• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1345-1352
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• 2021

Most of weather radar systems are used to monitor the whole weather situation for the very wide and medium-to-long range area. However, as the likelihood of occurrence of the local weather hazards is increased in recent days, it is very important to detect these wether phenomena with a local weather radar. For this purpose, it is necessary to detect the fast varying low altitude weather conditions and the effect of the ground surface clutter is more evident. Therefore, in this paper, the newly suggested method is explained and analyzed for detection of weather hazards such as the gust and wind shear using the fluctuation of wind velocities and the gradient of wind velocities among range cells. It is shown that the suggested method can be used efficiently in the future for faster detection of weather change through the simple algorithm implementation and also the effect of the ground clutter can be minimized in the detection procedure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.205-210
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• 2002

In a Doppler weather radar, high resolution windspeed profile measurements are needed to provide the reliable detection of hazardous weather conditions. For this purpose, the pulse pair method is generally considered to be the most efficient estimator, However, this estimator has some bias errors due to asymmetric spectra and may yield meaningless results in the case of a multimodal return spectrum. Although the poly-pulse pair method can reduce the bias errors of skewed weather spectra, the modes of spectrum may provide more reliable information than the statistical mean for the case of a multimodal or seriously skewed spectrum. Therefore, the idea of relatively simple mode estimator for a weather radar is developed in this paper, Performance simulations show promising results in the detection of hazardous weather conditions.

• Atmosphere
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• v.30 no.4
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• pp.347-360
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• 2020

The automatic algorithm optimized for the Korean Peninsula was developed to detect and track the center of typhoon based on a geometrical method using high-resolution retrieved WISSDOM (WInd Syntheses System using DOppler Measurements) wind and reflectivity data. This algorithm analyzes the center of typhoon by detecting the geometric circular structure of the typhoon's eye in radar reflectivity and vorticity 2D field data. For optimizing the algorithm, the main factors of the algorithm were selected and the optimal thresholds were determined through sensitivity experiments for each factor. The center of typhoon was detected for 5 typhoon cases that approached or landed on Korean Peninsula. The performance was verified by comparing and analyzing from the best track of Korea Meteorological Administration (KMA). The detection rate for vorticity use was 15% higher on average than that for reflectivity use. The detection rate for vorticity use was up to 90% for DIANMU case in 2010. The difference between the detected locations and best tracks of KMA was 0.2° on average when using reflectivity and vorticity. After the optimization, the detection rate was improved overall, especially the detection rate more increased when using reflectivity than using vorticity. And the difference of location was reduced to 0.18° on average, increasing the accuracy.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.445-452
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• 2007

We proposed a detection scheme of weather information in CCTV video images in this paper. The scheme obtains the RGB distribution of shiny day and divide a target image into cloud, rain, snow and for RGB distributions. shiny day RGB distribution. Our scheme designed systematically to detection and separation special characteristics of images from complex weather information. Our algorithm has less overhead than the previous methods to use weather database DB at the view of time and space. And our algorithm can be use in real world system with low cost of implementation. Also, our algorithm use informations of temperature, humidity, date, and time to detect the information of weather with high quality.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.209-217
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• 2007

This paper proposed to a detection scheme of weather information that is a part of CCTV Images Weather Detection System using CCTV images and Temperature, Humidity. The previous Partial Weather Detection System uses how to acquire weather information using images on the Road. In the system the contrast and RGB Values using clear images are gained. This information is distributed a input images to cloud, rain, snow and fog images. That is, this information is compared the snow and the fog images for acquisition more correctness information us ing difference images and binary images. Currently, We use to environment sense system, but we suggest a new Weather Detection Algorithm to detect weather information using CCTV images. Our algorithm is designed simply and systematically to detect and separate special characteristics of images from CCTV images. and using temperature & humidity in formation. This algorithm, there is more complex to implement than how to use DB with high overhead of time and space in the previous system. But our algorithm can be implement with low cost' and can be use the system in real work right away. Also, our algorithm can detect the exact information of weather with adding in formation including temperature, humidity, date, and time. At last, this paper s how the usefulness of our algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.79-87
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• 2019

Radar detection range is decreased with an increase in the noise levels and detection thresholds in adaptive CFAR of a radar signal processor to the weather clutter reflection signal in the rain. When a high-velocity plot is generated in weather clutter, what are detected are not targets but false plots. Detection opportunity is reduced by radar time resource consumption from additional confirmations regarding the false plots. In this paper, the received signals are saved using a radar-received signal storage device. Based on the analysis of the received signals from weather clutter, the influence of the rainfall reflection has been mitigated by front-end attenuation of the signal processor. The improvement in the detection performance is verified through received signal and simulation results.

• 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 Navigation and Port Research
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• v.40 no.5
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• pp.271-278
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• 2016

Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.473-482
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• 2015

Doppler weather radar is an important tool for meteorological research. Through several decades of development, Doppler weather radar has enormous progress in understanding, detection and warning of meso and micro scale weather system. It makes a significant contribution to weather forecast and weather disaster warning. But the large amount of data process limits the application of Doppler weather radar. This paper proposed for fast weather radar data processing based on CUDA. CDUA is a powerful platform for highly parallel programming developed by NVIDIA. Through running plenty of threads, radar data can be calculated at same time. In experiment, CUDA parallel program can significantly improve weather data processing time.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.149-154
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• 2014

Detection performance of fire detection in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes. In this paper, a smoke detection in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation, and also, statistical characteristics of smoke is applied to detect the smoke for separated candidate region. Through the experiments with input videos obtained from a various weather conditions, the proposed algorithms were useful to detect smoke in the outdoor.