• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.347-354
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• 2011

This paper presents a time-domain based asynchronous IR-UWB ranging system. This system accomplishes the ranging by detecting peaks from the outputs of a correlator implemented by a FIR filter. To discriminate the peaks due to a signal component, we use windowing for the correlated data within which the data are sorted in amplitude-ascending order and the noise level is calculated. Comparing with the recently presented frequency-domain based ranging system, we show the system structure and explain how it operates for ranging. Moreover, through the simulations, the proposed system is compared with the frequency-domain based system in terms of performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.8
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• pp.351-358
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• 2001

This paper deals with the PSS modeling of Ulchin N/P #1 as well as the generator and excitation system modeling by utilizing the recorded data from AVR step test, which has been performed by entering small voltage signal into the AVR summing point. In addition to it. two recorded results obtained from the AVR step test with PSS sunning and without PSS running have not only been compared each other on the time domain, but also they heve been analyzed with FFT analysis on the frequency domain; thus, the desirable effects of running PSS in Ulchin N/P #1 on power system have been explicitly confirmed. Finally, the derived PSS model parameters lead to good matches between simulation results and recorded data.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.831-839
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• 2016

Seismic qualification by test is widely used as a way to show the integrity and functionality of equipment that is related to the overall safety of nuclear power plants. Another means of seismic qualification is by direct integration analysis. Both approaches require a series of time histories as an input. However, in most cases, the possibility of using real earthquake data is limited. Thus, artificial time histories are widely used instead. In many cases, however, response spectra are given. Thus, most of the artificial time histories are generated from the given response spectra. Obtaining the response spectrum from a given time history is straightforward. However, the procedure for generating artificial time histories from a given response spectrum is difficult and complex to understand. Thus, this paper presents a simple time-domain method for generating a time history from a given response spectrum; the method was shown to satisfy conditions derived from nuclear regulatory guidance.

• Bulletin of the Korean Chemical Society
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• v.18 no.12
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• pp.1281-1285
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• 1997

The time-dependent tracking inversion method is developed to extract the potential of the excited state from frequency-domain measurements, such as the absorption profile. Based on the relay of the regularized inversion procedure and time-dependent wave-packet propagation, the algorithm extract the underlying potential piece by piece by tracking the time-dependent data which can be synthesized from frequency-domain measurements. We have demonstrated the algorithm to extract the potential of excited state for a model diatomic molecule. Finally, we describe the merits of the time-dependent tracking inversion method compared to the time-dependent inversion and discuss several extensions of the algorithm.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

• Journal of Semiconductor Engineering
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• v.1 no.2
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• pp.64-73
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• 2020

As CMOS technology scales down, the design of analog signal processing circuit becomes far more difficult because of steadily decreasing supply voltage and smaller intrinsic gain of transistors. With sub-1V supply voltage, the conventional analog signal processing relying on high-gain amplifiers is not an effective solution and different approach has to be sought. One of the promising approaches is "time-domain analog signal processing" which exploits the improving switching speed of transistors in a scaled CMOS technology. In this paper, various time-domain analog signal processing techniques are explained with some experimental results.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.377-390
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• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• Transactions of the Society of Information Storage Systems
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• v.3 no.2
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• pp.94-112
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• 2007

Nano-sized inverted domain dots in ferroelectric materials have potential application in ultrahigh-density rewritable data storage systems. Herein, a data storage system is presented based on scanning non-linear dielectric microscopy and a thin film of ferroelectric single-crystal lithium tantalite. Through domain engineering, we succeeded to form an smallest artificial nano-domain single dot of 5.1 nm in diameter and artificial nano-domain dot-array with a memory density of 10.1 Tbit/$inch^2$ and a bit spacing of 8.0 nm, representing the highest memory density for rewritable data storage reported to date. Sub-nanosecond (500psec) domain switching speed also has been achieved. Next, long term retention characteristic of data with inverted domain dots is investigated by conducting heat treatment test. Obtained life time of inverted dot with the radius of 50nm was 16.9 years at $80^{\circ}C$. Finally, actual information storage with low bit error and high memory density was performed. A bit error ratio of less than $1\times10^{-4}$ was achieved at an areal density of 258 Gbit/inch2. Moreover, actual information storage is demonstrated at a density of 1 Tbit/$inch^2$.