• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.4
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• pp.365-375
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• 2014

This paper proposes an abnormal V-shaped-error-free non-blind deconvolution technique featuring an adaptively segmented forward-problem based iterative deconvolution (ASDCN) process. Unlike the algebraic based inverse operations, this eliminates any operations of differential and division by zero to successfully circumvent the issue on the abnormal V-shaped error. This effectiveness has been demonstrated for the first time with applying to a real analysis for the effects of the Random Telegraph Noise (RTN) and/or Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. It has been shown that the proposed ASDCN technique can reduce its relative errors of RTN deconvolution by $10^{13}$ to $10^{15}$ fold, which are good enough for avoiding the abnormal ringing errors in the RTN deconvolution process. This enables to suppress the cdf error of the convolution of the RTN with the RDF (i.e., fail-bit-count error) to $1/10^{10}$ error for the conventional algorithm.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

• Structural Engineering and Mechanics
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• v.21 no.6
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• pp.621-634
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• 2005

A direct transfer substructure method is presented in this paper for analyzing the dynamic characteristics and the seismic random responses of a series reactor. This method combines the concept of FRF (frequency response function) and the transfer matrix algorithm with the substructure approach. The inner degrees of freedom of each substructure are eliminated in the process of reconstruction and the computation cost is reduced greatly. With the convenient solution procedure, the dynamic characteristics analysis of the structure is valid and efficient. Associated with the pseudo excitation algorithm, the direct transfer substructure method is applied to investigating the seismic random responses of the series reactor. The numerical results demonstrate that the presented method is efficient and practicable in engineering. Finally, a precise time integration method is employed in performing a time-history analysis on the series reactor under El Centro and Taft earthquake waves.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.223-228
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• 2006

The main purpose of this paper is to describe the code tracking performance of a non-coherent digital delay lock loop (DLL) or coherent DLL while tracking GNSS signal in the presence of signal masking. The masking effect is usually caused by buildings that obscure the signal in either a periodic or random manner. In some cases, ideal masking is used to remove random or periodic interference. Three types of the masked signal are considered - no masking, periodic masking, and random masking of the signal input to the receiver. The mean time to lose lock (MTLL) of the code tracking loop are evaluated, and some numerical result and simulation results are reported. Finally, the steadystate tracking errors on the performance of the tracking loop in interference environment are also presented.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.156-157
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• 2007

For tens of years many advantages of Phase-Change Random Access Memory(PRAM) were introduced. Although the performance improved gradually, there are some portions which must be improved. So, we studied new constitution of $Ge_1Se_1Te_2$ chalcogenide material to improve phase transition characteristic. Actually, the performance properties have been improved surprisingly. However, crystallization time was as long as ever for amorphization time. We conducted this experiment in order to solve that problem by doping-Sb.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.49-53
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• 2023

In this paper, we propose an antirandom pattern-based test method considering power consumption to compensate for the problem that the fault coverage through random test decreases or the test time increases significantly when the DUT circuit structure is complex or large. In the proposed method, a group unit test pattern generation process and rearrangement process are added to improve the problems of long calculation time and high-power consumption, which are disadvantages of the previous antirandom test.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.660-661
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• 2020

본 논문에서는 기존 인공 신경망 기반 시계열 학습 기법인 Recurrent Neural Network (RNN)의 많은 연산량 및 고 사양 시스템 요구를 개선하기 위해 랜덤 포레스트 (Random Forest)기반의 새로운 시계열 학습 기법을 제안한다. 기존의 RNN 기반 방법들은 복잡한 연산을 통해 높은 성능을 달성하는 데 집중하고 있다. 이러한 방법들은 학습에 많은 파라미터가 필요할 뿐만 아니라 대규모의 연산을 요구하므로 실시간 시스템에 적용하는데 어려움이 있다. 따라서 본 논문에서는, 효율적이면서 빠르게 동작할 수 있는 시계열 다층 랜덤 포레스트(Time series Multilayered Random Forest)를 제안하고 산불 조기 탐지에 적용해 기존 RNN 계열의 방법들과 성능을 비교하였다. 다양한 산불화재 실험데이터에 알고리즘을 적용해본 결과 GPU 상에서 방대한 연산을 수행하는 RNN 기반 방법들과 비교해 성능적인 한계가 존재했지만 CPU 에서도 빠르게 동작 가능하므로 성능의 개선을 통해 다양한 임베디드 시스템에 적용 가능하다.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.25-36
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• 2022

This paper proposes a random sign reversal technique in space frequency block code for single carrier modulation. The traditional space time and frequency block coding technique may be confronted with radio environments openly, severe radio hijacking problems are to be overcome. In order to avoid such an open radio issue, random coded data protection technique for space-time block code was proposed, but this algorithm can change channel combination per an Orthogonal Frequency Division Multiplexing block. This kind of slow switching increases the probability that nearby receivers will detect the transmitted data. This paper proposes a fast switching algorithm per data symbols' basis which is a random sign reversal technique in space frequency block code for Single Carrier Modulation. It is shown in simulation that the proposed one has a superior performance in comparison with the performance of the receiver which do not know the random timing sequence of sign reversal.