• Journal of Digital Convergence
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• v.19 no.10
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• pp.383-394
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• 2021

This study examines the determinants that affect the continuous intention to use of augmented reality applications by applying the technology acceptance model2(TAM2). A survey was conducted on 241 people, and the main results were derived by performing confirmatory factor analysis, correlation analysis, and path analysis using SPSS 21.0 and AMOS 21.0 programs. Presenting the results, it was found that the user's interface, interactivity, and relative advantage of the augmented reality application had a positive effect on perceived usefulness, and technological self-efficacy had a positive effect on perceived usefulness and perceived ease. Perceived ease of use had a positive effect on perceived usefulness, and both perceived usefulness and perceived ease had a positive effect on continuous intention to use of augmented reality applications. In future research, it will be necessary to verify the user effect of augmented reality applications by applying the fields of education or games.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.4
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• pp.451-458
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• 2021

Currently, digital technology is being used in various fields of dental treatment. In particular, in the case of anterior esthetic restoration, the traditional restoration method cannot contain facial information and it is difficult for the patient to predict the treatment result. However, in the case of esthetic restoration through digital design, the visualization of the prosthesis design and the ease of reflecting patient feedback, and expecting the treatment result is available. In this case, the patient confirmed the results of restoration treatment using a digital method before treatment and obtained consent for treatment in an anterior tooth trauma patient. In addition, since the conventional digital smile design method uses only the patient's facial and smile information, the design was made on a two-dimensional plane, and its application was somewhat limited. However, in this case, a three-dimensional virtual patient was created and thus the designed restoration was viewed from various angles. Through this case, it was possible to obtain a high degree of satisfaction with the ease of communication with the patient and the technician during the esthetic restoration using the digital method, the simplicity of the procedure, and the treatment result.

• Korean journal of applied entomology
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• v.60 no.4
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• pp.369-377
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• 2021

Mass trapping of the western flower thrips, Frankliniella occidentalis, has been considered as an option to control this pest. This study applied the commercial lures to the hot pepper-cultivating greenhouses and assessed the enhancement of the attracting efficiency by adding to sticky traps. There was no color difference in the attracting efficiency between blue and yellow sticky traps. However, the installation position of the traps was crucial in the greenhouses. The more thrips were captured within host cropping area than outside areas of the crop. In vertical trap position, it was the most optimal to install the traps at the crop crown. Using these installation parameters, the yellow sticky traps captured approximately 1% population of the thrips. To enhance the trapping efficiency, the commercial lures containing aggregation pheromone or 4-methoxybenzaldehyde were added to the yellow sticky traps. However, these commercial lures did not significantly enhance the trapping efficiency compared to the yellow sticky trap alone. In contrast, Y-tube olfactometry assays confirmed the high efficiency of the aggregation pheromone or another plant volatile (methyl isonicotinate) to attract the thrips. Interestingly, these lure components had lower attracting efficiencies compared to the hot pepper flowers. The high attractive efficiency of the flowers was supported by the observation that the commercial lure was effective to enhance the trapping efficiency of the yellow sticky trap against F. occidentalis in Welsh onion (Allium fistulosum) field without any flowers. This study indicates the limitation of the commercial lures in application to hot pepper fields for the mass trapping of F. occidentalis. It also suggests active volatile component(s) from hot pepper flowers to attract F. occidentalis.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.4
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• pp.745-754
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• 2019

As GeoWeb 2.0 technologies are widely used, various kinds of services that mashup spatial data and user data are being developed. In particular, various spatial information platforms such as Google Maps, OpenStreetMap, Daum Map, Naver Map, olleh Map, and VWorld based on GeoWeb 2.0 technologies support mashup service. The mashup service which is supported through the Open APIs of the platforms, provides various kinds of spatial data such as 2D map, 3D map, and aerial image. Also, application fields using the mashup service are greatly expanded. Recently, as user data for mashup have been greatly increased, there was a problem in mashup performance. However, the research on the mashup performance improvement is currently insufficient, even the research on the mashup performance comparison of various platforms has not been performed. In this paper, we perform comparative analysis of the mashup performance for large amounts of user data and spatial data using various spatial information platforms available in Korea. Specifically, we propose two performance analysis indexes of mashup time and user interaction time in order to analyze the mashup performance efficiently. Also, we implement a system for the performance analysis. Finally, from the performance analysis result, we propose a spatial information platform that can be efficiently applied to cases when user data increases greatly and user interaction occurs frequently.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.125-131
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• 2021

