• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.235-237
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• 2005

This paper presents a method of incorporating weather effects in KEPCO systems reliability evaluation. The effects of adverse weather on composite power system FOR(Forced Outage Rate) increase considerably as the proportion of outages in adverse weather increases. It is therefore important to establish suitable and practical procedures for collecting adverse weather data which can be utilized in realistic KEPCO systems reliability assessment. The modeling procedure proposed in this paper also provides a realistic FOR in KEPCO systems using real historical data.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.60-62
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• 2004

Transmission line outage is influenced by several weather factors: wind, rain snow, temperature, cloud and humidity. And most power system reliability studies assume a failure rate. It can be calculated by transmission line outage data and weather data. Also weather is divided into normal weather and adverse weather by failure rate analysis. The effect of failure rate is discussed with both normal weather and adverse weather. It can be used in effective information about system operation and planing.

• Journal of Advanced Research in Ocean Engineering
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• v.4 no.2
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• pp.53-65
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• 2018

The load condition significantly influences ship maneuverability in calm water. In this research, the effect of the load condition on turning performance of a very large crude oil carrier (VLCC) sailing in adverse weather conditions is investigated by an MMG-based maneuvering simulation method. The relative drift direction of the ship in turning to the wave direction is $20^{\circ}-30^{\circ}$ in ballast load condition (NB) and full load condition (DF) with a rudder angle $35^{\circ}$ and almost constant for any wind (wave) directions. The drifting displacement in turning under NB becomes larger than that under DF at the same environmental condition. Advance $A_d$ and tactical diameter $D_t$ become significantly small with an increasing Beaufort scale in head wind and waves when approaching, although $A_d$ and $D_t$ are almost constant in following wind and waves. In beam wind and waves, the tendency depends on the plus and minus of the rudder angle.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.3
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• pp.237-247
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• 2016

Global climate change is becoming one of the greatest challenges facing humanity. This article proposes a psychological perspective of climate change adaptation. Climate change-related severe adverse weather events may trigger mental health problems, including increased post-traumatic stress disorder (PTSD), depression, anxiety, violence, and even suicide. Forced migration could be considered a coping method for dealing with weather events, but it may also pose a psychological threat. People respond to severe weather events in different ways based on their individual characteristics. Psychological risks from adverse weather events are mediated and moderated by these factors, which are influenced by personal cognition, affect, and motivation. Examinations from a psychological perspective, which have been neglected in the science of climate change thus far, may provide keys to successful adaptation and the prevention of serious psychological problems resulting from the experience of severe weather events. A new prevention strategy has been suggested for coping with climate threats through encouraging attitude change, establishing proactive support systems for vulnerable groups, establishing a PTSD network, and implementing a stress inoculation program.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.138-149
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• 2016

The study reviewed the effects of the variable speed limit under adverse weather conditions by using a driving simulator. The study assumed that the display of the reduced speed limit without any change of the weather condition and the display of the same speed limit under the change of the weather conditions violate the expectancy of drivers, so it brings the negative effects on the safety. The study regards drivers conformance as the index of the degree of the compliance of driver expectancy, and utilizes the cumulative probability density within the certain range of the speed including displayed speed limit as the quantitative measure of effectiveness. The study reviewed this assumptions by using a driving simulator. As the results, the cases assumed to violate the expectancy of drivers showed the negative effects on the driving behaviour of driver relatively.

• Korean Journal of Organic Agriculture
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• v.28 no.4
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• pp.459-475
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• 2020

This study analyzes weather derivatives as an alternative risk management tool to stabilize farm revenue to complement the existing crop insurance program which suffers from asymmetric information problems such as adverse selection, moral hazard, and verifiability. We estimated apple yield functions to observe the relationship between yields and weather indices such as temperature and precipitation. Based on the estimated yield functions we designed weather futures and options products underlying temperature and precipitation, and calculated the prices of futures and options by two different approaches, historical distribution and Monte Carlo simulation. We found that weather futures and options stabilize farm revenue based on the estimated four risk indicators: Coefficient of Variation, Value at Risk, Certainty Equivalence, and Risk Premium. As a result, weather derivatives could be considered as a potential farm risk management tool through studying more in legal and institutional strategies and developing various derivatives products.

• 한국도로학회:학술대회논문집
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• 2004.10a
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• pp.429-434
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• 2004

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.31-40
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• 1997

Potential impacts for unfavourable weather conditions and the assessment of the magnitudes of their adverse effects on crop yields were studied. EPIC model was investigated for its capability on crop yield predictions for rice and soybean. Weather generationmodel was used to generate long-term climatic data. The model was verified with ohserved climate data of Suwon city. Fifty years weather data including abnormal conditions were generated and used for crop yield simulation by EPIC model. Crop yield probability function was derived from simulated crop yield data, which followed normal distribution. Probable crop yield reductions due to abnormal weather conditions were also analyzed.