• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.861-866
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• 2018

In OLED (Organic Light-Emitting Diode) manufacturing, the aging process increases the manufacturing efficiency and measures the correction value to correct the aging. The correction value for correcting the aging of the OLED can be applied to the driving signal. The OLED aging process measures the current after outputting the light for a predetermined time according to the preset driving signal and temperature. In the OLED manufacturing process, aging is applied for non-uniformity by deposition and temperature. This time has little effect on OLED efficiency reduction. Heating devices and signal generation systems are required to create the conditions necessary for aging. The results measured by the heating system and signal generation system can be used as a basis for evaluating power requirements, uniformity and efficiency in OLED manufacturing. In this paper, we propose and implement a configuration for interlocking the driving signal generation and heating system for practical OLED aging correction.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.992-997
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• 1987

This paper describes the design and fabrication of an integrated circuit that can separate the horizontal SYNC., vertical SYNC. and composite SYNC. signal included in a composite video signal. The circuit that is based on the comparator level samplign method can separate a stable SYNC. signal even from an external circuit with large variation. It has been fabrivated by the SST bipolar process. Its chip size is 1.5x1.5mm\ulcorner As a result, we succeeded in fabrication of IC which satisfied DC characteristics and SYNC. singal separator function.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.618-624
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• 1987

This paper describes the design and fabrication of a signal processing integrated circuit required for the recording and playback of VTR audio signal. The integrated circuit was designed using 8\ulcorner design rule and its chip size is 2.5x2.5mm\ulcorner It was fabricated using SST bipolar standard process technology. The measurement analysis of the fabricated circuit proves the satisfactory DC characteristics and its proper audio signal processing funcstion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.148-154
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• 2016

In recent year, the interests of PQ is increase due to the increasing of non-linear load and distributed power sources in power system. For the parameters detection and feature extraction of PQ, and the PQ improvement method, continuous power signal monitering is needed. In this paper, the power signal compression and reconstruction method is suggested for power signal monitering. The power signal is compressed using DCT that has good compression performance, and the compressed signal is reconstructed through IDCT. And for the higher compression rate, DCT coefficients are arranged by magnitude in compression process, and in recouction process DCT coefficients are rearranged to original frequency position. The synthesized signal according to the IEC standard is used used in compression and reconstruction simulations. The performances of the proposed method are verified by comparing the error between synthesized signal and reconstructed signal.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.11
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• pp.616-621
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• 2000

This paper deals with the multiresolution analysis of wavelet transform for partial discharge(PD). Test arrangement is based on the needle-plane electrode system and applied AC high voltage. The measured PD signal was decomposed into "approximations" and "details". The approximation are the high scale, low-frequency components of the PD signal. The details are the low-scale, high frequency components. The decomposition process are iterated to 3 level, with successive approximation being decomposed in turn, so that PD signal is broken down into many lower-resolution components. Through the procedure of signal wavelet transform, signal noise extraction and signal reconstruction, the signal is analyzed to determine the magnitude of PD.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.8
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• pp.863-867
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• 2014

In this study, an acoustic emission (AE) sensor was used for measuring the abrasive and molecular-scale phenomena in chemical mechanical polishing (CMP). An AE sensor is a transducer that converts a mechanical wave into an electrical signal, and is capable of acquiring high-level frequencies from materials. Therefore, an AE sensor was installed in the CMP equipment and the signals were measured simultaneously during the polishing process. In this study, an AE monitoring system was developed for investigating the sensitivity of the AE signal to (a) the variations in the material properties of the pad, slurry, and wafer and (b) the change in conditions during the CMP process. This system was adapted to Oxide and Cu CMP processes. AE signal parameters including AE raw frequency, FFT, and amplitude were analyzed for understanding the abrasive and molecular-level phenomena in the CMP process. Finally, we verified that AE sensors with different bandwidths could function in complementary ways during CMP process monitoring.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.1-7
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• 2021

In order to acquire an image in a positron emission tomography, it is necessary to draw the position coordinates of the scintillation pixels of the detector module measured at the same time. To this end, in a detector module using a plurality of scintillation pixels and a small number of photosensors, it is necessary to obtain a flood image and divide a region of each scintillation pixel to obtain a position of a scintillation pixel interacting with a gamma ray. Alternatively, when the number of scintillation pixels and the number of photosensors to be used are the same, the position coordinates for the position of the scintillation pixels can be directly acquired as digital signal coordinates. A method of using a plurality of scintillation pixels and a small number of photosensors requires a process of obtaining digital signal coordinates requires a plurality of photosensors and a signal processing system. This complicates the signal processing process and raises the cost. To solve this problem, in this study, we developed a method of obtaining digital signal coordinates without performing the process of separating the planar image and region using a plurality of flash pixels and a small number of optical sensors. This is a method of obtaining the position coordinate values of the flash pixels interacting with the gamma ray as a digital signal through a look-up table created through the signals acquired from each flash pixel using the maximum likelihood function. Simulation was performed using DETECT2000, and verification was performed on the proposed method. As a result, accurate digital signal coordinates could be obtained from all the flash pixels, and if this is applied to the existing system, it is considered that faster image acquisition is possible by simplifying the signal processing process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.1
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• pp.31-36
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• 2009

Micro-machining technology has been adopted for shape accuracy of micrometer and sub-micrometer scale, surface roughness of tens nanometer in industries. In micro-machining process the quality of machined surface is derived from machining condition and tooling. This paper investigates AE(acoustic emission) and cutting force signals according to machined surface quality related to machining condition. Machined surface quality was analyzed by the AE and cutting force parameter which reflect surface morphology. The characteristics of signal were extracted for process optimization by monitoring both the tool condition and the machined surface texture in micro end milling process.

• Journal of Welding and Joining
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• v.28 no.4
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• pp.81-86
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• 2010

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.