• 제목/요약/키워드: Safety Data

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A Study on Safety Investment Moment for Safety Target (철도 안전목표 설성을 위한 안전투자 시점에 대한 연구)

  • Kwak, Sang Log
    • Journal of the Korean Society of Safety
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    • v.32 no.5
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    • pp.122-128
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    • 2017
  • Korean government announced long-term railway safety investment plan for the safety improvement by 2020. But no research have been done about differential analysis on railroad safety investment and safety improvement. In this study, recent 10 year data on safety investments and accident data are analysed for the differential analysis. Three main safety investments are analysed on regard to accident rate and accident fatalities. Three safety measures include level crossing accident, platform fatalities, and track trespass fatalities. About 90% of railway accident fatalities are caused by these three kind of accidents. Differential analysis shows about 4 to 6 years delay after railroad safety investment and safety improvement. This result can be utilized for the decision making on safety measures and safety target. Which required long term approach.

Evaluation of the Safety impact by Adaptive Cruise Control System (자동순항제어기에 의한 안전도 향상 효과 분석)

  • Lee, Taeyoung;Yi, Kyongsu;Lee, Chankyu;Lee, Jaewan
    • Journal of Auto-vehicle Safety Association
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    • v.4 no.1
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    • pp.5-11
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    • 2012
  • This paper discusses the evaluation of the safety impact of the Adaptive Cruise Control (ACC) system in Korea. To evaluate the safety impact, this paper suggests an analysis method by using the test scenario and field operational test data. The test scenario is composed to represent the main component factor of the ACC system and ACC related accident situation such as rear-end collision, lane-change, and road-curvature, etc. Also, from the field operation test data, the system's potential to increase the safety can be measured ideally. Besides, field operational testdata was used to revise the expected safety impact value as Korean road conditions. By using the proposed evaluation method, enhanced safety impact of the ACC system can be estimated scientifically.

Basic Study on Safety Accident Prediction Model Using Random Forest in Construction Field (랜덤 포레스트 기법을 이용한 건설현장 안전재해 예측 모형 기초 연구)

  • Kang, Kyung-Su;Ryu, Han-Guk
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2018.11a
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    • pp.59-60
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    • 2018
  • The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

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A Study on the Safety Index Service Model by Disaster Sector using Big Data Analysis (빅데이터 분석을 활용한 재해 분야별 안전지수 서비스 모델 연구)

  • Jeong, Myoung Gyun;Lee, Seok Hyung;Kim, Chang Soo
    • Journal of the Society of Disaster Information
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    • v.16 no.4
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    • pp.682-690
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    • 2020
  • Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

A Study on Data Mapping for Integrated Analysis of Railway Safety Data (철도 위험관리 데이터 연계 분석을 위한 기준 데이터 매핑 연구)

  • Byun, Hyun-Jin;Lee, Yong-Sang
    • Journal of the Korean Society for Railway
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    • v.20 no.5
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    • pp.703-712
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    • 2017
  • The railway system is an interface industry that can be safely operated by organically operating the lines, vehicles, controls, etc. Various data are generated in the operation and maintenance activities of the railway system. These data are utilized in cooperation with safety and maintenance activities in each field, but amount of data is insufficient for data analysis of safety management due to relevant data being produced without any synchronous criteria such as time or space. In particular, reference data such as location and time of failure data for each field are set to different criteria according to the work characteristics in each field. So, it is not easy to analyze data integrally based on location and time. Therefore, mapping of reference data can be required for integrated analysis of data defined in different formats. By selecting data mapping tools and verifying the results of safety relevant data with the same criteria, the purpose of this paper is to enable integrated analysis of railway safety management data occurring in different fields based on location and time.

A Study on Data Integration for Railway Safety Management System (철도안전관리시스템을 위한 데이터통합 연구)

  • Hong, Soon-Heum;Noh, Hee-Min;Kim, Young-Hoon;Kim, Kyung-Hee
    • Proceedings of the KSR Conference
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    • 2010.06a
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    • pp.1215-1221
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    • 2010
  • Railway safety management information systems are now being developed by various railway related organizations. A large number of railway safety related data were gathered from railway operators, railway infrastructure manager and railway intendance, etc. The meaning of these data can easily be defined by each organization or developer with little regard for reusing of data by others. Until the present, the main efforts for data management focused on classifying the railway safety data by subject for the developer's convenience and how to combine these data each other is not interested in. In this study, data integration is considered from the viewpoint of combining them semantically in order to get more useful information from partially informed data of multi agencies.

