• Journal of Bio-Environment Control
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• v.21 no.4
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• pp.428-436
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• 2012

This study aimed to develop the multi-span plastic greenhouse which is suitable for tomato cultivation and is safe against climatic disasters such as typhoon or heavy snow. The width and heights of eaves and ridge of newly developed tomato greenhouse are 7, 4.5 and 6.5 m, respectively. The width is the same but the eaves and ridge heights are 1.8 and 2 m higher than conventional 1-2 W greenhouses, respectively. Cross beam has been designed as a truss structure so it can sustain loads of tomato and equipment. Tomato greenhouse has been designed according to climatic disaster preventing design standard maintaining the high height. In other words, the material dimensions and interval of materials including column and rafter have been set to stand against $40m{\cdot}s^{-1}$ of wind and 40 cm of snow. Tomato greenhouse has been equipped with rack-pinion type roof vents which have been used in glass greenhouse in order to prevent excessive rise in air temperature. This vent type is different from that of 1-2 W type greenhouse which is made by rolling up and down the vinyl at upper part of column. Roof vents are installed at ridge, and thus external air inflow and natural ventilation are maximized. As the height increases, heating cost increase as well and, therefore, tomato greenhouse has been equipped with multi-layered thermal curtain, of which thermo-keeping is excellent, to prevent heat from escaping.

• Progress in Medical Physics
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• v.17 no.1
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• pp.40-46
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• 2006

Hepatoma is one of 3 most common malignancies in Korea, the survival rate is not improved since last decades because of delayed diagnosis and limited treatment conditions. Radiation was one of treatment options but the impact on the survival is not remarkable. High dose exposure to target area was suggested for improved effect but low tolerance dose of normal liver tissue is the main limited factor. IMRT is the advanced form of 3DCRT, for focusing high dose on target with minimal dose to surrounding normal tissues. Motion of the tumor by respiration, cardiac pulsation and peristalsis is the main treatment harrier of IMRT for treatment of hepatoma patients. Development of QA technique for acceptable geometrical uncertainties and dose error on target volume is essential for IMRT in clinical treatment but proper QA technique is not yet developed. This study compared the verification film dosimetry with measured dose in phantom and calculated dose in planning computer on exactly same conditions of patient treatments. Within 3% dose differences between 3 groups were confirmed. We suggest that our verification QA technique is easy, economic, iterative and acceptable in clinical application for advanced hepatoma patients.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.86-86
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• 2017

마늘파종기는 파종기구동부와 파종후 파종홀의 복토역할을 하는 파종기롤러, 지면과의 마찰을 통해 바퀴의 회전토크가 발생하며 발생된 토크는 파종기 내부 동력으로 전달되어 전체 파종시스템(배종, 호퍼캠, 파종부)을 동작 시키는 기능을 수행하는 파종바퀴, 마늘종구를 한알씩 집어올려 중간이송컵으로 이송하는 자세교정컵이 포함된 배종부와 자세교정컵에서 낙하된 마늘종구를 땅속으로 파종하는 파종장치로 구성된다. 배종율 95%, 2립 배종률 5% 성능을 확보할 수 있는 기술이 개발될 경우 세계적으로도 독보적인 기술 우위를 확보할 수 있다. 이와 같은 기본적인 기능을 구현할 수 있는 컨셉모델에 대한 설계를 수행하여 시제품개발 전 기구해석과 구조해석 등을 위한 기본설계를 수행하였다. 배종율 95% 이상을 확보하기 위해서는 기존의 현장경험의 의한 설계 방식으로는 한계가 있기 때문에 시뮬레이션 및 분석 개발이 필요한데, 프레임은 고정밀 마늘종구 배종부 장착을 위한 기본 구조물로써 작동시 동력을 얻기 위한 바퀴와 연계가 되도록 설계되었으며, 호퍼는 배종 수행을 위해 마늘을 저장해 두는 통으로써 배종부와 연결된다. 배종부의 배종판이 회전함에 따라서 배종판의 홈이 호퍼 내로 들어갔다 나오면서 마늘을 집게되며, 동력 전달부는 배종판을 회전시켜주고, 회전 속도 조절을 가능하게 한다. 파종부는 배종부에서 중간컵을 통해 하나씩 공급해준 마늘을 땅에 심는 부분으로서. 프레임의 바퀴 회전과 연동되어 회전하고 설계하였다. 배종판에서 중간컵으로 이송된 마늘을 파종부의 파종컵에 받아 회전하면서 땅속에 파종컵이 묻히면 파종컵이 열리면서 땅속에 마늘을 심는 원리이다. 조간조정은 7조식의 경우 초기설치시 고정되도록 설계되었으며 농촌진흥청 기계화 표준재배안에 따라 의성마늘 기준 $14{\times}14cm$(조간${\times}$주간)를 기준으로 개발하였다. 조간조정은 기계가 설치되면 조정하기 어려우므로 14cm로 설계하였으며, 주간조정은 원형배종장치의 구동기어부의 속도비로 간격을 조정할 수 있도록 기어장치를 설계하였다. 주간조정은 13에서 18cm의 범위에서 작동하도록 설계되었으며, 필요에 따라 간격조절이 가능함. 마늘은 그 크기가 다르고, 형상도 다르기 때문에 종자에 따른 개별적인 파종기술들이 개발되어야하기 때문에 개발 기간이 오래 걸리고, 수익에 비하여 개발비가 과다하게 요구되는 실정인데 축적된 시뮬레이션 툴을 이용한 파종기 분석 기술을 확보할 경우 다른 파종기의 연구 개발에도 크게 도움이 될 것으로 기대되며, 생육측정 실험과 동역학 해석 툴 RecurDyn을 통해 파종기의 기구학적 분석을 통한 설계반영 인자를 도출할 계획이다.

