• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.332-334
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• 2021

As fatal accidents due to industrial accidents and deaths due to civil accidents were pointed out as social problems, the Act on Punishment of Serious Accidents Occurred in the Workplace was enacted to ensure the safety of citizens and to prevent serious accidents in advance. Effort is required. In this paper, we propose a distance prediction model in relation to the case where an operator is hit by heavy equipment such as a forklift. For the data, actual forklift trucks and workers roaming environments were directly captured by CCTV, and it was conducted based on the Euclidean distance. It is thought that it will be possible to learn YOLO-v4 by directly building a data-set at the industrial site, and then implement a model that predicts the distance and determines whether it is a dangerous situation, which can be used as basic data for a comprehensive risk situation judgment model.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.263-268
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• 2021

The purpose of this study was to propose a model which is suitable for the actual delivery system by designing a fetal delivery hospital operation management and fetal health classification model. The number of deaths during childbirth is similar to the number of maternal mortality rate of 295,000 as of 2017. Among those numbers, 94% of deaths are preventable in most cases. Therefore, in this paper, we proposed a model that predicts the health condition of the fetus using data like heart rate of fetuses, fetal movements, uterine contractions, etc. that are extracted from the Cardiotocograms(CTG) test using a random forest. If the redundancy of the data is unbalanced, This proposed model guarantees a stable management of the fetal delivery health management system. To secure the accuracy of the fetal delivery health management system, we remove the outlier which embedded in the system, by setting thresholds for the upper and lower standard deviations. In addition, as the proportion of the sequence class uses the health status of fetus, a small number of classes were replicated by data-resampling to balance the classes. We had the 4~5% improvement and as the result we reached the accuracy of 97.75%. It is expected that the developed model will contribute to prevent death and effective fetal health management, also disease prevention by predicting and managing the fetus'deaths and diseases accurately in advance.

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.361-371
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• 2019

In 2017, the ratio of the number of victims and deaths in the construction industry was the highest with 25.2% and 29.6%, respectively. Especially, as safety accidents at construction sites continue to increase, the economic loss is greatly increased too. Therefore, in order to prevent safety accidents in the construction work, the safety risk assessment index by type of construction was developed, and the main results of this study are as follows. First, 17 factors related to safety accidents at construction sites were derived through survey and interview survey, and this study suggested 9 items(process, type of construction, progress rate, contract amount, number of floors, safety education, working days and weather) throughout the expert advisory meeting. Second, the risk assessment index for safety accidents was developed based on the ratio and intensity of safety accidents. Third, to verify the risk assessment model, the construction safety risk assessment index by type of construction was derived by surveying and analyzing the statistics of the construction accident. In addition, the risk strength was calculated by dividing human damage caused by construction safety accidents into those killed and injured. The risk assessment index based on the frequency and intensity of safety accidents by type of construction is expected to be utilized as basic data when assessing the risk of similar projects in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.323-323
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• 2011

산지가 많은 국내의 지형적인 여건은 토지의 이용에 다양한 영향을 준다. 산지주변에서는 능선을 따르거나 가로지르는 도로가 건설되고 산지계곡 하류에 발달한 선상지에는 가옥이 들어서거나 경작지로 활용되고 있다. 이러한 환경에서 우리나라는 지난 10년간 사면재해로 인한 사망자가 전체 자연재해에 27%에 달하고 이는 집중호우가 발생하는 시기에 집중되고 있어 이에 대한 연구가 필요한 실정이다. 본 연구에서는 호우 발생 시 산지하천에서 발생하는 토석류의 흐름특성을 연구하기 위해 국내 외 토석류 실험장치에 대한 조사와 토석류 실험장치를 개발하여 토석류발생 및 도달거리 예측을 위한 실험을 수행하였다. 토석류 실험장치는 수조길이 5.5m에 10-40cm의 가동폭 그리고 상 중 하류의 3단계 경사조절이 가능하며 토석류 퇴적장치를 결합하여 도달거리 및 퇴적분포를 계측할 수 있다. 토석류를 발생시키기 위해 토사공급장치 이용방법, 하상퇴적토사를 이용한 방법 등 다양한 시도를 하였다. 토석류 실험장치를 이용하여 발생시킨 토석류의 특성과 퇴적장치 에서의 도달거리를 측정하여 유량공급과의 상관관계를 분석하였다. 이러한 연구결과는 토석류 재해지도의 작성이나 토석류 해석모형의 개발에 필요한 기초자료로 활용될 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.697-700
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• 2022

Recently, as of March 18, 2022, Corona-19 (COVID-19) has 8,250,000 confirmed persons and 11,481 deaths, and has been increasing since the outbreak in 2020. By limiting the number of people and time, we are showing how our daily life changes depending on the number of confirmed coronas. Therefore, in this study, we implemented an algorithm that predicts the number of confirmed people the next day to help minimize damage to the limits of daily life. This algorithm is an algorithm that predicts the number of confirmed persons on the next day using the number of confirmed persons for 3 days. It is predicted by adding the RNN and Dense layers using the Sequential model, and the number of people is subdivided by region. In order to predict the limit, we matched the personnel limit based on the number of fixed persons per day based on Seoul.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of the Korean Geotechnical Society
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• v.29 no.5
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• pp.65-74
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• 2013

The natural disaster occurrences and the loss of lives caused by the steep-slope failures in Korea were investigated in this study. The investigation includes the frequency rate of the steep-slope failures with respect to the characteristics of precipitation, underlying bedrock, and weathered soils. Analysis on the problems in the existing estimation methods of steep-slope failure was also undertaken, and a new model using unsaturated infinite slope stability was developed for the better slope failure estimation. The slope analyses by the newly developed model were performed considering unsaturated infinite slope, the gradient of slope, and hydro/mechanical properties of soils. Steep-slope failure estimation criterion is proposed based on the analysis results. In addition, the precipitation amount corresponding to warning stages against steep-slope failure is provided as an equation of Intensity-Duration criterion.

• Tuberculosis and Respiratory Diseases
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• v.61 no.1
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• pp.41-45
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• 2006

Background; Several clinical scoring systems are currently being used to predict the outcome of sepsis, but they all have certain limitations. Therefore, we sought to identify the proteomic biomarkers, with wsing proteomic tools, that differed according to the outcome of sepsis patients. Methods; Upon admission to the ICU, blood samples were obtained from the 16 patients with sepsis who were enrolled in this study. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI -TOF MS) was used to identify the markers that could predict the outcome of sepsis. Results; We found six peaks, by using cation and anion chips, that statistically differed between those patients who died and those who survived. Conclusion; The biomarkers we found by using proteomic tools may help predict the prognosis and also plan the treatment of sepsis.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.