• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.2
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• pp.119-126
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• 2013

The concept of extraction in orthodontic treatment has been changed many times. Even today, criteria of extraction or nonextraction is still changing. In this study, changes depending on the evaluator's perception of treatment outcomes were compared in both extraction and nonextraction cases. In this study, premolar extracted 59 patients and nonextracted 60 patients, totally 119 patients who finished orthodontic treatment in Dankook University Dental Hospital orthodontic clinic were enrolled. Evaluation sections made up of specialists and laypersons assessed soft tissue traced from lateral cephalometric radiographs with visual analogue scale before and after the treatment. And the results were statistically analyzed. Thus, the conclusions drawn are as follows: 1. Average score is 5.76 in extraction, which is larger than 5.28 of nonextraction case. Improvement of facial profile was more favorably accepted in extraction case. 2. 5.875 in the group of specialists were higher evaluation than 5.165 in the group of layperson. 3. Specialists gave significantly higher ratings in the extraction than nonextraction. 4. A higher rating in extraction case of the layperson group has no significant difference with nonextraction case. 5. Nonextraction patients were given higher ratings from specialist group. 6. A higher rating of specialist group in extraction case has no significant difference with layperson group.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.28-37
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• 2009

In this paper, we propose an obstacle localization method using a road feature based on a V-disparity map binarized by a maximum frequency value. In a conventional method, the detection performance is severely affected by the size, number and type of obstacles. It's especially difficult to extract a large obstacle or a continuous obstacle like a median strip. So we use a road feature as a new decision standard to localize obstacles irrespective of external environments. A road feature is proper to be a new decision standard because it keeps its rough feature very well in V-disparity under environments where many obstacles exist. And first of all, we create a binary V-disparity map using a maximum frequency value to extract a road feature easily. And then we compare the binary V-disparity map with a median value to remove noises. Finally, we use a linear interpolation for rows which have no value. Comparing this road feature with each column value in disparity map, we can localize obstacles robustly. We also propose a post-processing technique to remove noises made in obstacle localization stage. The results in real road tests show that the proposed algorithm has a better performance than a conventional method.

• Smart Media Journal
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• v.6 no.1
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• pp.16-21
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• 2017

Long time has passed since computers which used to be a means of research were commercialized and available for the general public. People used writing instruments to write before computer was commercialized. However, today a growing number of them are using computers to write instead. Computerized word processing helps write faster and reduces fatigue of hands than writing instruments, making it better fit to making long texts. However, word processing programs are more likely to cause spelling errors by the mistake of users. Spelling errors distort the shape of words, making it easy for the writer to find and correct directly, but those caused due to users' lack of knowledge or those hard to find may make it almost impossible to produce a document free of spelling errors. However, spelling errors in important documents such as theses or business proposals may lead to falling reliability. Consequently, it is necessary to conduct research on high-level spelling error correction programs for the general public. This study was designed to produce a system to correct sentence-level spelling errors to normal words with Korean alphabet similarity algorithm. On the basis of findings reported in related literatures that corrected words are significantly similar to misspelled words in form, spelling errors were extracted from a corpus. Extracted corrected words were replaced with misspelled ones to correct spelling errors with spelling error detection algorithm.

• 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 Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.295-302
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• 2018

Face recognition (FR) has been widely used in various applications, such as smart surveillance systems, immigration control in airports, user authentication in smart devices, and so on. FR in well-controlled conditions has been extensively studied and is relatively mature. However, in unconstrained conditions, FR performance could degrade due to undesired characteristics of the input face image (such as irregular facial pose variations). To overcome this problem, this paper proposes a new method for determining if an input image is suitable for FR. In the proposed method, for an input face image, reconstruction error is computed by using a predefined set of reference face images. Then, suitability can be determined by comparing the reconstruction error with a threshold value. In order to reduce the effect of illumination changes on the determination of suitability, a preprocessing algorithm is applied to the input and reference face images before the reconstruction. Experimental results show that the proposed method is able to accurately discriminate non-frontal and/or incorrectly aligned face images from correctly aligned frontal face images. In addition, only 3 ms is required to process a face image of $64{\times}64$ pixels, which further demonstrates the efficiency of the proposed method.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Smart Media Journal
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• v.10 no.4
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• pp.54-62
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• 2021

As the volume of online e-commerce transactions increases, items diversify and the classification becomes complicated. E-commerce platforms that specialize in dealing only in one area are emerging, and the area is diversifying. Three problems were identified by researching the craft online e-commerce platform, one of the various types of professional e-commerce platforms. First of all, although craft materials are diversified and complex on the platform, the existing craft e-commerce system is fragmented in structure to categorize complex crafts, making it difficult to accurately present search results that meet various criteria. Second, although appearance is the main reason for purchasing artifacts, it is rare for users to categorize them according to appearance, so they have to judge and filter each work directly. Finally, the language entered when searching for artifacts by non-technical experts is not reflected in the language used to categorize artifacts in the taxonomic system, so the language used for searching is highly accurate. Therefore, the purpose of this study is to add and consider complex attributes in the field of technology to meet the search criteria. Properties to be added must include the main appearance in the search for artifacts. In addition, the government aims to develop a taxonomic system that can reflect non-experts' search languages in the search of works through artificial intelligence natural language processing technology.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.117-135
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• 2021

Condition assessment of analog records is enforced to establish a preservation strategy and identify the damaged records by the Public Records Management Act and the public standard in Korea. However, the number of record management organizations where the condition assessment according to the act and the standard are actually conducted is limited in Korea. To find out what to change in the system and the practice of the condition assessment, the system and situation on the condition assessment of the paper-based records in Korea and other countries were investigated through literature research and a survey. Whereas Korean archives try to assess entire individual records, archives and libraries overseas apply condition assessment selectively depending on not only historical and cultural values of the records but also the vulnerability of compositional materials and severity of the damage of the records. It seems that archives and libraries overseas have a specific reason to conduct the assessment. Most of them take advantage of a sampling method not assessing every single item. Moreover, the periodical assessment is carried out in only about 50% of the responses. Therefore, we have to consider changing our condition assessment system to a more efficient and flexible way, adopting a sampling method and applying the assessment for selective collections with more specific purposes.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.137-148
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• 2023

This study investigated the influence of major satisfaction on learning flow and self-efficacy of students majoring in culinary arts at local junior colleges. In the 2022-2 semester, 260 freshmen and sophomore college students majoring in culinary from five junior colleges in the Gwangju and Jeonnam regions were analyzed. For data processing, SPSS Ver. 25.0 was used. The data is used to measure reliability by Cronbach's α, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. The results of this study are as follows : First, there was a difference in satisfaction between freshmen and sophomores in major satisfaction with cooking related departments at local junior colleges. Second, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on learning immersion. Third, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on self-efficacy. In conclusion, it was found that major satisfaction affects learning immersion and self-efficacy for both students enrolled in cooking-related departments at local junior colleges. In the future, we suggest follow-up research on educational measures to increase learning immersion and self-efficacy for students who are not majoring in cooking in the high school curriculum and students who are insufficient in major classes due to part-time jobs during the semester.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.