scholarly journals Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter

2019 ◽  
Vol 9 (16) ◽  
pp. 3329 ◽  
Author(s):  
Donggil Kim ◽  
Dongik Lee
Keyword(s):  
Kalman Filter ◽  
Wind Turbine ◽  
Linear Models ◽  
Adaptive Fuzzy ◽  
Fault Parameter ◽  
Fault Parameters ◽  
Novel Method ◽  
Detection And Diagnosis ◽  
And Control ◽  
Early Detection And Diagnosis

Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models.

Identification of Hydrodynamic Coefficients of a Robotic Fish Using Improved Extended Kalman Filter

2017 ◽  
Author(s):  
Fengying Dang ◽  
Feitian Zhang
Keyword(s):  
Kalman Filter ◽  
Rapid Development ◽  
High Energy ◽  
Robotic Fish ◽  
Pressure Measurements ◽  
Hydrodynamic Coefficients ◽  
Simulation Based ◽  
Novel Method ◽  
Coefficients Identification ◽  
And Control

Bio-inspired robotic fish have received an increasingly rapid development in recent years due to their advanced performances, such as high energy efficiency and high maneuverability. An accurate dynamic model is essential to the design and control of such robots. Hydrodynamic co-efficients play an important role in modeling the robot, which are usually obtained from theoretical calculation or water tunnel experiments. This paper proposes a novel method for hydrodynamic coefficients identification using an improved Kalman filter with angular velocity and distributed pressure measurements, which are typically available from the robot’s on-board sensors. Simulation based on a Joukowski airfoil shaped robotic fish demonstrates the proposed method.

Short-Term Wind Power Prediction for Wind Turbine via Kalman Filter based on JIT Modeling

2015 ◽  
Vol 135 (1) ◽  
pp. 81-89 ◽  
Author(s):  
Tomoki Ishikawa ◽  
Takaaki Kojima ◽  
Toru Namerikawa
Keyword(s):  
Kalman Filter ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

A New Robust Adaptive Fuzzy Sliding Mode Controller for a Variable Speed Wind Turbine

2015 ◽  
Vol 8 (5) ◽  
pp. 338
Author(s):  
El Mehdi Mellouli ◽  
Zakaria Chalh ◽  
Mohammed Alfidi ◽  
Ismail Boumhidi
Keyword(s):  
Wind Turbine ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Variable Speed ◽  
Adaptive Fuzzy ◽  
Variable Speed Wind Turbine ◽  
Adaptive Fuzzy Sliding Mode ◽  
Fuzzy Sliding Mode ◽  
Robust Adaptive

Wind turbine fault detection and diagnosis using LSTM neural network

2020 ◽  
Author(s):  
Taoran Yang ◽  
Jing Teng ◽  
Changling Li ◽  
Yizhan Feng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Wind Turbine ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals Syndecans and Pancreatic Ductal Adenocarcinoma

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 349
Author(s):  
Nausika Betriu ◽  
Juan Bertran-Mas ◽  
Anna Andreeva ◽  
Carlos E. Semino
Keyword(s):  
Extracellular Matrix ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cell Types ◽  
Ductal Adenocarcinoma ◽  
Physiological Processes ◽  
Extracellular Matrix Components ◽  
Detection And Diagnosis ◽  
And Migration ◽  
Early Detection And Diagnosis

Pancreatic Ductal Adenocarcinoma (PDAC) is a fatal disease with poor prognosis because patients rarely express symptoms in initial stages, which prevents early detection and diagnosis. Syndecans, a subfamily of proteoglycans, are involved in many physiological processes including cell proliferation, adhesion, and migration. Syndecans are physiologically found in many cell types and their interactions with other macromolecules enhance many pathways. In particular, extracellular matrix components, growth factors, and integrins collect the majority of syndecans associations acting as biochemical, physical, and mechanical transducers. Syndecans are transmembrane glycoproteins, but occasionally their extracellular domain can be released from the cell surface by the action of matrix metalloproteinases, converting them into soluble molecules that are capable of binding distant molecules such as extracellular matrix (ECM) components, growth factor receptors, and integrins from other cells. In this review, we explore the role of syndecans in tumorigenesis as well as their potential as therapeutic targets. Finally, this work reviews the contribution of syndecan-1 and syndecan-2 in PDAC progression and illustrates its potential to be targeted in future treatments for this devastating disease.

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