• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.788-791
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• 2014

IoT (Internet of Things)는 사물들 (Things)이 각자의 판단에 의해 협업을 하는 시스템이다. 미래의 IoT 기술은 우리의 일상생활뿐만 아니라 전반적으로 영향력이 강해질 것 이다. 하지만 아직 우리가 추구하는 IoT시대에 접어들기 위해서는 사물간 통신을 하는데 있어 발생되는 여러 문제들이 해결되지 않았다. 예를 들면 통신 프로토콜은 어떠한 것을 사용해야 하는지와 같은 것들이다. IoT에서 센서 (Sensor)가 센싱 (Sensing)을 하는 것, 통신을 하는 것, 일을 처리하는 것은 모두 이벤트에 해당된다. 이벤트는 "어떤 상태의 변화에 의해 상황이 변하는 것"[2]을 말한다. 이러한 이벤트는 순서에 따라 시간관계가 생기게 된다. 이러한 이벤트의 시간관계와 기존에 시간 간격에 대한 논문을 접목하여 본 논문에서 새로운 이벤트 시간 관계를 정의하고 그에 대한 성질을 식별했다. 그 결과 7가지의 시간 성질 (AFTER, MEETS, EQUALS, BEFORE, OVERLAPS, STARTS, FINISHES)[3]을 가지고 상호 적용하여 새로운 성질들을 식별했다. 이 성질들을 이용하면 향후 시간과 관련된 이벤트를 식별하고 활용이 가능하다.

• Korean Journal of Environmental Biology
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• v.41 no.1
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• pp.18-30
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• 2023

Forest destruction is an inevitable result of the development processes. According to the environmental impact assessment, over 10% of the destroyed trees need to be recycled and transplanted to minimize the impact of forest destruction. However, the rate of successful transplantation is low, leading to a high rate of tree death. This is attributable to a lack of consideration for environmental factors when choosing a temporary site for transplantation and inadequate management. To monitor transplanted trees, a field survey is essential; however, the spatio-temporal aspect is limited. This study evaluated the applicability of remote sensing for the effective monitoring of transplanted trees. Vegetation indices based on satellite remote sensing were derived to detect time-series changes in the status of the transplanted trees at three temporary transplantation sites. The mortality rate and vitality of transplanted trees before and after the transplant have a similar tendency to the changes in the vegetation indicators. The findings of this study showed that vegetation indices increased after transplantation of trees and decreased as the death rate increased and vitality decreased over time. This study presents a method for assessing newly transplanted trees using satellite images. The approach of utilizing satellite photos and the vegetation index is expected to detect changes in trees that have been transplanted across the country and help to manage tree transplantation for the environmental impact assessment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.1-10
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• 2022

As the volume of marine hazardous and noxious substances (HNSs) transported in domestic and overseas seas increases, the risk of HNS spill accidents is gradually increasing. HNS leaked into the sea causes destruction of marine ecosystems, pollution of the marine environment, and human casualties. Secondary accidents accompanied by fire and explosion are possible. Therefore, various types of HNSs must be rapidly detected, and a control strategy suitable for the characteristics of each substance must be established. In this study, the ground HNS spill experiment process and application result of detection algorithms were presented based on hyperspectral remote sensing. For this, styrene was spilled in an outdoor pool in Brest, France, and simultaneous observation was performed through a hyperspectral sensor. Pure styrene and seawater spectra were extracted by applying principal component analysis (PCA) and the N-Findr method. In addition, pixels in hyperspectral image were classified with styrene and seawater by applying spectral matching techniques such as spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), and spectral angle mapper (SAM). As a result, the SDS and SSV techniques showed good styrene detection results, and the total extent of styrene was estimated to be approximately 1.03 m². The study is expected to play a major role in marine HNS monitoring.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1075-1084
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• 2023

