• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.41-41
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• 2017

본 연구는 초분광 영상을 이용하여 수박 모종의 수분함량을 비파괴적으로 추정하기 위해 수행되었다. 단계적으로 수분 스트레스를 받은 수박(n=45) 모종을 초분광 영상시스템으로 촬영하여 모종 영역의 반사율을 추출하였고, 매 촬영 후 모종의 생체중과 건물중을 측정하여 수분함량을 계산하였다. 모종의 반사율과 계측된 수분함량을 변수로 하여 Partial Least Square Regression(PLSR) 분석을 이용하여 수분 추정 모델을 구축하였다. 수분 추정모델을 작성한 결과 Calibration(Cal.)의 정확도($R^2$)는 0.66, 정밀도(RMSE 및 RE)는 각각 1.06%, 1.14%로 나타났다. 수박 모종의 수분함량 추정모델의 정밀도는 상당히 높게 나타났으나 정확도는 낮게 나타났다. 정확도를 개선하기 위해 Confidence ellipses의 신뢰구간을 95%로 설정하였을 때 3개의 모종이 타원 밖에 위치하는 것을 발견하였으며 이를 제거 후 재분석을 하였다. 3개의 모종을 제외한 수박 모종의 수분함량 추정모델의 정확도는 0.82, 정밀도는 0.73%, 0.78%로 나타났다. 3개의 모종을 제외함으로서 모델의 정확도 및 정밀도가 상승하여 3개의 모종이 정확도 및 정밀도를 낮추는 원인이라 판단된다. 작물은 가뭄스트레스를 받을수록 반사율이 낮아지지만(Yang et al., 2010) 3개의 모종은 다른 모종의 수분함량에 비해 반사율이 큰 차이를 나타내어 정확도 및 정밀도를 낮춘 것으로 판단된다. 본 연구를 통해 초분광 영상을 이용하여 수박 모종의 수분함량 추정가능성을 시사하였고, 모델의 정확도를 개선하기 위해 샘플 수 및 수분함량의 변이를 증가시키는 것이 필요하다고 판단된다.

• Journal of Biosystems Engineering
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• 제43권2호
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• 농촌계획
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• 제20권3호
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• pp.121-130
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• 2014

This study aimed at setting up the evaluation indicators system by rural village types to identify systematically the multi-valuedness embedded in rural villages. AHP(Analytic Hierarchy Process) was used for evaluating the relative importance weight evaluation of each indicator and quantitative analysis of rural village through computer works. The importance weight of evaluation indicators was converted into the score on the basis of maximum 1,000 point to increase the practicality. As a result, characteristics of 5 rural village types(Basic life-supporting, Agricultural promotion, Marketing/processing oriented, Urban-rural communication, Life-style choice types) differed in score of classified indicators. Also, These results are expected to be possible to quantitatively evaluate characteristics by rural village types.

• 농촌계획
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• 제20권3호
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• pp.131-141
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• 2014

This study aimed at setting up the grading indicator standards on development stages for phasing the continuous development process. To achieve the objective of this study, after referring to the proposed cases derived from the reference analysis, the development stages were classified. Also grading criteria of indicators according to development stages were established through the statistical analysis and expert group checking. Development stages of rural village were stepped into 4-stages through the reviews and analysis of reference literature ; preparation-entry-development-advanced stage. Reasonable achievement level of each development stage was established by considering the necessary development period and benchmarking reference target together. On the basis of these clear stepwise development phases, the grading criteria were proposed for determination of the incumbent development stages.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
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• pp.77-85
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• 2004

