• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.7
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• pp.715-725
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• 2010

Orthgonal Frequency Division Multiplexing(OFDM) system is effective for the high data rate transmission in the frequency selective fading channel. In this paper we propose PAPR(Peak to Average Power Ratio) reduction method of problem in OFDM system used Fuzzy theory that often control machine. This thesis proposes PAPR reducing method of OFDM system using Fuzzy theory. The advantages for using Fuzzy theory to reduce PAPR are that it is easy to manage the data and embody the hardware, and required smaller amount of operation. Firstly, we proposed simple algorithm that is reconstructed at receiver with transmitted overall PAPR which is reduced PAPR of sub-block using Fuzzy. Although there are some drawbacks that the operation of the system is increased comparing conventional OFDM system and it is needed to send the information about Fuzzy indivisually, it is assured that the performance of the system is enhanced for PAPR reducing. To evaluate the perfomance, the proposed search algorithm is compared with the proposed algorithm in terms of the complementary cumulative distribution function(CCDF) of the PAPR and the computational complexity. As a result of using the QPSK and 16QAM modulation, Fuzzy theory method is more an effective method of reducing 2.3 dB and 3.1 dB PAPR than exiting OFDM system when FFT size(N)=512, and oversampling=4 in the base PR of $10^{-5}$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2715-2720
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• 2014

Median filtering is very effective to remove impulse type noises, so it has been widely used in many signal processing applications. However, due to the time complexity of its non-linearity, median filtering is often used using a small filter window size. A lot of work has been done on devising fast median filtering algorithms, but most of them can be efficiently applied to input data with finite integer values like images. Little work has been carried out on fast 2-d median filtering algorithms that can deal with real-valued 2-d data. In this paper, a fast and simple median 2-d filter is presented, and its performance is compared with the Matlab's 2-d median filter and a heap-based 2-d median filter. The proposed algorithm is shown to be much faster than the Matlab's 2-d median filter and consistently faster than the heap-based algorithm that is much more complicated than the proposed one. Also, a more efficient median filtering scheme for 2-d real valued data with a finite range of values is presented that uses higher-bit integer 2-d median filtering with negligible quantization errors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.9-14
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• 2016

This study evaluated a digital locking device design using detection distance of 13.4mm of a human body sensing type magnetic field coil. In contrast to digital locking devices that are used nowadays, the existing serial number entering buttons, lighting, number cover, corresponding pcb, exterior case, and data delivery cables have been deleted and are only composed of control ON/OFF power switches and emergency terminals. When the magnetic field coil substrates installed inside the inner case detects the electric resistance delivered from the opposite side of the 12mm interval exterior contacting the glass body part, the corresponding induced current flows. At this time, the magnetic field coil takes the role as a sensor when coil frequency of the circular coil is transformed. The magnetic coil as a sensor detects a change in the oscillation frequency output before and after the body is detected. This is then amplified to larger than 2,000%, transformed into digital signals, and delivered to exclusive software to compare and search for embedded data. The detection time followed by the touch area of the body standard to a $12.8{\emptyset}$ magnetic field coil was 30% contrast at 0.08sec and 80% contrast at 0.03sec, in which the detection distance was 13.4mm, showing the best level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2339-2344
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• 2014

of a microstrip transmission line, this transmission line can operate as the microstrip line or the coplanar line according to open or short connection between the ungrounded copper plane and grounded plane on the base plane. Two different type operation of the transmission line means that one transmission line can have two different characteristic impedances. This paper proposes and fabricates the circuit to be operated 2-ports power transmission line or 2-way power divider with the stable input matching characteristic by using this dual-impedance transmission line. The proposed circuit operates 2-ports power transmission line in case of the coplanar line or 2-way power divider line in case of the microstrip line. The fabricated circuit shows $S_{21}$ > -0.2 dB and $S_{11}$ < -15 dB above 700 MHz when the circuit operates 2-ports power transmission line. And, it is $S_{21}$ > -3.8 dB, $S_{11}$ < -10 dB and $S_{21}/S_{31}$ < ${\pm}0.3dB$ above 700 MHz when the circuit operates 2-way power divider.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.504-510
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• 2017

This paper presents an AC-DC power module design and evaluates its efficiency and reliability when used for electronics appliances. This power module consists of a PWM control IC, power MOSFETs, a transformer and several passive devices. The module was tested at an input voltage of 220V (RMS) (frequency 60 Hz). A test was conducted in order to evaluate the operation and power efficiency of the module, as well as the reliability of its protection functions, such as its over-current protection (OVP), overvoltage protection (OVP) and electromagnetic interference (EMI) properties. Especially, we evaluated the thermal shut-down protection (TSP) function in order to assure the operation of the module under high temperature conditions. The efficiency and reliability measurement results showed that at an output voltage of 5 V, the module had a ripple voltage of 200 mV, power efficiency of 73 % and maximum temperature of $80^{\circ}C$ and it had the ability to withstand a stimulus of high input voltage of 4.2 kV during 60 seconds.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.309-316
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• 2019

In this paper, a low-jitter delay-locked loop that compensates for local clock skew is presented. The proposed DLL consists of a phase splitter, a phase detector(PD), a charge pump, a bias generator, a voltage-controlled delay line(VCDL), and a level converter. The VCDL uses self-biased delay cells using current mode logic(CML) to have insensitive characteristics to temperature and supply noises. The phase splitter generates two reference clocks which are used as the differential inputs of the VCDL. The PD uses the only single clock from the phase splitter because the PD in the proposed circuit uses CMOS logic that consumes less power compared to CML. Therefore, the output of the VCDL is also converted to the rail-to-rail signal by the level converter for the PD as well as the local clock distribution circuit. The proposed circuit has been designed with a $0.13-{\mu}m$ CMOS process. A global CLK with a frequency of 1-GHz is externally applied to the circuit. As a result, after about 19 cycles, the proposed DLL is locked at a point that the control voltage is 597.83mV with the jitter of 1.05ps.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.99-107
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• 2021

We live in a myriad of data. Various data are created in all situations in which we work, and we discover the meaning of data through big data technology. Many efforts are underway to find meaningful data. This paper introduces an analysis technique that enables humans to make better choices through the trend and prediction of time series data as a principal component analysis technique. Principal component analysis constructs covariance through the input data and presents eigenvectors and eigenvalues that can infer the direction of the data. The proposed method computes a reference axis in a time series data set having a similar directionality. It predicts the directionality of data in the next section through the angle between the directionality of each time series data constituting the data set and the reference axis. In this paper, we compare and verify the accuracy of the proposed algorithm with LSTM (Long Short-Term Memory) through cryptocurrency trends. As a result of comparative verification, the proposed method recorded relatively few transactions and high returns(112%) compared to LSTM in data with high volatility. It can mean that the signal was analyzed and predicted relatively accurately, and it is expected that better results can be derived through a more accurate threshold setting.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.