• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.185-196
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• 2023

Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.464-477
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• 2013

There is a great need to develop a training program proven to change behavior and improve knowledge. The purpose of this study was to evaluate employee hygiene knowledge, hygiene practice, and cleanliness, before and after HACCP system implementation at one small-size restaurant. The efficiency of the system was analyzed using time-temperature control after implementation of u-HACCP$^{(R)}$. The employee hygiene knowledge and practices showed a significant improvement (p<0.05) after HACCP system implementation. In non-heating processes, such as seasoned lettuce, controlling the sanitation of the cooking facility and the chlorination of raw ingredients were identified as the significant CCP. Sanitizing was an important CCP because total bacteria were reduced 2~4 log CFU/g after implementation of HACCP. In bean sprouts, microbial levels decreased from 4.20 logCFU/g to 3.26 logCFU/g. There were significant correlations between hygiene knowledge, practice, and microbiological contamination. First, personnel hygiene had a significant correlation with 'total food hygiene knowledge' scores (p<0.05). Second, total food hygiene practice scores had a significant correlation (p<0.05) with improved microbiological qualities of lettuce salad. Third, concerning the assessment of microbiological quality after 1 month, there were significant (p<0.05) improvements in times of heating, and the washing and division process. On the other hand, after 2 months, microbiological was maintained, although only two categories (division process and kitchen floor) were improved. This study also investigated time-temperature control by using ubiquitous sensor networks (USN) consisting of an ubi reader (CCP thermometer), an ubi manager (tablet PC), and application software (HACCP monitoring system). The result of the temperature control before and after USN showed better thermal management (accuracy, efficiency, consistency of time control). Based on the results, strict time-temperature control could be an effective method to prevent foodborne illness.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.861-874
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• 2012

To fulfill applicability of Soil and Water Assessment Tool (SWAT) model, it is important that this model passes through a careful calibration and uncertainty analysis. In recent years, many researchers have come up with various uncertainty analysis techniques for SWAT model. To determine the differences and similarities of typical techniques, we applied three uncertainty analysis procedures to Chungju Dam watershed (6,581.1 $km^2$) of South Korea included in SWAT-Calibration Uncertainty Program (SWAT-CUP): Sequential Uncertainty FItting algorithm ver.2 (SUFI2), Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol). As a result, there was no significant difference in the objective function values between SUFI2 and GLUE algorithms. However, ParaSol algorithm shows the worst objective functions, and considerable divergence was also showed in 95PPU bands with each other. The p-factor and r-factor appeared from 0.02 to 0.79 and 0.03 to 0.52 differences in streamflow respectively. In general, the ParaSol algorithm showed the lowest p-factor and r-factor, SUFI2 algorithm was the highest in the p-factor and r-factor. Therefore, in the SWAT model calibration and uncertainty analysis of the automatic methods, we suggest the calibration methods considering p-factor and r-factor. The p-factor means the percentage of observations covered by 95PPU (95 Percent Prediction Uncertainty) band, and r-factor is the average thickness of the 95PPU band.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.51-60
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• 2020

