• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.155-168
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• 2020

Open source is a key growth engine of the 4th industrial revolution, and the continuous development and use of various algorithms for image processing is expected. The purpose of this study is to examine the effectiveness of the UAS image processing open source based algorithm by comparing and analyzing the water reproduction and moving object filtering function and the time required for data processing in 3D reproduction. Five matching algorithms were compared based on recall and processing speed through the 'ANN-Benchmarks' program, and HNSW (Hierarchical Navigable Small World) matching algorithm was judged to be the best. Based on this, 108 algorithms for image processing were constructed by combining each methods of triangulation, point cloud data densification, and surface generation. In addition, the 3D reproduction and data processing time of 108 algorithms for image processing were studied for UAS (Unmanned Aerial System) images of a park adjacent to the sea, and compared and analyzed with the commercial image processing software 'Pix4D Mapper'. As a result of the study, the algorithms that are good in terms of reproducing water and filtering functions of moving objects during 3D reproduction were specified, respectively, and the algorithm with the lowest required time was selected, and the effectiveness of the algorithm was verified by comparing it with the result of 'Pix4D Mapper'.

• Journal of Korean Home Economics Education Association
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• v.34 no.3
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• pp.117-132
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• 2022

The purpose of this study was to examine the relationship between metabolic syndrome and sugar intake. This study was conducted on adults aged over 19 who participated in the 2013-2016 Korea National Health and Nutrition Examination Survey. Subjects were classified according to the ratio of sugar intake to total energy. We used 24-hour recall survey data to investigate the daily sugar intake. The energy intake ratio from the sugar <20% group had higher % KDRI's of calcium, iron, potassium, vitamin A, riboflavin, and vitamin C than the energy intake ratio from the sugar ≥20% group. The risk of blood pressure level was higher in the ≥20% group than in the <20% group. The highest tertile of sugar intake showed an increased risk of elevated blood pressure level. This study found that increased sugar intake was associated with the risk of metabolic syndrome. It is expected that these results can be used as useful information to prepare basic data for establishing and managing sugar-reducing nutrition policies for the prevention of chronic diseases.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

• Nutrition Research and Practice
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• v.16 no.sup1
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• pp.134-146
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• 2022

BACKGROUND/OBJECTIVES: Accumulating evidence has shown the beneficial effects of isoflavone on health. There is limited information on the usual isoflavone intake for Koreans. This study examined the usual intake of total isoflavone and its major food sources in Koreans according to age and gender. SUBJECTS/METHODS: The dietary intake data of 21,271 participants aged 1 yrs and older from the Korea National Health and Nutrition Examination Survey (KNHANES) VII 2016-2018 were analyzed. The average isoflavone intake was estimated based on the 24-h dietary recall data in KNHANES and the isoflavone database from the Korea Rural Development Administration (RDA) and literatures. The usual isoflavone intake was estimated by applying the ratio of within- and between-participant variance estimated from the 2009 KNHANES data to the 7th KNHANES (2016-2018) data. The variance of the isoflavone intake was calculated using MIXTRAN macro with intake data for two days in the 2009 KNHANES. Complex sample analysis with stratified variables and integrated weights was conducted. RESULTS: The mean total isoflavone intake in the Korean population aged 1 yrs and older (n = 21,271) was 139.27 mg/d, which was higher than the usual intake of 47.44mg/d. Legumes were a major contributing food group (91%), with arrowroot being a major individual contributor to the isoflavone intake (67.2%), followed by 21.3% of soybean, 5.4% of bean sprouts, and 2.1% of tofu. The usual isoflavone intake was highest in the participants aged 50 to 64 yrs old and increased with age until 50 to 64 yrs and then decreased with further increases in age. The usual isoflavone intake of participants aged 65 yrs and older was higher for men than for women, showing gender differences. CONCLUSIONS: The usual dietary intake of isoflavone varied according to age and gender in the Korean population. This study showed that the usual isoflavone intake was lower than the average isoflavone intake. The difference between percentiles of the usual isoflavone intake was similarly smaller than the average intake. An estimation of average intake can be hindered by the occasional consumption of foods high in isoflavones, suggesting that the usual intake estimation method can be more appropriate. Further research will be needed to establish isoflavone dietary guidelines regarding the effects of isoflavone intake on health outcomes.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of Food Hygiene and Safety
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• v.27 no.2
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• pp.194-201
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• 2012

