• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.7-12
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• 2022

This paper introduces the implementation of the AWS IoT service and MQTT based smart home system. The smart home system implemented in this study can monitor temperature and humidity, and can manually adjust the air conditioner heating, and can check the visitors with the camera and remotely control the door lock. The implemented smart home system controls door locks, heating and air conditioners using Arduino, and manages the collected data and control information using the AWS IoT service. In this study, the Android app has been developed to allow users to control IoT devices remotely, and the MQTT protocol was used for data communication and control between the app and the AWS IoT server and Arduino. The implemented smart home system has been implemented based on AWS IoT service, which has scalability to add sensors and devices.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.397-399
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• 2021

The increase in single-person households and the development of Home IoT technology make it important to improve the convenience of the residential environment. In addition, the increase in indoor activities caused by COVID-19 calls for the development of products to make life more convenient for single-person households. This trend of increased indoor activity has made it easier to interact with the current residential environment than before, and as a result, the need to develop technology for Home IoT is emerging. Therefore, the Home IoT system will be developed to monitor the information needed to maintain an ideal indoor environment such as temperature, humidity, and fine dust. The system will also interact with users, and propose a system that improves safety in indoor activities by equipping the home with IoT sensors for preventing safety accidents such as gas leakage and fire.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.25-27
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• 2022

Recently, with the development of the 4th industry, environmental issues such as air pollution have become serious, and in particular, a lot of air pollutants are generated in industrial sites. There are various types of air pollutants, and among them, carbon monoxide is essential for fires occurring in industrial sites, so it should be possible to monitor in real time. In addition, there is a need for a remote monitoring system that can measure various environmental factors other than air pollutants in real time. In this paper, we propose a monitoring system using wireless communication to remotely measure the industrial environment. The proposed monitoring system collects data to the Arduino of the transmitter by using a carbon monoxide sensor, a combustible gas sensor, a temperature and humidity sensor, and a flame sensor, and then transmits it to the receiver using ZigBee. The transmitted data is stored in the database of the receiver Raspberry Pi, and the stored data can be monitored in real time through the monitoring system.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Food Science and Preservation
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• v.30 no.4
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• pp.573-588
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• 2023

The purpose of this study was to monitor changes in the quality of ginseng and predict its shelf-life. As the storage period of ginseng increased, some quality indicators, such as water-soluble pectin (WSP), CDTA-soluble pectin (CSP), cellulose, weight loss, and microbial growth increased, while others (Na₂CO₃-soluble pectin/NSP, hemicellulose, starch, and firmness) decreased. Principal component analysis (PCA) was performed using the quality attribute data and the principal component 1 (PC1) scores extracted from the PCA results were applied to the multivariate analysis. The reaction rate at different temperatures and the temperature dependence of the reaction rate were determined using kinetic and Arrhenius models, respectively. Among the kinetic models, zeroth-order models with cellulose and a PC1 score provided an adequate fit for reaction rate estimation. Hence, the prediction model was constructed by applying the cellulose and PC1 scores to the zeroth-order kinetic and Arrhenius models. The prediction model with PC1 score showed higher R² values (0.877-0.919) than those of cellulose (0.797-0.863), indicating that multivariate analysis using PC1 score is more accurate for the shelf-life prediction of ginseng. The predicted shelf-life using the multivariate accelerated shelf-life test at 5, 20, and 35℃ was 40, 16, and 7 days, respectively.

• ALGAE
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• v.39 no.3
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• pp.177-186
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• 2024

Red rot disease is one of the notorious algal diseases that threaten the cultivation of Pyropia in China, and two Pythium pathogens, i.e., Pythium porphyrae and P. chondricola, have been reported as causative agents. To monitor the pathogens, a fluorescent quantitative polymerase chain reaction (PCR) method was developed to quantitatively detect their abundance. Using overlapping PCR and pathogen-specific primer pairs, two pathogen-specific fragments were concatenated to construct an internal standard plasmid, which was used for quantification. For zoospores of known numbers, the results showed that this method can detect as less as 100 and 10 zoospores mL^-1 in a 200 mL solution for P. porphyrae and P. chondricola, respectively. Using monthly collected seawater at 10 sites in Haizhou Bay, a typical aquaculture farm in China, a significantly higher temperature and a significantly lower salinity were determined in December 2021. P. porphyrae was determined to be more abundant than P. chondricola, though with similar temporal distribution patterns from December 2021 to February 2022. When a red rot disease occurred in December 2021, the two pathogens were significantly more abundant at two infected sub-sites than the uninfected sub-site within both seawater and sediment, though they were all significantly more enriched in sediment than in seawater. The present method provides the capability to quantify and compare the abundance of two pathogens and also has the potential to forecast the occurrence of red rot disease, which is of much significance in managing and controlling the disease.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.67-72
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• 2005

