• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.419-438
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• 2023

Purpose: This study examines the effectiveness of personalized goal setting and smartphone-based nutrition counseling among adults in their 20s and 30s. Methods: Nutrition counseling was conducted for a total of 30 adults through a 1:1 chat room of a mobile instant messenger, once a week for 8 weeks. The first week of counseling included a preliminary online questionnaire survey and a dietary intake survey. Based on the results of the preliminary survey, 2 dietary goals were set in the second week and the participants were asked to record their achievements on a daily checklist. From the third week onwards, counselors sent feedback messages based on the checklist and provided information on dietary guidelines in a card news format every week. Post-counseling questionnaires and dietary intake surveys were conducted in the seventh week. Changes in dietary habits during the counseling were reviewed in the eighth week, followed by a questionnaire survey on the evaluation of the counseling process. Results: The nutrition quotient (NQ) scores and self-efficacy scores were significantly higher after nutrition counseling. The NQ scores of consumption frequencies of fruits, milk and dairy products, nuts, fast food, Ramyeon, sweet and greasy baked products, sugarsweetened beverages, the number of vegetable dishes at meals, and breakfast frequency were significantly higher after nutrition counseling. The intake of protein, vitamin A, thiamin, riboflavin, folate, calcium, and iron, and the index of nutritional quality of vitamin A, riboflavin, folate, calcium, and iron were higher after nutrition education. The participants were satisfied with the nutrition counseling program and the provided nutrition information. Conclusion: Personalized goal setting and smartphone-based nutrition counseling were found to be effective in improving the quality of diet and self-efficacy in young adults. Similar results were obtained in both the underweight/normal weight and the overweight/obese groups.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Korea Science and Art Forum
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• v.30
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• pp.119-127
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• 2017

Multi-cultural societies in the era of globalization are now common phenomena all over the world. Since our country has already entered into a multi-cultural society, we can no longer stay in the ideology of a single nation. However, current national policies and researches related to multi-cultural society in Korea are limited to institutional aspects and unilateral education of Korean culture. Therefore, this study aims to overcome these practical limitations. The purpose of this study is to acquire design resources in the folk paintings reflecting the culture of each country. And We will develop textile design content that can be applied to most closely related textile products in daily life. Through this, it is aimed to raise awareness of various cultures and to suggest a communication method through cultural exchange. Therefore, this study has developed color and textile pattern design contents through analysis of characteristics of China, Vietnam, and Japan peoples of the three most frequent countries based on the status of domestic marriage immigrants. And tried to apply it immediately to various textile products. The results and contents of the study are as follows. First, the domestic multi-cultural society was formed through international marriage, and the largest number of marriage immigrants came from China, Vietnam, Japan, the Philippines, Cambodia, Thailand, Mongolia and others. Second, folk paintings are suitable for developing textile design contents as an important factor implied by different cultures of different countries. Thirdly, we have developed the pattern and coloring DB and textile pattern design contents by using folk paintings of China, Vietnam and Japan. As a result, we could verify the utilization of contents reflecting the cultural characteristics of each country and the possibility of commercialization. Based on the results of this research, we hope to contribute to the harmonization of the emotional and artistic aspects that naturally share the culture among multi-cultural society members and to develop differentiated related products.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• Korean Journal of Heritage: History & Science
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• v.56 no.3
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• pp.76-98
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• 2023

