• Smart Structures and Systems
• /
• v.29 no.5
• /
• pp.715-728
• /
• 2022

Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3554-3570
• /
• 2021

With the rapid development of the Chinese water project, the safety monitoring of dams is urgently needed. Many drawbacks exist in dams, such as high monitoring costs, a limited equipment service life, long-term monitoring difficulties. MEMS sensors have the advantages of low cost, high precision, easy installation, and simplicity, so they have broad application prospects in engineering measurements. This paper designs intelligent monitoring based on the collaborative measurement of dual MEMS sensors. The system first determines the endpoint coordinates of the sensor array by the coordinate transformation relationship in the monitoring system and then obtains the dam settlement according to the endpoint coordinates. Next, this paper proposes a dual-MEMS sensor collaborative measurement algorithm that builds a mathematical model of the dual-sensor measurement. The monitoring system realizes mutual compensation between sensor measurement data by calculating the motion constraint matrix between the two sensors. Compared with the single-sensor measurement, the dual-sensor measurement algorithm is more accurate and can improve the reliability of long-term monitoring data. Finally, the experimental results show that the dam subsidence monitoring system proposed in this paper fully meets the engineering monitoring accuracy needs, and the dual-sensor collaborative measurement system is more stable than the single-sensor monitoring system.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.5
• /
• pp.97-103
• /
• 2017

In this paper, we propose a method to investigate the safety consciousness of a analyst incident response. This study conducted a statistical survey on 154 analysts who hired as expert in environment monitoring organizations in South Korea. The results of the analyses showed that respondents had good awareness on the equipment incident response and complied with laboratory safety regulations very well. Secondly, respondents were aware of the importance in the order of equipment incident response, an analytical laboratory incident response, and the cause of a chemical substance associated incident in an analytical laboratory in regarding the regulation compliance for creating a safe laboratory environment and the securement of laboratory safety. Therefore, (it was identified that) it would be necessary to create a safe environment and integrate a safety management system.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1997.10a
• /
• pp.82-89
• /
• 1997

Due to aging of the imported seismic monitoring system of Uljin 1＆2 units it is difficult for this system to provide enough functions needed for the security of seismic safety and the evaluation of the earthquake data from the seismic instrumentation. For this reason, it is necessary to replace the seismic monitoring system of Uljin 1＆2 units with an upgraded system with corresponding softwars. With operation of this system which incorporates the man-machine interface technology, the operators in nuclear power plant can rapidly and correctly determine the exceedance of Operating Basis Earthquake.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.2
• /
• pp.335-338
• /
• 2006

This report introduces a National Vessel Monitoring System(NVMS) within the General Information Center On Maritime Safety and Security(GICOMS) project and a national legislation system for ship's safety management in Korea. We also represent future directions of NVMS in terms of E-Navigation(E-Nav), which is the new study item for COMSAR and NAV that has been adopted from the Maritime Safety Committee(MSC) 81st session. It concludes the future E-Nav system, when it is built, that all functions of NVMS should be used by NVMS controllers as well as mariners, and we should also consider the existing NVMS infrastructure including S/W and H/W.

• Journal of the Korea Institute of Building Construction
• /
• v.21 no.5
• /
• pp.397-408
• /
• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.2 no.1
• /
• pp.143-143
• /
• 1996

Recent Class requirements and IMO recommendations concerning Hull Strength Monitoring (HSM) have prompted an increasing number of shipowner to adopt monitoring systems on bulk carriers and tanker. Such systems are designed to give warning when stress levels and the frequency and magnitude of ship motions approach levels which require corrective action. When fitted these systems provide enhanced operational safety and efficiency. This paper describes a development beyond the standard BMT HSM system through the integration of stress, motion and radar-based sea state monitoring with powerful, on-board, artificial intelligence (AI) tools. The latter utilises conceptual clustering techniques as an aid to pattern recognition in stress, fatigue. motion and sea state data clusters. This, in turn, provides additional operational guidance for ship's staff. Feedback from applications of the standard BMT HSM and extended HSM systems on board the British Steel Bulk Shipping fleet is described.

• Journal of the Korean Society of Safety
• /
• v.33 no.6
• /
• pp.42-49
• /
• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.3
• /
• pp.239-245
• /
• 2002

Many civil and infrastructures continue to be used despite aging and the associated potential for damage accumulation. Therefore, the ability to monitor the health of these systems is becoming increasingly important. The purpose of this paper is to propose a real-time health monitoring system of cable-stayed bridge, based-on non-destructive measurement. And also this paper focuses on the safety assessment for bridge from health monitoring system to accomplish this safety assesment. Using the proposed health monitoring system, it helps bridge maintenance and reduces the economic cost of a life-cycle costs. Also it give important data to develop the design and analysis method for cable-stayed bridges.

• Journal of the Korean Society for Railway
• /
• v.3 no.3
• /
• pp.147-153
• /
• 2000

The train control and monitoring system (TCMS) is an on board computer system in railway vehicles performing the control, supervisory and diagnostic functions of the complete train system. This system replaces a lot of hard-wired relays and minimizes the necessary vehicle wiring thus increasing the reliability of the train. It is also one of more important equipment on vehicle to implement much higher safety and reliability train system. We studied a software design technique of TCMS using a CASE tool that is a kind of safety critical software engineering tool (SCADE). This tool has mainly four functions such as the graphical editor, the document maker, tile automatically code generator, and the test simulator. The several functions of TCMS are implemented in this software easily programmed using a functional block diagram and a graphic programming language. We applied to automatically generated TCMS modules on the SCADE each functional block for the Standard type EMU in Korea. We performed the combination test using TCMS simulator and the running test in Seoul subway 7 Line. We proved that this technique is more useful for the software design of TCMS in urban transit