• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.6 s.360
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• pp.31-39
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• 2007

In this paper, a system for real-time or periodic measurement and analysis of RF parameters such as forward transmit power and pilot power in CDMA base station systems is proposed. Such RF characteristic parameter measurement can be prevented from system fault and used to achieve optimal service quality and maximum investment return through cell coverage expansion, subscriber capacity increase and so on. For forward power measurement, the local oscillator frequency for the detector is varied so that the transmit power for all channels can be measured. The channel power measurement can be used to analyze the variation in transmit power for changes in voice traffic. By comparing to forward $E_c/I_o$, the pilot channel power can be deducted, which can be used to determine uy degradation in transmit section modules such as the high dover amplifier. Since an accurate analysis of carefully measured data using the CDMA level detector must be made, the system is designed so that measurement errors due to changes in crest factor with modulation method can be overcome.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.710-720
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• 2017

In Korea, many nuclear power plants operate at a single site based on geographical characteristics, but the population density near the sites is higher than that in other countries. Thus, multiunit accidents are a more important consideration than in other countries and should be addressed appropriately. Currently, there are many issues related to a multiunit probabilistic safety assessment (PSA). One of them is the quantification of a multiunit PSA model. A traditional PSA uses a Boolean manipulation of the fault tree in terms of the minimal cut set. However, such methods have some limitations when rare event approximations cannot be used effectively or a very small truncation limit should be applied to identify accident sequence combinations for a multiunit site. In particular, it is well known that seismic risk in terms of core damage frequency can be overestimated because there are many events that have a high failure probability. In this study, we propose a quantification method based on a Monte Carlo approach for a multiunit PSA model. This method can consider all possible accident sequence combinations in a multiunit site and calculate a more exact value for events that have a high failure probability. An example model for six identical units at a site was also developed and quantified to confirm the applicability of the proposed method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.6
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• pp.22-29
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• 2002

The conventional neon transformer systems are very bulky and heavy because it consist of leakage type transformers made of silicon steel plates. In addition, it has problems in noise by a neon transformer and in possibilities of fire and electrical shock when neon tubes are destroyed. A protection circuit is designed for all types of neon transformer loaded with one or more neon tubes. Whenever the neon tube fails to be started up, comes to the life end, encounters faults with open-circuits at the output terminals of the neon transformer, the protection circuit will be initiated to avoid more critical hazards. The input of the transformer is automatically cut off when the abnormal condition occurs, preventing waste of no-load power. To improve such problems, in this paper, a new type of neon power supply systems for neon tube is designed and implemented using inverter type circuits and a newly designed lightweight transformer. In the developed neon transformer system, a 60［Hz］power input is converted to 20［KHz］high frequency using half-wave inverters, thereby the transformer reduces its size by 1/5 in volume and 1/10 in weight.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.75-80
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• 2006

The disastrous accident happens and the economic loss comes into being at the power facilities that used a industry site, if the fault comes into being. This paper experiments the partial discharge in the GIS used a $SF_6$ insulation gas by the pressure change. We studied the influence of particles at the partial discharge in a $SF_6$ gas. We use UHF method and measure the partial discharge signal radiation electromagnetic waves and to be happened at the $SF_6$. And we analyzed the influence on the $SF_6$ gas to have the particles which the partial discharge analyzes a spectrum of the radiated electromagnetic waves and comes out. The paper results aided the prevention of breakdown accident that happened by particles when an inside pressure changes at the GIS & the power facilities used $SF_6$ gas.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.389-403
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• 2023

