• Title/Summary/Keyword: 토양 성능

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Development of Modeling Technique for Prediction of Driving Force and Kinetic Resistance of Agricultural Forklift (농업용 포크리프트의 구동력 및 운동저항 예측을 위한 모델링 기법 개발)

  • Jo, Jae-hyun;Kim, Jun-tae;Jeong, Jin-hyoung;Chang, Young-yoon;Park, Won-yeop;Lee, Sang-sik
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.3
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    • pp.299-305
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    • 2019
  • This study was initiated to solve the difficulties of aged and female workers in agriculture society due to aging and demise of young people. In the case of the conventional elevated lift, the risk of exposure to uneven road or work environment, not the difficulty of professional qualification and operation, and the risk of exposure to the uneven road or working environment, were also studied based on previous researches so that women could easily and efficiently perform productive agriculture. First, the simulation was carried out through the prediction model of traction performance using the object of agricultural forklift, and the soil of the Kimhae city in Gyeongnam (34.125kPa, internal friction angle 35.294deg, external friction angle 13.620deg, Adhesion force 5.750 kPa, average cone index 0-15 cm cl, 1001.8 kPa). In the case of the forklift for simulation, the driving force and the kinetic resistance prediction modeling of the agricultural electric forklift are modeled. Based on this model, the motor control drive adopts the 1232E model, which is a drive dedicated to AC motor, and divides the two drivers into master and slave And the model for the simulation was designed to control motor drive, hydraulic drive, and various outputs on the main PCB. The simulation model is undergoing continuous simulation, modification and supplementation. Based on this research, we will continue research for development of safer and more efficient agricultural electric forklift.

Analysis of Traction Performance for Agricultural Tractor According to Soil Condition (토양 조건에 따른 농업용 트랙터의 견인 성능 분석)

  • Lee, Nam Gyu;Kim, Yong Joo;Baek, Seung Min;Moon, Seok Pyo;Park, Seong Un;Choi, Young Soo;Choi, Chang Hyun
    • Journal of Drive and Control
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    • v.17 no.4
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    • pp.133-140
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    • 2020
  • Traction performance of a tractor varies depending on soil conditions. Sinkage and slip of the driving wheel for tractor frequently occur in a reclaimed land. The objective of this study was to develop a tractor suitable for a reclaimed land. Traction performance was evaluated according to soil conditions of reclaimed land and paddy field. Field experiments were conducted at two test sites (Fields A: paddy field; and Field B: reclaimed land). The tractor load measurement system was composed of an axle rotation speed sensor, a torque meter, a six-component load cell, GPS, and a DAQ (Data Acquisition System). Soil properties including soil texture, water content, cone index, and electrical conductivity (EC) were measured. Referring to previous researches, the tractor traveling speed was set to B3 (7.05 km/h), which was frequently used in ridge plow tillage. Soil moisture contents were 33.2% and 48.6% in fields A and B, respectively. Cone index was 2.1 times higher in field A than in field B. When working in the reclaimed land, slip ratios were about 10.5% and 33.1% for fields A and B, respectively. The engine load was used almost 100% of all tractors under the two field conditions. Traction powers were 31.9 kW and 24.2 kW for fields A and B, respectively. Tractive efficiencies were 83.3% and 54.4% for fields A and B, respectively. As soil moisture increased by 16.4%, the tractive efficiency was lowered by about 28.9%. Traction performance of tractor was significantly different according to soil conditions of fields A and B. Therefore, it is necessary to improve the traction performance of tractor for smooth operations in all soil conditions including a reclaimed land by reflecting data of this study.

Identifying sources of heavy metal contamination in stream sediments using machine learning classifiers (기계학습 분류모델을 이용한 하천퇴적물의 중금속 오염원 식별)

  • Min Jeong Ban;Sangwook Shin;Dong Hoon Lee;Jeong-Gyu Kim;Hosik Lee;Young Kim;Jeong-Hun Park;ShunHwa Lee;Seon-Young Kim;Joo-Hyon Kang
    • Journal of Wetlands Research
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    • v.25 no.4
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    • pp.306-314
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    • 2023
  • Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

Influence of Soil Temperature on Growth and Nodulation Competition of Bradyrhizobium sp. Strains in the Rhizosphere of Peanut (온도(溫度)가 땅콩근류균(根瘤菌)의 근류형성(根瘤形成) 경합(競合)에 미치는 영향(影響))

