• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.346-351
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• 1997

The creep characteristics of an Al-5wt.% Ag alloy including the stress exponent, the activation energy for creep and the shape of the creep curve were investigated at a normalized shear stress extending from $ 10^{-5}{\;}to{\;}3{\times}10^{-4}$ and in the temperature range of 640-873 K, where silver is in solid solution. The experimental results shows that the stress exponent is 4.6, the activation energy is 141 kJ/mole, and the stacking fault energy is $180{\;}mJ/m^2$, suggesting that the creep behavior of Al-5 wt.% Ag is similiar to that reported for pure aluminum, and that under the current experimental conditions, the alloy behaves as a class II(metal class). The above creep characteristics obtained for Al-5 wt.% Ag are discussed in the light of prediction regarding deformation mechanisms in solid solution alloys.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• The Korean Society of Law and Medicine
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• v.11 no.1
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• pp.59-84
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• 2010

Medical practice is characterized by various physiological response and uncapacity of prediction, therefore when medical accident occur it's hard to prove medical professionals' mistake. Though medical accident by medical professionals' mistake will be compensated anyhow, about irresistible medical accidents, no one should be not bound to compensate, victims get into very difficult situation. So, the nation don't negligent irresistible medical accidents but compensate anyway. As in the past, to the legal principle's constitution of irresistible medical accidents, theory of liability without fault was adapted, and it was said this theory was illogical in theory of liability with fault. But the subject of compensation to irresistible medical accidents is nation, nation don't participate in medical treatment therefore there is no room to occur mistake. And it is not reasonable to regard medical agency as a truster of public service, to cast to it responsibility of medical accidents. The problem of compensation to irresistible medical accidents is understood under the theory of social compensation. Social compensation is consisted of compensation to sacrifice and contribution to nation and society and compensation to sacrifice revealed under danger, the compensation to irresistible medical accidents belongs to the latter. This is near to concept of relief, is applied to national compensation system supplementarily, and compensation have no option but to compensate minimum. And there are not relation between national compensation system of irresistible medical accidents and proof liability transposition and theory of liability with out fault, merely in side of sharing responsibility burden between medical treater and victim, it is reasonable to discuss transportation of proof liability and compulsive liability insurance together.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.21-30
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• 2014

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Economic and Environmental Geology
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• v.51 no.4
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• pp.393-400
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• 2018

Despite some skeptical views on the possibility of earthquake prediction, observation and evaluation of precursory changes have been continued throughout the world. In Korea, the public concern on the earthquake prediction has been increased after 2016 $M_L5.8$ and 2017 $M_L5.4$ earthquakes occurred in Gyeongju and Pohang, the southeastern part in Korea, respectively. In this study, the abnormal increase of groundwater level was observed before the 2016 $M_L5.8$ Gyeongju earthquake in a borehole located in 52 km away from the epicenter. The well was installed in the Yangsan fault zone, and equipped for the earthquake surveillance. The abnormal change in the well would seem to be a precursor, considering the hydrogeological condition and the observations from previous studies. It is necessary to set up a specialized council to support and evaluate the earthquake prediction and related researches for the preparation of future earthquake hazards.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Economic and Environmental Geology
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• v.51 no.2
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• pp.185-201
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• 2018

Nuclear power plants(NPP) are constructed and operated to ensure safety against natural disasters and man-made disasters in all processes including site selection, site survey, design, construction, and operation. This paper will introduce a series of efforts conducted in Korea Hydro and Nuclear Power Co. Ltd., to assure the safety of nuclear power plant against earthquakes and other natural hazards. In particular, the present status of the earthquake, fault, and slope safety monitoring system for nuclear power plants is introduced. A earthquake observatory network for the NPP sites has been built up for nuclear safety and providing adequate seismic design standards for NPP sites by monitoring seismicity in and around NPPs since 1999. The Eupcheon Fault Monitoring System, composed of a strainmeter, seismometer, creepmeter, Global Positioning System, and groundwater meter, was installed to assess the safety of the Wolsung Nuclear Power Plant against earthquakes by monitoring the short- and long-term behavioral characteristics of the Eupcheon fault. Through the analysis of measured data, it was verified that the Eupcheon fault is a relatively stable fault that is not affected by earthquakes occurring around the southeastern part of the Korean peninsula. In addition, it was confirmed that the fault monitoring system could be very useful for seismic safety analysis and earthquake prediction study on the fault. K-SLOPE System for systematic slope monitoring was successfully developed for monitoring of the slope at nuclear power plants. Several kinds of monitoring devices including an inclinometer, tiltmeter, tension-wire, and precipitation gauge were installed on the NPP slope. A macro deformation analysis using terrestrial LiDAR (Light Detection And Ranging) was performed for overall slope deformation evaluation.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.603-615
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• 2007

Software changes for various kinds of reasons and they increase maintenance cost. Software metrics, as quantitative values about attributes of software, have been adopted for predicting maintenance cost and fault-proneness. This paper proposes relationship between some typical object-oriented metrics and software changes in industrial settings. We used seven metrics which are concerned with size, complexity coupling, inheritance and polymorphism, and collected data about the number of changes during the development of an Information system on .NET platform. Based on them, this paper proposes a model for predicting the number of changes from the object-oriented metrics using multiple regression analysis technique.