• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.542-542
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• 2015

비점오염원은 기상조건과 토지이용에 따라 시간에 따른 오염부하량의 변동폭이 크게 발생하며, 강우초기에 오염물질의 농도가 크게 나타난다. MFFn은 강우지속시간에 따라 다양하게 변화하는 오염물질의 부하량과 유출량을 특정시점에서 강우유출율과 오염물질 유출율을 계산할 수 있으며, 강우가 시작될 때 0, 종료될 때 1의 값을 나타내며, 1보다 크면 초기세척이 있음을 나타낸다. 예를 들면 MFF20에서 평균값이 2.5이면 초기우수유출수의 부피 20%에 오염물질 부하량의 부피 50%를 포함하는 것을 의미한다. 본 연구에서는 논에서의 초기세척비율 정량화하기 위해 영산강수계 논지역(이하, 학야지구)과 섬진강수계 논지역(이하, 적성지구) 각 1개유역을 선정하여 2009년부터 2012년까지 수문 및 수질 모니터링을 수행하였다. 유역면적은 학야지구는 13.69 ha 이며, 적성지구는 8.06ha 이다. 두 지역 모두 외부유입이 없으며, 배수로가 구조물화 되어 있어 관측이 용이한 지점이다. 논에서 강우시유출되는 오염물질을 산정하기 위해서 배수로 말단에 압력식 수위계와 자동채수기를 설치하여 일정간격으로 관측하였으며, 수위별 유량관측을 통해 수위-유량관계곡선식을 산정 후 유량으로 환산하였다. 채취된 수질은 수질공정시험법을 통해 BOD, COD, TOC, T-N, T-P, SS를 분석하였으며, 관측된 유량과 수질자료를 이용하여 부하량을 산정하고, MFFn을 이용하여 초기세척비율을 정량화 하였다. BOD COD, TOC, T-N, T-P, SS 의 논 초기세척비율은 n 값이 10% 때 중앙값이 각각 1.3, 1.18, 1.13, 1.2, 1.13, 1.1 였으며, 13%, 11.8%, 11.3%, 12%, 11.3%, 11% 가 유출되는 것으로 나타났으며, n 값이 20%일 때 1.3, 1.1, 1.1, 1.25, 1.2, 1.2 이였으며, 26%, 22%, 22%, 25%, 24%. 24%가 유출되는 것으로 나타났다. n 값이 30%일 때 1.25, 1.0, 1.0, 1.25, 1.13, 1.3 였으며, 37.5%, 30%, 30%, 37.5%, 33.9%, 39%가 유출되는 것으로 나타났다.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.225.2-225.2
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• 2012

저궤도 관측용 다중 카메라를 통해 고해상도 위성을 제공할 수 있으며, 지도 제작이나 환경, 농업, 해양 지역 모니터링 등의 목적으로 사용될 수 있다. 특히 항공촬영 및 지구 관측을 통해 수치표고모델(DEM) 추출을 함으로써 촬영지역의 고도정보를 포함하는 입체영상을 얻는데 유용하다. 또한, 달 관측을 위한 관측위성에 장착할 경우 달 표면의 지형을 정밀하게 얻어내어 달표면 고도 지형 지도제작 및 향후 달 탐사선을 통한 달 탐사 시 탐사지역 선정에 필요한 정보를 제공할 수 있다. 다중 카메라를 포함한 탑재체 시스템은 크게 광학부와 카메라 전자부로 구성된다. 광학부에서는 입체촬영 및 줌인이 가능한 광학계를 제공하며, 카메라 전자부에서는 광학계를 통해 검출기로 입사되는 빛에너지를 전자신호로 변환하고, 이를 카메라 전자부 영상출력 형식으로 변환하게 된다. 특히, 다중카메라를 각각 제어하기 위한 정밀제어로직, 다양한 촬영 지원 모드, 다중카메라 영상자료 및 영상처리를 위한 추가적인 영상정보를 제공한다. 본 논문에서는 저궤도 관측용 다중 카메라를 이용한 다양한 활용에 따른 각 모드별 성능분석방법을 제안한다. 이를 위해 각 촬영조건에 따라 필요한 파라미터를 분석하고 실제 활용시 예상되는 성능을 분석해 본다. 또한 다중카메라를 통해 얻어진 영상을 처리하는데 필요한 처리 과정 및 처리된 영상을 활용하는 방법을 제시한다. 특히 다중 카메라 촬영을 통해 얻어진 영상데이터의 특성을 알아보고, 이를 보정 및 처리하기 위해 필요한 추가 적인 정보, 영상파라미터, 처리 단계 및 최종결과물을 검증하는 방법을 제시한다.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2315-2322
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• 2008

