• Investigative Magnetic Resonance Imaging
• /
• v.7 no.1
• /
• pp.22-30
• /
• 2003

Purpose : The efficacy of magnetic resonance imaging for evaluating coronary artery disease has been reported. In this study, we evaluated the usefulness of breath-hold segmented K-space cine MR imaging for evaluating the patency of coronary artery bypass grafts (CABG). Materials and Method s : Thirty eight patients with a total of 92 CABGs (36 internal thoracic arteries and 56 saphenous vein grafts) were evaluated using segmented K-space cardiac-gated fast gradient echo sequence (2D-FASTCARD) MR imaging. MR magnitude images were evaluated from the hard copies by two independent observers. A graft was defined as patent if it was seen as a bright small round area on at least two consecutive images throughout the cardiac cycle at a position consistent with the expected location for that graft. Results : MR images were obtained successfully for 23 patients (61%). The sagittal planes were most helpful in visualizing the cross-section of sapheneous vein bypass graft to left circumflex artery branch, whereas the transverse planes were used for identification of internal mammary artery grafts to left anterior descending coronary artery or its branch and identification of saphenous vein grafts to right coronary artery. Forty five grafts were visible using this MR technique, while the grafts were not visible on seven saphenous vein grafts and two internal mammary artery grafts. In two patients showing symptoms of myocardial ischemia, one or two bypass grafts were not visible. Imaging, perpendicular plane to a CABG was important to visualize the flow inside the CABG with maximum sensitivity. Conclusion : Evaluation of patency of the bypass graft was clinically feasible by 2D-FASTCARD MR imaging, whereas any invisible bypass grafts should be further studied by contrast-enhanced MR angiography or by conventional angiography for confirmation of abnormalities.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.27 no.5
• /
• pp.535-548
• /
• 1994

Community structures and fish densit were investigated on three different types of artificial reefs, dice, turtle artificial reef and tubes, constructed in the Korean waters. Variations of fish fauna according to type of artificial reef and the proper artificial reef in each area for optimizing harvest were discussed. Fish were captured by trammel gill net during May, June, September and November, 1988 and both identified and counted. Fourty-five fish species were found in the artificial reefs. Of these, Sebastes spp., Hexagrammos otakii, Pleuronectidae, Navodon modestus and Stephanolepis cirrhifer showed high occurrence-frequency. The dominant species groups were coastal settlement, demersal or rock fishes such as Pleuronectidae, Rajiformes, Stephanolepis cirrhifer, Navodon modestus, Hexagrammos otakii and Sebastes spp. in all of the Artificial reefs except the oceanic area of southern waters. Scomber japonicus was predominant in the oceanic area of southern waters. Composition of demersal, rock and pelagic fishes were different depending on the types of artificial reef. Dice artificial reefs were occupied by rock fish, on the other hand turtle artificial reefs were dominated by dermersal fish. Fish density was high at the dice artificial reef in all survey areas except the middle area of Eastern waters, with high fish density evident in the Tube artificial reef. Fish community structures were remarkably different between Dice and Turtle artificial reefs. The Tube artificial reef showed intermediate characteristics between the above two types of artificial reefs. The coastal areas of Southern waters and the middle and southern areas of Western waters revealed similar fish fauna. Results from the oceanic areas of Southern waters were well associated with the middle and southern areas of Eastern waters.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.281-286
• /
• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.41 no.1
• /
• pp.47-55
• /
• 2023

In this study, the following conclusions were drawn by analyzing the size, proportion, shape, angle, distribution, etc. of stones in order to identify the principles of facade molding of stonework of the 5th to 7th centuries of the Silla Dynasty. First, the uniformity of the size of the stones of the stone foundations of the Silla Dynasty was low at -0.8 to 4.1. This means that stones of various sizes were used, from small stones to large stones. In addition, the distribution of large stones in stonework of the Silla Dynasty appeared evenly regardless of height. This was common in the stonework of the Silla Dynasty, regardless of structural classification such as wells and mountain fortresses. It is thought that the Silla people did not only pursue practicality and efficiency in stone construction, but also considered design elements. Second, the proportional deviation of the stones of the stone walls of the Silla Dynasty was high, ranging from 0.861 to 1.515. This means that the stonework of the Silla Dynasty did not use only long flagstone-shaped stones, but used a mixture of long and short stones. Third, the shape average of the stones of the stonework of the Silla Dynasty was low at 0.45, and the shape deviation was high at the maximum of 0.15. This means that the stones as a whole have irregular shapes, and each stone has a high difference in shape. Fourth, the angle deviation of the stones of the Silla Dynasty was 4.3 to 16.2, and the average angle was 2. This means that the angle of each stone on the stone axis of the Silla Dynasty is tilted to the left and right. Fifth, there was no correlation between stone size, slenderness ratio, shape, and angle in the stone axes of the Silla Dynasty. In the case of stone axes in the Joseon Dynasty, there was a positive correlation between stone size and slenderness, and a negative correlation between stone size and shape. It can be said that the stones of the Joseon Dynasty were relatively standardized, but the Silla Dynasty showed the beauty of moderation by keeping the nature of the material and becoming one with the material.

• Journal of Korea Water Resources Association
• /
• v.57 no.3
• /
• pp.181-193
• /
• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.