• Journal of the Korean Society of Safety
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• v.29 no.2
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• pp.76-81
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• 2014

Railroad traffic accident consists of train accident, level-crossing accident, traffic death and injury accident caused by train or vehicle, and it is showing a continuous downward trend over a long period of time. As a result of the frequency comparison of train accidents and level-crossing accidents using the railway accident statistics data of Railway Industry Information Center, the share of train accident is over 90% in the 1990s and 80% in the 2000s more than the one of level-crossing accidents. In this study, we investigated time series characteristic and short-term prediction of railroad crossing, as well as seasonal characteristic. The analysis data has been accumulated over the past 20 years by using the frequency data of level-crossing accident, and was used as a frequency data per month and year. As a result of the analysis, the frequency of accident has the characteristics of the seasonal occurrence, and it doesn't show the significant decreasing trend in a short-term.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.198-204
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• 2018

In this paper, It proposed the development of integrated data management's prototype system for aviation accident and incident system. With the recent development of the aviation accident investigation equipment, accident investigation system should collect and manage the various types of jpg, avi, and wav data files. However, the ECCAIRS system does not have a separate database for managing the various generated data during the accident investigation. And the Korea aviation accident management system also has the same problem. Therefore, in this paper, we analyze the aviation accident report system of major foreign countries and prepare a method to apply it to the domestic environment. Through the prototype of the integrated data management system, we confirmed the performance through inputting the existing data and the recently investigated data. We will use this result as basic data for completion of final integrated data management system.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.354-362
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• 2000

Increasing amount of road tragic in 90's has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving conditions based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we apply several data fusion, ensemble and clustering algorithms in an effort to increase the accuracy of individual classifiers for the accident severity. An empirical study results indicated that clustering works best for road traffic accident classification in Korea.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.29-36
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• 2014

The accident statistics use the data from police accident reports and statistics. Although the official accident statistics are useful, they provide very limited information about how accidents occur, the cause of the accident and the injury mechanisms. This limitations could be overcome by carrying out the in-depth accident study and analysing investigations, collecting more detailed information. Meanwhile a net of in-depth investigation teams are operating worldwide, such as NASS (National Accident Sampling System) and CIREN (Crash Injury Research and Engineering Network) in US, OTS (On the spot investigation) in UK. In this study, the database structure and variables of Korea in-depth accidents investigation system would be proposed through considering the database structure of GIDAS (Germany In-Depth Accidents Study). GIDAS is one of the best system on the in-depth accident study system in the world. GIDAS was established in 1999 as a cooperation project between Federal Highway Research Institute of Germany (BASt) and research association on automotive engineering of German Car Industry(FAT). The iGLAD (Initiative for the Global Harmonization of Accident Data) was also considered to introduce into the database variables of Korea in-depth accident study. Current police reports were compared with GIDAS and iGLAD. To collaborate of the Worldwide in-depth accident data, this paper proposed that the database of Korea in-depth accident study would be introduced the structure of GIDAS and the core variables of iGLAD. Harmonization of the structures and core variables of Korea in-depth accident study will be better than the making unique ones. The database structure and core variables of KIDAS(Korea In-Depth Accident Study) introduced of GIDAS and iGLAD.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.111-118
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• 2014

PURPOSES: The purposes are to analyze the pedestrian accident severity and to develop the accident models by arterial road function. METHODS: To analyze the accident, count data and ordered logit models are utilized in this study. In pursuing the above, this study uses pedestrian accident data from 2007 to 2011 in Cheongju. RESULTS : The main results are as follows. First, daytime, Tue.Wed.Thu., over-speeding, male pedestrian over 65 old are selected as the independent variables to increase pedestrian accident severity. Second, as the accident models of main and minor arterial roads, the negative binomial models are developed, which are analyzed to be statistically significant. Third, such the main variables related to pedestrian accidents as traffic and pedestrian volume, road width, number of exit/entry are adopted in the models. Finally, Such the policy guidelines as the installation of pedestrian fence, speed hump and crosswalks with pedestrian refuge area, designated pedestrian zone, and others are suggested for accident reduction. CONCLUSIONS: This study analyzed the pedestrian accident severity, and developed the negative binomial accident models. The results of this study expected to give some implications to the pedestrian safety improvement in Cheongju.

• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.179-185
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• 2017

Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.

• The Korean Journal of Emergency Medical Services
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• v.11 no.1
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• pp.65-72
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• 2007

The purpose of this study was teacher's perception on young children's safety life, safety accident, and safety education and provided basic data of administrating teacher's education for young children's safety. Subjects of this study were teachers of young children attending for their education. 230 questionnaires were provided and 181 were collected and 170 were used for data analysis. Data collected were analyzed with SPSS WIN 2.0 program. The results of the study were as follows : 1. Regarding teacher's perception on types of young children's safety accident, play accident was high(70.0%). 2. With regard to teacher's perception on causes of accident, lacks of perception was high(64.1%). 3. Of transportation means in accident, 119 ambulance use was high(60.5%) 4. Regarding teacher' perception on accident prevention, direct attention of education by paramedics was high(48.2%).

• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.176-182
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• 2018

In order to understand and track the trends of construction safety accident, this study shows the topic trends in the construction safety accident with LDA(Latent Dirichlet Allocation)-based topic modeling method for data analytics. Especially, it performs to figure out the main issue of construction safety accident with unstructured data analysis based on the topic modeling rather than a variety of structured data analysis for preventing to safety accident in construction industry. To apply this methodology, I randomly collected to 540 news article data about construction accident from January 2017 to February 2018. Based on the unstructured data with the LDA-based topic modeling, I found the 10 topics and identified key issues through 10 keyword in each 10 topics. I forecasted the topic issue related to construction safety accident based on analysis of time-series trends about the news data from January 2017 to February 2018. With this method, this research gives a hint about ways of using unstructured news article data to anticipate safety policy and research field and to respond to construction accident safety issues in the future.