• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1969-1977
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• 2004

To model and understand the physics of partial rub, a nonlinear rotor model is sought by applying a nonlinear parameter identification technique to the experimental data. The results show that the nonlinear terms of damping and stiffness should be included to model partial rotor rub. Especially, the impact and friction during the contact between rotor and stator are tried to explain with a nonlinear model on the basis of experimental data. The estimated nonlinear model shows good agreements between the numerical and the experimental results in its orbit. Also, the estimated nonlinear model could explain the backward whirling orbit and jump phenomenon, which are the typical phenomena of partial rub.

• International Journal of Reliability and Applications
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• v.14 no.1
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• pp.27-39
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• 2013

In this article, a new model based on the Rayleigh distribution is introduced. This model is useful and practical in physics, reliability, and life testing. The statistical and reliability properties of this model are presented, including moments, the hazard rate, the reversed hazard rate, and mean residual life functions, among others. In addition, it is shown that the distributions of the new model are ordered regarding the strongest likelihood ratio ordering. Four estimating methods, namely, method of moment, maximum likelihood method, Bayes estimation, and uniformly minimum variance unbiased, are used to estimate the parameters of this model. Simulation is used to calculate the estimates and to study their properties. Finally, the appropriateness of this model for real data sets is shown by using the chi-square goodness of fit test and the Kolmogorov-Smirnov statistic.

• Tribology and Lubricants
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• v.24 no.5
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• pp.215-220
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• 2008

An internal friction model was developed to model the frictional behavior of automotive Constant Velocity (CV) joints by using the test data from an instrumented CV joint friction apparatus with actual driveshaft assemblies. Experiments were conduced under different realistic operating conditions of oscillatory speeds, CV joint articulation angles, lubrication, and torque. The experimental data were used to develop a physics-based semi-empirical CV joint internal friction coefficient model as a function of different CV Joint operating parameters. It was found that the proposed friction model captures the experimental results well not only the static behavior of friction coefficient, but also the dynamic friction terms, which is the main source of force that causes vehicle vibration problems.

• Journal of the Korea Computer Graphics Society
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• v.16 no.2
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• pp.1-7
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• 2010

This paper proposes a real-time simulation technique for thin rods undergoing large rotational deformation. Rods are thin objects such as ropes and hairs that can be abstracted as 1D structures. Development of a satisfactory physical model that runs in real-time but produces visually convincing animation of thin rods has been remaining a challenge in computer graphics. We adopt the energy formulation based on continuum mechanics, and develop a modal warping technique for rods that can integrate the governing equation in real-time. This novel simulation framework results from making extensions to the original modal warping technique, which was developed for the simulation of 3D solids. Experiments show that the proposed method runs in real-time even for large meshes, and that it can simulate large bending and/or twisting deformations with acceptable realism.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• Geomechanics and Engineering
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• v.15 no.1
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• pp.677-686
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• 2018

Aimed at serious casing damage problem during the process of oilfield development by injecting water, based on seepage mechanics, fluid mechanics and the theory of rock mechanics, the multi-physics coupling theory was also taken into account, the mathematical model for production of petroleum with water flooding was established, and the method to solve the coupling model was presented by combination of Abaqus and Eclipse software. The Q9G3 block in Jibei oilfield was taken for instance, the well log data and geological survey data were employed to build the numerical model of Q9G3 block, the method established above was applied to simulate the evolution of seepage and stress. The production data was imported into the model to conduct the history match work of the model, and the fitting accuracy of the model was quite good. The main mechanism of casing damage of the block was analyzed, and some wells with probable casing damage problem were pointed out, the displacement of the well wall matched very well with testing data of the filed. Finally, according to the simulation results, some useful measures for preventing casing damage in Jibei oilfield was proposed.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Korean Journal of Optics and Photonics
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• v.1 no.2
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• pp.210-216
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• 1990

This study was performed to obtain fundamental data on temperature increases in the dental tissues irradiated by IO.opm laser radiation. For this purpose a experimental facility was established. which was composed of a CO2 laser. a shutter unit and a temperature sensing device. The temperature changes in the pulp chamber of extracted molars. during and after the laser irradiation. were measured as function of laser power. the time of irradration and the thickness of the sample. An empirical formula for the maximum temperature increases, $\DeltaT_m$ was derived from the measured data as follows; $\DeltaT_m=\alphaP\Delta\tauexp(-\betad)$$ where P. $\Delta\tau$ and d are the laser power(W). irradiation time{sec) and the thickness(mm) between pulp chamber and occlusal surface. respectively. Also a theoretical calculation model based on simplified assumptions were established and the results from the calculation were compared with the measured temperature data. A fairly good agreement was obtained.obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2513-2530
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• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.