  • Lee, Sand-Bok;Choi, Youn-Hee;So, Jae-Don;Kim, Moo-Key
    • Korean Journal of Soil Science and Fertilizer
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    • v.26 no.3
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    • pp.197-203
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    • 1993
  • Greenhouse experiments were conducted to avaluate strain competition, nodulation, patterns of nodule occupancy and population changes of Bradyrhizobium sp. strain HCR-46 $str^{r}cep^{r}$ and CB756 $str^{r}rif^{r}$ in the rhizosphere of peanut(Arachis hypogaea L.) under different root temperatures. Inoculated with two strains using seed coating with peat slurry under different root temperatures, population of each strain in the rhizosphere increased with plant growth and multiplication rate of inoculum in the unit weight of root were showed the highest from 10 to 15days after sowing. The multiplication rate of inoculum in the rhizosphere was $28^{\circ}C$>$34^{\circ}C$>$22^{\circ}C$. The density of HCR-46 $str^{r}cep^{r}$ was more increased than that of CB756 $str^{r}rif^{r}$ under $22^{\circ}C$ and $28^{\circ}C$. While the density of two strains showed no difference under $34^{\circ}C$. Inoculated with HCR-46 $str^{r}cep^{r}$ and CB756 $str^{r}rif^{r}$, respectively at 22, 28 and $34^{\circ}C$, nodulation of each strain was dominated in its inoculation portion. Inoculated with the mixture of HCR-46 $str^{r}cep^{r}$ and CB756 $str^{r}rif^{r}$, occupancy rate of HCR-46 $str^{r}cep^{r}$ was dominated over that of CB756 $str^{r}rif^{r}$ at $22^{\circ}C$ and $28^{\circ}C$, but that was similar between them at $34^{\circ}C$. Dry mass, nodulation, nitrogen content per plant and nitrogenase activity showed higher at $28^{\circ}C$ than at $32^{\circ}C$ and $22^{\circ}C$, while those were higher in HCR-46 $str^{r}cep^{r}$ and mixing HCR-46 $str^{r}cep^{r}$ with CB756 $str^{r}rif^{r}$ than in CB756 $str^{r}rif^{r}$.

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Wildfire Severity Mapping Using Sentinel Satellite Data Based on Machine Learning Approaches (Sentinel 위성영상과 기계학습을 이용한 국내산불 피해강도 탐지)

  • Sim, Seongmun;Kim, Woohyeok;Lee, Jaese;Kang, Yoojin;Im, Jungho;Kwon, Chunguen;Kim, Sungyong
    • Korean Journal of Remote Sensing
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    • v.36 no.5_3
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    • pp.1109-1123
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    • 2020
  • In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

The NCAM Land-Atmosphere Modeling Package (LAMP) Version 1: Implementation and Evaluation (국가농림기상센터 지면대기모델링패키지(NCAM-LAMP) 버전 1: 구축 및 평가)

  • Lee, Seung-Jae;Song, Jiae;Kim, Yu-Jung
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.18 no.4
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    • pp.307-319
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    • 2016
  • A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.

Policy Trend and Status of Aerosol Application Research on the Safety Issues of Nanotechnologies (나노기술 안전성 정책 동향 및 에어로졸 응용 연구 현황)

  • Ji, Jun Ho;Yu, Il Je
    • Particle and aerosol research
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    • v.6 no.3
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    • pp.107-121
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    • 2010
  • The number of nanotechnology based consumer products are growing rapidly. Thus, the customer likely to be exposed to such products continues to increase as the applications expand. This article describes the international and Korea's policies on the EHS(Environment, Safety and Health) issues of nanotechnologies. The strategic plan and coordination of OECD and ISO were summarized. This article also examines several new findings of Korean researchers as well as current and future challenges in the aerosol application study of EHS issues on the nanotechnologies.

Modification of Indophenol Reaction for Quantification of Reduction Activity of Nanoscale Zero Valent Iron (나노 영가철 환원 반응성의 정량 분석을 위한 수정된 인도페놀법 적용)

  • Hwang, Yuhoon;Lee, Wontae;Andersen, Henrik R.
    • Journal of Korean Society of Environmental Engineers
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    • v.38 no.12
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    • pp.667-675
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    • 2016
  • Nanoscale zero-valent iron (nZVI) has been effectively applied for environmental remediation due to its ability to reduce various toxic compounds. However, quantification of nZVI reactivity has not yet been standardized. Here, we adapted colorimetric assays for determining reductive activity of nZVIs. A modified indophenol method was suggested to determine reducing activity of nZVI. The method was originally developed to determine aqueous ammonia concentration, but it was further modified to quantify phenol and aniline. The assay focused on analysis of reduction products rather than its mother compounds, which gave more accurate quantification of reductive activity. The suggested color assay showed superior selectivity toward reduction products, phenol or aniline, in the presence of mother compounds, 4-chlorophenol or nitrobenzene. Reaction conditions, such as reagent concentration and reaction time, were optimized to maximize sensitivity. Additionally, pretreatment step using $Na_2CO_3$ was suggested to eliminate the interference of residual iron ions. Monometallic nZVI and bimetallic Ni/Fe were investigated with the reaction. The substrates showed graduated reactivity, and thus, reduction potency and kinetics of different materials and reaction mechanism was distinguished. The colorimetric assay based on modified indophenol reaction can be promises to be a useful and simple tool in various nZVI related research topics.

