• Smart Media Journal
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• v.6 no.3
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• pp.57-63
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• 2017

The most important, essential prerequisite for the improvement of vehicle license plate recognition is the acquisition of high-quality vehicle images. Because typical images acquired from roads are affected by different environmental factors including the time of day, sunlight, and the weather, the brightness and the shape of the license plates in the images are inconsistent. To this end, many image corrections are performed, resulting in slower recognition and lower recognition rate. Therefore, in this study, we used the images acquired from roads to test the proposed method for fast capturing of vivid, high-quality vehicle images by measuring the brightness around license plates during real-time image capturing to control in real time the factors, such as shutter speed, brightness, and gain of the camera, that affect the brightness and the quality of the images.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.21-29
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• 2011

U-health care services have been attracted to effectively solve some problems in promoting health and preparing aging society. Although the recent U-health care services have been developed to treat diseases, it requires environment information related to health for preventing fundamental diseases and for promoting health. In this study, a U-health environment service that reflects context recognition information is proposed. The proposed service draws environment information using local weather and healthcare information in users' residential areas. In the context recognition based U-health environment services, various services are provided to users not only health, living weather based menu, and exercise services but user location based warning messages for dangerous regions and remote emergency services. That is, based on such context recognition, some events that are to be occurred to users are detected and then it will provide proper services. Thus, it improves the satisfaction of U-health services and its service qualities.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.627-631
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• 2024

We attempted to check the observation failure and error status of rain detectors for weather observation introduced and used in the 1980s and improve the collection and calculation algorithm of 1-minute rain detector data to enhance observation efficiency. Error status analysis revealed that among weather observation devices, rain detectors undergo manual quality control (MQC) the most frequently. It was determined that the precipitation recognition rate could be improved by refining the precipitation calculation algorithm. We examined and selected domestic and international rainfall detection algorithms and compared their precipitation recognition rates using random data. The algorithm that determined 'rainfall' when precipitation was measured at least once every 10 seconds showed the highest precipitation recognition rate. Although the algorithm tends to oversimulate precipitation, this can be improved through quality control of raw data. Based on the results of this study, it is believed that it can contribute to reducing the error rate and improving the accuracy of rain detectors.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.199-204
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• 2019

This paper introduces smart mirror that provides the contents needed for an individual's daily life. When a command that is designated as voice recognition is entered, Smart Mirror is produced that outputs desired contents from a display. The contents of the current smart mirror include time, weather, subway information, schedule and photography. Smart mirror sold for commercial private households is difficult to distribute due to high prices, but the smart mirror production presented in this paper can lower the manufacturing cost and can be more easily used by voice recognition.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.147-159
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• 2002

This paper proposes a method to recognize the noisiness of road images connected with the extraction of lane-related information in order to prevent the usage of erroneous information. The proposed method uses a fuzzy neural network(FNN) with the back-Propagation loaming algorithm. The U decides road images good or bad with respect to visibility of lane marks on road images. Most input parameters to the FNN are extracted from an edge distribution function(EDF), a function of edge histogram constructed by edge phase and norm. The shape of the EDF is deeply correlated to the visibility of lane marks of road image. Experimental results obtained by simulations with real images taken by various lighting and weather conditions show that the proposed method was quite successful, providing decision-making of noisiness with about 99%.

• Journal of Science Education
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• v.34 no.1
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• pp.12-22
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• 2010

The purpose of this study was to investigate the effect of concept sketches on the understanding and scientific attitude in high school student's learning of weather change. Among the various fields of meteorology, especially in weather change, we often deal with the change of the spatiotemporal change in an abstract way. So making use of 'Concept Sketches'- simplified sketches which represent the main features, principles, processes and interrelationships of the learning contents using some concise explanations, signs and terms - could help the students learn the phenomena of weather change efficiently. This study's aim was to check up the effect and analyze the results of the lesson including the concept sketches. As a result of this study, concept sketches group showed significant improvement compared to the other groups in understanding of weather change and in scientific attitude, too. In students' recognition research of concept sketches showed that students found the class more interesting with improved concentration and had a chance to review through concept sketching, which is helpful for their learning. Considering the above research results, the study which applies concept sketching required the students to actively process their knowledge, and had a positive effect on the understanding of weather changes. Most of all, drawing the pictures which is a familiar activity helped the students to take part in the class eagerly.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.339-345
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• 2002

Extracting of car licens plate os important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images are distorted and the car license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.596-599
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• 2001

Extracting of car licens plate is important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images we distorted and the tar license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.