• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.79-80
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• 2014

Most of marine pollution have been occurred by oil spill accidents resulted from ship accidents in South Korea. This year there were two large oil spill accidents: the Yeosu Oil Spill Accident (2014.01.31.(Fri.) 09:35 LT) and the Captain Vangelis L. Oil Spill Accident (2014.02.15.(Sat.) 14:00 LT). In general, Synthetic Aperture Radar (SAR) is used in monitoring and detection of oil dumping and spilled oils by accident at sea. Therefore it is expected that KOMPSAT-5, launched successfully last year, will take part in that mission during a normal operation mode. After the two accidents, high spatial resolution optical satellite data including KOMPSAT-3 were acquired February 2 and 14, 2014. In this presentation, we analyzed optical properties of spilled oils from optical satellite imagery to estimate the spilled area and the volume at each region. Finally, a satellite application planning for ocean surveillance in South Korea will be presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.82-89
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• 2023

Due to the characteristics of ship structure, the compartment structure is complicated and narrow, so safety accidents frequently occur during the work process. The main causes of accidents include structural collisions, falling objects, toxic substance leaks, fires, explosions, asphyxiation, and more. Understanding the on-site conditions of workers during accidents is crucial for mitigating damages. In order to ensure safety, the on-site situation is monitored using CCTV in the ship, but it is difficult to prevent accidents with the existing method. To address this issue, a smart safety helmet equipped with location identification and voice/video communication capabilities is being developed as a safety technology. Additionally, the smart safety helmet incorporates environmental sensors for temperature, humidity, vibration, noise, tilt (gyro sensor), and gas detection within the work area. These sensors can notify workers wearing the smart safety helmet of hazardous situations. By utilizing the smart safety helmet and environmental sensors, the safety of workers aboard ships can be enhanced.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.27-33
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• 2024

As of 2023, the number of accident victims in the domestic construction industry is 26,829, ranking second only to other businesses (service industries). The accident types of casualties in all industries were falls (29,229 people), followed by falls (14,357 people). Based on the above data, this study attaches sensors to hard hats and insoles to predict fall accidents that frequently occur at construction sites, and proposes smart safety equipment that applies a random forest algorithm based on the data collected through this. The random forest model can determine fall accidents in real time with high accuracy by generating multiple decision trees and combining the predictions of each tree. This model classifies whether a worker has had a fall accident and the type of behavior through data collected from the MPU-6050 sensor attached to the hard hat. Fall accidents that are primarily determined from hard hats are secondarily predicted through sensors attached to the insole, thereby increasing prediction accuracy. It is expected that this will enable rapid response in the event of an accident, thereby reducing worker deaths and accidents.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.633-636
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• 1999

We present a robust scheme of detecting obstacles such as vehicles, human beings, and other artificial structures that may cause serious traffic accidents in the nighttime driving. Obstacle regions are detected by the evidential reasoning rules that combine the isolated regions obtained by the phase-directed edge-linking and the hot evidence information. Preliminary experimental results show that the performance is robust to nighttime infrared scenes having various types of obstacles

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.11
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.561-566
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• 2016

A subway platform is equipped with screen doors in oder to avoid accidents of passengers, where Area sensors are installed for detecting obstacles in the screen doors. However, there exist frequent operating errors in screen doors due to dusts, sunlight, snow, and bugs. It is required to develope a detection device which reduces errors and elaborates detection function. In this paper, we compared the detection rates of the Area sensor the 3D sensor using CCTV-based image data with installing sensors at the screen door in Munyang station Daegu, where 3D sensor is applied with the space division multiple detection algorithms. It is measured that the detection rate of 3D sensor and Area sensor is approximately 89.61% and 78.88%, respectively. The results confirmed that 3D senor has higher detection rate compared with Area sensor with the rate of 6.87~9.79%, and 3D sensor has benefit in the aspect of installation fee.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.269-278
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• 2018

Railway obstacle detecion system has been introduced with high-speed railway in 2004 to prevent accidents by obstacles such as landslide, rockfall and things fallen from the gauntry over the railway. But existing system has some limitation for landslide or fallen obstacle over railway. Therefore, In this study, we suggest new advanced obstacle detection system introducing the OTDR, optical fiber fences and detection cameras. This system can detect depression degree by the force to the fences and video for the specific region as well as detection wire Off condition. We produce and functional tests for fiber fence and OTDR, which are the core parts of the development system, and results were obtained to demonstrate improved detection capabilities. Several functions also been tested to verify the advanced detection performance and got some satisfactory results. Further we will conduct environment tests and field test.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.109-117
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• 2019

In modern society, due to various accidents and crime threats committed to an unspecified number of people, individual security awareness is increasing throughout society and various surveillance techniques are being actively studied. Still, there is a decline in robustness due to many problems, requiring higher reliability monitoring techniques. Thus, this paper suggests a real-time change detection technique to complement the low robustness problem in various environments and dynamic/static change detection and to solve the cost efficiency problem. We used the Self-Organizing Map (SOM) applied as a data clustering technique to implement change detection, and we were able to confirm the superiority of noise robustness and abnormal detection judgment compared to the detection technique applied to the existing image surveillance system through simulation in the indoor office environment.