• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4437-4445
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• 2024

During the process of bilateral teleoperation, operators not only need to perform complex maintenance tasks but also have to constantly monitor the safety of the operation, leading to reduced operational efficiency. Therefore, in this paper, we introduce a shared autonomous scheme that intervenes in the operator's command input when necessary, autonomously ensuring the safe operation of the manipulator by employing a rolling horizon planning controller based on Nonlinear Model Predictive Control (NMPC). This controller considers the motion boundaries and collision avoidance constraints of the manipulator, accompanied by the design of corresponding objective functions. To validate the effectiveness of the proposed method, we conduct tests on collision-free trajectory tracking and comprehensive performance with collision constraints, confirming the feasibility and excellent performance of the approach.

• Journal of Korean Academy of Nursing
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• v.35 no.7
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• pp.1285-1294
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• 2005

Purpose: This study was done to evaluate the predictive value of aspects of the Transtheoretical model (TTM) of behavior change as applied to smoking cessation in a rural population. Method: A convenience sample was recruited from a public health center in a community. A total of 484 participants were recruited, including 319 smokers, 116 ex-smokers and 49 non-smokers. A cross-sectional and descriptive design was used in this study. Data was analyzed using descriptive statistics, frequency statistics, ANOVA and Logistic regression. Result: The major findings were 1) The participants were assessed at baseline for their current Stage of Change resulting in a distribution with $42.1\%$ in Precontemplation, $24.1\%$ in Contemplation, $9.7\%$ in Preparation, $6.2\%$ in Active, and $17.9\%$ in the Maintenance stage. 2) There were statistically significant differences of processes of change, decisional balance and situational temptation across the stages of change. 3) The main factors that affect smoking cessation were age, number of years smoking, age when began smoking, self-liberation and negative/affective situations, which combined explained $33.2\%$ of the smoking cessation. Conclusion: TTM variables measured prior to a smoking cessation program added little predictive value for cessation outcome beyond that explained by demographic and smoking history variables.

• Tunnel and Underground Space
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• v.29 no.5
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• pp.356-366
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• 2019

Various TBM performance prediction models have been developed and most of them were considered penetration rate only. Despite the fact that some models have suggested equations and charts for estimating the utilization factor, but there are a few studies to estimate the TBM utilization factor. Utilization factor is affected by the type of TBM machine, operation, maintenance of machine, geological conditions, contractor experience and other factors. In this study, more than 100 case studies are analyzed to determine the relationship between the utilization factor and RMR, geological conditions, TBM types, tunnel length, and TBM diameter. Simple and multiple linear regression analysis are performed to develop predictive models for the utilization factor. The predictive model with explanatory variables of geological conditions, TBM types, tunnel length, and TBM diameter does not give a good correlation. The predictive models with explanatory variable of RMR give higher values of the coefficient of determination.

• Korean Journal of Childcare and Education
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• v.18 no.3
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• pp.19-36
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• 2022

Objective: This study aimed to analyze the changes and predictive factors of mothers' parenting competencies during their children's second to third grades in primary school. Methods: We used the data from the Panel study of Korean Parental Educational Involvement. We classified 373 mothers into three groups, 'reduced' parenting competency, 'maintained' parenting competency, and 'increased' parenting competency, and conducted one-way variance analysis and multinomial logistic regression analysis. Results: First, the mothers' parenting competency decreased between their children's 2nd year and 3rd year in primary school. Second, the 'reduced', 'maintained', and 'increased' groups differed from each other in the degree of change in self-system competency, level of understanding of school life, number of counseling sessions with homeroom teachers, and social networking. Third, the degree of change in self-system competency and social networking predicted the increase in mothers' parenting competency. The degree of change in self-system competency and the level of understanding of school life predicted the maintenance of mothers' parenting competency. Conclusion/Implications: This study, for the first time, has revealed the change in mothers' parenting competency and its predictive factors after the second year in primary school. How to support the growth of mothers' parenting competency was also discussed.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.321-327
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• 1998

This paper presented a case study on the application of on-line wear monitoring technique to a high duty air-turbo-compressor system. Main objects monitored were a gear unit and metal bearings, both shown frequent troubles due to the severe operation conditions at heavy dynamic load. The air-turbo-compressor system needs secure condition monitoring because it is one of the main utilities in steel making industry. Temperature and vibration characteristics have been mainly on-line monitored in this system for a predictive maintenance; however, it has been shown that they are not fairly good enough to give an early warning prior to the machine failure. In this work, an on-line Opto Magnetic Detector(OMD) was implemented for an on-line wear monitoring, which quantitatively measured the contamination level of both ferrous and non-ferrous wear particles by detecting the change in optical density of used oil. Results showed that the application of on-line OMD system was satisfactory in diagnosis of the machine system.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.