• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1067-1072
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• 2001

In this study, the vibration diagnosis program for onboard machinery has been developed. The developed program includes signal monitoring module, system diagnosis module, and system modification module. The signal monitoring module is to monitor the vibration signal in time and frequency domains. And the system diagnosis module, which is developed by using Neural Network with error back propagation algorithm, can detect the abnormal symptom indicating the malfunction of the machinery onboard ships. The investigations of the developed system are presented through the experiment using Rotor Kit. Abnormal vibration signals are created by adding additional weight, manually misaligning the shaft, and loosening the bolts.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.128-137
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• 1992

An automatic noise detection system is developed to sense abnormal noises in operating a microwave electronic range. A noise detection method is presented which accounts for the effects of backgound and dynamic noises of the range. A recursive formula used as a noise estimator is a special case of the discrete-time Kalman filter in stochastic processes. Noise levels were measured using a noise acquisition processor in a closed room free of background noise, and detected signals were processes using a microcomputer. The results obtaines showed that the fault detection system should be fast in response to the data acquired and should be high in accuracy and reliability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.292-299
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• 2012

The line-of-sight(LOS) rate is estimated using Kalman filter in Radio-Frequency(RF) seeker. For the two axis gimbaled seeker, proper system modeling is considered and the basic filter structure is set up. The main issue for Kalman filter is choosing the proper process and measurement noise. For the measurement process, the signal to noise ratio(SNR) and other components are introduced. To cope with the eclipse problem or other abnormal seeker status, the pseudo input signal concept is proposed. By conditioning abnormal signals, the LOS rate estimation performance is increased. The process noise is also an important factor in the propagation phase. The analytical approach on a process noise component is performed and a reliable region for the filter is calculated based on the eigenvalue analysis. Some numerical simulations are performed to check the validity of suggested algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.361-366
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• 2017

The abnormal state of the main equipment across the industry is accompanied by abnormal rise of temperature, change in the vibration and noise. In this study, we developed a smart sensing module equipped with BLE, and developed a smart sensing device that can detect abnormal vibration due to its own flaws of the equipment by interfacing with an MEMS-based acceleration sensor. The smart sensing device developed in this study can be easily installed in a small space and can monitor the vibration status of the equipment in real time, and can easily inform the user of the steady state and the problem occurrence status with array LED display.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.876-878
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• 2008

The standards of acceptable limit and compensation is one of the most important things in environmental noise and vibration disputes resolution. In this paper, review on the present acceptable limit level and compensation standard in National Environmental Dispute Resolution Commission is introduced. Discordance of standards between in the regulation law and in the dispute resolution commission and it's improvement are discussed. Abnormal reasoning for compensation standards is pointed out from a author's private view.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.7-13
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• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.577-582
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• 2012

Recently, noise reduction of air-conditioner is one of the important design factors for high quality product. Especially, customer complaints arise due to noise problem of the outdoor unit. After the operation of air-conditioner start, noise level of outdoor unit is increased gradually and sometimes abnormal noise occurs until it reaches steady state condition. The aim of this paper is to investigate the relation between noise of outdoor unit and thermodynamic properties of cycle at transient condition of room air-conditioner. In order to find out the noise characteristics of outdoor unit, noise and vibration measurements are carried out. Also, the thermodynamic properties of compressor and heat exchanger are measured by using temperature and pressure sensors and experimental results are discussed. Finally, we find out the relation between noise and cycle properties at starting of room air-conditioner and the improvement method to reduce noise level is proposed.

• Journal of Sensor Science and Technology
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• v.29 no.4
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• pp.249-254
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• 2020

It is difficult to control children who exhibit negative behavior in dental clinics. Various methods are used for preventing pediatric dental patients from being afraid and for eliminating the factors that cause psychological anxiety. However, when it is difficult to apply this routine behavioral control technique, sedation therapy is used to provide quality treatment. When the sleep anesthesia treatment is performed at the dentist's clinic, it is challenging to identify emergencies using the current breath detection method. When a dentist treats a patient that is under the influence of an anesthetic, the patient is unconscious and cannot immediately respond, even if the airway is blocked, which can cause unstable breathing or even death in severe cases. During emergencies, respiratory instability is not easily detected with first aid using conventional methods owing to time lag or noise from medical devices. Therefore, abnormal breathing needs to be evaluated in real-time using an intuitive method. In this paper, we propose a method for identifying abnormal breathing in real-time using an intuitive method. Respiration signals were measured using a 3M Littman electronic stethoscope when the patient's posture was supine. The characteristics of the signals were analyzed by applying the signal processing theory to distinguish abnormal breathing from normal breathing. By applying a short-time Fourier transform to the respiratory signals, the frequency range for each patient was found to be different, and the frequency of abnormal breathing was distributed across a broader range than that of normal breathing. From the wavelet transform, time-frequency information could be identified simultaneously, and the change in the amplitude with the time could also be determined. When the difference between the amplitude of normal breathing and abnormal breathing in the time domain was very large, abnormal breathing could be identified.

• Journal of KSNVE
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• v.10 no.2
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.37-42
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• 2011

An extended scalar adaptive filter for guided missiles using a global positioning system receiver is presented. A conventional scalar adaptive filter is adequate filter for eliminating sudden abnormal jumping measurements. However, if missile or vehicle velocities have variation, the conventional filter cannot eliminate abnormal measurements. The proposed filter utilizes an acceleration term, which is an improvement not used in previous conventional scalar adaptive filters. The proposed filter continuously estimates noise measurement variance, velocity error variance and acceleration error variance. For estimating the three variances, an innovation method was used in combination with the least square method for the three variances. Results from the simulations indicated that the proposed filter exhibited better position accuracy than the conventional scalar adaptive filter.