The requesting a consultation of the farm manager is about the diagnosis and prevention of the breeding and extermination for diseases and insects in greenhouse, the managing problem for diseases and insects turn up a main issue. To solve these problems, this paper proposes a PDA based greenhouse, diseases and insects management system for mobile(GDIMS) uses as keeping up with ubiquitous time, which makes prediction and management for diseases and insects more efficiently checked at any time and anywhere you want to, and go well with the motto of ubiquitous. This system is using the environmental data from the greenhouse attached sensors provide the accurate diagnosis and recipes, which supports to product clean crops. There are no need to visit the greenhouse because our system is based on mobile devices that obtain the information in the greenhouse, which makes management in efficient with little number of people. This wort builds simply virtual greenhouse model that assembles system component of environmental sensor for performance analysis and offers a PDA view of the greenhouse status.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.661-674
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• 2009

Recently, there has been much research and many attempts to enhance converged information technology services using new technology such as ubiquitous sensor networks (USN) in medical, environmental, industrial, and logistic areas. There has also been much research and various attempts to apply this new technology to agricultural areas. However, applications to the agricultural areas should be considered differently against the same areas such as medical, environmental, industrial, and logistics. Therefore, this paper suggests that an agricultural cultivating management and traceability system. This system is a unified system that supports the processes sowing seeds through selling agricultural products to consumers. Farmers can be provided with an effective calendar for cultivation and weather information in real time as well as the monitoring of the growth of farm products on the farm in real time using this system. Farmers can also control all equipment installed on the farm directly or remotely and the equipment can be controlled automatically when the measured values such as temperature and humidity deviate from the decent criteria which are set by farmers or this system. Additionally, the reliability for and the better quality of the agricultural products can be improved because farmers can use this unified system to cover all processes from sowing seeds to selling to consumers.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.452-459
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• 2022

One of the important factors among the smart farm factors is environmental measurement. This study tried to design an environmental measurement monitoring system through Bluetooth wireless communication with LoRa using the open source programs Arduino, App Inventor, and Node Red. This system consists of Arduino, LoRa shield, temperature and humidity sensor (SHT10), and carbon dioxide sensor (K30). The environmental measurement system is configured as a system that allows the sensor to collect environmental data and transmit it to the user through wireless communication to conveniently monitor the farm environment. As libraries used in the Arduino program, LoRa.h, Sensirion.h, LiquidCrystal_I2C.h and K30_I2C.h were used. When receiving environmental data from the sensor at regular intervals, coding using average value was used for data stabilization. An Android-based app was developed using Node Red and App Inventor program as the user interface. It can be seen that the environmental data for the sensor is well collected with the screen output to the serial screen of Arduino, the screen of the smartphone, and the user interface of Node Red. Through these open source-based platforms and programs will be applied to various agricultural applications.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.511-516
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• 2003

Computer vision technology has been utilized as one of the most powerful tools to automate various agricultural operations. Though it has demonstrated successful results in various applications, the current status of technology is still for behind the human's capability typically for the unstructured and variable task environment. In this paper, a man-machine interactive hybrid decision-making system which utilized a concept of tole-operation was proposed to overcome limitations of computer image processing and cognitive capability. Tasks of greenhouse watermelon cultivation such as pruning, watering, pesticide application, and harvest require identification of target object. Identifying water-melons including position data from the field image is very difficult because of the ambiguity among stems, leaves, shades. and fruits, especially when watermelon is covered partly by leaves or stems. Watermelon identification from the cultivation field image transmitted by wireless was selected to realize the proposed concept. The system was designed such that operator(farmer), computer, and machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. And the developed system was composed of the image monitoring and task control module, wireless remote image acquisition and data transmission module, and man-machine interface module. Once task was selected from the task control and monitoring module, the analog signal of the color image of the field was captured and transmitted to the host computer using R.F. module by wireless. Operator communicated with computer through touch screen interface. And then a sequence of algorithms to identify the location and size of the watermelon was performed based on the local image processing. And the system showed practical and feasible way of automation for the volatile bio-production process.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.1
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• pp.21-28
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• 2009

