• 제목/요약/키워드: time domain data

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Clustering non-stationary advanced metering infrastructure data

  • Kang, Donghyun;Lim, Yaeji
    • Communications for Statistical Applications and Methods
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    • 제29권2호
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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.

머신러닝을 이용한 빅데이터 도메인 자동 판별에 관한 연구 (A Study of Big Data Domain Automatic Classification Using Machine Learning)

  • 공성원;황덕열
    • 한국빅데이터학회지
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    • 제3권2호
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    • pp.11-18
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    • 2018
  • 본 연구는 빅데이터 품질 진단의 핵심 요소인 도메인 기반 품질 진단을 위한 도메인 자동 판별에 관한 연구다. 빅데이터의 가치와 활용도의 증가와 4차 산업혁명의 대두로, 법률, 의료, 금융 등 IT와 융합된 다양한 분야에서 빅데이터를 활용하여 새로운 가치를 창출하려는 노력을 진행중이다. 하지만, 신뢰도가 낮은 데이터에 기반한 분석은 과정과 결과 모두에서 치명적인 문제를 발생하며, 분석 결과에 따른 판단 또한 신뢰하기 어려워 진다. 이처럼 신뢰도가 높은 데이터의 필요성 또한 증가하였지만, 데이터의 품질 확보에 대한 연구와 그에 대한 결과는 미비하다. 본 연구는 데이터 품질 향상을 위한 진단 평가의 핵심적 요소인 도메인 기반 품질 진단에서, 수작업으로 진행되었던 도메인 판별 작업을 머신러닝을 이용하여 자동화 함으로써, 작업시간을 단축하는 것을 목표로 한다. 데이터 베이스에 저장된, 도메인이 판별되어 있는 데이터의 특성에 관한 정보들을 추출하여 변수화하고, 이를 머신러닝을 이용하여 도메인 판별을 자동화 한다. 이를 빅데이터 품질 진단에 활용하고, 품질 향상에 기여하도록 한다.

시간 영역 기반의 비동기 IR-UWB 거리추정 시스템 (Time-Domain Based Asynchronous IR-UWB Ranging System)

  • 김형래;양훈기;양성현;강봉순
    • 한국정보통신학회논문지
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    • 제15권2호
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    • pp.347-354
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    • 2011
  • 본 논문에서는 시간 영역 기반의 비동기 IR-UWB 거리추정 시스템을 제안한다. 제안하는 시스템은 시간 영역에 서 고속으로 샘플링된 IR-UWB 신호에 대해서 FIR 필터를 이용해 코릴레이션 연산을 하여 피크를 검출한다. 코릴레이션 과정에 의해 신호성분이 단계적으로 커지지만 잡음환경에서의 성공적인 동작을 위해서 본 연구에서는 윈도우를 이용해서 잡음레벨을 추정한다. 주파수 영역 기반의 방식과의 비교 관점에서 시스템 구조 및 동작과정을 설명하고 시뮬레이션에 의해 제시된 시스템이 주파수 영역 기반의 시스템과 유사하게 우수한 성능을 나타냄을 보인다.

AVR 스템시험에 의한 울진 N/P 1호기 PSS 모델링 연구 (A Study of PSs Modeling of Ulchin N/P #1 by AVR Step Test)

  • 김동준;문영환;전동훈;김태균
    • 대한전기학회논문지:전력기술부문A
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    • 제50권8호
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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.

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A Time-Domain Method to Generate Artificial Time History from a Given Reference Response Spectrum

  • Shin, Gangsig;Song, Ohseop
    • Nuclear Engineering and Technology
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    • 제48권3호
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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.

From the Absorption Profile to the Potential by a Time-dependent Inversion Method

  • 김화중;김영식
    • Bulletin of the Korean Chemical Society
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    • 제18권12호
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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.

심층신경망을 이용한 시간 영역 음향 이벤트 검출 알고리즘 (Time-domain Sound Event Detection Algorithm Using Deep Neural Network)

  • 김범준;문현기;박성욱;정영호;박영철
    • 방송공학회논문지
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    • 제24권3호
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    • pp.472-484
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    • 2019
  • 본 논문에서는 심층신경망을 이용한 시간 영역 음향 이벤트 검출 알고리즘을 제시한다. 본 시스템에서는 주파수 영역으로 변환되지 않은 시간 영역의 음향 데이터를 심층신경망의 입력으로 사용한다. 전반적인 구조는 CRNN 구조를 사용하였으며, GLU, ResNet, Squeeze-and-excitation 블럭을 적용하였다. 그리고 여러 계층에서 추출된 특징을 함께 고려하는 구조를 제안하였다. 또한 본 연구에서는 강한 라벨이 있는 훈련 데이터를 확보하는 것이 현실적으로 어렵다는 전제 아래에서 약한 라벨이 있는 훈련 데이터 약간 그리고 다수의 라벨이 없는 훈련 데이터를 활용하여 훈련을 수행하였다. 적은 수의 훈련 데이터를 효과적으로 사용하기 위해 타임 스트레칭, 피치 변화, 동적 영역 압축, 블럭 혼합 등의 데이터 증강 방법을 적용하였다. 라벨이 없는 데이터에는 의사 라벨을 붙여 부족한 훈련 데이터를 보완하였다. 본 논문에서 제안한 신경망과 데이터 증강 방법을 사용하는 경우, 종래의 방식으로 CRNN 구조의 신경망을 훈련하여 사용하는 경우보다, 음향 이벤트 검출 성능이 약 6 % (f-score 기준)가 개선되었다.

Time-Domain Analog Signal Processing Techniques

  • Kang, Jin-Gyu;Kim, Kyungmin;Yoo, Changsik
    • Journal of Semiconductor Engineering
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    • 제1권2호
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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.

Classification of Time-Series Data Based on Several Lag Windows

  • Kim, Hee-Young;Park, Man-Sik
    • Communications for Statistical Applications and Methods
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    • 제17권3호
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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.

Ferroelectric ultra high-density data storage based on scanning nonlinear dielectric microscopy

  • Cho, Ya-Suo;Odagawa, Nozomi;Tanaka, Kenkou;Hiranaga, Yoshiomi
    • 정보저장시스템학회논문집
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    • 제3권2호
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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/$inch^2$.

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