• 대기
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• 제20권2호
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• pp.153-160
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• 2010

In this paper, an attempt has been made to build and test self-configuring weather sensor networks and internet based observation system to gather atmospheric data. The aim is to provide integrated or real-time weather information in standard form using network data access protocol. This system was successfully developed to record weather information both digital as well as visual using sensor network and web-enabled surveillance cameras. These data were transformed by network based data access protocol to access and utilize for public domain. The competed system has been successfully utilized to monitor different types of weather. The results show that this is one of the most useful weather monitoring system.

• 한국빅데이터학회지
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• 제2권2호
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• pp.129-140
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• 2017

낮은 시정, 강우, 강풍, 고온 등 기상 상태는 운전 능력, 차량 성능(예: 마찰, 안정성, 조작력), 노면 마찰력, 도로 인프라, 추돌 위험, 교통 흐름 및 도로 관리자 생산성 등에 영향을 미친다. 최근에는 CCTV, 도로 센서, 차량 센서 등 다양한 도로 기상 빅데이터 소스들이 개발되면서 이러한 기상 관련 문제들 해결에 적용되고 있다. 본 연구는 이러한 도로 기상 빅데이터 소스들의 유형과 특징을 정의하고 국내외 실증 사례들을 통해 도로 기상 빅데이터 유형별로 관련 문제들 해결에 활용하는 전략에 대해 제시하고자 한다.

• 센서학회지
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• 제8권3호
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• pp.253-258
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• 1999

본 논문은 일기에 관한 대표적인 정보인 온도, 습도, 그리고 기압의 변화를 감지하여 일기를 예측하는 일기예보시스템을 개발함으로써, 가정에서 쉽게 일기에 대한 정보를 얻을 수 있도록 하는데 목적이 있다. 이를 위해 기상청으로부터 기상정보와 일기와의 관계를 분석하여, 차후 측정된 기상정보로부터 일기예보를 하는데 필요한 판단기준을 마련하였다. 또한, 자체적인 데이터 수집을 위해 반도체 압저항성을 이용한 압력센서와 온습도센서를 제작하고, 마이크로프로세서를 이용하여 시스템을 제작하였다.

• 산업경영시스템학회지
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• 제46권3호
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

• 로봇학회논문지
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• 제17권3호
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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.

• International journal of advanced smart convergence
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• 제12권4호
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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.

• 대기
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• 제24권3호
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• 대기
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• 제24권3호
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• pp.445-456
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• 2014

A real-time quality control system for meteorological data (air temperature, air pressure, relative humidity, wind speed, wind direction, and precipitation) measured by an integrated meteorological sensor has been developed based on comparison of quality control procedures for meteorological data that were developed by the World Meteorological Organization and the Korea Meteorological Administration (KMA), using time series and statistical analysis of a 12-year meteorological data set observed from 2000 to 2011 at the Incheon site in Korea. The quality control system includes missing value, physical limit, step, internal consistency, persistence, and climate range tests. Flags indicating good, doubtful, erroneous, not checked, or missing values were added to the raw data after the quality control procedure. The climate range test was applied to the monthly data for air temperature and pressure, and its threshold values were modified from ${\pm}2{\sigma}$ and ${\pm}3{\sigma}$ to ${\pm}3{\sigma}$ and ${\pm}6{\sigma}$, respectively, in order to consider extreme phenomena such as heat waves and typhoons. In addition, the threshold values of the step test for air temperature, air pressure, relative humidity, and wind speed were modified to $0.7^{\circ}C$, 0.4 hPa, 5.9%, and $4.6m\;s^{-1}$, respectively, through standard deviation analysis of step difference according to their averaging period. The modified quality control system was applied to the meteorological data observed by the Weather Information Service Engine in March 2014 and exhibited improved performance compared to the KMA procedures.

• 한국항행학회논문지
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• 제22권6호
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• pp.551-556
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• 2018

과거에 비해 최근에는 항공기 비행 및 기상정보측정을 위하여 드론을 많이 활용하고 있다. 관련 응용분야로는 저고도 대기자료 측정, 대기미세먼지측정, 대기 오염측정 등이 있다. 그러나 대기자료 측정센서의 장착위치는 드론비행체의 구조적 특징 때문에 프로펠러 유동의 영향, 전자파 영향, 드론의 무게중심의 변화를 고려하여 장착하여야 한다. 이중에서 프로펠러에 의한 기체 상부의 공기유동은 센서의 풍속 및 풍향에 영향을 미치므로 최적 위치를 분석하여 선정해야 한다. 본 연구는 대기자료 측정센서의 적정 높이 선정에 대한 연구로, 유동 해석을 통하여 유동특성을 파악하고 실험 데이터를 비교 분석하여 적정 센서 장착 높이를 제시한다.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.233-240
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• 2011

산악지역이나 어떤 한정된 지역에서 급작하게 나타나는 기상변화를 모니터링하기 위해 유비쿼터스 센서 네트워크 기술을 이용한 필드서버를 개발하였다. 국지적 기상변화에 의한 인명 및 재난 피해를 최소화하기 위해 국지역내에 배치된 필드서버간의 데이터 전달이 매우 중요한 기술이다. 일정한 국지적 지역에 배치 된 필드서버들로부터 일정한 시간 간격으로 수집되는 온도, 습도, 조도, 기압, 이슬점, 수위의 기상데이터를 전송하기 위한 플러딩 라우팅 프로토콜 전송방식을 구현하였으며, 이를 제작 된 외부센서모듈과 Telosb 계열의 센서노드에 nesC 언어를 사용한 초소형 무선센서네트워크 플랫폼인 TinyOS로 프로그램 하여서 서버 컴퓨터에서 모니터링 할 수 있도록 하였다.