• Journal of Trauma and Injury
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• v.20 no.1
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• pp.12-18
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• 2007

Purpose: Recently, trauma is more frequent due to the increases in the population, the number of traffic accident, and the incidence of violence. Especially, abdominal trauma is a leading cause of morbidity and mortality. We analyzed the clinical features and the factors associated with morbidity and mortality. Methods: We analyzed 136 patients of abdominal trauma who were admitted at the Department of Surgery, Chonnam National University Hospital, from January 2003 to June 2005. We analyzed the cause of trauma, the injured organ, combined injuries, mental status, blood pressure, laboratory findings, morbidity, and mortality. The relationships between by variable were assesed by using the independent samples test and the Kruskal?Wallis test. Results: The causes of trauma were traffic accidents (98 cases, 72%), falling accidents (9 cases, 6.6%), violence (6 cases, 4.4%), and stab injuries (6 cases, 4.4%). The injured organs were the small intestines (47 cases, 34.6%), the liver (35 cases, 25.7%), the spleen (26 cases, 19.1%), the mesentery (17 cases, 12.5%), the large intestines (15 cases, 11.0%), the pancreas (14 cases, 10.3%), etc. The most common combined injury was chest injury (53 cases, 39%). Comatose or semicomatose mental status and shock on admission (<60 mmHg in systolic) were related to high mortality (85.7%). In laboratory findings, decreased hemoglobin (<8 g/dL), and platelet count (<$50,000/mm^3$), and increased creatinine level (>1.6 mg/dL) were significant prognostic factors. The incidence of postoperative complications was 40.4%, and frequent complications were wound infection (8.1%) and re-bleeding (8.1%). The overall mortality rate was 18.4%, and most common cause was hypovolemic shock (18 cases, 13.2%), however, there was no statistical difference according to injurd organ. Conclusion: In the multivariate analysis, mental status, hemoglobin, and serum creatinine level were the most significant prognostic factors. When an abdominal trauma patient arrives at the emergency room, a rapid and accurate evaluation of the patient's status and risk factors, and resuscitation, if necessary, have to be performed to lower the morbidity and mortality.

• Journal of the Korea Concrete Institute
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• v.19 no.2
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• pp.189-197
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• 2007

Concrete tunnel lining must be designed to having the fireproof performance because the lining are sometimes exposed to very high temperature due to traffic accident. Such fire temperature may cause explosion of concrete, or collapse of tunnel structure. The purpose of this study is to obtain the fundamental fireproof behavior of fire resistance-engineered cementitious composites(FR-ECC) under fire temperature in order to use the fire protection material in tunnel lining system. The present study conducted the experiment to simulate fire temperature by employing 2 types of FR-ECC and investigated experimentally the explosion and cracks in heated surface of these FR-ECC. Employed temperature curve were hydro carbon(HC, ECl) criterion, which are severe in various criterion of fire temperature. The numerical analysis is carried out the nonlinear transient heat flow analysis and verified against the experimental data. The complex features of behavior in fire conditions, such as thermal expansion, plasticity, cracking or crushing, and material properties changing with temperature are considered. By the use of analytical model, the concrete tunnel subjected to fire loads were analyzed and discussed. With comparison of current concrete materials and FR-ECC, the experimental and analytical results of FR-ECC shows the better fire resistance performance than the other.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.