• Korean Journal of Clinical Laboratory Science
• /
• v.42 no.1
• /
• pp.16-21
• /
• 2010

It has been well known that organic industrial solvents such as toluene, xylene and styrene are environmental health hazards causing occupational diseases to workers. The detection of biological metabolites of toluene, xylene and styrene in blood, urine, breast milk, saliva, and hair of workers exposed to these organic industrial solvents is a useful tool for assessing the amount of organic industrial solvents. Therefor, the external analytical quality assurance program is extremely important work for increasing reliability of the data analyzed in the special health examination laboratory. We evaluated the results of analysis by Korean analytical quality assurance program from the second half 2007 to the first half 2009. The number of participants for analytical quality assurance program for both mandatory item and one of optional items, hippuric acid and methylhippuric acid were 116-122, 116-122 and 105-115 laboratories, respectively. The proficient rate of the special health examination laboratories analyzed both mandatory item and optional item, hippuric acid and methylhippuric acid were 92-98%, 92-98% and 96-98%, respectively. We can conclude from our data that the analytical quality assurance program is a essential tool for improvement of reliability on the data analyzed in the special health examination laboratories.

• Journal of Sensor Science and Technology
• /
• v.24 no.5
• /
• pp.306-310
• /
• 2015

IoT(Internet of Things) based underground risk assessment system surrounding water pipeline enables an advanced monitoring and prediction for unexpected underground hazards such as abrupt road-side subsidence and urban sinkholes due to a leak in water pipeline. For the development of successful assessment technology, the PSU(Water Pipeline Safety Unit) which detects the leakage and movement of water pipes. Then, the IoT-based underground risk assessment system surrounding water pipeline will be proposed. The system consists of early detection tools for underground events and correspondence services, by analyzing leakage and movement data collected from PSU. These methods must be continuous and reliable, and cover certain block area ranging a few kilometers, for properly applying to regional water supply changes.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.369-372
• /
• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.206-207
• /
• 2007

Islanding is the electrical phenomenon in a part of a power network disconnected from the utility, where the loads are entirely supplied by PV systems, and where the voltage and frequency are maintained around nominal values. But islanding may result in power-quality issues, interference to grid-protection devices, equipment damage, and even personnel safety hazards[4]. Therefore, islanding must be detected and stopped. The existing anti-islanding methods have NDZ(None-detection Zone) or power quality degradation. But PV-AF power generation system has the function of not only shunt active filter but also anti-islanding method without NDZ. In this paper, It is proved that PV-AF system has originally the function of anti-islanding without not only NDZ and but current distortion

• Molecular & Cellular Toxicology
• /
• v.2 no.1
• /
• pp.11-14
• /
• 2006

In this study we attempted to develop a novel genomic method to selectively isolate target functional microbial genomes from environmental samples. For this purpose, stable isotope probing (SIP) was applied in selectively isolating organic pollutant-assimilating populations. When soil microbes were fed with $^{13}C-labeled $ biphenyl, biphenyl-utilizing cells were incorporated with the heavy carbon isotope. The heavy DNA portion was successfully separated by CsCl equilibrium density gradient. And the diversity in the heavy DNA was sufficiently reduced, being suitable for the current DNA microarray techniques to detect biphenyl-utilizing populations in the soil. In addition, we proposed a new way to get more genetic information by combining this SIP method with selective metagenomic approach. The increased selective power of these new DNA isolation methods will be expected to provide a good quality of new genetic information, which, in turn, will result in development of a variety of biomarkers that may be used in assessing ecotoxicology issues including the impacts of organic hazards, and antibiotic-resistant pathogens on human and ecological systems.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.3
• /
• pp.429-438
• /
• 2021

The mandatory installation of the ECDIS (Electronic Chart Display and Information System) became an important navigational equipment for navigation officer. In addition, ECDIS is a key component of the ship's digitalization in conjunction with various navigational equipment. Meanwhile, cyber-attacks emerge as a new threat along with digitalization. Damage caused by cyber-attacks is also reported in the shipping sector, and IMO recommends that cybersecurity guidelines be developed and included in International Security Management (ISM). This study analyzed the cybersecurity hazards of ECDIS, where various navigational equipment are connected. To this end, Importance-Performance Analysis (IPA) was conducted on navigation officer using ECDIS. As a result, the development of technologies for cyber-attack detection and prevention should be priority. In addition, policies related to 'Hardware and Software upgrade', 'network access control', and 'data backup and recovery' were analyzed as contents to be maintained. This paper is significant in deriving risk factors from the perspective of ECDIS users and analyzing their priorities, and it is necessary to analyze various cyber-attacks that may occur on ships in the future.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.117-122
• /
• 2022

