• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.350-354
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• 1995

The Tension Leveller of Cold Rolling Mill In POSCO performs levelling the strip in high speed line. But minor variations in operating condition of driving machines such as motor, gear box, and support bearings, a small gap-variation of supporter and strip slip by poor roll revolutions can cause serious problems in the quality of strip. In this study, firstly, A condition monitoring standard for each sensor is made through with the detail analysis of vibration and strip slip. Secondly, An automatic monitoring and diagnosing system was developed to monitor the condition of Tension Leveller, and diagnose the cause of abnormal condition. Finally, A diagnosing algorithm for abnormal condition and man-machine interface (MMI) for easy operation are developed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1405-1409
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• 2000

The electro-magnetic noise generated from fan-driving motor is one of important items in view of abnormal noise which affect the quality of air conditioner noise. The electro-magnetic noise of the outdoor unit is identified by the serial experiments for related parts, whose effects are verified to give proper measures for reduced noise level. The weight of fan appears to be the most important factor, and the stiffness of the frame, such as motor bracket is shown to be important also.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.332-333
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• 2006

The driving forces and the probable processes of WC-Co grain growth are reanalysed from recent data of interface energy and microstructure. Grain growth is driven by the disappearing of the high energy WC/WC and WC/Co interfaces with habit planes different from {0001}, ${10\bar{1}0}$ and ${11\bar{2}0}$ facets and by the area decrease of the WC/WC and WC/Co interfaces with {0001} and ${10\bar{1}0}$ habit planes. Grain growth mainly results of dissolution-precipitation. Abnormal grains are likely formed by defects assisted nucleation.

• Journal of the Korean Ceramic Society
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• v.48 no.6
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• pp.641-647
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• 2011

The present study aims to identify the effect of sintering atmosphere [$O_2$, 75$N_2$-25 $H_2$ (mol%) and $H_2$] on microstructural evolution at the relatively low sintering temperature of 1040$^{\circ}C$. Samples sintered in $O_2$ showed a bimodal microstructure consisting of fine matrix grains and large abnormal grains. Sintering in 75 $N_2$ - 25 $H_2$ (mol %) and $H_2$ caused the extent of abnormal grain growth to increase. These changes in grain growth behaviour are explained by the effect of the change in step free energy with sintering atmosphere on the critical driving force necessary for rapid grain growth. The results show the possibility of fabricating $(K_{0.5}Na_{0.5})NbO_3$ at low temperature with various microstructures via proper control of sintering atmosphere.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of the Korean Society for Heat Treatment
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• v.35 no.2
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• pp.57-65
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• 2022

In this study, the effect of the initial particle size distribution (PSD) of (K_0.5Na_0.5)NbO₃ powders on the microstructure of sintered ceramics was investigated. (K_0.5Na_0.5)NbO₃ powders with uni-, bi-, tri-, and quad-modal PSDs were obtained through a planetary ball-mill. For the specimens sintered at 1080℃, the growth of abnormal grains was promoted from the powders exhibiting quad- and tri-modal PSDs with a high content of large particles, resulting in a microstructure in which huge abnormal grains were predominant. However, as the number of peaks in PSD and the overall particle size decreased, the abnormal grain growth was suppressed and the grain growth of small particles started, resulting in a microstructure with a uniform grain size. For the specimens sintered at 1100℃, huge abnormal grains were not observed due to the decrease in the critical driving force for 2D nucleation even when powders with quad- and tri-modal PSDs were used. It was confirmed that when powder with unimodal PSD was used, a uniform microstructure that was not significantly affected by the sintering temperature could be obtained. The results of this study demonstrate that the microstructure of (K_0.5Na_0.5)NbO₃-based ceramics can be controlled by controlling the particle size of the initial powder.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.63-70
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• 2018

In this work, we analyze the relationship between the accumulated mileage of hybrid electric vehicle(HEV) and the data provided from vehicle parts. Data were collected while traveling over 70,000 Km in various paths. The data collected in seconds are aggregated for 10 minutes and characterized in terms of centrality, variability, normality, and so on. We examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval of a hybrid car. When the cumulative mileage interval is categorized into =< 30,000, <= 50,000, and >50,000, the statistical properties are classified by the mileage interval as 82.3% accuracy. This indicates that if the data of the vehicle parts is collected by operating the hybrid vehicle for 10 minutes, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect the abnormality of the vehicle part relative to the accumulated mileage. It can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.11-16
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• 2019

This paper suggests an algorithm for detecting fault of longitudinal controller in autonomous vehicles. Guaranteeing safety in fault situation is essential because electronic devices in vehicle are dependent each other. Several methods like alarm to driver, ceding control to driver, and emergency stop are considered to cope with fault. This research investigates the fault monitoring process in fail-safe system, for controller which is responsible for accelerating and decelerating control in vehicle. Residual is computed using desired acceleration control command and actual acceleration, and detection of its abnormal increase leads to the decision that system has fault. Before computing residual for controller, health monitoring process of acceleration signal is performed using hardware and analytic redundancy. In fault monitoring process for controller, a process model which is fitted using driving data is considered to improve the performance. This algorithm is simulated via MATLAB tool to verify performance.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.135-141
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• 2023

The volatilization of alkali ions in (K,Na)NbO₃ (KNN) ceramics was inhibited by doping them with alkaline earth metal ions. In addition, the grain growth behavior changed significantly as the sintering duration (t_s) increased. At 1,100 ℃, the volatilization of alkali ions in KNN ceramics was more suppressed when doped with alkaline earth metal ions with smaller ionic size. A Ca²⁺-doped KNN specimen with the least alkali ion volatilization exhibited a microstructure in which grain growth was completely suppressed, even under long-term sintering for t_s = 30 h. The grain growth in Sr²⁺-doped and Ba²⁺-doped KNN specimens was suppressed until t_s = 10 h. However, at t_s = 30 h, a heterogeneous microstructure with abnormal grains and small-sized matrix grains was observed. The size and number of abnormal grains and size distribution of matrix grains were considerably different between the Sr²⁺-doped and Ba²⁺-doped specimens. This microstructural diversity in KNN ceramics could be explained in terms of the crystal growth driving force required for two-dimensional nucleation, which was directly related to the number of vacancies in the material.