing new rail transit lines was forecasted by using incremental logit model with the intial share values calculated the previous analysis step. It also reflected changes in level of service for automobile in highway due to changes in highway systems and changes in mode shares after introducing new lines of rail transit. It can be expected that the proposed method would more realistically duplicates phenomena of mode choice behavior for rail transit and that it would be more theoretically logical than the typical existing methods using SP data and incremental logit model or using addictive logit model in this country.

• Applied Microscopy
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• v.30 no.2
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• pp.153-163
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• 2000

This study aims to demonstrate the effect of squalene (SQ), one of the natural chelator, on the ultrastructural changes in the mouse liver caused by $HgCl_2$. A total of 40 healthy ICR that weighted 30 gm $({\pm}2gm)$ was used for experiment. The experimental group was divided into two groups; group A and B. The group A administrated $HgCl_2$ (4.0mg/kg) to the intraperitoneal. The group B administrated $HgCl_2$ (4.0 mg/kg) to the intraperitoneal treated with SQ (180 mg/kg, 2 times/day). Each group was observed at 24, 48, 72, 96 hours after injected $HgCl_2$. The results were as follows: 1. Group A Nucleus showed condensation of nuclear membrane at the 24 hours. At the 48 hours, observed distinct condensation. But nuclear membrane be seen relative rounded-shape at the 96 hours. At overall the time, inner cavity of mitochondria swollen and development of cristae weakened. Also electron density of matrix was a little low. At the 72 hours, destruction of the inner and outer membrane of mitochondria observed occasionally. Swelling of inner cavity of rER and destruction of lamellae be found from 24 hours to 72 hours, but at the 96 hours, only some swelling 2. Group B Nuclear membrnae and chromatin be seen normal shape at overall the time. Mitochondria showed destruction of the inner membrane until the 48 hours, but mostly normal shapes. Electron density showed high on the all groups. RER be found swelling of inner cavity at the 24 and 48 hours, but found typical lamellae and observed a number of transfer vesicles around rER at the 72 and 96 hours. These results suggest that squalene attenuates the toxic effect of the $HgCl_2$ in the mouse liver.

• Journal of Hospice and Palliative Care
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• v.17 no.3
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• pp.179-184
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• 2014

Purpose: We investigated the safety and efficacy of peripherally inserted central catheters (PICCs) in terminally ill cancer patients. Methods: A retrospective review was conducted on patients who underwent PICC at the hospice-palliative division of KEPCO (Korea Electric Power Corporation) Medical Center between January 2013 and December 2013. All PICCs were inserted by an interventional radiologist. Results: A total of 30 terminally ill cancer patients received the PICC procedure during the study period. Including one patient who had had two PICC insertions during the period, we analyzed a total of 31 episodes of catheterization and 571 PICC days. The median catheter life span was 14.0 days (range, 1~90 days). In 25 cases, catheters were maintained until the intended time (discharge, transfer, or death), while they were removed prematurely in six other cases (19%; 10.5/1000 PICC days). Thus, the catheter maintenance success rate was 81%. Of those six premature PICC removal cases, self-removal due to delirium occurred in four cases (13%; 7.0/1000 PICC days), and catheter-related blood stream infection and thrombosis were reported in one case, each (3%; 1.8/1000 PICC days). Complication cases totaled eight (26%; 14.1/1000 PICC days). The time to complication development ranged from two to 14 days and the median was seven days. There was no PICC complication-related death. Conclusion: Considering characteristics of terminally ill cancer patients, such as a poor general condition, vulnerability to trivial damage, and a limited period of survival, PICC could be a safe intravenous procedure.

• Korean Journal of Food Science and Technology
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• v.10 no.4
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• pp.398-403
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• 1978

Squid was dried on the fluidized-bed in the drying chamber filled with solid particles which were also fluidized with hot-air, and effects of the fluidized particles, the squid's height from the grid and the drying temperature on the drying rate and quality of the squid were observed The mechanism of moisture transfer during the falling rate period was also derived. 1. Sodium chloride was found to be the most suitable fluidized particles and at an air velocity of 3.8 m/sec, optimal fluidization state of this particle was obtained. 2. Uniform profiles of temperature were obtained at a point 4 cm above the grid and the location of squid on the fluidized-bed observed to be suitable when it was 4 cm above the grid. 3. At an air velocity of 3.8 m/sec and when the location height of the squid on the fluidized-bed was 4 cm, the optimal temperature for the drying time which is required to reduce the moisture from 80.8% to 18-22% was 8.5 hours. 4. Drying data followed the empirical equation of unsteady state diffusion $log\;(\frac{W-We}{Wc-We})=-m{\theta}$ in the region of the moisture contents measured and the drying constant (m) was calculated as $0.32hr^{-1}$. These results suggested that the migration of moisture during the falling rate period is due to a diffusion type mechanism. 5. The short constant rate period was observed in the early stage and thereafter, drying was controlled by the falling rate period, and the time ratio of the fluidized bed drying to the through circulation drying for reducing the squid's moisture contents to the same level at the same drying temperature was 1 : 1.4 6. Comparisons of fluidized-bed dried squid and sun dried squid in sale showed that there was no significant change in qualities such as external appearance and hydrogen ion concentration of dry product.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.