Facing a number of global food-related accidents, the concept and system for food traceability have been designed and introduced in many countries to manage the food-safety risks. To connect and harmonize the various food traceability-information in food traceability system according to the food supply chain, the coding system of identification-number for food-traceability has to be standardized. The GTIN (Global Trade Item Number) barcode system which has been globally standardized and implemented, is reviewed with the mandatory food-labeling regulation in expiration date of processed foods. The integration of GTIN-13 bar-code system for food-traceability is a crucial factor to expand its function in the food-related industrial areas. In this literature, the standard coding system of identification-number for food-traceability is proposed with 20 digit coding number which is combined with GTIN-13 bar-code (13 digit), expiration date (6 digit), and additional classification code (1 digit). This proposed standard coding system for identification-number has a several advantages in application for prohibiting the sale of hazard goods, food-recall, and inquiring food traceability-information. And also, this proposed coding system could enhance the food traceability system by communicating and harmonizing the information with the national network such as UNI-PASS and electronic Tax-invoice system. For the global application, the identification-number for food-traceability needs to be cooperated with the upcoming global standards such as GTIN-128 bar-code and GS1 DataBar.

• Journal of Nutrition and Health
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• v.47 no.4
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• pp.268-276
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• 2014

Purpose: The aim of this study is to estimate total sugar intake and identify major food sources of total sugar intake in the diet of the Korean population. Methods: Dietary intake data of 33,745 subjects aged one year and over from the KNHANES 2008-2011 were used in the analysis. Information on dietary intake was obtained by one day 24-hour recall method in KNHANES. A database for total sugar content of foods reported in the KNHANES was established using Release 25 of the U.S. Department of Agriculture National Nutrient Database for Standard Reference, a total sugar database from the Ministry of Food and Drug Safety, and information from nutrition labeling of processed foods. With this database, total sugar intake of each subject was estimated from dietary intake data using SAS. Results: Mean total sugar intake of Koreans was 61.4 g/person/day, corresponding to 12.8% of total daily energy intake. More than half of this amount (35.0 g/day, 7.1% of daily energy intake) was from processed foods. The top five processed food sources of total sugar intake for Koreans were granulated sugar, carbonated beverages, coffee, breads, and fruit and vegetable drinks. Compared to other age groups, total sugar intake of adolescents and young adults was much higher (12 to 18 yrs, 69.6 g/day and 19 to 29 yrs, 68.4 g/day) with higher beverage intake that beverage-driven sugar amounted up to 25% of total sugar intake. Conclusion: This study revealed that more elaborated and customized measures are needed for control of sugar intake of different subpopulation groups, even though current total sugar intake of Koreans was within the range (10-20% of daily energy intake) recommended by Dietary Reference Intakes for Koreans. In addition, development of a more reliable database on total sugar and added sugar content of foods commonly consumed by Koreans is warranted.

• Journal of Nutrition and Health
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• v.47 no.4
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• pp.258-267
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• 2014

Purpose: The current study was designed to investigate the relationship of dietary calcium and riboflavin and their main dietary source (milk and dairy products) with the risk of periodontitis using data from 2007 to 2010 Korea National Health and Nutrition Examination Surveys. Methods: A total of 1,690 adults aged ${\geq}40$ years were included. We used results of dental examination regarding all sextant information on probing depth of at least two index teeth, nutritional assessment by a single 24-hour dietary recall, and demographic and medical information. The periodontitis group was defined as those who had 3-4 points, and the normal group was defined as those who had 0 points of Community Periodontal Index at all locations of six examination sites using a probe. Results: Using multiple logistic regression analysis, after adjustment for age, body mass index, energy intake, income, smoking, and alcohol drinking, we found an inverse relationship between consumption of dairy products and risk for periodontitis (OR: 0.465, 95% CI: 0.224-0.964) and between dietary riboflavin intake more than the estimated average requirements and risk for periodontitis (OR: 0.535, 95% CI: 0.300-0.954) in males. Conclusion: Adequate intake of milk dairy products and riboflavin may be recommended for prevention of periodontitis in the Korean male population.