• 식물병연구
• /
• 제7권2호
• /
• pp.102-106
• /
• 2001

2000년에 전국적으로 이삭누룩병이 발생한 포장의 비율은 7.5%이었으며, 지역별로는 충북이 13.7%로 가장 높았고 전남이 1.5%로 가장 낮았다. 벼 품종별로는 남천벼가 가장 감수성이었고 흑진주벼는 1999년과 2000년에 전혀 발생이 되지 않아 가장 저항성인 것으로 조사되었다. 이삭누룩병 약제방제 선발 시험결과 터부코나졸 수화제의 방제가는 83.0% 이상으로 가장 우수하였고, 훼림존 성분이 함유된 두 약제도 방제가가 60.9%∼75.9%로 나타났다. 남원 지역의 포장에서 이삭누룩병 발생이 높은 원인은 일조부족 및 강우량이 많아 발병이 조장된 것으로 분석되었다.

• International Journal of Concrete Structures and Materials
• /
• 제11권2호
• /
• pp.199-213
• /
• 2017

Chloride penetration is among the main causes of corrosion initiation in reinforced concrete (RC) structures producing premature degradations. Weather and exposure conditions directly affect chloride ingress mechanisms and therefore the operational service life and safety of RC structures. Consequently, comprehensive chloride ingress models are useful tools to estimate corrosion initiation risks and minimize maintenance costs for RC structures placed under chloride-contaminated environments. This paper first presents a coupled thermo-hydro-chemical model for predicting chloride penetration into concrete that accounts for realistic weather conditions. This complete numerical model takes into account multiple factors affecting chloride ingress such as diffusion, convection, chloride binding, ionic interaction, and concrete aging. Since the complete model could be computationally expensive for long-term assessment, this study also proposes model simplifications in order to reduce the computational cost. Long-term chloride assessments of complete and reduced models are compared for three locations in France (Brest, Strasbourg and Nice) characterized by different weather and exposure conditions (tidal zone, de-icing salts and salt spray). The comparative study indicates that the reduced model is computationally efficient and accurate for long-term chloride ingress modeling in comparison to the complete one. Given that long-term assessment requires larger climate databases, this research also studies how climate models may affect chloride ingress assessment. The results indicate that the selection of climate models as well as the considered training periods introduce significant errors for mid- and long- term chloride ingress assessment.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2010년도 춘계 학술논문 발표대회 1부
• /
• pp.95-97
• /
• 2010

This study revealed unusual weather phenomena by comparing and analyzing monthly average temperature and amount of snowfall for the past 10 years, and, based on the weather phenomena, analyzed damage cases of concrete structures in winter. As a result, the temperature for the recent one year became greatly low compared with the monthly average for the past 10 years, and the snowfall increased by 4-5 times compared with the past, so that the frost damage of concrete structures also greatly occurred. Accordingly, in case of concrete construction, because there may occur various variables owing to abnormal weather conditions, it is required that thorough quality control should be performed even from the stage of construction plan, execution and maintenance.

• 한국농공학회지
• /
• 제41권6호
• /
• pp.25-32
• /
• 1999

The purpose of this study is to analyse the accuracy of weather forecasts of temperature, precipitation probability , and sky condition and to evaluate the applicability of weather forecasts for the estimation of potential evapotranspiration for irrigation scheduling. Five weather station s were selected to compare forecasted and measured climatcal data. The error between forecasted and measured temperature was calculated and discussed. The accuracy of temperature forecast using relative frequency of the error was calculated . The temperature forecasting showed considerably high accuracy. Average sunshine hours for forecasted sky conditions were calculated and showed reasonable quality. From the reliability graphs, the forecasting precipation probabililty was reliable. Potential evapotranspirations were calculated and compared using forecast and measured temperatures. The weather forecast is considered usable for irrigation scheculing.

