• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.83-83
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• 1998

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5607-5611
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• 2015

Thousands of people die each year from cancer due to occupational causes. To reduce cancer in workers, preventive strategies should be used in the high-risk workplace. The effective prevention of occupational cancer requires knowledge of carcinogen agents. Like other areas of healthcare industry, occupational health has been affected by information technology solutions to improve prevention, early detection, treatment and finally the efficiency and cost effectiveness of the healthcare system. Information technology solutions are thus an important issue in the healthcare field. Information about occupational cancer in information systems is important for policy makers, managers, physicians, patients and researchers; because examples that include high quality data about occupational cancer patients and occupational cancer causes are able to determine the worker groups which require special attention. As a result exposed workers who are vulnerable can undergo screening and be considered for preventive interventions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.525-534
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• 2023

Gas leak detection system is a key to minimize the loss of life due to the explosiveness and toxicity of gas. Most of the leak detection systems detect by gas sensors or thermal imaging cameras. To improve the performance of gas leak detection system using single-modal methods, the paper propose multimodal approach to gas sensor data and thermal camera data in developing a gas type identification model. MultimodalGasData, a multimodal open-dataset, is used to compare the performance of the four models developed through multimodal approach to gas sensors and thermal cameras with existing models. As a result, 1D CNN and GasNet models show the highest performance of 96.3% and 96.4%. The performance of the combined early fusion model of 1D CNN and GasNet reached 99.3%, 3.3% higher than the existing model. We hoped that further damage caused by gas leaks can be minimized through the gas leak detection system proposed in the study.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.1
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• pp.45-52
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• 2016

Blast furnaces are crucial equipment for steel production. A typical furnace risks unexpected accidents caused by contraction and expansion of the walls under an environment of high temperature and pressure. In this study, an acoustic emission (AE) monitoring system was tested for evaluating the large-scale structural health of a blast furnace. Based on the growth of shell cracks with the emission of high energy levels, severe damage can be detected by monitoring increases in the AE energy parameter. Using this monitoring system, steel mill operators can establish a maintenance period, in which actual shell cracks can be verified by cross-checking the UT. From this study, we expect that AE systems permit early fault detection for structural health monitoring by establishing evaluation criteria based on the severity of shell cracking.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.607-614
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• 2019

It has been an issue among researchers that the tsunamis that occurred on the west coast of Japan in 1983 and 1993 damaged the coastal cities on the east coast of Korea. In order to predict and reduce the damage to the Korean Peninsula effectively, it is necessary to install offshore tsunami observation instruments as part of the system for the early detection of tsunamis. The purpose of this study is to recommend the optimal deployment of tsunami observation instruments in terms of the higher probability of tsunami detection with the minimum equipment and the maximum evacuation and warning time according to the current situation in Korea. In order to propose the optimal location of the tsunami observation equipment, this study will analyze the tsunami propagation phenomena on the east sea by considering the potential tsunami scenario on the west coast of Japan through numerical modeling using the COrnell Multi-grid COupled Tsunami (COMCOT) model. Based on the results of the numerical model, this study suggested the optimal deployment of Korea's offshore tsunami observation instruments on the northeast side of Ulleung Island.

• Smart Structures and Systems
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• v.22 no.1
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• pp.1-11
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• 2018

This study presents early crack detection of steel fiber-reinforced concrete (SFRC) under varying temperature and humidity conditions using an instantaneous electrical impedance acquisition system. SFRC has the self-sensing capability of electrical impedance without sensor installation thanks to the conductivity of embedded steel fibers, making it possible to effectively monitor cracks initiated in SFRC. However, the electrical impedance is often sensitively changed by environmental effects such as temperature and humidity variations. Thus, the extraction of only crack-induced feature from the measured impedance responses is a crucial issue for the purpose of structural health monitoring. In this study, the instantaneous electrical impedance acquisition system incorporated with SFRC is developed. Then, temperature, humidity and crack initiation effects on the impedance responses are experimentally investigated. Based on the impedance signal pattern observation, it is turned out that the temperature effect is more predominant than the crack initiation and humidity effects. Various crack steps are generated through bending tests, and the corresponding impedance damage indices are extracted by compensating the dominant temperature effect. The test results reveal that propagated cracks as well as early cracks are successfully detected under temperature and humidity variations.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.373-378
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• 2023

Untypical symptoms and lack of diagnostic records make it difficult for even medical specialists to detect rare diseases. Thus, it takes a lot of time and money from the onset of symptoms to an accurate diagnosis, which seriously results in physical, mental, and economic pressure on patients. In this paper, we propose and implement an early detection assistance system for rare diseases using web crawling and text mining, which can suggest the names of suspected rare diseases so that medical staffs can easily recall the disease names and make a final diagnosis of the rare diseases.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.433-440
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• 2016

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure can significantly affect railway operations with potentially disastrous consequences, early stress detection of point machine is critical for monitoring and managing the condition of rail infrastructure. In this paper, we propose a stress detection method for point machine in railway condition monitoring systems using sound data. The system enables extracting sound feature vector subset from audio data with reduced feature dimensions using feature subset selection, and employs support vector machines (SVMs) for early detection of stress anomalies. Experimental results show that the system enables cost-effective detection of stress using a low-cost microphone, with accuracy exceeding 98%.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.