• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.803-812
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• 2008

Along with the increasing demand for automatic and mechanical tunnel excavation methods in Korea, the Tunnel Boring Machine (TBM) method of tunnel excavation has become increasingly popular. However, in spite of this rising demand, few studies have been performed on the TBM method, in Korea. For this reason, this study focused on evaluation of the applicability of TBM performance prediction models based on field data in order to contribute to the basic and essential parts of TBM designation and the TBM method of tunnel excavation in Korea. These rock properties can be defined as the mechanical and physical factors of rock that have an influence on a disc cutter's ability to cut rock, and provide information for the evaluation of the applicability of field data. Based on outcomes from these tests, applicability of the prediction model was evaluated and the predicted performance of a TBM was compared with real field data obtained from four different TBM construction sites in Korea.

• Wind and Structures
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• 제38권2호
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.

• 4차산업연구
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• 제3권1호
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• pp.13-20
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• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• 대한간호학회지
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• 제34권1호
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• pp.172-181
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• 2004

Purpose: This study was designed to construct a structural model for explaining negative outcomes of anger in female adolescents. Methods: Data was collected by questionnaires from 199 female adolescents ina female high school in Seoul. Data analysis was done with SAS for descriptive statistics and a PC-LISREL Program for Covariance structural analysis. Results: The fit of the hypothetical model to the data was moderate, thus it was modified by excluding 7 paths and adding free parameters to it. The modified model withthe paths showed a good fit to the empirical data($x^2$ =5.62, p=.69, GFl=.99, AGFl=.97, NFI=.99, NNFI=l.01, RMSR=.02, RMSEA=.00). Trait anger, state anger, and psychosocial problems were found to have a significant direct effect on psychosomatic symptoms. State anger, psychosocial problems, and learning behaviorswere found to have direct effects on depression of female adolescents. Conclusion: The derived modelis considered appropriate for explaining and predicting negative outcomes of anger in female adolescents. Therefore, it can effectively be used as a reference model for further studies and is a suggested direction in nursing practice.

• 한국안전학회지
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• 제31권3호
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• pp.162-167
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• 2016

As the simple empirical and phenomenological model applied to the analysis of leakage and explosion of chemical substances does not regard numerous variables, such as positional density of installations and equipment, turbulence, atmospheric conditions, obstacles, and wind effects, there is a significant gap between actual accident consequence and computation. Therefore, the risk management of a chemical plant based on such a computation surely has low reliability. Since a process plant is required to have outcomes more similar to the actual outcomes to secure highly reliable safety, this study was designed to apply the CFD (computational fluid dynamics) simulation technique to analyze a virtual prediction under numerous variables of leakages and explosions very similarly to reality, in order to review the computation technique of the practical safety distance at a process plant.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Computers and Concrete
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• 제24권5호
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.