• The Research Journal of the Costume Culture
• /
• v.24 no.1
• /
• pp.13-26
• /
• 2016

The purpose of the present study is to investigate the effect of time remaining to use mileage in the notification message from retailers on consumer responses. A total of 577 consumers participated in experiments involving different notification messages of the time remaining to use mileage. Results showed: 1) a significant difference in mileage benefit perception, positive emotion, negative emotion, attitude toward retailers, and repurchase intention according to the remaining time to use mileages, 2) benefit perception positively affected positive emotion and negatively affected negative emotion; positive emotion positively affected and negative emotion negatively affected attitude toward retailers; and attitude positively affected repurchase intention on retailers, and 3) the remaining time to use mileages moderates the relationship between attitude and repurchase intention. Findings highlighted the importance of timing of the message to notify the consumer as to remaining time to use mileage. In the case of a message indicating long remaining time to use mileage, consumers showed more positive responses toward retailers than did consumers who had a message indicating short remaining time to use mileage. These results can be used as guidelines to select the optimal time to send notification messages of remaining time to use mileage in order to generate positive consumer responses.

• Nuclear Engineering and Technology
• /
• v.56 no.8
• /
• pp.3167-3179
• /
• 2024

In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

• Computers and Concrete
• /
• v.18 no.1
• /
• pp.69-97
• /
• 2016

Performance-based remaining life assessment of reinforced concrete bridge girders, subject to chloride-induced corrosion of reinforcement, is addressed in this paper. Towards this, a methodology that takes into consideration the human judgmental aspects in expert decision making regarding condition state assessment is proposed. The condition of the bridge girder is specified by the assignment of a condition state from a set of predefined condition states, considering both serviceability- and ultimate- limit states, and, the performance of the bridge girder is described using performability measure. A non-homogeneous Markov chain is used for modelling the stochastic evolution of condition state of the bridge girder with time. The thinking process of the expert in condition state assessment is modelled within a probabilistic framework using Brunswikian theory and probabilistic mental models. The remaining life is determined as the time over which the performance of the girder is above the required performance level. The usefulness of the methodology is illustrated through the remaining life assessment of a reinforced concrete T-beam bridge girder.

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.153-162
• /
• 2010

The finite buffer GI/G/1/K system is set up by using an unconventional arrangement of the state space, in which the remaining interarrival time or service time is chosen as the level. The stationary distributions of resulting Markov chain can be explicitly determined, and the chain is positive recurrent without any restriction. This is an advantage of this method, compared with that using the elapsed time approach [2].

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.4
• /
• pp.536-545
• /
• 2020

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

• Biomedical Science Letters
• /
• v.30 no.2
• /
• pp.49-55
• /
• 2024

This study uses the 9th 1st year (2022) National Health and Nutrition Examination Survey to analyze the relationship between sleep time and the number of existing teeth for the adult population aged 19 or older to provide basic data on related dental development. There is a purpose. This program is designed to improve sleep quality and maintain the number of viable teeth in the future. The subjects were 53,220 people who answered the questions. The collected data were analyzed using SPSS (ver 21.0) program using complex samples, and chi-square analysis and logistic return analysis were performed. As a result, it was found that 2.537 times more existing teeth remained when sleep time was 9 hours or more than when sleep time was 6 hours or less, and there was a statistically significant difference. In conclusion, it is necessary to recognize the importance of the number of remaining teeth and to make efforts to manage personal immunity, such as sleep management for adults, and to promote and prevent oral care and oral health education in order to maintain the number of remaining teeth.

• Journal of Korean Institute of Industrial Engineers
• /
• v.43 no.3
• /
• pp.176-183
• /
• 2017

In recent years, studies about multitasking becomes more important. During multitasking, operators can feel frustration when they are interrupted during the task and frustration can affect operator's emotional state and performance. However there is no research on measuring the frustration quantitatively in multitasking environment. In this paper, we suggested quantitative measurement of frustration during multitasking. In order to measure the frustration, we made a mathematical representation with emotional decay model and the initial intensity of frustration based on cognitive closure theory. The amount of initial intensity could be represented as the ratio of actual remaining time to expected remaining time. By the experiment, we measured the frustration during the experiment and compared this values with values of frustration dimension of NASA-TLX. Finally we got the linear regression model with a good accuracy ($R^2=0.986$). This study contributes to measuring the emotion quantitatively by the relation of expected and actual remaining time in multitasking environment.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.4
• /
• pp.2401-2406
• /
• 2015

Pipelines are used by large heavy industries to deliver various types of fluids. Since this is important to maintain the performance of large systems, it is necessary to accurately predict remaining life of the corroded pipeline. However, predicting the remaining life is difficult due to uncertainties in the associated variables, such as geometries, material properties, corrosion rate, etc. In this paper, a statistical method for predicting corrosion remaining life is proposed using Bayesian inference. To accomplish this, pipeline failure probability was calculated using prior information about pipeline failure pressure according to elapsed time, and the given experimental data based on Bayes' rule. The corrosion remaining life was calculated as the elapsed time with 10 % failure probability. Using 10 and 50 samples generated from random variables affecting the corrosion of the pipe, the pipeline failure probability was estimated, after which the estimated remaining useful life was compared with the assumed true remaining useful life.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.1
• /
• pp.48-55
• /
• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Journal of Internet Computing and Services
• /
• v.21 no.3
• /
• pp.83-92
• /
• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.