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Abstract

A moving average (MA) is a commonly used noise reduction method in signal processing. Several studies on wheeze auscultation have used MA analysis for preprocessing. The present study compared the performance of MA analysis with that of differential operation (DO) by observing the produced spectrograms. These signal preprocessing methods are not only applicable to wheeze signals but also to signals produced by systems such as machines, cars, and flows. Accordingly, this comparison is relevant in various fields. The results revealed that DO increased the signal power intensity of episodes in the spectrograms by more than 10 dB in terms of the signal-to-noise ratio (SNR). A mathematical analysis of relevant equations demonstrated that DO could identify high-frequency episodes in an input signal. Compared with a two-dimensional Laplacian operation, the DO method is easier to implement and could be used in other studies on acoustic signal processing. DO achieved high performance not only in denoising but also in enhancing wheeze signal features. The spectrograms revealed episodes at the fourth or even fifth harmonics; thus, DO can identify high-frequency episodes. In conclusion, MA reduces noise and DO enhances episodes in the high-frequency range; combining these methods enables efficient signal preprocessing for spectrograms.
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Authors and Affiliations

Meng-Lun Hsueh
1
Jin-Peng Chen
2
Bing-Yuh Lu
2
Huey-Dong Wu
3
Pei-Yi Liu
2

  1. Graduate Institute of Intelligent Robotics, Hwa Hsia University of Technology, New Taipei City, Taiwan
  2. Faculty of Automation, Guangdong University of Petrochemical Technology, Guangdong, China
  3. Department of Integrated Diagnostics and Therapeutics, National Taiwan University Hospital, Taipei, Taiwan
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Abstract

Most of the existing statistical forecasting methods utilize the historical values of wind power to provide wind power generation prediction. However, several factors including wind speed, nacelle position, pitch angle, and ambient temperature can also be used to predict wind power generation. In this study, a wind farm including 6 turbines (capacity of 3.5 MW per turbine) with a height of 114 meters, 132-meter rotor diameter is considered. The time-series data is collected at 10-minute intervals from the SCADA system. One period from January 04th, 2021 to January 08th, 2021 measured from the wind turbine generator 06 is investigated. One period from January 01st, 2021 to January 31st, 2021 collected from the wind turbine generator 02 is investigated. Therefore, the primary objective of this paper is to propose a combined method for wind power generation forecasting. Firstly, response surface methodology is proposed as an alternative wind power forecasting method. This methodology can provide wind power prediction by considering the relationship between wind power and input factors. Secondly, the conventional statistical forecasting methods consisting of autoregressive integrated moving average and exponential smoothing methods are used to predict wind power time series. Thirdly, response surface methodology is combined with autoregressive integrated moving average or exponential smoothing methods in wind power forecasting. Finally, the two above periods are performed in order to demonstrate the efficiency of the combined methods in terms of mean absolute percent error and directional statistics in this study.
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Authors and Affiliations

Tuan-Ho Le
1
ORCID: ORCID

  1. Faculty of Engineering and Technology, Quy Nhon University, Quy Nhon, Binh Dinh Province, 820000, Vietnam
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Abstract

The work considers a one-dimensional time series protocol packet intensity, measured on the city backbone network. The intensity of the series is uneven. Scattering diagrams are constructed. The Dickie Fuller test and Kwiatkowski-Phillips Perron-Shin-Schmitt test were applied to determine the initial series to the class of stationary or nonstationary series. Both tests confirmed the involvement of the original series in the class of differential stationary. Based on the Dickie Fuller test and Private autocorrelation function graphs, the Integrated Moving Average Autoregression Model model is created. The results of forecasting network traffic showed the adequacy of the selected model.
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Authors and Affiliations

Tansaule Serikov
1
Аinur Zhetpisbayeva
1
Ainur Аkhmediyarova
2
Sharafat Mirzakulova
3
Aigerim Kismanova
1
Aray Tolegenova
1
Waldemar Wójcik
4

  1. S.Seifullin Kazakh AgroTechnical University, Nur-Sultan, Kazakhstan
  2. Institute of Information and Computational Technologies, Almaty, Kazakhstan
  3. Turan University, Almaty, Kazakhstan
  4. Lublin University of Technology, Poland

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