• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• 생물환경조절학회지
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• 제29권3호
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• pp.277-284
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• 2020

스마트 팜 관리의 활용 효율성을 높이기 위해서는 작물 및 환경 변화에 대한 사전 검사를 실시간으로 평가하기 위한 모델링 기법이 필요하다. 시설 온실 내부의 CO₂와 같은 필수 환경 요소는 다양한 상관 변수가 밀접하게 결합 된 시간 영역에서 신뢰할 수 있는 추정 모델을 확립하기가 어렵다. 따라서 본 연구는 입력 영역과 출력 변수를 CO₂와 같은 시간 관점에서 인접 영역에 분포된 환경 정보를 이용하여 시간 복잡도를 줄이기 위한 인공 신경망을 개발하기 위해 수행되었다. 스마트 팜을 계측하기 위한 센서 모듈을 통해 환경 요소를 지속적으로 측정하였다. 실험기간의 평균 데이터로 예측하는 모델링 1, 전일 데이터로 예측하는 모델링 2을 구성하여 CO₂ 환경인자의 상호관계를 예측하였다. 전일의 데이터 학습으로 예측하는 모델링 2가 60일 평균값으로 예측한 모델링 1에 비해 성능이 우수하였다. 30일 이전까지는 대부분 0.70~0.88사이의 결정계수를 보였으며 모델링 2가 약0.05정도 높게 나타났다. 하지만 30일 이후에는 두 가지 모델링 모두 결정 계수 값이 0.50 이하로 낮은 값을 보였다. 모델링 접근법에 따라 결정 요인의 값을 비교하고 분석 한 결과 인접한 시간대의 데이터는 고정 신경망 모델을 사용하는 대신 예측이 필요한 지점에서 상대적으로 높은 성능을 나타냈다.

• Journal of Biosystems Engineering
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• 제8권2호
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• pp.69-85
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• 1983

Nonlinear Goal Programming(NGP)이 Multiple Objective Decision Making(MODM) 문제(問題)를 적정화(適正化)할 수 있는 방법(方法)으로 소개(紹介)가 되었다. 그리고 6 가지의 벼 조제(調製) 및 저장시설(貯藏施設)을 분석(分析)한 후, NGP에 의(依)하여 적정시설설계(適正施設設計)를 하기 위하여 Model Systems를 개발(開發)하고 그에 의(依)하여 MODM의 수학적(數學的) 모형(模型)을 개발(開發)하였다.

• Agricultural and Biosystems Engineering
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• 제2권2호
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• pp.41-49
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• 2001

Advanced Life Support Systems(ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses heave been delivered through the internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. they include objected-oriented approach for top-level models, procedureal approach for process-level models, and application of commercially available modeling tools such as $MATLAB^{R}$/$Simulink^{R}$. Every paradigm has its particular applicability for the purpose of modeling work. an overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Journal of Biosystems Engineering
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• 제31권6호
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• pp.524-528
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• 2006

This study was performed to develop experimental model for milling degree and whiteness from analysis the CBB (colored bran balance) index using NMG dying and image processing. The endosperm layer increased and the pericarp, aleurone layer and CBB index decreased as the milling degree and whiteness increased. So, CBB index was closely linked to milling degree and whiteness. A empirical CBB index model was developed according to the milling degree and whiteness. The model were found to fit adequately to all test data with a coefficient of determination of 0.99, and therefore the CBB model proved to be quite applicable.

• Journal of Biosystems Engineering
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• 제20권2호
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• pp.173-179
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• 1995

A modeling technique of land rearrangement was proposed using Geographical Information System(GIS) based on PC ARC/INFO. Three regions with different slopes were chosen. At first, slope angle and direction were calculated, and lands were rearranged using various parameters. The required parameters are obtained from PC ARC/INFO and the design factors for land rearrangement. The shape of land before rearrangement could be compared with the shape of land after rearrangement. Besides the shape comparison, drain site could be determined and the information for cost of land rearrangement could be obtained. The system was integrated on a menu driven style with SML files and subprograms. GIS was proved to be useful for land rearrangement design.

• Journal of Biosystems Engineering
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• 제17권1호
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• pp.5-17
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• 1992

To develop an analytic method of combustion characteristics in a small sized and single cylinder type diesel engine for a power tiller, 1) the theoritical analysis of combustion gas in engine cylinder was performed based on thermoscience and 2) the computer program which could be used to calculate those values of the apparent burning rate, the heat loss, the gas temperature and the fuel-air equivalence ratio with the experimental cylinder pressure data, was developed. This method would provide the practical and quantative data for the diesel combustion process. Through the use of this method, following details would be obtained: 1) the application in the modeling of combustion process without detail knowledeg of combustion process, 2) the basis for the complete modeling of diesel engine, and 3) the basic information for the design of combustion chamber by the prediction of engine performance.

• Asian-Australasian Journal of Animal Sciences
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• 제33권10호
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.48-54
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• 2013

Purpose: This study evaluated the feasibility of using a discrete wavelet transform (DWT) method as a preprocessing tool for visible/near-infrared spectroscopy (VIS/NIRS) with a spectroscopic transmittance dataset for predicting the internal quality of cherry tomatoes. Methods: VIS/NIRS was used to acquire transmittance spectrum data, to which a DWT was applied to generate new variables in the wavelet domain, which replaced the original spectral signal for subsequent partial least squares (PLS) regression analysis and prediction modeling. The DWT concept and its importance are described with emphasis on the properties that make the DWT a suitable transform for analyzing spectroscopic data. Results: The $R^2$ values and root mean squared errors (RMSEs) of calibration and prediction models for the firmness, sugar content, and titratable acidity of cherry tomatoes obtained by applying the DWT to a PLS regression with a set of spectra showed more enhanced results than those of each model obtained from raw data and mean normalization preprocessing through PLS regression. Conclusions: The developed DWT-incorporated PLS models using the db5 wavelet base and selected approximation coefficients indicate their feasibility as good preprocessing tools by improving the prediction of firmness and titratable acidity for cherry tomatoes with respect to $R^2$ values and RMSEs.

• 농업생명과학연구
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• 제52권6호
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• pp.127-138
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• 2018

본 연구에서는 국내에서 주로 사용되는 채소 정식기를 대상으로 전체적인 작동 특성을 분석하였다. 식부장치 거동에 영향을 주는 주요 구성요소 및 동력전달경로를 파악하였으며, 식부장치의 링크 구조를 기구적으로 분석하고 3D모델링 및 시뮬레이션을 수행하였다. 이를 바탕으로 식부호퍼 극하단점의 궤적을 분석하였다. 또한 엔진회전속도 및 식부변속단수 변화에 따른 주간거리를 도출하고 필드시험을 통해 검증하였다. 주요 결과로써, 식부장치는 10개의 링크와 13개의 회전 조인트로 구성된 1 자유도의 기구이며 각 부분들은 4절 링크식으로 구성되어 있었다. 링크장치의 거동에 의해 식부호퍼는 일정한 자세를 유지하면서 연직 방향으로 묘를 심어준다. 동력은 엔진을 통해 주행부 및 식부로 전달되었으며 식부장치의 최대 및 최소 주간거리는 각각 428.97mm, 261.20mm로 나타났다.