• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.42-45
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• 2003

Optical fiber vibrations sensors (OFVSs) and extrinsic Fabry-Perot interferometer (EFPI) were used in damage monitoring of fiber-metal laminates(FML). The optical fiber vibration sensor and EFPI were applied in order to detect and evaluate the strain, damage and failure of FML. Damages in composites, such as matrix cracks, delamination and fiber breakage may occur as a result of excessive load, fatigue and low-velocity impacts. Indentation test was performed with the measurement of optical signal and acoustic emission (AE). The signals of the optical fiber vibration sensor due to damages were quantitatively evaluated by wavelet transform. It was found that damage information of comparable in quality to acoustic emission data could be obtained from the optical fiber vibration sensor signals.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.49-50
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• 2011

In this paper we collect data concerning sleep environments in a bedroom and analyze the relationship between the collected condition data and sleep. In addition, this paper detects scene changes from the subjects in a sleeping state and presents the physical conditions, reactions during sleep, and physical sensations and stimuli. To detect scene changes in image sequences, we used color histogram for the difference between the preceding frame and the current frame. In addition, to extract the tossing and turning for different situations, the subjects were instructed to enter the level of fatigue, the level of drinking, and the level of stomach emptiness.

• Computers and Concrete
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• v.14 no.1
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• pp.73-90
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• 2014

The traditional destructive tests in damage detection require high cost, long consuming time, repairing of damaged members, etc. In addition to these, powerful equipments with advanced technology have motivated development of global vibration based damage detection methods. These methods base on observation of the changes in the structural dynamic properties and updating finite element models. The existence, location, severity and effect on the structural behavior of the damages can be identified by using these methods. The main idea in these methods is to minimize the differences between analytical and experimental natural frequencies. In this study, an application of damage detection using model updating method was presented on a one storey reinforced concrete (RC) building model. The model was designed to be 1/2 scale of a real building. The measurements on the model were performed by using ten uni-axial seismic accelerometers which were placed to the floor level. The presented damage identification procedure mainly consists of five steps: initial finite element modeling, testing of the undamaged model, finite element model calibration, testing of the damaged model, and damage detection with model updating. The elasticity modulus was selected as variable parameter for model calibration, while the inertia moment of section was selected for model updating. The first three modes were taken into consideration. The possible damaged members were estimated by considering the change ratio in the inertia moment. It was concluded that the finite element model calibration was required for structures to later evaluations such as damage, fatigue, etc. The presented model updating based procedure was very effective and useful for RC structures in the damage identification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2353-2358
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• 2011

Sleep is very important physiology to our human, about one third of human life was sent over to sleep. This paper measures of sleep and proposes sleep quality and future direction in order to improve the sleep environment. Sleep measure was determined by using X2 histogram that is one of the scene change detection method. X2 histogram method is one of the statistical scene change detection and is used in many studies because of the histogram method performs better than the other. And find out their relationship by entering the degree of fatigue, alcohol, and hungry in order to develop quality of sleep and extracting to tossing and turning according to each situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3820-3841
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• 2018

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.45-51
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• 2004

Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.223-229
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• 2008

In this paper. we propose the real-time vision system which judges drowsiness driving based on levels of drivers' fatigue. The proposed system is to prevent traffic accidents by warning the drowsiness and carelessness using face-image analysis and fuzzy logic algorithm. We find the face position and eye areas by using fuzzy skin filter and virtual face model in order to develop the real-time face detection algorithm, and we measure the eye blinking frequency and eye closure duration by using their informations. And then we propose the method for estimating the levels of drivel's fatigue based on measured data by using the fuzzy logic and for deciding whether drowsiness driving is or not. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.423-424
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• 2021

In this paper, we propose a system that collects data from sensors that detect the sleeping environment and adjusts the sleeping environment optimally based on the environment to help you get a good night's sleep. The sleep environment analysis is based on the determination of the sleep stage by detection of twisting through the load cell. In addition, based on data such as temperature, humidity, and illuminance, heat devices, humidifiers, blinds, etc. are controlled to create an environment in which to have a good sleep. The sleep environment control according to the sleep state can reduce fatigue when waking up by inducing a sleep state that is easy to wake up.

• Composites Research
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• v.37 no.4
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• pp.343-349
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• 2024

The use of composite materials for structural fasteners is increasingly common, making it crucial to assess the deformation of these fasteners under fatigue behavior. In this study, clamp-type fasteners were manufactured using carbon fiber reinforced composites, and their structural stability and sectional damage rates were evaluated using electrical resistance measurement during fatigue behavior. While clamp-type composite fasteners exhibited minimal deformation in flat sections, significant deformation occurred in the bent sections due to fatigue. It was observed that insufficient angular stability led to concentrated damage in the bent sections. The dynamic fatigue behavior showed that the length change rate of the composite fasteners was within 0.6%, but the angular change rate reached up to 6%, indicating that the bent sections are the most critical areas. By utilizing the self-sensing capability of the composite fasteners, sectional damage behavior was assessed through electrical resistance measurement. Significant damage was noted in the bent sections due to fatigue, and 3D-CT results revealed substantial deformation and interfacial damage when the initial bend angle of the fasteners was less than 90 degrees. These findings highlight the importance of reinforcing the stiffness of the bent sections and establishing systematic angular standards in the development of composite fasteners.