• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.729-736
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• 2017

In this study, the physics-based model and machine learning technique were used to conduct model-assisted probability of detection (MAPOD) experiments. The possibility of using in-service cracked parts was also investigated. Bolt hole shaped specimens with fatigue crack on the hole surface were inspected using eddy current inspection. Owing to MAPOD, the number of experimental factors decreased significantly. The uncertainty in the crack length measurement for in-service cracked parts was considered by the application of Monte Carlo simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.657-665
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• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.465-466
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• 2002

In any mechanical system consisting of gears, shafts and/or bearings, the majority of metallic particles deposited into and carried by the lubrication system originate from the deterioration of oil-wetted working surfaces, even in proper lubrication system, due to failure mechanism (s) such as wear, fatigue and fretting corrosion. Determination of the point at which transition from normal to abnormal or to actual damage occurs has become a focus of attention in research activities for years, because it has been recognized that reliable, economic operation can be achieved through appropriate preventative measures. Known collectively from 'all size wear debris analysis' as early failure detection, the methods of testing for damage differ considerably, range from a micron or a submicron size debris analysis to Magnetic Chip Detector (MCD) ferrous debris analysis. This paper will be focused on the utilization of the low-cost analysis techniques for evaluation of industrial machinery condition.

• Journal of the Korean Society for Nondestructive Testing
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• v.26 no.6
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• pp.440-446
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• 2006

The reactor vessel body and closure head are fastened with the stud bolt that is one of crucial parts for safety of the reactor vessels in nuclear power plants. It is reported that the stud bolt is often experienced by fatigue cracks initiated at threads. Stud bolts are inspected by the ultrasonic technique during the overhaul periodically for the prevention of failure which leads to radioactive leakage from the nuclear reactor. The conventional ultrasonic inspection for stud bolts was mainly conducted by reflected echo method based on shadow effect. However, in this technique, there were numerous spurious signals reflected from every oblique surfaces of the thread. In this study, ultrasonic phased array technique was applied to investigate detectability of flaws in stud bolts and characteristics of ultrasonic images corresponding to different scanning methods, that is, sector and linear scan. For this purpose, simplified stud bolt specimens with artificial defects of various depths were prepared.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.909-914
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• 2007

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially moving window. The main purpose of this paper is to prevent a damage or crack from the fatigue in advance. 16 channel sensors data are considered and maximum stress is computed by a sensor fusion method every moving window. After the maximum stress every window is compared with a given threshold, this overload detecting algorithm decides overload or not.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.195-198
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• 2001

Backside defect of plate structure may grow due to fatigue or overload to cause critical failure during operation, so it is important to detect this kind of defect in line. For this purpose, nondestructive, non-contact and highly sensitive method is required. ESPI and Shearography are considered as useful method to satisfy these requirements. In this paper, the possibility of application of ESPI and Shearography to detect the backside defect of steel plate and to quantify the defect size was tested. For the experiment, some steel plates with defect on the backside were prepared. Experimental results for these plates showed that location and size of defect could be detected correctly by both of ESPI and Shearography.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.227-233
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• 2002

Suspension system is one of the most important components indespensible for stability and reliability of automobiles. The demands to more safe and durable suspension system have been increased as the automobiles get popular and improve in quality. The crack in the coil spring of the suspension system produced during manufacturing may grow under a fatigue load and cause a severe safety problems which lead to a catastrophic damage to the passengers. Many conventional NDT techniques including ET, RT, and UT are less sensitive or hard to apply to detect the surface breaking crack in the suspension coils partly because the techniques are point-to-point measurement methods, thus take too long time to inspect the coil spring longer than 1m. Contrary to this, Lamb wave technique is full-field measurement method that make it possible to examine the whole coil spring in real time. In this paper, the Lamb wave is applied to the coil spring to investigate the possibility to detect the cracks on the surface of the coil spring.

• Tuberculosis and Respiratory Diseases
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• v.40 no.4
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• pp.353-356
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• 1993

Advent of new imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound contributed greately to the specific imaging diagnosis. However plain chest X-ray is still most prequently used for imaging diagnosis of respiratory disease in clinical pratic and it is important to make a good quality of X-ray film and good interpretation. The optimal chest X-ray should be taken with full inspiration without rotation and motion and the exposure is at the level of barely demonstrable thoracic vertebral disc space. It is recommended that higk KVP technique for detection of lesions which is overlaped by mediastinum, heart and rib cage. It is better to examine chest X-ray film start at some distance(6-8 feet) and closer to the film later on and the reader should not read a film in fatigue condition. The reading room should be quiet and relately dark illumination. It is important, to make a good X-ray film and good interpretation to reduce the observer error.