In order to explore the influence of tooth root cracks on the dynamic characteristics of multi-stage planetary gear transmission systems, a concentrated parameter method was used to construct a nonlinear dynamic model of the system with 30-DOF in bending and torsion, taking into account factors such as crack depth, length, angle, error, time-varying meshing stiffness (TVMS), and damping. In the model, the energy method was used to establish a TVMS model with cracks, and the influence of cracks on the TVMS of the system was studied. By using the Runge- Kutta method to calculate the differential equations of system dynamics, a series of system vibration diagrams containing cracks were obtained, and the influence of different crack parameters on the vibration of the system was analyzed. And vibration testing experiments were conducted on the system with planetary gear cracks. The results show that when the gear contains cracks, the TVMS of the system will decrease, and as the cracks intensify, the TVMS will decrease. When cracks appear on the II-stage planetary gear, the system will experience impact effects with intervals of rotation cycles of the II-stage planetary gear. There will be obvious sidebands near the meshing frequency doubling, and the vibration trajectory of the gear will also become disordered. These situations will become more and more obvious as the degree of cracks intensifies. Through experiments, the theoretical results are in good agreement with experimental results, verifying the correctness of the theoretical model. This provides a theoretical basis for fault diagnosis and reliability research of the system.

• Journal of the Earthquake Engineering Society of Korea
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• v.24 no.6
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• pp.253-266
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• 2020

The extended slip-weakening model was investigated by using a compiled set of source-spectrum-related parameters, i.e. seismic moment M_o, S-wave velocity V_s, corner-frequency f_c, and source-controlled high-cut frequency fmax, for 113 shallow crustal earthquakes (focal depth less than 25 km, M_W 3.0~7.5) that occurred in Japan from 1987 to 2016. The investigation was focused on the characteristics of stress drop, radiation energy-to-seismic moment ratio, radiation efficiency, and fracture energy release rate, G_c. The scaling relationships of those source parameters were also investigated and compared with those in previous studies, which were based on generally used singular models with the dimensionless numbers corresponding to f_c given by Brune and Madariaga. The results showed that the stress drop from the singular model with Madariaga's dimensionless number was equivalent to the breakdown stress drop, as well as Brune's effective stress, rather than to static stress drop as has been usually assumed. The scale dependence of stress drop showed a different tendency in accordance with the size category of the earthquakes, which may be divided into small-moderate earthquakes and moderate-large earthquakes by comparing to M_o = 10¹⁷~10¹⁸ Nm. The scale dependence was quite similar to that shown by Kanamori and Rivera. The scale dependence was not because of a poor dynamic range of recorded signals or missing data as asserted by Ide and Beroza, but rather it was because of the scale dependent V_r-induced local similarity of spectrum as shown in a previous study by the authors. The energy release rate G_c with respect to breakdown distance D_c from the extended slip-weakening model coincided with that given by Ellsworth and Beroza in a study on the rupture nucleation phase; and the empirical relationship given by Abercrombie and Rice can represent the results from the extended slip-weakening model, the results from laboratory stick-slip experiments by Ohnaka, and the results given by Ellsworth and Beroza simultaneously. Also the energy flux into the breakdown zone was well correlated with the breakdown stress drop, ${\tilde{e}}$ and peak slip velocity of the fault faces. Consequently, the investigation results indicate the appropriateness of the extended slip-weakening model.

• Korean Journal of Optics and Photonics
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• v.17 no.6
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• pp.507-513
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• 2006

In this paper, we propose an improved image encryption and fault-tolerance decryption method using phase wrapping and phase encoding in the frequency domain. To generate an encrypted image, an encrypting key which denotes the product of a phase-encoded virtual image, not an original image, and a random phase image is zero-padded and Fourier transformed and its real-valued data is phase-encoded. The decryption process is simply performed by performing the inverse Fourier transform for multiplication of the encrypted key with the decrypting key, made of the proposed phase wrapping method, in the output plane with a spatial filter. This process has the advantages of solving optical alignment and pixel-to-pixel mapping problems. The proposed method using the virtual image, which does not contain any information from the original image, prevents the possibility of counterfeiting from unauthorized people and also can be used as a current spatial light modulator technology by phase encoding of the real-valued data. Computer simulations show the validity of the encryption scheme and the robustness to noise of the encrypted key or the decryption key in the proposed technique.