• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.138-147
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• 2024

Objectives: Fine particulate matter pollution has emerged as a significant life-threatening issue in Thailand. Recognizing the importance of environmental health literacy (EHL) in disease prevention is crucial for protecting public health. This study investigated EHL levels and aimed to identify associated factors among village health volunteers (VHVs) in the upper northern region of Thailand. Methods: A cross-sectional study was conducted to collect data from 710 VHVs using the EHL assessment tool developed by the Department of Health, Thailand. Results: The overall EHL score was moderate (mean, 3.28 out of a possible 5.0), with the highest and lowest domain-specific mean score for the ability to make decisions (3.52) and the ability to access (3.03). Multiple linear regression revealed that the factors associated with EHL score were area of residence (urban areas in Chiang Mai: B=0.254; urban areas in Lampang: B=0.274; and rural areas in Lampang: B=0.250 compared to rural areas in Chiang Mai), higher education levels (senior high school: B=0.212; diploma/high vocational certificate: B=0.350; bachelor's degree or above: B=0.528 compared to elementary school or lower), having annual health checkups compared to not having annual health check-ups (B=0.142), monthly family income (B=0.004), and individuals frequently facing air pollution issues around their residence (B=0.199) compared to those who reported no such issues. Conclusions: The VHVs exhibited moderate EHL associated with residence area, education, health check-ups, family income, and residential air pollution. Considering these factors is vital for enhancing VHVs' EHL through strategic interventions.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.3
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• pp.121-128
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• 2024

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f^0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune's source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1256-1263
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• 2024

In the realm of Architecture, Engineering, and Construction (AEC) education, various factors play a crucial role in shaping students' acceptance of the learning environments facilitated by visualization technologies, such as virtual reality (VR). Works on leveraging the heterogeneous educational information (i.e., pedagogical data, student performance data, and student survey data) to identify essential factors influencing students' learning experience and performance in virtual environments are still insufficient. This research proposed KnowLearn, an interactive learning assistant system, to integrate an educational knowledge graph (KG) and a locally deployed large language model (LLM) to generate real-time personalized learning recommendations. As the knowledge base of KnowLearn, the educational KG accommodated multi-faceted educational information from twelve perspectives, such as the teaching content, students' academic performance, and their perceived confidence in a specific course from the AEC discipline. A heterogeneous graph attention network (HAN) was utilized to infer the latent information in the KG and, thus, identified the perceived confidence, intention to use, and performance in a relevant quiz as the top three indicators that significantly influenced students' learning outcomes. Based on the information preserved in the KG and learned from the HAN model, the LLM enhanced the personalization of recommendations concerning adopting virtual learning environments while protecting students' privacy. The proposed KnowLearn system is expected to feasibly provide enhanced recommendations on the teaching module design for educators from the AEC domain.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.114-129
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• 2006

Ontology mapping is the task of finding semantic correspondences between two ontologies. In order to improve the effectiveness of ontology mapping, we need to consider the characteristics and constraints of data models used for implementing ontologies. Earlier research on ontology mapping, however, has proven to be inefficient because the approach should transform input ontologies into graphs and take into account all the nodes and edges of the graphs, which ended up requiring a great amount of processing time. In this paper, we propose a multi-strategic mapping approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the topic maps. Our multi-strategic mapping approach includes a topic name-based mapping, a topic property-based mapping, a hierarchy-based mapping, and an association-based mapping approach. And it also uses a hybrid method in which a combined similarity is derived from the results of individual mapping approaches. In addition, we don't need to generate a cross-pair of all topics from the ontologies because unmatched pairs of topics can be removed by characteristics and constraints of the topic maps. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Yahoo german literature dictionary as input ontologies. Our experiments show that the automatically generated mapping results conform to the outputs generated manually by domain experts, which is very promising for further work.

