• Title/Summary/Keyword: Periodicity

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Experimental Study on Application of an Anomaly Detection Algorithm in Electric Current Datasets Generated from Marine Air Compressor with Time-series Features (시계열 특징을 갖는 선박용 공기 압축기 전류 데이터의 이상 탐지 알고리즘 적용 실험)

  • Lee, Jung-Hyung
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.1
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    • pp.127-134
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    • 2021
  • In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

Use of various drought indices to analysis drought characteristics under climate change in the Doam watershed

  • Sayed Shajahan Sadiqi;Eun-Mi Hong;Won-Ho Nam
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.178-178
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    • 2023
  • Drought and flooding have historically coexisted in Korea, occurring at different times and with varying cycles and trends. The drought indicators measured were (PDSI), (SPI), and (SPEI) in order to statistically analyze the annual or periodic drought occurrence and objectively evaluate statistical characteristics such as the periodicity, tendency, and frequency of occurrence of droughts in the Doam watershed. To compute potential evapotranspiration (PET), both Thornthwaite (Thor) and Penman-Monteith (PM) parameterizations were considered, and the differences between the two PET estimators were analyzed. Hence, SPIs 3 and SPIs 6 revealed a tendency to worsen drought in the spring and winter and a tendency to alleviate drought in the summer in the study area. The seasonal variability trend did not occur in the SPIs 12 and PDSI, as it did in the drought index over a short period. As a result of the drought trend study, the drought from winter to spring gets more severe, in addition to the duration of the drought, although the periodicity of the recurrence of the drought ranged from 3 years to 6 years at the longest, indicating that SPIs 3 showed a brief time of around 1 year. SPIs 6 and SPIs 12 had a term of 4 to 6 years, and PDSI had a period of roughly 6 years. Based on the indicators of the PDSI, SPI, and SPEI, the drought severity increases under climate change conditions with the decrease in precipitation and increased water demand as a consequence of the temperature increase. Therefore, our findings show that national and practical measures are needed for both winter and spring droughts, which happen every year, as well as large-scale and extreme droughts, which happen every six years.

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Prediction for Bicycle Demand using Spatial-Temporal Graph Models (시-공간 그래프 모델을 이용한 자전거 대여 예측)

  • Jangwoo Park
    • Journal of Internet of Things and Convergence
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    • v.9 no.6
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    • pp.111-117
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    • 2023
  • There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.

Selective Extraction of a Single Optical Frequency Component from an Optical Frequency Comb (광 주파수 빗으로부터 단일 광 주파수 성분의 선택적 추출)

  • Han Seb Moon
    • Korean Journal of Optics and Photonics
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    • v.34 no.6
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    • pp.225-234
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    • 2023
  • Mode-locked pulse lasers have a temporal periodicity up over a short period of time. However, in the time-frequency domain, a pulsed laser with temporal periodicity is described as an optical frequency comb with constant frequency spacing. Each frequency component of the optical frequency comb in the frequency domain is then a continuous-wave (CW) laser with hundreds of thousands of single-frequency-component CW lasers in the time domain. This optical frequency comb was developed approximately 20 years ago, enabling the development of the world's most precise atomic clocks and precise transmission of highly stable optical frequency references. In this review, research on the selective extraction of the single-frequency components of optical frequency combs and the control of the frequency components of optical combs is introduced. By presenting the concepts and principles of these optical frequency combs in a tutorial format, we hope to help readers understand the properties of light in the time-frequency domain and develop various applications using optical frequency combs.

Empirical model of over-all ship's magnetism (총체적 선체현장의 실험모델)

  • 박길현;정태권;이상집
    • Journal of the Korean Institute of Navigation
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    • v.13 no.3
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    • pp.1-20
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    • 1989
  • In order to provide a basic information to locate the sensor of remote-indicating magnetic compass onboard, an empirical model for the over-all ship's magnetism was developed based on the periodicity of the observed magnetic field around the vessels. The values of model parameters were determined by least-square method and optimum numbers of them were fixed using Akaike's information criterion theory, and also an approximation method to determine parameter was proposed based on the symmetrical characteristic of observed data versus ship's length. The confidence level of the newly developed models was tested by analysis of variance method. The agreement between the modelled and real values was found to be remarkably accurate.