This study aimed to develop a modular smart clothing system for heart rate monitoring that reduces the inconvenience caused by battery charging and the large size of measurement devices. The heart rate monitoring system was modularized into a temporary device and a continuous device to enable heart rate monitoring depending on the requirement. The temporary device with near-field communication (NFC) and heart rate sensors was developed as a clothing attachment type that enables heart rate monitoring via smart phone tagging when required. The continuous device is based on Bluetooth Low Energy (BLE) communication and batteries and was developed to enable continuous heart rate measurement via a direct connection to the temporary device. Furthermore, the temporary device was configured to connect with a textile electrode made of a silver-based knitted fabric designed to be located below the pectoralis major muscle for heart rate measurement. Considering the user-experience factors, key functions, and the ease of use, we developed an application to automatically log through smart phone tagging to improve usability. To evaluate the accuracy of the heart rate measurement, we recorded the heart rate of 10 healthy male subjects with a modular smart clothing system and compared the results with the heart rate values measured by the Polar RS800. Consequently, the average heart rate value measured by the temporary system was 85.37, while that measured by the reference device was 87.03, corresponding to an accuracy of 96.73%. No significant difference was found in comparison with the reference device (T value = -1.892, p = .091). Similarly, the average heart rate measured by the continuous system was 86.00, while that measured by the reference device was 86.97, corresponding to an accuracy of 97.16%. No significant difference was found in terms of the heart rate value between the two signals (T value = 1.089, p = .304). The significance of this study is to develop and validate a modular clothing system that can measure heart rates according to the purpose of the user. The developed modular smart clothing system for heart rate monitoring enables dual product planning by reducing the price increase due to unnecessary functions.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Food Science and Preservation
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• v.14 no.1
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• pp.35-41
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• 2007

As the consumption of environmentally friendly agricultural products increases, food safety is at the forefront of public health concerns. We analyzed the biological hazards of 26 species of leaf vegetables and 4 species of fruit according to types of cultivation (conventional or organic filming) and distribution system (giant retailers or organic food stores) using various culture media, automatic bacterial identification systems, and microscopy, Total bacterial count of unwashed agricultural product ranged from $5.2{\times}10^{3}\;to\;1.5{\times}10^{5}\;CFU/mL$ (from 0.1 g of agricultural products), and the average count dropped 25-fold (range, 8-60-fold) after water washing. Microbial levels of washed organic agricultural products were $6.0{\times}10^{2}-1.9{\times}10^{4}\;CFU/mL$, and were not significantly different f개m the microbial loads on conventionally farmed produce. There was no significant difference in bacterial count from agricultural produce purchased from giant retailers or organic food stores. Total microbial count of Chinese cabbage, welsh onion, red chicory and kale were comparatively high, and Enterobacter cloacae was isolated most frequently. Parasites were detected in agricultural products purchased from conventional farm products in the stores of giant retailers, and in organic food stores, and parasite prevalence was especially high in Chinese cabbages and welsh onion. The study indicated that cultivation methods and distribution systems did not cause significant differences in biological contamination levels of agricultural produce. Some vegetables and fruits were highly contaminated effective sanitizing methods to reduce these biological hazards are needed.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.152-157
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• 2008

The customer satisfaction of WAP service greatly relies on the usability of the service due to the limited display size of a mobile phone and limitation in realizing UI (User Interface) for function keys, browser, and OS (operating system) Currently, a number of contents providers develop and deliver varying services, and thus, it is critical to control quality level of UI in consistent standards and manner. This study suggests usability index evaluation system to achieve consistent UI quality control of various WAP services. The system adopts both top-down and bottom-up approaches. The former concerns deriving UI design components and evaluation checklists for the WAP, based on the usability attributes and UI principles. The latter concerns deriving usability-related evaluation checklists from the established UI design features, and then grouping them from the viewpoint of usability principles and attributes. This bidirectional approach has two outstanding advantages: it allows thorough examination of potential elements that can cause usability problems from the standpoint of usability attributes, and also derives specific evaluation elements from the perspective of UI design components that are relevant to the real service environment. The evaluation system constitutes a hierarchical structure by networking usability attributes, UI guideline which indicates usability principles for each attribute, and usability evaluation checklist for each UI component that enables specific evaluation. Especially, each evaluation checklist contains concrete contents and format so that it can be readily marked in O/X. The score is based on the ratio of number of items that received positive answer to the number of total items. This enables a quantitative evaluation of the usability of mobile WAP service. The validity of the proposed evaluation system has been proved through comparative analysis with the real usability problems based on the user test. A software was developed that provides guideline for evaluation objects, criteria and examples for each checklist, and automatically calculates a score. The software was applied to evaluating and improving the real mobile WAP service.