A significant portion (about 8% in human genome) of mammalian mRNA sequences contains AU(Adenine and Uracil) rich elements or AREs at their 3' untranslated regions (UTR). These mRNA sequences are usually stable. ARE motifs are assorted into three classes. The importance of AREs in biology is that they make certain mRNA unstable. We analyzed the occurrences of AREs and Alu, and propose a possible mechanism on how human mRNA could acquire and keep A REs at its 3' UTR originated from Alu repeats. Interspersed in the human genome, Alu repeats occupy 5% of the 3' UTR of mRNA sequences. Alu has poly-adenine (poly-A) regions at the end that lead to poly -thymine (poly-T) regions at the end of its complementary Alu. It has been discovered that AREs are present at the poly -T regions. In the all ARE's classes, 27-40% of ARE repeats were found in the poly -T region of Alu with mismatch allowed within 10% of ARE's length from the 3' UTRs of the NCBI's reference m RNA sequence database. We report that Alu, which has been reported as a junk DNA element, is a source of AREs. We found that one third of AREs were derived from the poly -T regions of the complementary Alu.

• 한국환경과학회지
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• 제19권9호
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• pp.1153-1159
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• 2010

Identification of probability distribution for water quality constituents from specific land use is important to achieve successful implementation of TMDL program. In this 3-year study, distribution of discharge and BOD(Biological Oxygen Demand) concentration from paddy rice fields were monitored. Four probability distributions, normal, log-normal, Gamma and Weibull were fitted and the goodness-of-fit was assessed using probability plots and Kolmogorov-Smirnov test. $EMC_s$ of BOD in runoff from paddy field ranged 0.37 to $7.99\;mgL^{-1}$, and all four probability distributions were acceptable. But the normal distribution would be preferred for BOD from paddy fields considering nature of straight forward application.

• Smart Structures and Systems
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• 제7권3호
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Molecules and Cells
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• 제39권2호
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• pp.141-148
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• 2016

Oriental melon (Cucumis melo L. var. makuwa) is one of six subspecies of melon and is cultivated widely in East Asia, including China, Japan, and Korea. Although oriental melon is economically valuable in Asia and is genetically distinct from other subspecies, few reports of genome-scale research on oriental melon have been published. We generated 30.5 and 36.8 Gb of raw RNA sequence data from the female and male flowers, leaves, roots, and fruit of two oriental melon varieties, Korean landrace (KM) and Breeding line of NongWoo Bio Co. (NW), respectively. From the raw reads, 64,998 transcripts from KM and 100,234 transcripts from NW were de novo assembled. The assembled transcripts were used to identify molecular markers (e.g., single-nucleotide polymorphisms and simple sequence repeats), detect tissue-specific expressed genes, and construct a genetic linkage map. In total, 234 single-nucleotide polymorphisms and 25 simple sequence repeats were screened from 7,871 and 8,052 candidates, respectively, between the KM and NW varieties and used for construction of a genetic map with 94 F2 population specimens. The genetic linkage map consisted of 12 linkage groups, and 248 markers were assigned. These transcriptome and molecular marker data provide information useful for molecular breeding of oriental melon and further comparative studies of the Cucurbitaceae family.

• 한국농공학회논문집
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• 제63권5호
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• pp.39-47
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• 2021

With rising concerns about pesticide spray drifts, this study analyzed the drift patterns of two typically-used nozzles, XR nozzle and AI nozzle, concerning their working pressures and wind speeds by wind tunnel experiments. AI nozzle showed low drift potential with larger droplet sizes compared to XR nozzle. Airborne and deposition drifts of XR nozzle were two times higher than those of AI nozzle under high wind speeds (≥2 m s^-1). In all cases, higher working pressures decreased the droplet sizes, thereby increasing the airborne and deposition drifts. Higher wind speeds also resulted in more airborne drifts, while ground deposition was increased under lower wind speeds. These effects of working pressures and wind speeds on the airborne and deposition drifts were observed at leeward distances less than 4 m from the nozzles. However, the airborne and deposition drifts were barely affected by the working pressures and wind speeds at leeward distances more than 11 m. The measurements were fitted to regression models of the drift curve with acceptable R² values greater than 0.8, demonstrating that further studies will be useful to settle domestic issues of spray drifts.

• 한국농공학회논문집
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• 제64권5호
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.