• Safety and Health at Work
• /
• v.12 no.1
• /
• pp.1-9
• /
• 2021

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.31 no.3
• /
• pp.42-49
• /
• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.28 no.4
• /
• pp.89-101
• /
• 2020

In order to enhance the safety of the international aviation industry, the International Civil Aviation Organization has recommended establishing an operational foundation for systematic and integrated collection, storage, analysis and sharing of aviation safety data. Accordingly, the Korea aviation industry also needs to comprehensively manage the safety data which generated and collected by various stakeholders related to aviation safety, and through this, it is necessary to previously identify and remove hazards that may cause accident. For more effective data management and utilization, a standard structure should be established to enable integrated management and sharing of safety data. Therefore, this study aims to propose the framework about how to manage and integrate the aviation safety data for big data-based aviation safety management and shared platform.

• Environmental Analysis Health and Toxicology
• /
• v.22 no.1 s.56
• /
• pp.91-101
• /
• 2007

The new chemicals are developed and circulated without the verified toxicity data. So, the accidents and occupational diseases, such as explosion, fire, suffocation about deadly poisons etc. are frequently to workers. Classifications of chemicals suited with guideline and an offer of correct chemical information data are the molt important thing for the establishment of suitable chemical management system. The GHS (Globally Harmonized System of classification and labeling of chemicals) is based with the chemical classifications and unification plan. The warning symbol and phrases are established for improvements of chemical information data system. According to these unified and improved systematic form of data, and the chemical information data, the workplaces will be presented many chemical safety and risk data correctly. In this paper, we will present constructions and accomplishment contents-based chemical management of workplace through development of chemical information data and the nice using for new chemical investigation and risk assessment of chemicals in workplaces.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.599-608
• /
• 2018

As natural disasters are increasing due to the unusual weather and the modern society is getting complicated, the rapid change of the urban environment has increased human disasters. Thus, citizens are becoming more anxious about social safety. The importance of preparation for safety has been suggested by providing the disaster safety services such as regional safety index, life safety map, and disaster safety portal application. In this paper, we propose an application framework to predict the urban safety index based on user's location with realtime weather/atmosphere data after creating a predication model based on the machine learning using number of occurrence cases and weather/atmosphere history data. Also, we implement an application to provide traffic safety index with executing preprocessing occurrence cases of traffic and weather/atmosphere data. The existing regional safety index, which is displayed on the Si-gun-gu area, has been mainly utilized to establish safety plans for districts vulnerable to national policies on safety. The proposed system has an advantage to service useful information to citizens by providing urban safety index based on location of interests and current position with realtime related data.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.300-312
• /
• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.5
• /
• pp.57-64
• /
• 2016

In this paper, we propose the design of railway safety common data model to provide common transformation method for collecting data from railway facility fields to Real-time railway safety monitoring and control system. This common data model is divided into five abstract sub-models according to the characteristics of data such as 'StateInfoMessage', 'ControlMessage', 'RequestMessage', 'ResponseMessage' and 'ExtendedXXXMessage'. This kind of model structure allows diverse heterogeneous data acquisitions and its common conversion method to DDS (Data Distribution Service) format to share data to the sub-systems of Real-time railway safety monitoring and control system. This paper contains the design of common data model and its DDS Topic expression for DDS communication, and presents two kinds of data transformation case studied for verification of the model design.

• Korean Journal of Organic Agriculture
• /
• v.30 no.1
• /
• pp.21-35
• /
• 2022

Measuring the food safety has been focused only on the psychological consumers' recognition of food safety. The actual measurement tool should consist of the evidence-based statistical data to assess the level of national food safety in scientific perspectives. This paper described the development of a concept to measure the food safety of the food chain based on OECD PSR framework. This paper discusses the elaboration of a set of 8 food safety related data issued as statistical data, and which were same weighted. These food safety statistical data (FSDs) were derived as the basis of measuring the variation of food safety during 2013-2019. The values of the primary production indicator (PPI), the processing and manufacturing indicator (PMI), and the distribution and consumption indicator (DCI) are 0.558-0.859, 0.533-0.691, and 0.979-0.982, respectively. The food safety status (FSS) derived from the safety indicator values of each of the three stages is 0.700-0.810. In order to increase the level of food safety, it is necessary to pay attention to PMI and PPI management. In the future, continuously calculating the level of food safety, managing it like the level of psychological safety, and further expanding it to the level of food safety between countries will help establish policies to improve the level of food safety in Korea.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.29 no.4
• /
• pp.105-116
• /
• 2021

The importance of a systematic approach to collect, process, analyze, and share safety data in aviation safety management is continuously increasing. Accordingly, the domestic aviation industry has been making efforts to build a Big-data platform that can utilize multi-field safety data generated and managed by various stakeholders and to develop safety management technology based on them. Big data platforms not only must meet appropriate technical requirements, but also need to establish a management system for effective operation. The purpose of this study is to suggest the requirements of the aviation safety big data platform operation procedure and plan by reviewing the advanced overseas cases (FAA ASIAS). This study can provide overall framework and managerial direction for the operation of the aviation safety big data platform.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.721-727
• /
• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.