• Progress in Medical Physics
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• v.25 no.2
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• pp.79-88
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• 2014

The dose distributions within the real volumes of tumor targets and critical organs during internal target volume-based intensity-modulated radiation therapy (ITV-IMRT) for liver cancer were recalculated by applying the effects of actual respiratory organ motion, and the dosimetric features were analyzed through comparison with gating IMRT (Gate-IMRT) plan results. The ITV was created using MIM software, and a moving phantom was used to simulate respiratory motion. The doses were recalculated with a 3 dose-volume histogram (3DVH) program based on the per-field data measured with a MapCHECK2 2-dimensional diode detector array. Although a sufficient prescription dose covered the PTV during ITV-IMRT delivery, the dose homogeneity in the PTV was inferior to that with the Gate-IMRT plan. We confirmed that there were higher doses to the organs-at-risk (OARs) with ITV-IMRT, as expected when using an enlarged field, but the increased dose to the spinal cord was not significant and the increased doses to the liver and kidney could be considered as minor when the reinforced constraints were applied during IMRT plan optimization. Because the Gate-IMRT method also has disadvantages such as unsuspected dosimetric variations when applying the gating system and an increased treatment time, it is better to perform a prior analysis of the patient's respiratory condition and the importance and fulfillment of the IMRT plan dose constraints in order to select an optimal IMRT method with which to correct the respiratory organ motional effect.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.121-121
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• 2017

