scholarly journals EEG Signal Enhancement Using OWA Filter

2021 ◽  
Vol 40 ◽  
pp. 01010
Author(s):  
Soham Yadav ◽  
Jeevika Pawar ◽  
Girish Patil ◽  
Shivangi Agarwal
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Medical Diagnosis ◽  
Median Filter ◽  
Kernel Density ◽  
Linear Filter ◽  
Control Mechanisms ◽  
Non Linear ◽  
Estimation Function

Biomedical signal monitoring and recording are an integral part of medical diagnosis and treatment control mechanisms. For this, enhanced signals with appropriate peak preservation are required. The OWA (OrderedWeighted Aggregation) Filter used in this paper helps in non-linear signal filtering and preservation of peaks for accurate medical diagnosis. Weights are an important aspect of the OWA filter, the Gaussian method and the KDE (Kernel Density Estimation) function are used to obtain a precise output which helps in filtering the signal. This filter is further compared with another non-linear filter that is the median filter to understand the compatibility and the preciseness of the filter in a much deeper sense. OWA | filter | peak | kernel density estimation | probability density | EPD (Estimated Probability Density)

scholarly journals Benchmarking the reliability of medium-voltage lines

2017 ◽  
Vol 68 (3) ◽  
pp. 212-215 ◽  
Author(s):  
Michal Kolcun ◽  
Mirosław Kornatka ◽  
Anna Gawlak ◽  
Zsolt Čonka
Keyword(s):  
Density Distribution ◽  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Power Lines ◽  
Probability Density Distribution ◽  
Nonparametric Method ◽  
Medium Voltage

Abstract This paper discusses the problem of reliability of the Polish medium voltage power lines. It presents the benchmarking of power lines and values of SAIDI. The probability density distribution of the medium voltage lines length as well as selected indicators of the MV power lines reliability, being operated by the distribution companies under test, are also introduced. The analysis is based on the actual MV lines and uses the nonparametric method of kernel density estimation.

scholarly journals Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

2019 ◽  
Vol 11 (24) ◽  
pp. 6954
Author(s):  
Fuqiang Li ◽  
Shiying Zhang ◽  
Wenxuan Li ◽  
Wei Zhao ◽  
Bingkang Li ◽  
...  
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Kernel Density ◽  
Support Vector ◽  
Average Width ◽  
Power Load ◽  
Density Forecasting

In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation

2014 ◽  
Vol 8 (1) ◽  
pp. 501-507
Author(s):  
Liyang Liu ◽  
Junji Wu ◽  
Shaoliang Meng
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Forecast Error ◽  
Kernel Density ◽  
Wind Power Forecast ◽  
Proposed Model ◽  
Chi Squared

Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function for wind power forecast error based on kernel density estimation was proposed. The proposed model is a non-parametric statistical algorithm and can directly obtain the probability density function from the error data, which do not need to make any assumptions. This paper also presented an optimal bandwidth algorithm for kernel density estimation by using particle swarm optimization, and employed a Chi-squared test to validate the model. Compared with Gaussian distribution and Beta distribution, the mean squared error and Chi-squared test show that the proposed model is more effective and reliable.

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