• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.417-423
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• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.

• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.111-115
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• 2017

Blood pressure is one of the important vital signs for monitoring the medical condition of a patient. Automated NIBP(non-invasive blood pressure) monitoring devices calculate systolic and diastolic blood pressures from the oscillation in cuff pressure caused by a pulsation of an artery. To validate the NIBP devices, we developed a simulator to supply the oscillometric waveforms obtained from human subjects. The simulator provided pressure pulses to device-under-test and device readings were compared to the auscultatory references. Fully automated simulation system including OCR(optical character recognition) were developed and used for NIBP monitoring devices. The validation results using the simulator agreed well with previous clinical validation. More validation studies using the standardized oscillometric waveforms would be required for the replacement of clinical trials to validate a new automated NIBP monitoring device.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.583-589
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• 2016

Volatile organic compounds(VOCs) are regarded as a harmful cause substance not only causing air pollutions but also causing global warming phenomenon. For this reason, VOCs are managed politically to reduce emissions by each country. In particular, the vapor from the gas station contains VOCs which is harmful to the human body such as carcinogens benzene and pollute the atmosphere, the Ministry of Environment defined every gas station must install vapor recovery equipment to recover volatile organic compounds. Recently, there are many accidents caused by existing vapor treatment methods, the liquefaction recovery technology is getting the spotlight to cool the vapor at the field. However, because the liquefaction recovery technology have risks of fire or explosion in accordance with temperature, the real time monitoring is critical factor. In this paper, we implement an Android-based monitoring application for liquified vapor recovery device which attached sensor module for temperature and power to monitoring real time information.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1007-1011
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• 2020

Recently, natural and anthropogenic disasters have rapidly increased due to the on-going climate change and various human activities. Remote sensing (RS) technology enables the continuous monitoring and rapid detection of disastrous events thanks to its advantages covering vast areas at high temporal resolution. Moreover, RS technology has been very actively used in disaster monitoring and assessment since cluster- and micro-satellites and drones were introduced and became popular. In this special issue, nine papers were introduced, including the processing and applications of remote sensing data for monitoring, assessment, and prediction of various natural disasters. These papers are expected to serve as useful references for disaster management in the future.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 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 IKEEE
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• v.26 no.3
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• pp.396-400
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• 2022

In this study, an ICT-based underwater communication monitoring device for underwater structures is implemented based on lifting fixture that transport human bodies found on the seabed to sea level. The lifting fixture is packaged with a retback, sideback, and cartridge that injects air. Monitoring systems are developed in a mobile manner in a portable structure. The underwater ultrasonic sensor signal is supplied using a USB port, and the O/S consists of Linux. For the underwater communication dong test, a measurement test was conducted in real time from 6m to 40m in depth on the east coast. The ultrasonic sound sensor is converted to 2,400 bps to verify the transmission error according to the duality. The communication speed of sensor to monitoring is 115,200 bps, and the speed of communication from controller to receiver is 2,400 bps. In the commercialization stage of the lifting device, it is easy to develop a low-end type and the compatibility is wide.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.375-388
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• 2017

United States of America started NSDI in 1994 for the first time in the world. EU and other many countries invested lots of budget on NSDI due to necessity to manage countries and social economy. As skepticism for effect of such investment has risen, developed countries predicted higher effect compared to investment using ROI and other methods. FGDC clarified that geospatial information is a critical national assets. USA has managed NSDI by introducing portfolio concept for it recognizing NSDI as financial assets from fixed assets. Currently directions of NSDI and its advancement has been proceeded variably depending on corresponding organizations, human resources, budget and national policies. This study analyzed recent trends regarding NSDI assessment methods from developed countries and researchers. Assessment of NSDI is introduced only by some countries such as EU, USA and Canada. This study analyzed USA's assessment model and indicator that assess NSDI in a way that various external organizations (COGO, URISA) participate, EU INSPIRE Directives, monitoring and Canada's CGDI assessment methods. Besides these, this study analyzed STIG that adopted Financial Infrastructure from European studies and Korea's NSDI monitoring assessment indicator research. Further this study suggested assessment directions for future NSDI through implications of NSDI assessment method analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.673-676
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• 2017

Accompanying the Internet of Things (IoT) is a demand of advanced applications and services utilizing the potential of the IoT environment. Monitoring the environment for a provision of context-aware services to the human beings is one of the new trends in our future life. The IoTivity Cloud is one of the most notable open-source platform bringing an opportunity to collect, analyze, and interpret a huge amount of data available in the IoT environment. Based on the IoTivity Cloud, we aim to develop a novel platform for comprehensive monitoring of a future network, which facilitates on-demand data collection to enable the network behavior prediction and the quality of user experience maintenance. In consideration of performance evaluation of the monitoring platform, this paper presents results of a preliminary test on the data acquisition/supply process in the IoTivity Cloud.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.