Satellite-derived data is collected by observing the Earth and is used in various fields such as national defense, natural disasters, location-based services, infrastructure, environment, energy, marine, and insurance. This study aims to present the virtuous cycle structure of the satellite information data industry and the business ecosystem model of the industry. As a research method, cases were collected and categorized from the following areas: literature, online, application, and content. The results show that the ecosystem model of the satellite information data industry provides an approach to content services in public and commercial areas, and develops various algorithmic technologies to facilitate content production and services at the level of complex general-purpose technologies. Second, in terms of content typology, satellite information data can be subdivided into monitoring content, urban space monitoring content, and satellite information content. Third, the consumption value of satellite content could be subdivided into informational value, environmental, social and governance (ESG) value, educational value, and content value. In order to expand the global content market, Korea will need to focus on creating an ecosystem for the satellite information industry and discovering differentiated content. It will also need to increase the popularization and accessibility of data to the general public and promote the Korean K-Satellite Information Data Industry ecosystem through government support, policy efforts, and policies such as establishing legal systems, increasing investment, and training human resources.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.376-381
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• 1998

In this study, we was measured the radiance reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the multi-purpose satellite(KOMPSAT) to use the data in analyzing water pollution. Also we investigated the possibility of extraction of water quality factors in rivers and water body by using high resolution remote sensing data such as Airborne MSS. Especially, we tried to extract the environmental factors related with eutrophication, and also tried to develop the process technique and the radiance feature of reflectance related with eutrophication. The results were summarized as follows: First, the spectrum of sun's rays which reaches the surface of the earth was consistent with visible rays bands of 0.4${\mu}{\textrm}{m}$~0.7${\mu}{\textrm}{m}$ and about 50% of total quantity of radiation were there. And at around 0.5${\mu}{\textrm}{m}$ of green spectral band in visible rays bands, the spectrum was highest. Second, as a result of the radiance reflectance Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and suspended sediments and turbidity represented high spectral reflectance at 0.8${\mu}{\textrm}{m}$ and at 0.57${\mu}{\textrm}{m}$ each. Third, as a result of the water quality analysis by using Airborne MSS, Chlorophyll-a could have a distribution chart when carried out ratio of B3 and BS to B7. And Band 7 was useful for making the distribution chart of suspended sediments. And when we carried out PCA, suspended sediments and turbidity had distributions at PC 1 , PC 4 each similarly to ground truth data. Above results can be changed according to the change of season and time. Therefore, in order to analyze more exactly the environmental factors of water quality by using LRC data, we need to investigate constantly the ground truth data and the radiance feature of reflectance of water body. Afterward in this study, we will constantly analyze the radiance feature of the surface of water in water body by measuring the on-the-spot radiance reflectance and using low resolution satellite image(SeaWiFs). Besides, we will gather the data of water quality analysis in water body and analyze the pattern of water pollution.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.105-105
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• 2023

Heavy metals, including lead (Pb) toxicity, are increasing in soil and are considered toxic in small amounts. Pb contamination is mainly caused by industrialization - smelting, mining. Agricultural practices - sewage sludge, pests and urban practices - lead paint. It can seriously damage and threaten crop growth. Pb can adversely affect plant growth and development by affecting the photosystem, cell membrane integrity, and excessive production of reactive oxygen species (ROS) such as hydrogen peroxide (H₂O₂)andsuperoxide(O₂.-). NO is produced via enzymatic and non-enzymatic antioxidants to scavenge ROS and lipid peroxidation substrates in terms of protecting cells from oxidative damage. Thus, NO improves ion homeostasis and confers resistance to metal stress. Our results here suggest that exogenous NO may aid in better growth under lead stress. These enhancements may be aided by NO's ability in sensing, signaling and stress tolerance in plants under heavy metal stress in combination with lead stress. Our results show that GSNO has a positive effect on soybean seedling growth in response to axillary pressure and that NO supplementation helps to reduce chlorophyll maturation and relative water content in leaves and roots following strong burst under lead stress. GSNO supplementation (200 µM and 100 µM) reduced compaction and approximated oxidative damage of MDA, proline and H₂O₂. Under plant tension, a distorted appearance was found in the relief of oxidative damage by ROS scavenging by GSNO application. In summary, modulation of these NO, PCS and prolongation of metal past reversing GSNO application confirms the detoxification of ROS induced by toxic metal rates in soybean. In summary, these NO, PCS and metal traditionally sustained rates of reverse GSNO application confirm the detoxification of ROS induced by toxic metal rates in soybean.