3 Robust Performance of the Multiloop Perturbation Compensator

2005 ◽  
pp. 41-65 ◽  
Author(s):  
SangJoo Kwon ◽  
Wan Kyun Chung
Keyword(s):  
Robust Performance ◽  
Perturbation Compensator

scholarly journals Robust deep learning pipeline for PVC beats localization

2021 ◽  
Vol 29 ◽  
pp. 475-486
Author(s):  
Bohdan Petryshak ◽  
Illia Kachko ◽  
Mykola Maksymenko ◽  
Oles Dobosevych
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Ecg Signal ◽  
Ventricular Contraction ◽  
Localization Task ◽  
Robust Performance ◽  
Extensive Evaluation ◽  
Decoder Architecture ◽  
Public Datasets

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

scholarly journals Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 287
Author(s):  
Matteo Corno ◽  
Sara Furioli ◽  
Paolo Cesana ◽  
Sergio M. Savaresi
Keyword(s):  
Autonomous Navigation ◽  
Incidence Angle ◽  
Autonomous Driving ◽  
Localization Algorithm ◽  
Robust Performance ◽  
Cluttered Environment ◽  
Experimental Campaign ◽  
Commercial Applications ◽  
Agricultural Tractors ◽  
Selection Stage

Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees.

Regularized error function-based extended Kalman filter for estimating the cancer chemotherapy dosage: impact of improved grey wolf optimization

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Utkarsha L. Mohite ◽  
Hirenkumar G. Patel
Keyword(s):  
Tumor Cells ◽  
Error Function ◽  
Drug Dosage ◽  
Parameters Estimation ◽  
Gray Wolf ◽  
Robust Performance ◽  
Parameter Uncertainties ◽  
Initial State ◽  
Robust Controller ◽  
Fine Tune

AbstractObjectivesThe main aim of this work is to introduce a robust controller for controlling the drug dosage.MethodsThe presented work establishes a novel robust controller that controls the drug dosage and it also carried out parameters estimation. Along with this, a Regularized Error Function-based EKF (REF-EKF) is introduced for estimating the tumor cells that could be adapted for different conditions. It also assists in solving the overfitting problems, which occur during the drug dosage estimation. Moreover, the performance of the adopted controller is compared over other conventional schemes, and the attained outcomes reveal the appropriate impact of drug dosage injection on immune, normal, and tumor cells. It is also ensured that the presented controller does a robust performance on the parameter uncertainties. Moreover, to enhance the performance of the proposed system and for fast convergence, it is aimed to fine-tune the initial state of EKF optimally using a new Improved Gray Wolf Optimization (GWO) termed as Adaptive GWO (AGWO). Finally, analysis is held to validate the betterment of the presented model.ResultsThe outcomes, the proposed method has accomplished a minimal value of error with an increase in time, when evaluated over the compared models.ConclusionsThus, the improvement of the proposed REF-EKF-AGWO model is proved from the attained results.

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