Detailed information on soil characteristics is of great importance for the use and conservation of soil resources that are essential for human welfare and ecosystem sustainability. This paper introduces soil inventory of Korea focusing on national soil database establishment, information systems, use, and future direction for natural resources management. Different scales of soil maps surveyed and soil test data collected by RDA (Rural Development Administration) were computerized to construct digital soil maps and database. Soil chemical properties and heavy metal concentrations in agricultural soils including vulnerable agricultural soils were investigated regularly at fixed sampling points. Internet-based information systems for soil and agro-environmental resources were developed based on 'National Soil Survey Projects' for managing soil resources and for providing soil information to the public, and 'Agroenvironmental Change Monitoring Project' to monitor spatial and temporal changes of agricultural environment will be opened soon. Soils data has a great potential of further application in estimation of soil carbon storage, water capacity, and soil loss. Digital mapping of soil and environment using state-of-the-art and emerging technologies with a pedometrics concept will lead to future direction.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.168-168
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• 2017

ICT 융합 스마트팜 내의 환경계측 센서, 영상 및 사양관리 시스템의 증가에도 불구하고 이들 장비에서 확보되는 데이터를 적절히 유효하게 활용하는 기술이 미흡한 실정이다. 돈사의 경우 가축의 복지수준, 성장 변화를 실시간으로 모니터링 및 예측할 수 있는 데이터 분석 및 모델링 기술 확보가 필요하다. 이를 위해선 가축의 생리적 변화 및 행동적 변화를 조기에 감지하고 가축의 복지수준을 실시간으로 감시하고 분석 및 예측 기술이 필요한데 이를 위한 대표적인 정보 통신 공학적 접근법 중에 하나가 Data mining 이다. Data mining에 대한 연구 수행에 필요한 다양한 소프트웨어 중에서 Open source로 제공이 되는 4가지 도구를 비교 분석하였다. 스마트 돈사 내에서 열환경 모델링을 목표로 한 데이터 분석에서 고려해야할 요인으로 데이터 분석 알고리즘 도출 시간, 시각화 기능, 타 라이브러리와 연계 기능 등을 중점 적으로 분석하였다. 선정된 4가지 분석 도구는 1) R(https://cran.r-project.org), 2) OpenCV(http://opencv.org), 3) OpenNN (http://www.opennn.net), 4) Orange(http://orange.biolab.si) 이다. 비교 분석을 수행한 운영체제는 Linux-Ubuntu 16.04.4 LTS(X64)이며, CPU의 클럭속도는 3.6 Ghz, 메모리는 64 Gb를 설치하였다. 개발언어 측면에서 살펴보면 1) R 스크립트, 2) C/C++, Python, Java, 3) C++, 4) C/C++, Python, Cython을 지원하여 C/C++ 언어와 Python 개발 언어가 상대적으로 유리하였다. 데이터 분석 알고리즘의 경우 소스코드 범위에서 라이브러리를 제공하는 경우 Cross-Platform 개발이 가능하여 여러 운영체제에서 개발한 결과를 별도의 Porting 과정을 거치지 않고 사용할 수 있었다. 빌트인 라이브러리 경우 순서대로 R 의 경우 가장 많은 수의 Data mining 알고리즘을 제공하고 있다. 이는 R 운영 환경 자체가 개방형으로 되어 있어 온라인에서 추가되는 새로운 라이브러리를 클라우드를 통하여 공유하기 때문인 것으로 판단되었다. OpenCV의 경우 영상 처리에 강점이 있었으며, OpenNN은 신경망학습과 관련된 라이브러리를 소스코드 레벨에서 공개한 것이 강점이라 할 수 있다. Orage의 경우 라이브러리 집합을 제공하는 것에 중점을 둔 다른 패키지와 달리 시각화 기능 및 망 구성 등 사용자 인터페이스를 통합하여 운영한 것이 강점이라 할 수 있다. 열환경 모델링에 요구되는 시간 복잡도에 대응하기 위한 부가 정보 처리 기술에 대한 연구를 수행하여 스마트팜 열환경 모델링을 실시간으로 구현할 수 있는 방안 연구를 수행할 것이다.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.53-63
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• 2024

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.