To investigate the feasibility of a new concept of storing Liquefied Natural Gas (LNG) in a lined hard rock cavern, and to develop essential technologies for constructing underground LNG storage facilities, a small pilot plant storing liquid nitrogen (LN2) has been constructed at the Korea Institute of Geoscience and Mineral Resources (KIGAM). The LN2 stored in the cavern will subject the host rock around the cavern to very low temperatures, which is expected to cause the development of an ice ring and the change of ground condition around the storage cavern. To investigate and monitor changes in ground conditions at this pilot plant site, geophysical, hydrogeological, and rock mechanical investigations were carried out. In particular, geophysical methods including borehole radar and three-dimensional (3D) resistivity surveys were used to identify and monitor the development of an ice ring, and other possible changes in ground conditions resulting from the very low temperature of LN2 in the storage tank. We acquired 3D resistivity data before and after storing the LN2, and the results were compared. From the 3D images obtained during the three phases of the resistivity monitoring survey, we delineated zones of distinct resistivity changes that are closely related to the storage of LN2. In these results, we observed a decrease in resistivity at the eastern part of the storage cavern. Comparing the hydrogeological data and Joint patterns around the storage cavern, we interpret this change in resistivity to result from changes in the groundwater flow pattern. Freezing of the host rock by the very low temperature of LN2 causes a drastic change in the hydrogeological conditions and groundwater flow patterns in this pilot plant.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Ocean Science Journal
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• v.40 no.1
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• pp.25-44
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• 2005

Long-term, continuous, and real-time ocean monitoring has been undertaken in order to evaluate various oceanographic phenomena and processes in the East/Japan Sea. Recent technical advances combined with our concerted efforts have allowed us to establish a real-time monitoring system and to accumulate considerable knowledge on what has been taking place in water properties, current systems, and circulation in the East Sea. We have obtained information on volume transport across the Korea Strait through cable voltage measurements and continuous temperature and salinity profile data from ARGO floats placed throughout entire East Sea since 1997. These ARGO float data have been utilized to estimate deep current, inertial kinetic energy, and changes in water mass, especially in the northern East Sea. We have also developed the East Sea Real-time Ocean Buoy (ESROB) in coastal regions and made continual improvements till it has evolved into the most up-to-date and effective monitoring system as a result of remarkable technical progress in data communication systems. Atmospheric and oceanic measurements by ESROB have contributed to the recognition of coastal wind variability, current fluctuations, and internal waves near and off the eastern coast of Korea. Long-tenn current meter moorings have been in operation since 1996 between Ulleungdo and Dokdo to monitor the interbasin deep water exchanges between the Japanese and Ulleung Basins. In addition, remotely sensed satellite data could facilitate the investigation of atmospheric and oceanic surface conditions such as sea surface temperature (SST), sea surface height, near-surface winds, oceanic color, surface roughness, and so on. These satellite data revealed surface frontal structures with a fairly good spatial resolution, seasonal cycle of SST, atmospheric wind forcing, geostrophic current anomalies, and biogeochemical processes associated with physical forcing and processes. Since the East Sea has been recognized as a natural laboratory for global oceanic changes and a clue to abrupt climate change, we aim at constructing a 4-D continuous real-time monitoring system, over a decade at least, using the most advanced techniques to understand a variety of oceanic processes in the East Sea.

• Korean Journal of Environment and Ecology
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• v.25 no.6
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• pp.903-910
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• 2011

The research was carried out in order to find climate factors which determine the distribution of Machilus thunbergii, and the potential habitats under the current climate and three climate change scenario by using classification tree (CT) model. Four climate factors; the minimum temperature of the coldest month (TMC), the warmth index (WI), summer precipitation (PRS), and winter precipition (PRW) : were used as independent variables for the model. The model of distribution for Machilus thunbergii (Mth-model) constructed by CT analysis showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of M. thunbergii. The area above the $-3.3^{\circ}C$ of TMC revealed high occurrence probability of the M. thunbergii. Potential habitats was predicted $9,326km^2$ under the current climate and $61,074{\sim}67,402km^2$(South Korea: $58,419{\sim}61,137km^2$, North Korea: $2,655{\sim}6,542km^2$) under the three climate change scenarios (CCCMA-A2, CSIRO-A2, HADCM3-A2). The Potential habitats was to predicted increase by 51~56%(South Korea: 49~51%, North Korea: 2~5%) under the three climate change scenarios. The potential expand of M. thunbergii habitats has been expected that it is competitive with warm-temperate deciduous broadleaf forest. M. thunbergii is evaluated as the indicator of climate change in Korea and it is necessary for M. thunbergii to monitor of potential habitats.