• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.253-273
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• 2022

Automated vehicles rely on information collected through sensors to drive. Therefore, the uncertainty of the information collected from a sensor is an important to address. To this end, research is conducted in the field of road and traffic to solve the uncertainty of these sensors through infrastructure or facilities. Therefore, this study developed a traffic cone that can maintaing the gaze guidance function in the construction site by securing sufficient LiDAR detection performance even in rainy conditions and verified its improvement effect through demonstration. Two types of cones were manufactured, a cross-type and a flat-type, to increase the reflective performance compared to an existing cone. The demonstration confirms that the flat-type traffic cone has better detection performance than an existing cone, even in 50 mm/h rainfall, which affects a driver's field of vision. In addition, it was confirmed that the detection level on a clear day was maintained at the 20 mm/h rain for both cones. In the future, improvement measures should be developed so that the traffic cones, that can improve the safety of automated driving, can be applied.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.883-893
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• 2019

In this study, crop coefficients were calculated in two different methods and the results were evaluated. In the first method, appropriateness of GLDAS-based evapotranspiration was evaluated by comparing it with observed data of Cheongmi-cheon (CMC) Flux tower. Then, crop coefficient was calculated by dividing actual evapotranspiration with potential evapotranspiration that derived from GLDAS. In the second method, crop coefficient was determined by using MLR (Multiple Linear Regression) analysis with vegetation index (NDVI, EVI, LAI and SAVI) derived from MODIS and in-situ soil moisture data observed in CMC, In comparison of two crop coefficients over the entire period, for each crop coefficient GLDAS K_c and SM&VI K_c, shows the mean value of 0.412 and 0.378, the bias of 0.031 and -0.004, the RMSE of 0.092 and 0.069, and the Index of Agree (IOA) of 0.944 and 0.958. Overall, both methods showed similar patterns with observed evapotranspiration, but the SM&VI-based method showed better results. One step further, the statistical evaluation of GLDAS K_c and SM&VI K_c in specific period was performed according to the growth phase of the crop. The result shows that GLDAS K_c was better in the early and mid-phase of the crop growth, and SM&VI K_c was better in the latter phase. This result seems to be because of reduced accuracy of MODIS sensors due to yellow dust in spring and rain clouds in summer. If the observational accuracy of the MODIS sensor is improved in subsequent study, the accuracy of the SM&VI-based method will also be improved and this method will be applicable in determining the crop coefficient of unmeasured basin or predicting the crop coefficient of a certain area.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.344-352
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• 2019

Drought stress caused by global climate change is a serious problem for rice cultivation. Increasingly frequent abnormal weather occurrences could include severe drought, which could cause water stress to rice during the seedling stage. This experiment was conducted to clarify the effects of drought during the seedling period on yield and quality of rice. Drought conditions were created in a rain shelter house facility. The drought treatment was conducted at 3, 10, and 20 days after transplanting. Soil water content was measured by a soil moisture sensor during the whole growth stage. In this study, we have chosen 3 rice cultivars which are widely cultivated in Korea: 'Haedamssal' (Early maturing), 'Samkwang' (Medium maturing), and 'Saenuri' (Mid-late maturing). The decrease in yield due to drought treatment was most severe 3 days after transplanting because of the decrease in the number of effective tillers. The decrease in grain quality due to drought treatment was also most severe 3 days after transplanting because of the increased protein content and hardness of the grains. The cultivar 'Haedamssal' was the most severely damaged by water stress, resulting in about a 30% yield loss. Drought conditions diminished the early vigorous growth period and days to heading in early-maturing cultivars. The results show that drought stress affects yield components immediately after transplanting, which is a decisive factor in reducing yield and grain quality. This study can be used as basic data to calculate damage compensation for drought damage on actual rice farms.