• 한국통신학회논문지
• /
• 제23권8호
• /
• pp.1990-1997
• /
• 1998

기상레이다로 정확한 정보를 추출하기 위해서는 강력한 지표면 및 이동 클러터의 제거가 매우 중요하다. 따라서 본 논문에서는 기존의 기상레이다의 단점을 극복하기 위하여 적응배열 안테나의 적용을 제안하였다. 우선 모의 클러터 및 기상신호를 효율적으로 발생시키는 방법을 제시하였으며 이러한 데이터를 이용하여 제안된 기상레이다의 성능정도를 분석하였다. 이러한 모의실험을 통해 기상정보 추출을 위한 펄스페어 추정치의 정확도가 비약적으로 향상될 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권8호
• /
• pp.3567-3582
• /
• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• 한국정보통신학회논문지
• /
• 제25권10호
• /
• pp.1345-1352
• /
• 2021

대부분의 기상 레이다 시스템은 중장거리용으로 매우 넓은 지역의 전체적인 기상 현상을 파악하는 목적으로 사용된다. 그러나 최근에 와서는 국지적인 재난현상의 빈발 가능성이 높아짐에 따라 국지적인 기상 레이다를 활용한 기상이변 현상의 탐지가 매우 중요한 문제이다. 따라서 이러한 국지적인 기상 이변 탐지목적의 기상 레이다는 저고도 탐지 및 급변하는 기상상황의 빠른 탐지가 필요하다. 또한 상대적으로 지표면 클러터가 큰 영향을 미치게 된다. 따라서 본 논문에서에서는 풍속의 변화정도 및 거리에 따른 풍속의 변화율을 이용하여 돌풍 및 풍속 전단현상 등의 급변하는 기상 위험 등을 탐지할 수 있는 방법을 제안하고 분석하였다. 제안한 방법은 탐지과정에서의 지표면 클러터에 의한 영향을 최소화 할 수 있고 빠른 탐지를 위한 간단한 알고리즘 구현이 가능한 방식으로서 향후 기상변화 탐지에 유용하게 활용될 수 있음을 보였다.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• 제4B권3호
• /
• pp.146-152
• /
• 2004

Cost effective simulation schemes for Wind Power Generation Systems (WPGS) considering wind turbine types, generators and load capacities have been strongly investigated by researchers. As an alternative, a true weather condition based simulation method using a real-time digital simulator (RTDS) is experimented in this paper for the online real-time simulation of the WPGS. A stand-alone WPGS is, especially, simulated using the Simulation method for WPGS using Real Weather conditions (SWRW) in this work. The characteristic equation of a wind turbine is implemented in the RTDS and a RTDS model component that can be used to represent any type of wind turbine in the simulations is also established. The actual data related to weather conditions are interfaced directly to the RTDS for the purpose of online real-time simulation of the stand-alone WPGS. The outcomes of the simulation demonstrate the effectiveness of the proposed simulation scheme. The results also signify that the cost effective verification of efficiency and stability for the WPGS is possible by the proposed real-time simulation method.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송∙미디어공학회 2020년도 추계학술대회
• /
• pp.60-63
• /
• 2020

With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

• 대한전기학회논문지
• /
• 제34권10호
• /
• pp.389-398
• /
• 1985

Recently maximum power demand of our country has become to be under the great in fluence of electric cooling and air conditioning demand which are sensitive to weather conditions. This paper presents the technique and algorithm to forecast the long-term maximum power demand considering the characteristics of electric power and weather variable. By introducing a weather load model for forecasting long-term maximum power demand with the recent statistic data of power demand, annual maximum power demand is separated into two parts such as the base load component, affected little by weather, and the weather sensitive load component by means of multi-regression analysis method. And we derive the growth trend regression equations of above two components and their individual coefficients, the maximum power demand of each forecasting year can be forecasted with the sum of above two components. In this case we use the coincident dry bulb temperature as the weather variable at the occurence of one-day maximum power demand. As the growth trend regression equation we choose an exponential trend curve for the base load component, and real quadratic curve for the weather sensitive load component. The validity of the forecasting technique and algorithm proposed in this paper is proved by the case study for the present Korean power system.