This paper proposes a method for evaluating the work of manufacturing workers using MediaPipe as a risk factor for musculoskeletal diseases. Recently, musculoskeletal disorders (MSDs) caused by repeated working attitudes in industrial sites have emerged as one of the biggest problems in the industrial health field while increasing public interest. The Korea Occupational Safety and Health Agency presents tools such as NIOSH Lifting Equations (NIOSH), OWAS (Ovako Working-posture Analysis System), Rapid Upper Limb Assessment (RULA), and Rapid Entertainment Assessment (REBA) as ways to quantitatively calculate the risk of musculoskeletal diseases that can occur due to workers' repeated working attitudes. To compensate for these shortcomings, the system proposed in this study obtains the position of the joint by estimating the posture of the worker using the posture estimation learning model of MediaPipe. The position of the joint is calculated using inverse kinetics to obtain an angle and substitute it into the REBA equation to calculate the load level of the working posture. The calculated result was compared to the expert's image-based REBA evaluation result, and if there was a result with a large error, feedback was conducted with the expert again.

• Nuclear Engineering and Technology
• /
• v.54 no.12
• /
• pp.4644-4651
• /
• 2022

Due to its short half-life, thoron gas has been assumed to have negligible health hazards on humans compared to radon. But, one of the decay products with a long half-life can make it to be transported to a long distance and to cause a severe internal dose through respiration. Since most commercial radon detectors can not discriminate thoron signals from radon signals, it is very common to overestimate radon doses which in turn result in biased estimation of lung cancer risk in epidemiological studies. Though some methods had been suggested to measure thoron and radon separately, they could not be used for real-time measurement because of CR-39 or LR-115. In this study, an effective method was suggested to measure radon and thoron separately from the free air. It was observed that the activity of thoron decreases exponentially due to delay time caused by a long pipe between two chambers. Therefore from two ion chambers apart in time, it was demonstrated that thoron and radon could be measured separately and simultaneously. We also developed a collimated alpha source and with this source and an SBD, we could convert the ion chamber reading to count rate in cps.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1202-1208
• /
• 2022

Techniques based on computer vision are becoming increasingly important in construction safety monitoring. Using AI algorithms can automatically identify conceivable hazards and give feedback to stakeholders. However, the construction site remains various potential hazard situations during the project. Due to the site complexity, many visual devices simultaneously participate in the monitoring process. Therefore, it challenges developing and operating corresponding AI detection algorithms. Safety information resulting from computer vision needs to organize before delivering it to safety managers. This study proposes a framework for computer vision-aided construction safety monitoring using collaborative 4D BIM information to address this issue, called CSM4D. The suggested framework consists of two-module: (1) collaborative BIM information extraction module (CBIE) extracts the spatial-temporal information and potential hazard scenario of a specific activity; through that, Computer Vision-aid Safety Monitoring Module (CVSM) can apply accurate algorithms at the right workplace during the project. The proposed framework is expected to aid safety monitoring using computer vision and 4D BIM.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2377-2398
• /
• 2023

In our daily lives, we engage in a variety of tasks that rely on our senses, such as seeing. Blindness is the absence of the sense of vision. According to the World Health Organization, 2.2 billion people worldwide suffer from various forms of vision impairment. Unfortunately, blind people face a variety of indoor and outdoor challenges on a daily basis, limiting their mobility and preventing them from engaging in other activities. Blind people are very vulnerable to a variety of hazards, including falls. Various barriers, such as stairs, can cause a fall. The Internet of Things (IoT) is used to track falls and send a warning message to the blind caretakers. One of the gaps in the previous works is that they were unable to differentiate between falls true and false. Treating false falls as true falls results in many false alarms being sent to the blind caretakers and thus, they may reject the IoT system. As a means of bridging this chasm, this paper proposes an intelligent shoe that is able to precisely distinguish between false and true falls based on three sensors, namely, the load scale sensor, the light sensor, and the Flex sensor. The proposed IoT system is tested in an indoor environment for various scenarios of falls using four models of machine learning. The results from our system showed an accuracy of 0.96%. Compared to the state-of-the-art, our system is simpler and more accurate since it avoids sending false alarms to the blind caretakers.