The results of analyzing the outfits of male and female characters depicted in the "Hoehonryedocheop" (回婚禮圖帖, Deoksu 6375) held by the National Museum of Korea and estimating its production date of the "Hoehonryedocheop"are as follows. Firstly, an elderly groom is depicted wearing a patterned heukdanryeong (黑團領) with rank badges, a garment commonly donned by government officials in works such as "Jeonanryedo (奠鴈禮圖)" and "Gyobaeryedo (交拜禮圖)". And the old groom in "Heonsuryedo (獻壽禮圖)" "Jeobbindo (接賓圖)" and "Jungroeyeondo (重牢宴圖)" is shown wearing a jarip (purple hat) without a paeyoung (ornamental jewelry strap), accompanied by jade colored robe with a red strap belt. Gireokabeom (雁夫) is observed wearing a jarip (紫笠) adorned with a paeyoung (貝纓) and a patternless heukdanryeong with rank badges. Adult male descendants are depicted wearing dopo (道袍), while guests wear dopo, cheolrik (帖裏), and jikryeong (直領), accompanied by red and blue straps denoting their social status. Jingssi (徵氏), sidong (侍童), and young grandchildren are observed wearing jungchimak(中赤莫). The young servants are wearing jungchimak, and the boys carrying the food have braided their hair and worn sochangu (小氅衣), while adults servants wore jeonrip (氈笠) and sochangui. Performers are seen clad in a sochangui, jeonbok, and a blue sash around their waists. Secondly, the elderly bride is portrayed wearing a keunmeori (ceremonial headdress) and a green wonsam (圓衫) in "Gyobaeryedo," while in "Heonsuyeondo," she is depicted in a blue skirt and a jade colored jeogori (赤古里). Women descendants are shown adorning headdress decorations, such as binyeo(簪), banja(斑子) and pearl daenggi (眞珠唐紒) on their eoyeomeori (於于味, ceremonial headdress). They are further dressed in skirts of navy, red, and jade hues, paired with various-colored jeogori. Additionally, a woman wearing a navy skirt and a green jangot (長衣) is also depicted. The dongnyeo (童女, unmarried women) wear beolsaengmeri (娘子雙髻), headdress) with long binyeo and long dodaik-daenggi (都多益唐只). They wear chilbo-jokduri (七寶簇頭里) and a red skirt with a green hoejang-jeogori (回裝赤古里). Bija (婢子) wears garima (加里亇) on her eoyeomeori and is seen dressed in skirts and jeogori resembling those worn by noble women, albeit with lighter colors, shorter skirt length, and a subdued volume. Ginyeo's attire bears similarities to that of noble women, although with a dress with less vibrant tones and devoid of decorations on the eoyeomeori. Thirdly, based on the main character's jarip, along with the cheolrik and jikryeong worn by the guests, as well as the performances by musicians of the military camp, it is suggested that the main character of the 60th wedding anniversary is connected to the Ministry of Military Affairs or the military camp. Judging by the military band's short-sleeved vest, the silhouettes of the women dress, and the headdresses, it is likely that the "Hoehonryedocheop" was produced between the 1760s and 1780s.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.714-729
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• 2023

Purpose: Since the coronavirus pandemic, the number of obese people has increased, and accelerated aging has been reported, particularly among young population. Therefore, this study analyzed the dietary habits of university students according to their risk of obesity to provide data for improving their eating habits. Methods: Ninety students at Daegu Catholic University were surveyed for their eating habits and photo-enhanced meal intake. The obesity risk was categorized as normal if the body mass index (BMI) and percent body fat (PBF) were normal, at-risk if both indicated overweight or obese, obesity in BMI alone were analyzed as BMI group and those with obesity in percent body fat alone were analyzed as PBF group. Results: There were 23 (25.5%) in the normal group, 10 (11.1%) in the BMI group, 24 (26.7%) in the PBF group and 33 (36.7%) in the at-risk group. The dietary survey showed that the risk groups had lower consumption frequencies of vegetables (p < 0.05) than the normal group, had less sleep time (p < 0.05) and higher frequency of fast food consumption (p < 0.001). The intake of vegetables was significantly higher in the normal group than in the risk group (p < 0.05). In terms of the daily nutrient intake, there was no significant difference in energy intake according to the obesity risk, but the intakes of dietary fiber (p < 0.01), vitamin A (p < 0.01), vitamin C (p < 0.01) were higher in the normal group than in the risk groups. Conclusion: Therefore, it is important to consider the BMI and percent body fat together to diagnose obesity and provide nutrition education and counseling.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.902-909
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• 2023

This study evaluated the performance test of a small-sized LTE-Maritime(LTE-M) transceiver that was developed and promoted to expand the use of intelligent maritime traf ic information services led by the Ministry of Oceans and Fisheries with the aim of supporting the prevention of maritime accidents. Accoriding to statistics, approximately 30% of all marine accidents in Korean water occur with ships weighing less than 3 tons. Therefore, the blind spots of maritime safety must be supplemented through the development of small-sized transceivers. The small transceiver may be used in fishing boats that are active near coastal waters and in water leisure equipment near the coastline. Therefore, verifying whether sufficient performance and stable communication quality are provided is necessary, considering the environment of their real usage. In this study, we reviewed the communication quality goals of the LTE-M network and the performance requirements of small-sized transceivers suggested by the Ministry of Oceans and Fisheries, and proposed a test plan to appropriately evaluate the performance of small-sized transceivers. The validity of the proposed test method was verified for six real-sea areas with a high frequency of marine accidents. Consequently, the downlink and uplink transmission speeds of the small-sized LTE-M transceiver showed performances of 9 Mbps or more and 3 Mbps or more, respectively. In addition, using the coverage analysis system, coverage of more than 95% and 100% were confirmed in the intensive management zone (0-30 km) and interesting zone (30-50 km), respectively. The performance evaluation method and test results proposed in this paper are expected to be used as reference materials for verifying the performance of transceivers, contributing to the spread of government-promoted e-navigation services and small-sized transceivers.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.