• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.83-90
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• 2010

The demands of analysis for the physical errors of systems and prediction system using this has increased steadily with computing environment growth linking real system just like IT Convergence. The physical errors are unpredictable because of relations of various elements such as natural phenomenon and mechanical errors. Especially, the elevator system occurs various problems because of the complexity of system so that we need to efficient approach for this. In this paper, we propose the analysis and management system for elevator based on data minining that predict the error to gather information about physical or natural phenomenon. This helps actively responding in early stage and saving lives through prediction of error and an early warning for just such an eventuality.

• Journal of the Korean Society for Railway
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• v.15 no.3
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• pp.278-285
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• 2012

The Service Availability (SA) in the viewpoint of passenger is used as the key performance indicator (KPI) of quality of service in Light Rail Transit (LRT) Public-Private Partnerships projects. But there are many disputes on the target value of SA because of the lack of experience in SA. The target value of SA should be set at an early stage of the project to be specified on the system specifications and operation plan. Therefore, this paper developed the quantitative prediction model of SA to set the reasonably achievable target value of SA at an early stage of the LRT project. Also this paper analyzed the relationship and differentiation of SA and Train Punctuality (TP) that is mostly compared with SA.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.53-63
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• 2023

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

• Tribology and Lubricants
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• v.40 no.1
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• pp.1-7
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• 2024

Ball bearings are a major component of mechanical parts for transmitting rotation. Compared to tapered roller bearings, ball bearings offer less rolling resistance, which leads to reduced heat generation during operation. Because of these characteristics, ball bearings are widely used in electric vehicles and machine tools. The design of ball bearing cages has recently emerged as a major issue in ball bearing design. Cage design requires pre-verification of performance using theoretical or experimental formula or computational fluid dynamics (CFD). However, CFD analysis is time-consuming, making it difficult to apply in case studies for design decisions and is mainly used in performance prediction following design confirmation. To use CFD in the early stages of design, main-taining analytical accuracy while reducing the time required for analysis are necessary. Accordingly, this study proposes a laminar steady-state segment CFD technique to solve the problem of long CFD analytical times and to enable the use of CFD analysis in the early stages of design. To verify the reliability of the CFD analysis, a bearing drag torque test is performed, and the results are compared with the analytical results. The proposed laminar steady-state segment CFD technique is expected to be useful for case studies in bearing design, including cage design.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.771-780
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• 2009

Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.

• Journal of the Korea Concrete Institute
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• v.16 no.1 s.79
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• pp.102-110
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• 2004

Recently, production cost of ready mixed concrete(remicon) has been increased due to the rising cost of raw materials such as cement and aggregate etc. cause by the upturn of oil price and increase of shipping charge. The delivery cost of remicon companies, however, has been decreased owing to their excessive competition in sale. Consequently, remicon companies began to manufacture the concrete by mixing ground granulated blast furnace slag(GGBF) in order to lower the production cost. Therefore, the objective of this study was to predict 28-day strength of GGBF slag concrete by early strength(1 day-strength, 7 day-strength) for the sake of managing with ease the quality of remicon. In experimental results, the prediction equation for 28 day-strength of GGBF slag concrete could be produced through the linear regression analysis of early strength and 28 day-strength. In order to acquire the reliability, all mixture were repeated as 3 times and each mixture order was carried out by random sampling. The prediction equation for 28 day-strength of GGBF slag concrete by 1-day strength(hot-water method) won the good reliability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.2A
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• pp.159-166
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• 2008

In this paper, we proposed an algorithm to solve the problem that can't maintain hop synchronization using only early-late gate tracking loop due to the drift of geo-stationary satellite in frequency hopping satellite communication system. When the signal is transferred to downlink through DRT(Dehop-Rebop Transponder), the problem with synchronization loss is occurred periodically when using only early-late gate tracking loop, because of energy loss in each side portion of hop due to orbital variation of the satellite. To solve this problem, we have developed Anti-Shrink synchronization tracking algorithm which uses the prediction value of transmission timing and the structure of inner-outer gate instead of early-late gate with the ranging information. Through simulations, we showed that the performance of the Anti-Shrink algorithm is better than that of simple inner-outer energy ratio algorithm and similar to that of conventional early-late tracking loop algorithm with ranging information. No synchronization failure in the proposed algorithm was occurred because of less energy loss and robustness without the ranging information.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.21-28
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• 2016

Numeric weather prediction is important to prevent meteorological disasters such as heavy rain, heat wave, and cold wave. The Korea meteorological administration provides a realtime special weather report and the rural development administration demonstrates information about 2-day warning of agricultural disasters for farms in a few regions. To improve the early warning systems for meteorological hazards, a nation-wide high-resolution dataset for weather prediction should be combined with web-based GIS. This study aims to develop a web service prototype for early warning of meteorological hazards, which integrates web GIS technologies with a weather prediction database in a temporal resolution of 1 hour and a spatial resolution of 1 km. The spatially and temporally high-resolution dataset for meteorological hazards produced by downscaling of GME was serviced via a web GIS. In addition to the information about current status of meteorological hazards, the proposed system provides the hourly dong-level forecasting of meteorologic hazards for upcoming seven days, such as heavy rain, heat wave, and cold wave. This system can be utilized as an operational information service for municipal governments in Korea by achieving the future work to improve the accuracy of numeric weather predictions and the preprocessing time for raster and vector dataset.