Using IoT and Apache Spark Analysis Technique to Monitoring Architecture Model for Fruit Harvest Region (IoT 기반 Apache Spark 분석기법을 이용한 과수 수확 불량 영역 모니터링 아키텍처 모델)

  • Oh, Jung Won;Kim, Hangkon
    • Smart Media Journal
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    • v.6 no.4
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    • pp.58-64
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    • 2017
  • Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

A Benchmark of Hardware Acceleration Technology for Real-time Simulation in Smart Farm (CUDA vs OpenCL) (스마트 시설환경 실시간 시뮬레이션을 위한 하드웨어 가속 기술 분석)

  • Min, Jae-Ki;Lee, DongHoon
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 2017.04a
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    • pp.160-160
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    • 2017
  • 자동화 기술을 통한 한국형 스마트팜의 발전이 비약적으로 이루어지고 있는 가운데 무인화를 위한 지능적인 스마트 시설환경 관찰 및 분석에 대한 요구가 점점 증가 하고 있다. 스마트 시설환경에서 취득 가능한 시계열 데이터는 온도, 습도, 조도, CO2, 토양 수분, 환기량 등 다양하다. 시스템의 경계가 명확함에도 해당 속성의 특성상 타임도메인과 공간도메인 상에서 정확한 추정 또는 예측이 난해하다. 시설 환경에 접목이 증가하고 있는 지능형 관리 기술 구현을 위해선 시계열 공간 데이터에 대한 신속하고 정확한 정량화 기술이 필수적이라 할 수 있다. 이러한 기술적인 요구사항을 해결하고자 시도되는 다양한 방법 중에서 공간 분해능 향상을 위한 다지점 계측 메트릭스를 실험적으로 구성하였다. $50m{\times}100m$의 단면적인 연동 딸기 온실을 대상으로 $3{\times}3{\times}3$의 3차원 환경 인자 계측 매트릭스를 설치하였다. 1 Hz의 주기로 4가지 환경인자(온도, 습도, 조도, CO2)를 계측하였으며, 계측 하는 시점과 동시에 병렬적으로 공간통계법을 이용하여 미지의 지점에 대한 환경 인자들을 실시간으로 추정하였다. 선행적으로 50 cm 공간 분해능에 대응하기 위하여 Kriging interpolation법을 횡단면에 대하여 분석한 후 다시 종단면에 대하여 분석하였다. 3 Ghz에 해당하는 연산 능력을 보유한 컴퓨터에서 1초 동안 획득한 데이터에 대한 분석을 마치는데 소요되는 시간이 15초 내외로 나타났다. 이는 해당 알고리즘의 매우 높은 시간 복잡도(Order of $O=O^3$)에 기인하는 것으로 다양한 시설 환경의 관리 방법론에 적절히 대응하기에 한계가 있다 할 수 있다. 실시간으로 시간 복잡도가 높은 연산을 수행하기 위한 기술적인 과제를 해결하고자, 근래에 관심이 증가하고 있는 NVIDIA 사에서 제공하는 CUDA 엔진과 Apple사의 제안을 시작으로 하여 공개 소프트웨어 개발 컨소시엄인 크로노스 그룹에서 제공하는 OpenCL 엔진을 비교 분석하였다. CUDA 엔진은 GPU(Graphics Processing Unit)에서 정보 분석 프로그램의 연산 집약적인 부분만을 담당하여 신속한 결과를 산출할 수 있는 라이브러리이며 해당 하드웨어를 구비하였을 때 사용이 가능하다. 반면, OpenCL은 CUDA 엔진이 특정 하드웨어에서 구동이 되는 한계를 극복하고자 하드웨어에 비의존적인 라이브러리를 제공하는 것이 다르며 클러스터링 기술과 연계를 통해 낮은 하드웨어 성능으로 인한 단점을 극복하고자 하였다. 본 연구에서는 CUDA 8.0(https://developer.nvidia.com/cuda-downloads)버전과 Pascal Titan X(NVIDIA, CA, USA)를 사용한 방법과 OpenCL 1.2(https://www.khronos.org/opencl/)버전과 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea)를 사용한 방법을 비교 분석하였다. 50 cm의 공간 분해능에 대응하기 위한 4차원 행렬($100{\times}200{\times}5{\times}4$)에 대하여 정수 지수화를 위한 Quantization을 거쳐 CUDA 엔진과 OpenCL 엔진을 적용한 비교한 결과, CUDA 엔진은 1초 내외, OpenCL 엔진의 경우 5초 내외의 연산 속도를 보였다. CUDA 엔진의 경우 비용측면에서 약 10배, 전력 소모 측면에서 20배 이상 소요되었다. 따라서 우선적으로 OpenCL 엔진 기반 하드웨어 가속 기술 최적화 연구를 통해 스마트 시설환경 실시간 시뮬레이션 기술 도입을 위한 기술적 과제를 풀어갈 것이다.

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