• Proceedings of the KSME Conference
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• 2003.11a
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• pp.308-313
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• 2003

Thermal mass flow meters (TMFMs) are most widely used for measuring mass flow rates in the semiconductor industry. A TMFM should have a short response time in order to measure the time-varying flow rate rapidly and accurately. Therefore it is important to study transient heat transfer phenomena in the sensor tube of a TMFM that is the most critical part in the TMFM. In the present work, a simple numerical model for transient heat transfer phenomena of the sensor tube of a TMFM is presented. Numerical solutions for the tube and fluid temperatures in a transient state are obtained using the proposed model and compared with experimental results to validate the proposed model. Based on numerical solutions, heat transfer mechanism in a transient state in the sensor tube is explained. Finally, a correlation for predicting the response time of a sensor tube is presented. The correlation is verified by experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.369-373
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• 2001

In this paper, we proposes the steps adopted to construct the neural network model for GMAW welds. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters are influenced by numerous factors, such as welding current, arc voltage, torch travel speed, electrode condition and shielding gas type and flow rate etc. In traditional work, the structural mathematical models have been used to represent this relationship. Contrary to the traditional model method, neural network models are based on non-parametric modeling techniques. For the welding process modeling, the non-linearity at well as the coupled input characteristics makes it apparent that the neural network is probably the most suitable candidate for this task. Finally, a suitable proposal to improve the construction of the model has also been presented in the paper.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.1-6
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• 2007

The integrated thermal-chemical-mechanical (TCM) material removal model presented in the companion paper is dynamically simulated in this work. The model is applied to a Cu CMP process for the simulation and the results of the three individual ingredients composing the model are presented separately first. These results are then incorporated to calculate the total material removal rate (MRR) of the Cu CMP. It is shown that the non-linear trend of MRR with respect to the applied mechanical power (i.e., non-Prestonian behavior), which is not well explained with the models established in principle on conventional contact mechanics, may be due to the chemical reaction(s) varying non-linearly with the temperature in the wafer.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• Korea Journal of Hospital Management
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• v.8 no.3
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• pp.72-91
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• 2003

The purpose of this study is to investigate the recognition of health information managers, and to analyze the level of job satisfaction and organizational commitment of medical record technicians. The data for this study were collected through a self-administered survey with a structured questionnaire to 172 subjects from medical record technicians working in hospitals in Seoul and Gyeonggi Province as well as the faculty of medical schools across South Korea. In this analysis frequency, t-test, ANOVA, factor analysis and structural equation model were used. The main findings of this study are as follows: 1. As for recognition of the seven dimensions in the role of health information managers, the role as clinical data specialist received the most positive feedback, followed by document & repository managers, patient information coordinators, health information managers, data quality managers, security officers and research & decision support analyst. 2. The level of job satisfaction among medical information handlers and managers averaged 3.14. In terms of the factors in the work environment concerned with job satisfaction, being able to work independently and as team players reached the top among 6 factors with the average of 3.39, followed by professional position, salary & rewards, expectations for job performance and administration. 3. The average rate of organizational commitment stood at 3.09. Respondents tend to be focused on present tasks rather than future-oriented tasks. 4. The result of the analysis based on the relationship between recognition as health information managers, job satisfaction and organizational commitment found that all analysis are statistically meaningful. The more the respondents were aware of their roles as health information managers, the more they tended to be committed to their work and satisfied with their work. The more the respondents were committed to their work, the more satisfaction was seen. The effects of recognition as health information managers on organizational commitment measured 0.27 and for job satisfaction it was 0.17. The effects of organizational commitment on job satisfaction stood at 0.71. The feasibility of the model meets the standard at Chi-square value of 66.755 and the P value of 0.057. The Normed Fit Index (NFI) of 0.930 was in compliance with the standard for model feasibility and the squared multiple correlation coefficient of this model was 8% in organizational commitment and 60% in job satisfaction.

• Tribology and Lubricants
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• v.13 no.4
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• pp.71-78
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• 1997

Wear is affected by numerous factors-contact load, sliding velocity and distance, friction coefficient, material properties and environmental conditions. Among these wear factors, surface hardness is one of very important factors to determine wear. But surface hardness is varied by work hardening during repeated sliding contact. In this reason wear rate is increased or decreased with varying surface hardness, and transition of wear mechanism is happened. In this study, the surface hardening by accumulating residual stress was analyzed by considering the repeated sliding Hertzian contact model. The results showed that surface hardness was increased with increasing contact load, friction coefficient and contact number. And the depth of hardening layer, plastic layer and elastic layer depended upon contact load and number, but they didn't depend upon friction coefficient. The predicted surface hardness was about 1.5-1.8 times as hard as the material.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.458-461
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• 2016

In this study, a finite element analysis for high-speed blanking of mild steel is performed. A thermomechanically coupled simulation model of a blanking process was developed using ABAQUS/Explicit. Through a simulation of the high-speed blanking process of mild steel, the influence of the punch speed, tool edge radius, and work material thickness on the development of the plastic heat and punch load were studied. The results of the study revealed that a higher punch speed caused thermal softening of the work material and decreased the punch load. Decreasing tool edge radius could help reduce the punch load. In addition, the results of the study revealed that the thermal softening effect was more dominant in the blanking of a mild steel sheet with a greater thickness as compared to that in the blanking of a mild steel sheet with a lower thickness.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.61-68
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• 2023

The phenomenon of fretting wear due to the flow-induced vibration in steam generator (SG) tube is a significant degradation mechanism in nuclear power plants. Fretting wear in SG tube is primarily attributed to the friction and impact forces between the SG tube and the tube support structures, experienced during nuclear power plants operation. While the Archard model has generally been used for the prediction of fretting wear in SG tube, it is limited by its linear nature. In this study, we introduced an "Impact Shear Work-rate" (ISW) model, which takes into account the combined effects of impact and sliding. The ISW model was evaluated using existing experimental data on fretting wear in SG tube and was compared against the Archard model. The prediction results using the ISW model were more accurate than those using the Archard model, particularly for impact forces.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1902-1912
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• 2024

This paper presents results from system-level modeling of a water-based reactor cavity cooling system using RELAP5-3D. The computational model is benchmarked with experimental data from a half-scale RCCS test facility at Argonne National Laboratory. The model prediction is first compared with a two-phase oscillatory baseline experimental case where mixed accuracy is obtained. The model shows reasonable prediction of mass flow rate, pressure, and temperature but significant overprediction of void fraction. The model prediction is then compared with a fault case where the inlet of the risers is gradually reduced using a throttling valve. As the valve is closed, the model is able to predict some major flow phenomena observed in the experiment such as the dampening of oscillations, the reintroduction of oscillations, as well as boiling, flashing, and geysering in the risers. However, the timeline of these events are not well captured by the model. The model is also used to investigate the evolution of flow regime in the chimney. This work highlights that the semi-empirical constitutive relations used in RELAP-3D could have a strong influence on the accuracy of the model in two-phase oscillatory flows.