• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• 한국산업융합학회 논문집
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• 제27권2_1호
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• pp.257-268
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• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

• 산업융합연구
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• 제22권2호
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• pp.13-22
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• 2024

현대 학습자들은 배경, 학습 스타일, 능력 등에서 다양한 차이를 보인다. 하지만 모든 학습자에게 동일한 학습 내용을 전달하는 전통적 교육 방법은 이러한 학습자의 다양성을 충분히 고려하지 못한다. 따라서 개별 학습자의 특성에 따라 최적의 학습 경험을 제공하는 맞춤형 학습 시스템의 구현은 오늘날 에듀테크 시대에 더욱 중요해졌다. 본 논문은 증가하는 학습자 중심의 교육 요구에 따라 학습자의 특성, 관심사, 학습 이력 등을 종합적으로 분석할 수 있는 모델들을 파악한 후 이를 기반으로 맞춤형 학습 시스템을 설계했다. 본 시스템은 학습자의 학습 이력을 기반으로 학습자의 현재 수준과 목표에 맞춘 자기주도적 학습을 지원하기 위해 강점과 약점을 파악할 수 있도록 설계되었으며 이 과정에서 시스템의 설계 변경 없이 필요한 학습 요소들을 확장할 수 있도록 구성하였다. 본 연구를 통해 사용자 맞춤형 학습 시스템 구축에 필요한 주요 기반을 파악하고 맞춤형 학습을 지원하기 위한 시스템 아키텍처를 효과적으로 구축할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• 대한방사선기술학회지:방사선기술과학
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• 제43권6호
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• pp.495-502
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• 2020

In this study, the prevalence of osteoporosis was predicted based on 10 independent variables such as age, weight, and alcohol consumption and 4 tree-based machine-learning models, and the performance of each model was compared. Also the model with the highest performance was used to check the performance by clearing the independent variable, and Area Under Curve(ACU) was utilized to evaluate the performance of the model. The ACU for each model was Decision tree 0.663, Random forest 0.704, GBM 0.702, and XGBoost 0.710 and the importance of the variable was shown in the order of age, weight, and family history. As a result of using XGBoost, the highest performance model and clearing independent variables, the ACU shows the best performance of 0.750 with 7 independent variables. This data suggests that this method be applied to predict osteoporosis, but also other various diseases. In addition, it is expected to be used as basic data for big data research in the health care field.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• 산업경영시스템학회지
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• 제43권1호
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• 디지털융복합연구
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• 제12권2호
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• pp.467-473
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• 2014

적응적 학습 시스템은 학습자의 학습 요구에 따라서 학습 자료를 적응적으로 제공해주는 시스템을 의미한다. 적응적 학습 시스템은 전문가 모델, 수업 모델, 학습자 모델로 구성되어있다. 전문가 모델은 가르치는 정보를 저장하고 있다. 학습자 모델은 학생들의 학습 정보와 학습 이력에 대한 데이터를 저장한다. 수업 모델은 실제 학습자에게 필요한 학습 자료를 제공해주는 모델이다. 본 논문에서는 학습자 프로파일 정보를 통하여 학습자 모델을 구성하였으며, 동적 시나리오 구축을 통하여 수업 모델을 구성하였다. 이후 학습자의 프로파일 정보 기반의 동적 시나리오를 구축해줌으로써 학습자에게 적응적으로 학습 콘텐츠를 제공해주는 시스템을 개발하였다. 마지막으로 시스템에 대한 만족도 결과는 88%로 높은 만족도를 보였다.

• 패션비즈니스
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• 제25권2호
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• pp.98-109
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• 2021

The purpose of this study was to present the case of class operation by paralleling a general instructor's lecture class and a IC-PBL class in a fashion design major course and comparing the educational effects. The teaching model of this study was designed to improve the ability to use the knowledge gained in accordance with the needs of the industrial field and to develop an independent learning ability. It will provide meaningful data. This study measured and considered the qualitative items of self-efficacy and changes in class perception through interviews and questionnaires completed by the learners after experiencing each general class and IC-PBL class. The results of this study are, first, that in the History of Fashion and Costume class, the general teaching method and the IC-PBL teaching method were applied in parallel to design a class, and a method case was presented. Second, as a result of comparing the educational effects of the two teaching methods through a student questionnaire, IC-PBL was more effective in improving learning attitude, learning achievement and self-efficacy. In addition, after the IC-PBL class on History of Fashion and Costume, the students' negative perception of team activities improved, and the students' cooperative ability and creativity improved.