• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1393-1405
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• 2020

UAV (Unmanned Aerial Vehicle) can acquire high-resolution images due to low-altitude flight, and it can be photographed at any time. Therefore, the UAV images can be updated at any time in map production. Due to these advantages, studies on the possibility of producing large-scale digital maps using UAV images are actively being conducted. Precise digital maps can be used as base data for digital twins or smart cites. For producing a precise digital map, precise sensor modeling using GCPs (Ground Control Points) must be preceded. In this study, geometric models of UAV images were established through a precision sensor modeling algorithm developed in house. Then, a digital map by stereo plotting was produced to evaluate the possibility of large-scale digital map. For this study, images and GCPs were acquired for Ganseok-dong, Incheon and Yeouido, Seoul. As a result of precision sensor modeling accuracy analysis, high accuracy was confirmed within 3 pixels of the average error of the checkpoints and 4 pixels of the RMSE was confirmed for the two study regions. As a result of the mapping accuracy analysis, it satisfied the 1:1,000 mapping accuracy announced by the NGII (National Geographic information Institute). Therefore, the precision sensor modeling technology suggested the possibility of producing a 1:1,000 large-scale digital map by UAV images.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.517-523
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• 2021

The outcome of the consultation is important to both the counselor and the researcher. Analyzing the trends of research on the results of counseling that have been carried out so far will help to comprehensively structure the results of consultations. The purpose of this research is to analyze research trends in Korea, focusing on research related to the outcomes of counseling published in 「Korean Journal of Counseling」 from 2011 to 2021, which is one of the well-known academic journals in the field of counseling in Korea. This is to explore the direction of future research by navigating the knowledge structure of research. There were 197 studies used for analysis, and the final 339 keyword were extracted during the node extraction process and used for analysis. As a result of extracting potential topics using the LDA algorithm, "Measurement and evaluation of counseling outcomes", "emotions and mediate factors affecting interpersonal relationships", and "career stress and coping strategies" are the main topics. Identifying major topics through trend analysis of counseling performance research contributed to structuring counseling performance. In-depth research on these topics needs to continue thereafter.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• Journal of Conservation Science
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• v.38 no.2
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• pp.154-165
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• 2022

The color appearance of cultural heritage are essential factors for manufacturing technique interpretation, conservation treatment usage, and condition monitoring. Therefore, this study systematically established color reproduction procedures based on the digital color management system for the portrait of Gwon Eungsu. Moreover, various application strategies for recording and conserving the cultural heritage were proposed. Overall color reproduction processes were conducted in the following order: photography condition setting, standard color measurements, digital photography, color correction, and color space creation. Therefore, compared with the color appearance, the digital image applied to a camera maker profile indicated an average color difference of 𝜟10.1. However, the digital reproduction result based on the color management system exhibits an average color difference of 𝜟1.1, which is close to the color appearance. This means that although digital photography conditions are optimized, recording the color appearance is difficult when relying on the correction algorithm developed by the camera maker. Therefore, the digital color reproduction of cultural heritage is required through color correction and color space creation based on the raw digital image, which is a crucial process for documenting the color appearance. Additionally, the recording of color appearance through digital color reproduction is important for condition evaluation, conservation treatment, and restoration of cultural heritage. Furthermore, standard data of imaging analysis are available for discoloration monitoring.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.387-394
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• 2022

120 kVp FBP reconstruction image standard by using raw data after scanning by changing tube voltage among the NECK CT protocols that are broad applied in clinical practice using a human phantom including thyroid gland The usefulness of the DLIR reconstruction technique was investigated. As a result, CTDIvol decreased when the DLIR reconstruction technique was applied, and in particular, the image quality obtained under the same standard scanning conditions at a lower dose for ASIR-V and DLIR reconstruction was reached than when FBP was applied at the same kVp In addition, as a result of SNR and CNR analysis, the DLIR reconstructed image was analyzed with high SNR and CNR values, and SSIM analysis, the SSIM index of the 100 kVp, DLIR reconstructed image was measured to be close to 1, and it was analyzed that the similarity of the reconstructed image to the original image was high (p>0.05). If the results of this study are used to supplement clinical image evaluation and further develop an algorithm applicable to various anatomical structures, it is thought that it will be useful for clinical application as it is possible to maintain the image quality while lowering the examination dose.