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Factors Influencing Safety Care Activities of Hospital Nurses (병원 간호사의 안전 간호활동에 영향을 미치는 요인)

  • Yang, Ya Ki
    • Journal of Korean Academy of Fundamentals of Nursing
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    • v.26 no.3
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    • pp.188-196
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    • 2019
  • Purpose: The purpose of this study was to investigate the relationships among fatigue, patient safety culture and safety care activities of hospital nurses, and to identify and explain factors influencing safety care activities. Methods: The research participants were 187 nurses from a urban general hospital located in Korea. Self-evaluation questionnaires were used to collect the data. Data collection was done from January 10 to 31, 2019. Data were analyzed using descriptive statistics, independent t-test, One-way ANOVA, Pearson correlation coefficients and multiple regression with the SPSS 24.0 program. Results: There were significant negative relationships between fatigue and safety care activities (r=-.22, p=.003), and significant positive relationships between patient safety culture and safety care activities (r=.22, p=.003). Factors influencing safety care activities in hospital nurses were identified as type of unit (ICU) (${\beta}=.28$), patient safety culture (${\beta}=.24$) and fatigue (${\beta}=-.19$). The explanation power of this regression model was 16% and it was statistically significant (F=8.29, p<.001). Conclusion: These results suggest the need to develop further management strategies for enhancement of safety care activities in hospital. To improve the levels of patient safety, education programs on patient safety should be developed and provided to nurses in hospitals.

A deep neural network to automatically calculate the safety grade of a deteriorating building

  • Seungho Kim;Jae-Min Lee;Moonyoung Choi;Sangyong Kim
    • Smart Structures and Systems
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    • v.33 no.4
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    • pp.313-323
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    • 2024
  • Deterioration of buildings is one of the biggest problems in modern society, and the importance of a safety diagnosis for old buildings is increasing. Therefore, most countries have legal maintenance and safety diagnosis regulations. However, the reliability of the existing safety diagnostic processes is reduced because they involve subjective judgments in the data collection. In addition, unstructured tasks increase rework rates, which are time-consuming and not cost-effective. Therefore, This paper proposed the method that can calculate the safety grade of deterioration automatically. For this, a DNN structure is generated by using existing precision inspection data and precision safety diagnostic data, and an objective building safety grade is calculated by applying status evaluation data obtained with a UAV, a laser scanner, and reverse engineering 3D models. This automated process is applied to 20 old buildings, taking about 40% less time than needed for a safety diagnosis from the existing manual operation based on the same building area. Subsequently, this study compares the resulting value for the safety grade with the already existing value to verify the accuracy of the grade calculation process, constructing the DNN with high accuracy at about 90%. This is expected to improve the reliability of aging buildings in the future, saving money and time compared to existing technologies, improving economic efficiency.

A HAZARDOUS AREA IDENTIFICATION MODEL USING AUTOMATED DATA COLLECTION (ADC) BASED ON BUILDING INFORMATION MODELLING (BIM)

  • Hyunsoo Kim;Hyun-Soo Lee;Moonseo Park;Sungjoo Hwang
    • International conference on construction engineering and project management
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    • 2011.02a
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    • pp.17-22
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    • 2011
  • A considerable number of construction disasters occur on pathways. Safety management is usually performed on construction sites to prevent accidents in activity areas. This means that the safety management level of hazards on pathways is relatively minimized. Many researchers have noted that hazard identification is fundamental to safety management. Thus, algorithms for helping safety managers to identify hazardous areas are developed using automated data collection technology. These algorithms primarily search for potential hazardous areas by comparing workers' location logs based on a real-time location system and optimal routes based on BIM. Potential hazardous areas are filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help to improve safety on construction sites.

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A Study on De-Identification Methods to Create a Basis for Safety Report Text Mining Analysis (항공안전 보고 데이터 텍스트 분석 기반 조성을 위한 비식별 처리 기술 적용 연구)

  • Hwang, Do-bin;Kim, Young-gon;Sim, Yeong-min
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.29 no.4
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    • pp.160-165
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    • 2021
  • In order to identify and analyze potential aviation safety hazards, analysis of aviation safety report data must be preceded. Therefore, in consideration of the provisions of the Aviation Safety Act and the recommendations of ICAO Doc 9859 SMM Edition 4th, personal information in the reporting data and sensitive information of the reporter, etc. It identifies the scope of de-identification targets and suggests a method for applying de-identification processing technology to personal and sensitive information including unstructured text data.