• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.426-431
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material in real time. In this paper a realtime detection of the brazing defect of thin Inconel plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (23 kHz) ultrasonic transducer was used to infuse the welded Inconel plates with a short pulse of sound for 280 ms. The ultrasonic source has a maximum power of 2 kW. The surface temperature of the area under inspection is imaged by an infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the bound between the two faces of the Inconel plates near the defective brazing point and heated up highly, are observed. And the weak thermal signal is observed at the defect position of brazed plate also. Using the image processing technology such as background subtraction average and image enhancement using histogram equalization, the position of defective brazing regions in the thin Inconel plates can be located certainly.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.679-683
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• 2013

BSR noise emerges in a vehicle as a result of road vibrations, engine vibrations, and speaker vibrations. BSR noise occurs with an irregular impact or stick slip friction phenomenon as the influence of the resonance mode when the vibration input load is transferred along poor joint and contacting pairs of the system. A sub-structure method of finite element analysis is required to detect impacts and slip in the full vehicle model. This study presents a method for sub-structure modeling and a rattle and squeak detection methodology that considers the characteristics of road vibration inputs.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.427-433
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• 1999

The purpose of the study is to develop an improved pitch extraction method that can be used in a variety of speech applications such as high-puality compression and vocoding, and recognition and synthesis of speech. To do so, we develop a new electroglottograph (EGG) measurement system that is based on the four modulation-demodulation type spot electrodes for detecting the EGG signals. Then, the glottal closure instant(GCI) is determined from the EGG signals on a real-time basis. We can obtain the pitch contour using the information on the GCI. It turns out that the new pitch contour algorithm (PCA) operates more reliably as compared to the conventional speech-only-based algorithm. In addition, we study the speech source models and glottal vibratory patterns for Koreans by measuring and analyzing the diversified vibration patterns of the vocal from the EGG signals.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.96-101
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• 2014

Wind power is the fastest growing renewable energy source in the world and it is expected to remain so for some times. Recently, there is a constant need for the reduction of Operational and Maintenance(O&M) costs of Wind Energy Conversion Systems(WECS). The most efficient way of reducing O&M cost would be to utilize CMS(Condition Monitoring System) of WECS. CMS allows for early detection of the deterioration of the wind generator's health, facilitating a proactive action, minimizing downtime, and finally maximizing productivity. There are two types of faults such as mass unbalance and aerodynamic asymmetry which are related to wind turbine's rotor faults. Generally, these faults tend to generate various vibrations. Therefore, in this work a simple fault detection algorithm based on spectrums of vibration signals and simple max-min decision logic is proposed. Furthermore, in order to verify its feasibility, several simulation studies are carried out by using GH-bladed software.

• KSBB Journal
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• v.22 no.6
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• pp.443-446
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• 2007

A prognostic indicator of coronary heart disease, C-reactive protein, was tried to be determined by a batch-type quartz crystal microbalance immunosensor. The sensor was operated by direct-binding mode and the optimum concentration for the corresponding antibody for immobilization was $50{\mu}g/ml$. The reaction buffer for the system was 0.1 M sodium phosphate (pH 7.0) and system operation was performed in the order of baseline stabilization, analyte addition and measurement, and regeneration of the sensor chip with 10 mM NaOH. When plotted in double-logarithmic scale, the sensor showed a linear detection range of 0.27-106.00 nM for rat C-reactive protein with the limit of detection of 0.53 nM. It also showed a good reusability.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.416-423
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• 2009

The ultrasonic pressure fields for the 3 MHz linear phased array transducer with sixteen piezoelectric elements of which one may not be operated by defect were simulated theoretically and measured experimentally using a visualization system of the Schlieren method. The simulation results for steering angles of $0^{\circ}$ and $30^{\circ}$ show that the side-lobe patterns of the transducer including a defective element is quite different from the transducer with all normal elements, and those patterns are in good agreement with the results of visualization. It is shown that the defective elements in a linear array transducer can be detected by comparison of the simulated and the visualized side-lobe patterns in two dimensional acoustic fields.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.30-39
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• 2012

In this paper, a safe driving assistance system is proposed by detecting the status of driver's doze based on face and eye detection. By the level of the fatigue, safe driving system alarms or set the seatbelt on vibration. To reduce the effect of backward light and too strong solar light which cause a decrease of face and eye detection rate and false fatigue detection, post processing techniques like image equalization are used. Haar transform and PCA are used for face detection. By using the statistic of the face and eye structural ratio of normal Koreans, we can reduce the eye candidate area in the face, which results in reduction of the computational load. We also propose a new eye status detection algorithm based on Hough transform and eye width-height ratio, which are used to detect eye's blinking status which decides doze level by measuring the blinking period. The system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. In this paper, four algorithms are implemented and proposed algorithm is made based on the probability model and we achieves 84.88% of correct detection rate through indoor and in-car environment experiments. And also we achieves 69.81% of detection rate which is better result than that of other algorithms using IR camera.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.29-39
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• 2015

This paper describes a design and implementation of fuzzy-based algorithm for hand-shake state detection and error compensation in the mobile optical image stabilization(OIS) motion detector. Since the gyro sensor output of the OIS motion detector includes inherent error signals, accurate error correction is required for prompt hand-shake error compensation and stable hand-shake state detection. In this research with a little computation overhead of fuzzy-based algorithm, the hand-shake error compensation could be improved by quickly reducing the angle and phase error for the hand-shake frequencies. Further, stability of the OIS system could be enhanced by the hand-shake states of {Halt, Little vibrate, Big vibrate, Pan/Tilt}, classified by subdividing the hand-shake angle. The performance and stability of the proposed algorithm in OIS motion detector is quantitatively and qualitatively evaluated with the emulated hand-shaking of ${\pm}0.5^{\circ}$, ${\pm}0.8^{\circ}$ vibration and 2~12Hz frequency. In experiments, the average error compensation gain of 3.71dB is achieved with respect to the conventional BACF/DCF algorithm; and the four hand-shake states are detected in a stable manner.