• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.151-167
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• 2021

This study reports on the development of a dietary education program for Korean young adults in single-person households. The 7th National Health and Nutrition Survey (2016-2018) was used to compare and analyze the dietary behavior of single-person households and multi-person households, and an online survey was conducted on 350 young adults (age 19-39 years) living in Seoul. According to the analysis, single-person households had higher rates of breakfast and eating out than multi-person households, and significantly lower average intake of energy and nutrients (p<0.05). In particular, in the case of single-person households, the lower the frequency of cooking at home, the higher the rate of breakfast and the higher the frequency of eating out and delivery food (p<0.05). Based on the survey, a dietary education program for young adults single-person households was developed by applying the DESIGN six-step procedure and social cognitive theory as a conceptual model. The first session consisted of the health and economic benefits of home-cooked meals, the second session of the importance of the breakfast and the effect of exercise in life, the third session of the importance of balanced nutrition and the principles of a healthy diet, the fourth session of food safety and storage, and the fifth session of social dining. Each session was composed of a combination of theoretical lectures to motivate 'more making and eating healthy home-cooked meals' and cooking practice for improving behavioral performance.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.33-40
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• 2020

As the number of automobile registrations increases and luxury expectations grow, consumers are increasingly interested in indoor environment of vehicles. Therefore, manufacturers have an increasing interest in improving the indoor comfort as well as automobile performance. Research on indoor automobile comfort can help manufacturers increase driver satisfaction and reduce driver stress and discomfort, thereby reducing the risk of traffic accidents. Using electroencephalogram (EEG) measurements, we investigated the change in comfort and comfortable temperature according to the ventilating seat temperature change for both men and women. Results showed that the sensation of comfort was statistically significantly higher at 25℃ than at 28℃. Secondly, there was no statistically significant difference in temperature-based comfort feeling between male and female subjects. In the future, if the correlation between the driver's comfort feeling and the change in ventilating seat temperature is analyzed, it is possible to reduce traffic accidents caused by human error and reduce the electric energy consumption of the automobile.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.2
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• pp.263-277
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• 2021

Taint analysis techniques are popularly used to detect web vulnerabilities originating from unverified user input data, such as Cross-Site Scripting (XSS) and SQL Injection, in web applications written in JavaScript. To detect such vulnerabilities, it would be necessary to trace variables affected by user-submitted inputs. However, because of the dynamic nature of JavaScript, it has been a challenging issue to identify those variables without running the web application code. Therefore, most existing taint analysis tools have been developed based on dynamic taint analysis, which requires the overhead of running the target application. In this paper, we propose a novel static taint analysis technique using symbol information obtained from the TypeScript (a superset of JavaScript) compiler to accurately track data flow and detect security vulnerabilities in TypeScript code. Our proposed technique allows developers to annotate variables that can contain unverified user input data, and uses the annotation information to trace variables and data affected by user input data. Since our proposed technique can seamlessly be incorporated into the TypeScript compiler, developers can find vulnerabilities during the development process, unlike existing analysis tools performed as a separate tool. To show the feasibility of the proposed method, we implemented a prototype and evaluated its performance with 8 web applications with known security vulnerabilities. We found that our prototype implementation could detect all known security vulnerabilities correctly.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1697-1707
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• 2021

On 19 February 2020, the 2nd Geostationary Ocean Color Imager (GOCI-II), a maritime sensor of GEO-KOMPSAT-2B, was launched. The GOCI-II instrument expands the scope of aerosol retrieval research with its improved performance compared to the former instrument (GOCI). In particular, the newly included UV band at 380 nm plays a significant role in improving the sensitivity of GOCI-II observations to the absorbing aerosols. In this study, we calculated the aerosol index and detected absorbing aerosols from January to June 2021 using GOCI-II 380 and 412 nm channels. Compared to the TROPOMI aerosol index, the GOCI-II aerosol index showed a positive bias, but the dust pixels still could be clearly distinguished from the cloud and clear pixels. The high GOCI-II aerosol index coincided with ground-based observations indicating dust aerosols were detected. We found that 70.5% of dust and 80% of moderately-absorbing fine aerosols detected from the ground had GOCI-II aerosol indices larger than the 75th percentile through the whole study period.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1233-1242
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• 2021

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

• Ocean and Polar Research
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• v.43 no.4
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• pp.229-243
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• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.76-83
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• 2021

This study examines the bending behavior of lap-spliced ultra-high-strength fiber-reinforced concrete members and evaluates the safety of the design codes for ultra-high-strength fiber-reinforced concrete structures. An experiment on a total of six beams was performed. The main variables were the fiber-inclusion and the lap-spliced length at the center of the beams. The steel fibers in a volume fraction of 2% were used, and the lap-splice lengths were determined to be 8db and 16db. As a result of the test, the specimens not reinforced with fiber lost abrupt load-bearing capacity at the lap region and did not experience yielding of the reinforcing bar. In the case of fiber-reinforced concrete, if a lap-splice length of 16db is secured, the yielding of the main reinforcing bar can be experienced, and appropriate flexural strength can be expressed. Based on the experimental results of this study, as a result of reviewing the lap-splice length calculation formulas of the current design standards and the ultra-high-strength concrete structural design recommendations, it was found that all of them were evaluated conservatively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.115-123
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• 2021

In recent years, the automobile industry has been facing a major change with the introduction of new technologies represented by autonomous driving, electrification, and digitalization. Major domestic and overseas automakers are trying to use a systematic approach to customer satisfaction through user interfaces to provide customers with a special experience and value beyond just making products with high performance. This study proposes the Kano model as a systematic and qualitative research method for satisfaction. As a case study, 17 functions of a product were sorted (3 operation functions, 7 safety functions, and 7 convenience functions). This was done by analyzing the use case and the customers' requirements for a smart trunk system. 18 new functions were derived via creative ideation codes. In addition, a scientific analysis method is proposed for product quality attributes and the strength of customer satisfaction. Using the Kano methodology, 25 functions were classified into quality attributes: 18 attractive qualities, 3 one-dimensional qualities, and 4 complex qualities, which are combinations of one-dimension qualities and must-have qualities. The functions that have one-dimensional quality and complex qualities were found to have higher customer ratings than the functions that have attractive qualities. Based on this, enterprises could effectively reduce customer complaints and enhance customer satisfaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.520-528
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• 2021

In this study, we attempted to derive major management factors related to the quality control of formwork and identified differences in the perceptions of construction engineers, construction managers, and field workers about the importance and performance of the derived factors. As a result of the study, 18 management factors related to the formwork were derived. Among the derived factors, the field workers placed importance on the factors that influence the detailed process of the formwork, while construction engineers placed importance on the factors related to the judgment of the progress and completion of the construction. The construction managers placed importance on the factors that affect the quality of the building frame. Based on the results of this study, it is expected that a plan to improve the quality of formwork that accepts the various opinions of each participant will be established.