• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.11-19
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• 2020

A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.613-620
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• 2021

The purpose of this study is to propose a convolutional neural network model that can classify normal and abnormal(cardiomegaly) in chest X-ray images. The training data and test data used in this paper were used by acquiring chest X-ray images of patients diagnosed with normal and abnormal(cardiomegaly). Using the proposed deep learning model, we classified normal and abnormal(cardiomegaly) images and verified the classification performance. When using the proposed model, the classification accuracy of normal and abnormal(cardiomegaly) was 99.88%. Validation of classification performance using normal images as test data showed 95%, 100%, 90%, and 96% in accuracy, precision, recall, and F1 score. Validation of classification performance using abnormal(cardiomegaly) images as test data showed 95%, 92%, 100%, and 96% in accuracy, precision, recall, and F1 score. Our classification results show that the proposed convolutional neural network model shows very good performance in feature extraction and classification of chest X-ray images. The convolutional neural network model proposed in this paper is expected to show useful results for disease classification of chest X-ray images, and further study of CNN models are needed focusing on the features of medical images.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.222-243
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• 2011

The article is organized as follows. The first section clarifies research questions, the purpose of the study and the methodology used when researching cultural properties & storytelling marketing of intangible cultural heritages in Korea. The Pimatgol's DNAs are liberated areas of the nation, cooked rice served in soup, a broth to chase a hangover, makgeolli, so forth. The second section deals with methods of study, the literature review consisting of previous research, the author's previous research and the theoretical background of cultural heritage's storytelling marketing. The third section presents select storytelling marketing cases about our regional cultural heritage as well as some foreign cases. The fourth section provides a few examples and cases of cultural heritage about government officials, the 'Pimatgol' peddler, the story of Chunbo and Okseom and the idea for fostering storytelling marketing. The last section concluded the study. The findings support the importance of understanding the characteristics and differences of cultural heritage & storytelling marketing because if the stories are well told, the cultural heritages will be successfully promoted.

• The Journal of Korean Academy of Sensory Integration
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• v.17 no.1
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• pp.1-8
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• 2019

Objective : This paper aimed to measure the relationship between the performance of Korean handwriting and visual fixation for children. Methods : Twenty-one typically developing children aged 7 to 9 years participated in the study. The children performed Korean handwriting task wearing Tobii Pro Glasses 2. The Korean handwriting task consisted of 10 words from elementary school textbooks. The handwriting skill was measured by the coefficient variation of the letter size and the fixation cound and duration. Correlation analysis was performed to investigate the relation between visual fixation and the coefficient variation of the letter size. Results : The results showed that the visual fixation per second was positively correlated with Korean handwriting vertical size coefficient variation, indicating that the more consistent the vertical size of the letter, the smaller the fixation count per second. Conclusion : The results suggested a relation between the performance of Korean handwriting and visual fixation in typically developing children. This study is the first attempt to measure eye movement during the Korean handwriting process, and suggests a future direction for research on students' development in writing Korean.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.1-12
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• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.

• Journal of IKEEE
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• v.25 no.2
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• pp.243-251
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• 2021

In this paper, we propose a measurement technique of indoor location based on markerless applicable to AR. The proposed technique has the following originality. The first is to extract feature points and use them to generate local patches to enable faster computation by learning and using only local patches that are more useful than the surroundings without learning the entire image. Second, learning is performed through deep learning using the convolution neural network structure to improve accuracy by reducing the error rate. Third, unlike the existing feature point matching technique, it enables indoor location measurement including left and right movement. Fourth, since the indoor location is newly measured every frame, errors occurring in the front side during movement are prevented from accumulating. Therefore, it has the advantage that the error between the final arrival point and the predicted indoor location does not increase even if the moving distance increases. As a result of the experiment conducted to evaluate the time required and accuracy of the measurement technique of indoor location based on markerless applicable to AR proposed in this paper, the difference between the actual indoor location and the measured indoor location is an average of 12.8cm and a maximum of 21.2cm. As measured, the indoor location measurement accuracy was better than that of the existing IEEE paper. In addition, it was determined that it was possible to measure the user's indoor location in real time by displaying the measured result at 20 frames per second.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.289-309
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• 2022

In recent years, astronomy has been snowballing: including Higgs particle discovery, black hole imaging, extraterrestrial exploration, and deep space observation. Students are also largely interested in astronomy. The purpose of this study is to discover what needs to be improved in the current astronomy curriculum in light of recent scientists' researches and discoveries. We collected keywords from all papers published from 2011 to 2020 in four selected journals-ApJ, ApJL, A&A, and MNRAS- by R package to examine research trends. The curriculum contents were extracted by synthesizing the in-service teachers' coding results in the 2015 revised curriculum document of six subjects (Science, Integrated Science, Earth Science I, Earth Science II, Physics II, Convergence Science). The research results are as follows: first, keywords that appear steadily in astronomy are 'galaxies: formation, galaxy: active, star: formation, accretion, method: numerical.' Second, astronomy curriculum includes all areas except the 'High Energy Astrophysical Phenomena' area within the common science curriculum learned by all students. Third, it is necessary to review the placement of content elements by subject and grade and to consider introducing new concepts based on astronomy research keywords. This is an exploratory study to compare curriculum and the field of scientific research that forms the basis of the subject. We expect to provide implications for a future revision of the astronomy curriculum as a primary ground investigation.