In recent years, Convolutional Neural Networks (CNNs) have achieved outstanding performance in the fields of computer vision such as image classification, object detection, visual quality enhancement, etc. However, as huge amount of computation and memory are required in CNN models, there is a limitation in the application of CNN to low-power environments such as mobile or IoT devices. Therefore, the need for neural network compression to reduce the model size while keeping the task performance as much as possible has been emerging. In this paper, we propose a method to compress CNN models by combining matrix decomposition methods of LR (Low-Rank) approximation and CP (Canonical Polyadic) decomposition. Unlike conventional methods that apply one matrix decomposition method to CNN models, we selectively apply two decomposition methods depending on the layer types of CNN to enhance the compression performance. To evaluate the performance of the proposed method, we use the models for image classification such as VGG-16, RestNet50 and MobileNetV2 models. The experimental results show that the proposed method gives improved classification performance at the same range of 1.5 to 12.1 times compression ratio than the existing method that applies only the LR approximation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.247-254
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• 2021

As COVID-19 spread, non-face-to-face activities were required, and the use of service robots is gradually increasing. This paper analyzed the relationship between the increasing trend of service robots before and after COVID-19 through keyword search containing the keyword 'service robot AND non-face-to-face' over the past three years (2018.10-20219) using BigKines, a news big data analysis system. As a result, there were 0 cases in the first period (2018.10~2019.9), 52 cases in the second period (2019.10~2020.9) and 112 cases in the third period (2020.10~2021.9), an increase of 115% compared to the second period. The keywords commonly mentioned in the analysis of related words in the second and third periods were COVID-19, AI, the Ministry of Trade, Industry, and Energy, and LG Electronics, and the weight of COVID-19 was the largest, confirming that the analysis keyword. Due to the spread of Corona 19, non-face-to-face is required, and with the development of information and communication technology, the field of application of service robots is rapidly increasing. Accordingly, for the commercialization of service robots that will lead the non-face-to-face economy, there is an urgent need to nurture human resources that require standardization and expertise in safety and performance fields.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.568-573
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• 2018

UAV(Unmanned Aerial Vehicle) is widely used in space information construction, agriculture, fisheries, weather observation, communication, and entertainment fields because they are cheaper and easier to operate than manned aircraft. In particular, UAV have attracted much attention due to the speed and cost of data acquisition in the field of spatial information construction. However, ortho image images produced using UAVs are distorted in buildings and forests. It is necessary to solve these problems in order to utilize the geospatial information field. In this study, fixed wing, rotary wing, vertical take off and landing type UAV were used to detect distortions of ortho image of UAV under various conditions, and various object areas such as construction site, urban area, and forest area were captured and analysed. Through the research, it was found that the redundancy of the unmanned aerial vehicle image is the biggest factor of the distortion phenomenon, and the higher the flight altitude, the less the distortion phenomenon. We also proposed a method to reduce distortion of orthoimage by lowering the resolution of original image using DTM (Digital Terrain Model) to improve distortion. Future high-quality unmanned aerial vehicles without distortions will contribute greatly to the application of UAV in the field of precision surveying.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.43-47
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• 2019

The aim of this study was to apply the Importance-Performance Map (IPM) method based on PLS-SEM by extending the importance-performance analysis (IPA), which is an existing method to grasp strategic policies through the difference of importance and satisfaction of existing competitiveness factors. For this application, this study was applied to research related to policy measures that can survive and spread in global competition by analyzing strategic factors of information technology (IT) convergence industry. The development of IT convergence industry, which is the subject of this study, has the effect of revitalizing related industry development and employment activation. Therefore, this study expanded the possibility of applying this research methodology to research the strategic factors to activate exports of SMEs (Small and medium-sized enterprises) in the convergency industry. In order to achieve this goal, the analytical methodology of this study was applied and the policy measures for IT SMEs. Therefore, based on the analysis results of this study, this study can apply this research methodology to other fields as a strategic tool in establishing and enforcing policies for export activation of IT convergence industry.