• Quality Improvement in Health Care
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• v.11 no.1
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• pp.32-45
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• 2004

Objective : The aim of this study were to measure quality of life(QOL) in liver transplant recipients, to compare QOL between living donor liver transplant recipients and cadaveric liver transplant recipients and to investigate whether SF-36 may be used as a disease-specific instrument in liver transplant recipients. Methods : We conducted a single-center cross-sectional study of 133 LT recipients ages 13 to 65 years, all of whom had had Liver Transplantation(LT) at least 1 months previously. QOL was assessed using a self-completion questionnaire consisting of the Bang Whal Ran(1991) instruments and the 36-Item Short-Form Health Survey(SF-36) health status profile measure. We investigated whether the SF-36 instrument may be used as a disease-specific instrument in LT recipients. Individual scale scores range from 0 to 100, with higher score reflecting better health. Data on demographics, clinical status at pre transplantation 1 day, post transplantation clinical status, and graft function were collected to identify predictors of post transplantation QOL. Results : Standard measures for test-retest reliability, internal consistency, and discriminant and concurrent validity were examined. The reliability of the SF-36, as measured by test-retest correlation(Pearson coefficients: 0.729, p=0.002) and by internal consistency(Cronbach's alpha: 0.9431) exceeded conventional acceptability criteria. The correlation between domain scores of SF-36 and the Bang Whal Ran(l991) was clear and logical in that the clinical characteristics of SF-36 strongly correlated with the clinical component summary score of the Bang Whal Ran(l991)(r = 0.8155, P<.01). SF-36 scale scores were compared between Cadaveric Liver Transplant recipients and Living Donor Liver Transplant recipients. Donor types of post LT did not influence HRQOL(p>0.05). 87% of the liver transplant recipients were satisfied to get LT. Satisfaction of post LT showed significantly greater HRQOL(p<0.001). Conclusion : SF-36 is found reliable and valid. This study indicates thet Donor Type did not influence HRQOL after LT. The information gained from this study will help us to better define expectations and the clinical course after liver transplantation to patients and their families.

• Journal of the Korean Geotechnical Society
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• v.21 no.4
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• pp.71-81
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• 2005

Surface wave techniques were initially based on 2-D plane waves and were later improved to the techniques based the 3-D based cylindrical waves. However, body-wave interference, near-field effect and limited technology in surface wave measurements restricted the use of 3-D cylindrical waves to the 1-D evaluation of subgrade stiffness. In this study, by the numerical simulation of SASW measurements, the dispersion properties of surface waves including vertical, horizontal Rayleigh waves and Love waves were thoroughly investigated in the 3-D domain, and a new filter criteria to minimize the near-field effect was established, which led to CAP (common-array-profiling)-SASW technique. The CAP-SASW technique enabled the evaluation of subgrade stiffness fur a specific subgrade segment, not for a whole section of measurement array. Therefore, a contour plot of subgrade stiffness with a ground-truth quality can be obtained by the CAP-SASW technique. The procedure proposed in this study was verified by comparing the shear-wave velocity profiles with the shear-wave velocity profiles of downhole testing at two geotechnical sites.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.203-211
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• 2011

Recently, information communication technologies such as smartphone or mobile app greatly affect various application fields including geo-spatial domain. And development scheme of mobile web app or hybrid app regards as the most important computing technology which is combined each advantage of mobile app and mobile web. Despite these trends, it is general case that satellite images are used for the background image for other contents services. With this motivation, hybrid app for geo-metadata as the base for dissemination and service is designed and implemented as the prototype, in this study. At the design stage, HTML5, which is the core technology on an international standardization process for hybrid app, is applied. In the implementation, PhoneGap and Sencha Touch as mobile SDK(Software Development Kit) supporting HTML5 on cross-platform in open sources are used. In prototype, some KOMPSAT-2 images covering small area and mandatory elements in geo-metafata standard are tested. As mobile industry applications and business service models based on satellite images on mobile platform are progressing and diversifying, it is expected that this approach and implemented prototype are considered as an important reference.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.463-474
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• 2009

By employing the subscriber concept in broadcasting services, IPTV (Internet Protocol Television) operators provide various grades of services to subscribers based on the billing level of the subscribers. With the income from subscribers for a basis, IPTV operators plan to provide high quality services. Since Web browser-based IPTV provides T-commerce and E-commerce services as well as television services, users may frequently visit other service domains to buy goods or content. To provide the user with charged or private services, these service domains request authentication of user. The existing authentication system is not appropriate for the IPTV service environment because the environment unavoidably forces the user to cross from one authentication-based service domain to another. Single sign-on provides a user with transparent authentication services by enabling an authenticated user to move between authentication-based service domains without any re-authentication. Like this distributed environment, since the IPTV service environment also provides a variety of authentication-based services, transparent authentication service needs to be provided to subscribers who want to access charged or private services. In this paper, we propose a new user authentication scheme for the IPTV environment. This scheme integrates the Security Assertion Markup Language (SAML), which is a standard for XML-based single sign on. We validate this scheme using a simple use case scenario.