오늘날 시장에 유통되는 버섯은 대부분 환경이 조절되는 시설 내부에서 재배된다. 버섯은 다른 식물과 달리 버섯의 종류, 품종 등에 따라 요구되는 환경이 매우 다르다. 특히 대부분의 버섯은 버섯이 생육되는 공간의 온도, 수분, 이산화탄소, 조도 등의 관리가 필수적이다. 버섯의 단위면적당 생산량을 극대화하기 위해서는 이들 버섯이 필요로 하는 환경을 버섯의 생육특성에 따라 적합하게 유지해 주어야만 한다. 현재 대부분의 버섯재배사에는 이들 환경을 컨트롤하는 장치가 설치되어 있고, 이들 시스템의 환경 설정은 농업인이 현장에서 버섯의 상태를 확인 한 후 그때그때 경험에 의해 채득한 정보를 기반으로 환경설정을 하고 있는 실정이다. 이렇다 보니 버섯을 재배하는 기간에는 농업인이 재배사 내부의 환경을 관리하기 위해서 항상 버섯재배사에 머물러야 하고, 재배사의 환경관리 때문에 원거리 또는 장기 출타가 어려운 실정이다. 본 연구에서는 기존에 사람이 버섯재배 현장에서 컨트롤하던 환경관리를 원격에서 컴퓨터 또는 모바일로 구현할 수 있는 시스템을 개발하였다. 시스템에서 모니터링 및 제어하는 환경은 온도, 습도, 이산화탄소, 배기팬 및 입기팬의 가동 등 버섯재배 환경관리에 필요한 모든 요소를 모니터링 및 관리할 수 있도록 하였다. 이들 관리 요소는 인터넷이 연결된 컴퓨터에 접속하여 버섯재배사의 환경을 실시간으로 모니터링 하고 필요에 따라 제어를 할 수 있도록 하였다. 또한 컴퓨터에서 볼 수 있는 환경을 스마트폰을 통해서도 볼 수 있고 또한 제어할 수 있도록 하였다. 그리고 버섯배지를 입상 한 후부터 수확시기까지의 관리 환경을 데이터베이스로 만들어 농민이 버섯배지를 입상하고 데이터베이스와 연동한 제어가 되도록 설정하면 버섯재배사 내부의 환경은 데이터베이스의 정보를 읽어 들여 버섯의 생육단계에 따라 자동으로 내부환경이 제어되도록 하였다. 또한 농업인이 원격에서 버섯재배사 내부 버섯의 생육상황을 모니터링 할 수 있도록 재배사 상부에 CCD 카메라를 달아 실시간으로 버섯의 생육상활을 모니터링 할 수 있도록 구성하였다. 이와 같은 시스템을 사용하면 버섯재배 경험이 없는 농업인도 경험자와 같은 재배관리가 가능하고 원격에서 재배사 내부환경을 모니터링하고 제어 할 수 있기 때문에 농업인이 재배사에 매여 있지않아도 될 것으로 판단된다.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.20-26
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• 2018

This study was conducted to develop a device for measuring the air flow by space variation through monitoring program, which acquires data by each point from each environmental sensor located in the greenhouse. The distribution of environmental factors(air temperature, humidity, wind speed, etc.) in the greenhouse is arranged at 12 points according to the spatial variation and a large number of measurement points (36 points in total) on the X, Y and Z axes were selected. Considering data loss and various greenhouse conditions, a bit rate was at 125kbit/s at low speed, so that the number of sensors can be expanded to 90 within greenhouse with dimensions of 100m by 100m. Those system programmed using MATLAB and LabVIEW was conducted to measure distributions of the air flow along the greenhouse in real time. It was also visualized interpolated the spatial distribution in the greenhouse. In order to verify the accuracy of CFD modeling and to improve the accuracy, it will compare the environmental variation such as air temperature, humidity, wind speed and $CO_2$ concentration in the greenhouse.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.51-55
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• 2017

In this paper, we designed a LBS-based smart platform for Lifelog service that can utilize the other's lifelog information. The conventional Lifelog service means that the system records the daily activities of the smart device user so the user can retrieve the early-recorded information later. The proposed Lifelog service platform uses the GPS/UFID location information and the various information extracted from the image as the lifelog data. Further, the proposed Lifelog platform using DB can provide the user with the Lifelog data recorded by the other service user. The system usually provide the other's Lifelog data within the 500m distance from the user and the range of distance can be adjustable. The proposed smart platform based on image recognition for Lifelog can acquire the image from the smart device directly and perform the various image recognition processing to produce the useful image attributes. And it can store the location information, image data, image attributes and the relevant web informations on the database that can be retrieved by the other use's request. The attributes stored and managed in the image information database consist of the followings: Object ID, the image type, the capture time and the image GPS coordinates. The image type attribute has the following values: the mountain, the sea, the street, the front of building, the inside of building and the portrait. The captured image can be classified into the above image type by the pattern matching image processing techniques and the user's direct selection as well. In case of the portrait-attribute, we can choose the multiple sub-attribute values from the shirt, pant, dress and accessory sub-attributes. Managing the Lifelog data in the database, the system can provide the user with the useful additional services like a path finding to the location of the other service user's Lifelog data and information.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.