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Channel Compensation for Cepstrum-Based Detection of Laryngeal Diseases (켑스트럼 기반의 후두암 감별을 위한 채널보상)

  • Kim Young Kuk;Kim Su Mi;Kim Hyung Soon;Wang Soo-Geun;Jo Cheol-Woo;Yang Byung-Gon
    • MALSORI
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    • no.50
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    • pp.111-122
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    • 2004
  • Automatic detection of laryngeal diseases by voice is attractive because of its non-intrusive nature. Cepstrum based approach to detect laryngeal cancer shows reliable performance even when the periodicity of voice signals is severely lost, but it has a drawback that it is not robust to channel mismatch due to different microphone characteristics. In this paper, to deal with mismatched training and test microphone conditions, we investigate channel compensation techniques such as Cepstral Mean Subtraction (CMS) and Pole Filtered CMS (PFCMS). According to our experiments, PFCMS yields better performance than CMS. By using PFCMS, we obtained 12% and 40% error reduction over baseline and CMS, respectively.

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Quality Improvement of Bandwidth Extended Speech Using Mixed Excitation Model (혼합여기모델을 이용한 대역 확장된 음성신호의 음질 개선)

  • Choi Mu Yeol;Kim Hyung Soon
    • MALSORI
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    • no.52
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    • pp.133-144
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    • 2004
  • The quality of narrowband speech can be enhanced by the bandwidth extension technology. This paper proposes a mixed excitation and an energy compensation method based on Gaussian Mixture Model (GMM). First, we employ the mixed excitation model having both periodic and aperiodic characteristics in frequency domain. We use a filter bank to extract the periodicity features from the filtered signals and model them based on GMM to estimate the mixed excitation. Second, we separate the acoustic space into the voiced and unvoiced parts of speech to compensate for the energy difference between narrowband speech and reconstructed highband, or lowband speech, more accurately. Objective and subjective evaluations show that the quality of wideband speech reconstructed by the proposed method is superior to that by the conventional bandwidth extension method.

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Determination of Initial Conditions for Tetrahedral Satellite Formation

  • Yoo, Sung-Moon;Park, Sang-Young
    • Journal of Astronomy and Space Sciences
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    • v.28 no.4
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    • pp.285-290
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    • 2011
  • This paper presents an algorithm that can provide initial conditions for formation flying at the beginning of a region of interest to maximize scientific mission goals in the case of a tetrahedral satellite formation. The performance measure is to maximize the quality factor that affects scientific measurement performance. Several path constraints and periodicity conditions at the beginning of the region of interest are identified. The optimization problem is solved numerically using a direct transcription method. Our numerical results indicate that there exist an optimal configuration and states of a tetrahedral satellite formation. Furthermore, the initial states and algorithm presented here may be used for reconfiguration maneuvers and fuel balancing problems.

Approximate Detection Method for Image Up-Sampling

  • Tu, Ching-Ting;Lin, Hwei-Jen;Yang, Fu-Wen;Chang, Hsiao-Wei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.2
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    • pp.462-482
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    • 2014
  • This paper proposes a new resampling detection method for images that detects whether an image has been resampled and recovers the corresponding resampling rate. The proposed method uses a given set of zeroing masks for various resampling factors to evaluate the convolution values of the input image with the zeroing masks. Improving upon our previous work, the proposed method detects more resampling factors by checking for some periodicity with an approximate detection mechanism. The experimental results demonstrate that the proposed method is effective and efficient.

The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals

  • Lee, Han-Wook;Lee, Ju-Won;Jung, Won-Geun;Lee, Gun-Ki
    • International Journal of Control, Automation, and Systems
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    • v.5 no.6
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    • pp.701-706
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    • 2007
  • The measurement accuracy for heart rate or $SpO_2$ using photoplethysmography (PPG) is influenced by how well the noise from motion artifacts and other sources can be removed. Eliminating the motion artifacts is particularly difficult since its frequency band overlaps that of the basic PPG signal. Therefore, we propose the Periodic Moving Average Filter (PMAF) to remove motion artifacts. The PMAF is based on the quasi-periodicity of the PPG signals. After segmenting the PPG signal on periodic boundaries, we average the $m^{th}$ samples of each period. As a result, we remove the motion artifacts well without the deterioration of the characteristic point.