• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.803-806
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• 2016

'Pokemon Go',which is game program, provides a clue to solve the problem of healthcare in the sense of leading changes in behavior of the users. 'Pokemon Go'is a spin-off of the $Pok{\acute{e}}mon$ game series and uses Augmented Reality(AR) technology. AR, which can be said to complement the real world, has been used in many fields such as medical applications, broadcasting, manufacturing, the mobile sector as a wide range of technologies. In particular, the medical field as area of the active application from the start of AR, provides a great help in medical fields, that is accurate medical diagnosis and prevention of unnecessary dissection by synthesizing the patient information and the image of actual patient on three-dimensional data of the sensor such as MRI or ultrasonic wave. In this study, we analyze the VR technology trends, application examples, and the future of VR and AR based medical services in healthcare.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.485-490
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• 2020

Recently, amid active research on domestic shipbuilding industry and IT convergence technology, with the development of satellite detection technology for ship safety operation, ships monitored the movement of ships with the mandatory long-range identification & tracking of vessels and automatic identification system. It is possible to help safe navigation, but it is necessary to develop safety device that alert the marine officer who rely on radar to correct conditions in case of weightlessness. Therefore, an ocean fog alarm system was developed to detect and inform using photo sensors. The fabricated ocean fog detect and alarm system consists of a small, low-power optical sensor transceiver and data sensing processing module. Through experiment, it is confirmed that the fabricated ocean fog detect and alarm system measure the corresponding concentration of ocean fog for fogless circumstance and fogbound circumstance, respectively. Furthermore, the fabricated system can control RPM of ship engine according to the concentration of ocean fog, and consequently, the fabricated system can be applied to assistant device for ship safety operation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of Sensor Science and Technology
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• v.30 no.4
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• pp.229-235
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• 2021

This study presents a simple approach for the assembly of a free-standing conductive electronic nanofilm of single-walled carbon nanotubes (SWNTs) suitable for enzymatic electrochemical biosensors. A large-scale SWNT electronic film was successfully produced by the dialysis of p-Terphenyl-4,4''-dithiol (TPDT)-treated SWNTs. Furthermore, Horseradish peroxidase (HRP) was immobilized on the TPDT-SWNT electronic film, and the enzymatic detection of hydrogen peroxide (H₂O₂) was demonstrated without mediators. The detection of H₂O₂ in the negative potential range (-0.4 V vs. Ag/AgCl) was achieved by direct electron transfer of heme-based enzymes that were immobilized on the TPDT-SWNT electronic film. The SWNT-based biosensor exhibited a wide detection range of H₂O₂ from 10 µM to 10 mM. The HRP-doped SWNT electronic film achieved a high sensitivity of 342 ㎛A/mM·cm² and excellent selectivity against a variety of redox-active interfering substances, such as ascorbic acid, uric acid, and acetaminophen.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.19-31
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• 2022

Purpose: This study was conducted to identify indoor air quality in various living spaces using sensors that can measure noise, vibration, fine dust, and odor in real time and to propose optimal indoor air quality maintenance management using Internet of Things(IoT). Research design, data and methodology: Using real-time sensors to monitor physical factors and environmental air pollutants that affect the comfort of the residential environment, Noise, Vibration, Atmospheric Pressure, Blue Light, Formaldehyde, Hydrogen Sulfide, Illumination, Temperature, Ozone, PM10, Aldehyde, Amine, LVOCs and TVOCs were measured. It were measured every 1 seconds from 4 offices and 4 stores on a small scale from November 2018 to January 2019. Results: The difference between illuminance and blue light for each measuring point was found to depend on lighting time, and the ratio of blue light in total illumination was 0.358 ~ 0.393. Formaldehyde and hydrogen sulphide were found to be higher than those that temporarily attract people in an indoor office space that is constantly active, requiring office air ventilation. The noise was found to be 50dB higher than the office WHO recommendation noise level of 35 ~ 40dB. The most important factors for indoor environmental quality were temperature> humidity> illumination> blue light in turn. Conclusions: Various factors that determine the comfort of indoor living space can be measured with real-time sensors. Further, it is judged that the use of IoT can help maintain indoor air quality comfortably.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.566-573
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• 2024

Applying autonomous driving technology to construction sites is very difficult due to safety issues. However, the application of various positioning and sensing devices, such as cameras and radars, to construction equipment is very active. Based on these technological trends, the government is making various efforts, including the Serious Accident Punishment Act and support for industrial safety management expenses, to reduce the incidence of accidents caused by construction equipment and industrial vehicles. And, related industries have been developing various safety equipment over the past few years and applying them to the field. In this paper, we investigate the current status of safety equipment-related technologies currently applied to construction equipment and industrial vehicles, and propose a direction for the development of safety technology in construction equipment based on artificial intelligence. Improving the safety and work efficiency of construction equipment based on the technology proposed in this paper should be reviewed through simulation in the future.

• Journal of KIISE:Information Networking
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• v.35 no.5
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• pp.453-463
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• 2008

The missing child problem is no more a personal problem. It became a social problem that all parents must consider. To this, this study applies USN/RFID technology integrated with GIS for the prevention of missing child. Although RFID is not designed for location sensing, but now it is regarded as a device to facilitate real time location awareness. Such advantages of RFID can be integrated with 4S(GIS/GPS/LBS/GNSS) achieving much synergy effects. In order to prevent kidnapping and missing child, it is necessary to provide a missing child preventing system using a ubiquitous computing system. Therefore, the missing child preventing system has been developed using high-tech such as RFID, GPS network, CCTV, and mobile communication. The effectiveness of the missing child prevention system can be improved through an accurate location tracking technology. This study propose and test a location sensing system using the active RFID tags. This study verifies technical applied service, and presents a system configuration model. Finally, this paper confirms missing child prevention system utilization possibility.