• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.469-476
• /
• 2021

The framework plan for the development of an integrated underground space map was established of preventing ground subsidence. The mapping process is expected to be completed to the level of nationwide municipal government standards by end of this year. To facilitate the utilization of the integrated underground space map, paper-based drawings for specialized organizations in underground safety impact assessment have been provided since September 2018, and services for local government officials have been provided in the underground information utilization system since May 2019. However, the map is utilized based on the information at the time of the initial development of the map, without any updates, thereby resulting in a lack of accuracy and latest information. This has led to a decrease in the utilization and reliability of the information. Therefore, in this study, for the underground structures(subway, underground shopping mall, underground passage, underground roadway, underground parking lot, utility tunnel), which are the key components of the integrated underground space map, a standard format for the submission of completed drawings is designed in accordance with Article 42 (2) of the Special Act on Underground Safety Management, which aims at laying the foundation for establishing the updated system of the integrated underground space map. In addition, through the verification of the automatically updated underground structure data based on the standard format, the reliability of the data can be assured. This format is expected to contribute to the improved utilization of the integrated underground space map in the future.

• Journal of Korea Water Resources Association
• /
• v.55 no.11
• /
• pp.979-989
• /
• 2022

In hydrological surveys, observation through representative location is essential due to temporal and spatial limitations and constraints. Regarding the use of hydrological data and the accuracy of the data, there are still insufficient observatories to be used in a specific watershed. In addition, since there is virtually no standard for the location of the current evapotranspiration, this study proposes a method for determining the location of the evapotranspiration. To determining the location of evapotranspiration, a grid is selected in consideration of the operating range of the Flux Tower using the eddy covariance measurement method, which is mainly used to measure evapotranspiration. The grid of representative location was calculated using the factors affecting evapotranspiration and satellite data of evapotranspiration. The grid of representative location was classified as good, fair, and poor. As a result, the number of good grids calculated was 54. It is judged that the classification of the grid has been achieved regarding topography and land use as a characteristic that appeared in the classification of the grid. In particular, in the case of elevation or city area, there was a large deviation, and the calculated good grid was judged to be a group between the two distributions.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.11-17
• /
• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Knowledge Management Research
• /
• v.23 no.2
• /
• pp.123-142
• /
• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• Computers and Concrete
• /
• v.31 no.3
• /
• pp.267-276
• /
• 2023

The main purpose of the current research is to develop an efficient two variables trigonometric shear deformation beam theory to investigate the buckling behavior of symmetric and non-symmetric functionally graded carbon nanotubes reinforced composite (FG-CNTRC) beam resting on an elastic foundation with various boundary conditions. The proposed theory obviates the use to shear correction factors as it satisfies the parabolic variation of through-thickness shear stress distribution. The composite beam is made of a polymeric matrix reinforced by aligned and distributed single-walled carbon nanotubes (SWCNTs) with different patterns of reinforcement. The material properties of the FG-CNTRC beam are estimated by using the rule of mixture. The governing equilibrium equations are solved by using new analytical solutions based on the Galerkin method. The robustness and accuracy of the proposed analytical model are demonstrated by comparing its results with those available by other researchers in the existing literature. Moreover, a comprehensive parametric study is presented and discussed in detail to show the effects of CNTs volume fraction, distribution patterns of CNTs, boundary conditions, length-to-thickness ratio, and spring constant factors on the buckling response of FG-CNTRC beam. Some new referential results are reported for the first time, which will serve as a benchmark for future research.

• Composites Research
• /
• v.36 no.1
• /
• pp.48-52
• /
• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• Journal of Platform Technology
• /
• v.10 no.4
• /
• pp.47-55
• /
• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of Korea Water Resources Association
• /
• v.56 no.9
• /
• pp.529-541
• /
• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Imaging Science in Dentistry
• /
• v.53 no.3
• /
• pp.193-198
• /
• 2023

Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion: AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.

• Journal of the Korea Society for Simulation
• /
• v.19 no.1
• /
• pp.143-151
• /
• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.