• 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.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.525-532
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• 2022

Accidents at construction sites in Korea account for more than half of all industrial accidents. To solve this problem, a policy to strengthen safety education was implemented to ensure the safety of workers. However, it was analyzed that there is a high possibility of accidents because workers did not receive proper safety information for each risk factor due to general lecture-style education. In addition, statistics show that the accident status of workers with fewer years of period is high, indicating that a customized information delivery method needs to be proposed for unskilled workers with fewer years of period. Research on the importance of education has been conducted, but no information delivery method has been identified. For unskilled workers to effectively receive safety information, appropriate delivery formats (text, photos, illustrations, 4D-BIM, 360-based panorama, video, animation) were analyzed, and a new method of education was proposed. If customized safety information is provided according to this proposal, effective information delivery to unskilled workers will be possible, and it is expected to be verified in various ways.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1057-1064
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• 2024

The construction industry has witnessed a concerning rise in ladder-related accidents, necessitating the implementation of stricter safety measures. Recent statistics highlight a substantial number of accidents occurring while using ladders, emphasizing the mandatory need for preventative measures. While prior research has explored computer vision-based automatic monitoring for specific aspects such as ladder stability with and without outriggers, worker height, and helmet usage, this study extends existing frameworks by introducing a rule set for co-workers. The research methodology involves training a YOLOv5 model on a comprehensive dataset to detect both the worker on the ladder and the presence of co-workers in real time. The aim is to enable smooth integration of the detector into a mobile application, serving as a portable real-time monitoring tool for safety managers. This mobile application functions as a general safety tool, considering not only conventional risk factors but also ensuring the presence of a co-worker when a worker reaches a specific height. The application offers users an intuitive interface, utilizing the device's camera to identify and verify the presence of coworkers during ladder activities. By combining computer vision technology with mobile applications, this study presents an innovative approach to ladder safety that prioritizes real-time, on-site co-worker verification, thereby significantly reducing the risk of accidents in construction environments. With an overall mean average precision (mAP) of 97.5 percent, the trained model demonstrates its effectiveness in detecting unsafe worker behavior within a construction environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.9
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• pp.1001-1008
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• 2015

This paper introduces the architecture of the power supply in order to achieve the safety integrity target for power supply which is a part of safety related system. The integrity level for safety is set 4 and according to the IEC 62425 which is standard for railway application the architecture design is conducted and process for design is developed. The procedure for design consists with 6 steps. The architecture of power supply that is able to keep the safety integrity against of failure of power supply is derived through the analysis and it is suggested that the power supply adopted the result in this paper is suitable to apply in safety system. Also, the failure frequency that is a quantitative value for the power supply is proposed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.136-144
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• 2022

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.31-40
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• 2014

While Korea had achieved radical growth of construction industry, it also had accumulated problems in material, human and economical loss due to its low quality of safety level. Therefore, not only enterprises but also the nation is putting in a great deal of efforts for construction safety. However, its effect is not satisfiable. This research aims for change of construction cite by introduction of professional Safety Patrol Management System(SPMS) and consideration of its necessity. To consideration of its necessity, we compared and anaylzed average numbers of indicated dangers and safety accident incidences in each construction cites and we researched changes in worker's safety sense. It will establish the suitable design standards and suggest the basic database for estimating disaster and accident ratio.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of the Korea Safety Management & Science
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• v.16 no.2
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• pp.43-52
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• 2014

In weapon systems development, live fire tests have been frequently adopted to evaluate the performance of the systems under development. Therefore, it is necessary to ensure safety in the test ranges where the live fire tests can cause serious hazards. During the tests, a special care must be taken to protect the test and evaluation (T&E) personnel and also test assets from potential danger and hazards. Thus, the development and management of the range safety process is quite important in the tests of guided missiles and artillery considering the explosive power of the destruction. Note also that with a newly evolving era of weapon systems such as laser, EMP and non-lethal weapons, the test procedure for such systems is very complex. Therefore, keeping the safety level in the test ranges is getting more difficult due to the increased unpredictability for unknown hazards. The objective of this paper is to study on how to enhance the safety in the test ranges. To do so, an approach is proposed based on model-based systems engineering (MBSE). Specifically, a functional architecture is derived utilizing the MBSE method for the design of the range safety process under the condition that the derived architecture must satisfy both the complex test situation and the safety requirements. The architecture developed in the paper has also been investigated by simulation using a computer-aided systems engineering tool. The systematic application of this study in weapon live tests is expected to reduce unexpected hazards and test design time. Our approach is intended to be a trial to get closer to the recent theme in T&E community, "Testing at the speed of stakeholder's need and rapid requirement for rapid acquisition."

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.463-478
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• 2019

The most important non-functional requirements for dependability of any Embedded Real-Time Safety Systems are safety, availability and reliability requirements. System architecture plays the primary role in achieving these requirements. Compliance with these non-functional requirements should be ensured early in the development cycle with appropriate considerations during architectural design. In this paper, we present an application of system architecture modeling for quantitative assessment of system dependability. We use probabilistic model checker (PRISM), for dependability analysis of the DTMC model derived from system architecture model. In general, the model checking techniques do not scale well for analyzing large systems, because of prohibitively large state space. It limits the use of model checking techniques in analyzing the systems of practical interest. We propose abstraction based compositional analysis methodology to circumvent this limitation. The effectiveness of the proposed methodology has been demonstrated using the case study involving the dependability analysis of safety system of a large Pressurized Water Reactor (PWR).

• Safety and Health at Work
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• v.15 no.3
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• pp.373-378
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• 2024

Introduction: Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules. Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results: The functional evaluation shows that the main function of this SAP architecture was operated successfully. Discussion: The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.