• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.459-468
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• 2019

Purpose: The cavity exploration of the lower part of the road is carried out to prevent ground-sinking. However, the detected communities cannot be identified by the cavity location and history information, such as repackaging the pavement. Therefore, the field applicability of RFID systems was evaluated in this study to enable anyone to accurately identify information. Method: During temporary recovery, tag recognition distance and recognition rate were measured according to underground burial materials and telecommunication tubes using RFID systems with electronic tag chips attached to the bottom of the rubber cap. Result: The perceived distance and perceived rate of depth for each position of the electron tag did not significantly affect the depth up to 15cm, but it did have some effect if the depth was 20cm. In addition, water effects from nearby underground facilities and rainfall are relatively small, and the effects of wind will need to be considered during the weather conditions of the road. Conclusion: The RFID tags for field application of the pavement management system store various information such as location and size of cavity, identification date, cause of occurrence, and surrounding underground facilities to maximize cavity management effect with a system that can be computerized and mobile utilization.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.29-38
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• 2016

As publicizing of electromagnetic devices such as smart_phone and drone etc. which are relate with geomagnetic direction, and recognition about the importance to space weather effect and their hazards rises up recently, it is required heavily that the study on the effective measurement of geomagnetic declination and geomagnetic field effects of space weather. The purpose of this study is that the investigation of the feasibility of the absolute geomagnetic measurement in a place, where man-made geomagnetic contamination is low or negligible, with replacing the azimuth marks used for the absolute geomagnetic declination measurement with unified control points(UCP) which established at suburb. Further to this, have first derived the correlation of daily variations and disturbance level between the published indices($K_P$ and $K_K$) and geomagnetic element calculated from geomagnetic data of Cheongyang observatory located at the middle stage in Korea and is a member of INTERMAGNET. In addition, have carried out that the absolute measurement for the geomagnetic declination at three places near unified control point and one place with wide open field in Korea. The world magnetic models(WMMs) are selected as the criteria for comparison on the feasibility of geomagnetic declination investigation near unified control points. We compared deviations of declination from absolute measurement with that obtained from WMMs, also those from WMMs inter-comparison. The result through examination and analysis show that the feasibility of the absolute geomagnetic declination measurement with replacing the azimuth marks with UCP which established at suburb is possible.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.457-473
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• 2010

Patterns and regional distribution of disaster-related place names have been analyzed to confirm the recognition and probability of disaster and to explore the possibility of disaster prevention measures. 106 terms and 37,901 place names related to disaster and prevention measures have been collected from the Korean gazetteers "Hanguk Jimyeong Chongnam". Based on this, some conclusions have been drawn: firstly, place names related to the geomorphic processes and prevention measures are more common than any other disasters; secondly, place names related to heavy rain, flooding and drowning are most common. Analysis of the regional distribution pattern shows that disaster-related place names are most common in Jeolla and Gyeongsang Provinces and general place names reflecting environmental concern such as water, sand, plain, rain and dam are distributed evenly throughout the whole country; howe, r, place names such as dumbeong, gureong, yeoul, tan(灘), bangjuk, je(堤), and ji(池) are restricted to the specific region, which shows that place names reflects the locational and toprn sucic ainuations. Case st, anindicates that prevention measures should be focused on tributaries and srill villeys conaid ring that disasters originated from the combination of weather and landform conditions are most common throughout the whole country.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• Journal of IKEEE
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• v.24 no.1
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• pp.232-237
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• 2020

In this paper, we propose an efficient sensor fusion method for autonomous vehicle recognition and distance measurement. Typical sensors used in autonomous vehicles are radar, lidar and camera. Among these, the lidar sensor is used to create a map around the vehicle. This has the disadvantage, however, of poor performance in weather conditions and the high cost of the sensor. In this paper, to compensate for these shortcomings, the distance is measured with a radar sensor that is relatively inexpensive and free of snow, rain and fog. The camera sensor with excellent object recognition rate is fused to measure object distance. The converged video is transmitted to a smartphone in real time through an IP server and can be used for an autonomous driving assistance system that determines the current vehicle situation from inside and outside.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.223-230
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• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.

• Journal of Platform Technology
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• v.12 no.3
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• pp.62-70
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• 2024

Recent surveillance systems employ multiple sensors, such as cameras and radars, to enhance the accuracy of intrusion detection. However, object recognition through camera (RGB, Thermal) sensors may not always be accurate during nighttime, in adverse weather conditions, or when the intruder is camouflaged. In such situations, it is possible to detect intruders by utilizing the trajectories of objects extracted from camera or radar sensors. This paper proposes a method to detect intruders using only trajectory information in environments where object recognition is challenging. The proposed method involves training an LSTM-Attention based trajectory classification model using normal and abnormal (intrusion, loitering) trajectory data of animals and humans. This model is then used to identify abnormal human trajectories and perform intrusion detection. Finally, the validity of the proposed method is demonstrated through experiments using real data.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1020-1026
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• 2005

In the era of ubiquitous computing, human-friendly man-machine interface is getting more attention due to its possibility to offer convenient services. For this, in this paper, we introduce a 'Half-Mirror Interface System (HMIS)' as a novel type of human-friendly man-machine interfaces. Basically, HMIS consists of half-mirror, USB-Webcam, microphone, 2ch-speaker, and high-speed processing unit. In our HMIS, two principal operation modes are selected by the existence of the user in front of it. The first one, 'mirror-mode', is activated when the user's face is detected via USB-Webcam. In this mode, HMIS provides three basic functions such as 1) make-up assistance by magnifying an interested facial component and TTS (Text-To-Speech) guide for appropriate make-up, 2) Daily weather information provider via WWW service, 3) Health monitoring/diagnosis service using Chinese medicine knowledge. The second one, 'display-mode' is designed to show decorative pictures, family photos, art paintings and so on. This mode is activated when the user's face is not detected for a time being. In display-mode, we also added a 'healing-window' function and 'healing-music player' function for user's psychological comfort and/or relaxation. All these functions are accessible by commercially available voice synthesis/recognition package.