• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• The Journal of Korean Philosophical History
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• no.27
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• pp.417-446
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• 2009

This study was to review the features of the preventive dimension in connection with adolescent deviation on the basis of the morality and ethics held by Confucian doctrine. To find solutions to the problems of adolescent deviation is never easy. As adolescent deviation always does occur, it is important to consider the methods that can minimize and prevent it. The traditional society of Korea laid weight on the education and training in the aspect of preventive measure against such adolescent deviation by emphasizing moral edification and realization of spiritual understanding for it. In this paper, the researcher tried to understand the problem situations by examining the image of such deviation and its type as well as the method on response thereto targeting the young generation of Korea. In addition, the researcher analyzed how the adolescent was recognized in the traditional society that was established on the Confucian values, and moral standards that applied to them, and the process of education as well. Through the moral concepts of Confucianism that were revealed in the Doctrine of the Mean (中庸, pronounced 'Jungyong' in Korean) in particular, the researcher sought the possibility of education on morality and ethics that will be able to prevent adolescent deviation. This study suggests that the morality and ethics held by Confucian doctrine can prevent adolescent deviation and open a new horizon of ethics education.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.50-53
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• 2022

Recently, there have been cases in which counterfeit foreign ships have entered and left domestic ports several times. Vessels have a ship-specific serial number given by the International Maritime Organization (IMO) to identify the vessel, and IMO marking is mandatory on all ships built since 2004. In the case of airports and ports, which are representative logistics platforms, a security system is essential, but it is difficult to establish a security system at a port and there are many blind spots, which can cause security problems due to insufficient security systems. In this paper, a port security system is designed using deep learning object recognition and OpenCV. The security system process extracts the IMO number of the ship after recognizing the object when entering the ship, determines whether it is the same ship through feature point matching for ships with entry records, and stores the ship image and IMO number in the entry/exit DB for the first arrival vessel. Through the system of this paper, port security can be strengthened by improving the efficiency and system of port logistics by increasing the efficiency of port management personnel and reducing incidental costs caused by unauthorized entry.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Journal of the Korean Society of Food Culture
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• v.25 no.3
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• pp.270-284
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• 2010

The objective of this study was to familiarize people with the native local food of Chungnam Province by developing its regional food as a culture resource. Native Local Food Search and luxury itemization based on regionally-characterized stories enhances self-perception of the national culture, promotes appropriate local images to the public, and contributes to the local economy by increasing regional tourism. Therefore, this study researched local stories of cultural significance, that is, those connected to the history and originality of Chungnam Province, and developed contents related to Chungnam native local food. Features of the native local food were introduced by a story telling method in order to appeal to the five senses. The story was composed for easy understanding of the value of food, and the brand image of Chungnam was developed based on representative historical stories of the region. In this study, the following were developed as representative images of Chungnam: 'Royal meal table of King Moo-ryung' in Kongju was presented by recomposing the story of King Moo-ryung, a famouns king of the Baekje era; 'Mong-yoo-do-won's rice meal table' was introduced via Mongyoo-do-won's painting by Kyeon An, a famous painter of the Chosun era who was born in Seosan. The 'Royal meal table of King Moo-ryung' was set with food made from local farm products, demonstrating the flavor and elegance of the Baekje era. 'Mong-yoo-do-won's rice meal table' resembled Kyeon An's Mong-yoo-do-won-do, which has a background featuring early spring. The rice table was designed to remind people of a flamboyant painting like a scattered peach blossom leaf in the early spring. To verify the health effectiveness of each rice table, the function of each ingredient was investigated through 'Sik-ryo-chan-yo:a dietary treatment', which was published by Soon-Ui Cheon during the Chosan era. According to the results, most of ingredients are certainly beneficial to health, as was recognized in the early Choson era.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.151-156
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• 2019

In recent years, the Korean realistic theme of the film momentum gradually rising. Realistic films do not stick to the business and market, and do not simply cater to the audience's needs for watching movies. They reflect social violence and cruel reality, allowing the audience to observe the structural contradictions in reality and think about the direction when watching movies. At the recent cannes film festival, "parasite" won the top prize palm in cannes by an overwhelming margin, with the highest score of 3.3 issues. Although this film is positioned as a thriller with comedy elements, it presents the opposite life images of Korean classes to the audience in a parasitic way, which not only expands the possibility and artistry of realistic film aesthetics, but also enhances the appreciation of the film and gives play to its own aesthetic value. Focusing on the technical and literary nature of the film, and having a high degree of attention to real life, it is an excellent work that tells about class opposition and thinking about reality. This paper considers and analyzes the content, form and creation method of parasite, and discusses the continuous exploration and attempt of realistic film to image language under the demand of market and system, evolving into new aesthetic expression.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.339-346
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• 2023

The uncertainties of microstructural features affect the properties of materials. Numerous pores that are randomly distributed in materials make it difficult to predict the properties of the materials. The distribution of pores in cementitious materials has a great influence on their mechanical properties. Existing studies focus on analyzing the statistical relationship between pore distribution and material responses, and the correlation between them is not yet fully determined. In this study, the mechanical response of cementitious materials is predicted through an image-based data approach using a convolutional neural network (CNN), and the correlation between pore distribution and material response is analyzed. The dataset for machine learning consists of high-resolution micro-CT images and the properties (tensile strength) of cementitious materials. The microstructures are characterized, and the mechanical properties are evaluated through 2D direct tension simulations using the phase-field fracture model. The attributes of input images are analyzed to identify the spot with the greatest influence on the prediction of material response through CNN. The correlation between pore distribution characteristics and material response is analyzed by comparing the active regions during the CNN process and the pore distribution.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Journal of the Korean Society of Radiology
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• v.81 no.5
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• pp.1164-1174
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• 2020

Purpose To evaluate a deep learning model to predict recurrence of thyroid tumor using preoperative ultrasonography (US). Materials and Methods We included representative images from 229 US-based patients (male:female = 42:187; mean age, 49.6 years) who had been diagnosed with thyroid cancer on preoperative US and subsequently underwent thyroid surgery. After selecting each representative transverse or longitudinal US image, we created a data set from the resulting database of 898 images after augmentation. The Python 2.7.6 and Keras 2.1.5 framework for neural networks were used for deep learning with a convolutional neural network. We compared the clinical and histological features between patients with and without recurrence. The predictive performance of the deep learning model between groups was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve served as a summary of the prognostic performance of the deep learning model to predict recurrent thyroid cancer. Results Tumor recurrence was noted in 49 (21.4%) among the 229 patients. Tumor size and multifocality varied significantly between the groups with and without recurrence (p < 0.05). The overall mean area under the curve (AUC) value of the deep learning model for prediction of recurrent thyroid cancer was 0.9 ± 0.06. The mean AUC value was 0.87 ± 0.03 in macrocarcinoma and 0.79 ± 0.16 in microcarcinoma. Conclusion A deep learning model for analysis of US images of thyroid cancer showed the possibility of predicting recurrence of thyroid cancer.