• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1071-1085
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• 2015

The researchers attempted to develop a way to extract a near real-time online nation image using social media. Referring to previous studies about nation images and the categories defined in Wikipedia, an ontology considering the characteristics of nation image was constructed. Separately, data sets from various social media were compared and the click view of Wikipedia English-edition was selected. The ontology was applied to the recent six years of the data extracted of the three big exporting countries of the east Asia, China, Japan, and Korea. To compare the nation images, correspondence analysis was employed to show images in the area of politics, society, culture, and economy. The nation images extracted are indeed the reasonable representation of them. The researchers verified them to a few known government policies and confirmed that it could be used to help government officers to make foreign policies to boost nation's export and to employ as a key performance index for them.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1153-1164
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• 2012

In this paper, we propose an efficient car management system based on object-oriented modeling using car number recognition and smart phone. The proposed system perceives car number of repair vehicle after recognizing the licence plate using an IP camera in real time. And then, existing repair history information of the recognized car is be displayed in DID. In addition, maintenance process is shooting video while auto maintenance mechanic repairs car through IP-camera. That will be provide customer car identification and repairs history management function by sending key frames extracted from recorded video automatically. We provide user graphic interface based on web and mobile for your convenience. The module design of the proposed system apply software design modeling based on granular object-oriented considering reuse and extensibility after implementation. Car repairs center and maintenance companies can improve business efficiency, as well as the requested vehicle repair can increase customer confidence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.274-276
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• 2016

Recently due to increase of tobacco users, many problems have been issued. Not only smoking in public places, smoking indoors as well causes harm to non-smoking people. Smoking booths that are installed but the quality is considerably less and purification devices are not correctly installed, which leads to harm the people around the smoking booth. In this paper, we introduce the "Augmented Reality Billboard" in order for smokers to effectively recognize the non-smoking warning image and healthy warning messages, Kinect Camera Sensor and Augmented Reality (AR) functions are used to recognize the motion of a person to coordinate the corresponding coordinate values.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.3
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• pp.359-364
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• 2013

In order to use a linear vibration motor for the actuator of a haptic interface, the motor must provide a higher reaction rate and longer service life than typical rotational motors with an eccentric mass. In this paper, we propose a linear vibration motor that is equipped with a voice-coil actuator and permanent-magnet springs. To concentrate the magnetic flux in the actuator, a Halbach-style magnetization pattern is used. Permanent-magnet springs replace mechanical springs to help increase the service life. We use the method of equivalent current sheets and the method of images to analyze and model the proposed vibration motor. These methods are validated using finite element analyses and experiments. A prototype motor is designed and fabricated. Tests with the prototype show the feasibility of the proposed linear vibration motor.

• Composites Research
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• v.32 no.5
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• pp.258-264
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• 2019

In this paper, a progressive failure analysis method was developed using the Hashin failure criterion and crack band model. Using the failure criterion, the failure initiation was evaluated. If the failure initiation is occurred, the damage variables at each failure modes (fiber tension & compression, matrix tension & compression) was calculated according to linear softening degradation behavior and the variables are used to derive the damaged stiffness matrix. The damaged stiffness matrix is reflected to damaged material and the progressive failure analysis is continued until the damage variables to be 1 that complete failure of material. A series of processes were performed using FE commercial code ABAQUS with user defined material subroutine (UMAT). To evaluate the proposed progressive failure model, the experimental results of open hole composite laminate tests was compared with numerical result. Using digital image correlation system, the strain behavior also was compared. The proposed numerical results were coincided well with the experimental results.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.