The least square estimation of the basic frequency for periodically non-stationary random signals

2021 ◽  
pp. 103333
Author(s):  
Ihor Javorskyj ◽  
Roman Yuzefovych ◽  
Ivan Matsko ◽  
Zbigniew Zakrzewski
Keyword(s):  
Least Square ◽  
Least Square Estimation ◽  
Basic Frequency ◽  
Random Signals

High-Accuracy Fundamental Frequency Detection in Power System

2011 ◽  
Vol 383-390 ◽  
pp. 4962-4966
Author(s):  
Ling Li ◽  
Guo Bin Jin ◽  
Shao Ping Huang ◽  
Xiao Peng
Keyword(s):  
Power System ◽  
Fundamental Frequency ◽  
Frequency Measurement ◽  
Least Square ◽  
Signal Model ◽  
Least Square Estimation ◽  
Signal Process ◽  
Frequency Detection ◽  
Novel Method ◽  
Subspace Estimation

A novel method on frequency measurement based on improved TLS-ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) is proposed in this paper with the research on fundamental frequency measurement in power system. TLS-ESPRIT is belong to subspace estimation in modern signal process. Noise is included in signal model, so it is independent on noise. But the same multi-poles cannot be taken when signal is in noise and based on TLS-ESPRIT. Multiple poles restoring is presented to take the true poles accurately. It is revealed that fundamental frequency is detected accurately in harmonics, interharmonics, noise and frequency fluctuations and better anti-noise ability in particular better adaptiveness on time varying signal in amplitude by simulation results.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

Least square estimation-based adaptive complimentary filter for attitude estimation

2018 ◽  
Vol 41 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Parag Narkhede ◽  
Alex Noel Joseph Raj ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
Shashi Poddar
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Unmanned Vehicles ◽  
Least Square ◽  
Least Square Estimation ◽  
Micro Electro Mechanical Systems ◽  
Moving Vehicle ◽  
Complementary Filter ◽  
Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

scholarly journals Coupled Harmonic Admittance Identification Based on Least Square Estimation

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2600 ◽  
Author(s):  
Yaqiong Li ◽  
Zhanfeng Deng ◽  
Tongxun Wang ◽  
Guoliang Zhao ◽  
Shengjun Zhou
Keyword(s):  
Equivalent Circuit ◽  
Time Domain ◽  
Field Data ◽  
Mutual Coupling ◽  
Least Square ◽  
Estimation Technique ◽  
Least Square Estimation ◽  
Harmonic Current ◽  
Emission Estimation ◽  
Current Emission

Norton equivalent circuit is a commonly used model in estimating harmonic current emissions of harmonic sources. It however cannot reflect the mutual coupling relationships among voltage and current in different harmonic orders. This paper proposes a new method to identify parameters in a coupled harmonic admittance model. The proposed method is conducted using voltage and current measurements and is based on least square estimation technique. The effectiveness of the method is verified through time-domain simulations for a grid-connected converter and also through field data obtained from a ±800 kV converter station. The experimental results showed that the proposed method presents higher accuracy in terms of harmonic current emission estimation compared with three Norton-base methods.

Mathematical properties of the Kumaraswamy-Lindley distribution and its applications

2017 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Hamdy Salem ◽  
Abd-Elwahab Hagag
Keyword(s):  
Hazard Function ◽  
Likelihood Estimation ◽  
Real Data ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Least Square ◽  
Estimation Of Parameters ◽  
Least Square Estimation ◽  
Mathematical Properties ◽  
Composite Distribution

In this paper, a composite distribution of Kumaraswamy and Lindley distributions namely, Kumaraswamy-Lindley Kum-L distribution is introduced and studied. The Kum-L distribution generalizes sub-models for some widely known distributions. Some mathematical properties of the Kum-L such as hazard function, quantile function, moments, moment generating function and order statistics are obtained. Estimation of parameters for the Kum-L using maximum likelihood estimation and least square estimation techniques are provided. To illustrate the usefulness of the proposed distribution, simulation study and real data example are used.

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