scholarly journals Short Transient Parameter-Varying IIR Filter Based on Analog Oscillatory System

2019 ◽  
Vol 9 (10) ◽  
pp. 2013 ◽  
Author(s):  
Piotr Okoniewski ◽  
Jacek Piskorowski
Keyword(s):  
Transient Response ◽  
Real Life ◽  
Digital Filtering ◽  
Oscillatory System ◽  
Optimization Method ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Dynamical Properties ◽  
Simulation Results ◽  
Discretization Process

This paper presents a concept for digital infinite impulse response (IIR) lowpass filter with reduced transient response. The proposed digital filtering structure is based on an analog oscillatory system. In order to design the considered digital filter, the analog prototype is subjected to a discretization process and, then, the parameters describing the dynamical properties of the oscillatory system are temporarily varied in time, so as to suppress the transient response of the designed filter. An optimization method, aimed at reducing the settling time by proper parameter manipulation, is presented. Simulation results, along with a real-life application proving the usefulness of the proposed concept, are also shown and discussed.

scholarly journals A Concept of the Non-Stationary Filtering Network with Reduced Transient Response

2019 ◽  
Vol 9 (21) ◽  
pp. 4570
Author(s):  
Katarzyna Wiechetek ◽  
Jacek Piskorowski
Keyword(s):  
Transient Response ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Gain Factor ◽  
Time Varying ◽  
Swarm Optimization ◽  
First Order ◽  
Analog System ◽  
Time Varying Parameters ◽  
Discretization Process

This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.

scholarly journals A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 38
Author(s):  
Amr Mohamed AbdelAziz ◽  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi
Keyword(s):  
Medical Care ◽  
Real Life ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Admission Process ◽  
Multi Objective ◽  
Admission Time ◽  
King Faisal Specialist Hospital ◽  
Number Of Patients ◽  
Massive Number

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

2016 ◽  
Vol 24 (6) ◽  
pp. 1086-1100
Author(s):  
Utku Boz ◽  
Ipek Basdogan
Keyword(s):  
Computational Complexity ◽  
Vibration Control ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Active Vibration

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Influence of digital filtering parameters on the pulse waveform in reflectance photoplethysmography

2021 ◽  
Author(s):  
D.G. Lapitan ◽  
A.A. Glazkov ◽  
D.A. Rogatkin
Keyword(s):  
Morphological Analysis ◽  
Pulse Wave ◽  
Cutoff Frequency ◽  
Digital Filtering ◽  
Infinite Impulse Response ◽  
Pulse Waveform ◽  
Filter Order ◽  
Technical Requirements ◽  
Dicrotic Notch ◽  
Lower Cutoff

Photoplethysmography (PPG) is an optical method for recording pulse wave (PW) propagating in the tissue microvasculature. As a rule, filters with infinite impulse response (Butterworth, Bessel, etc.) often used in PPG signal processing introduce distortions in the PW signal. At the same time, the filtering parameters for a more accurate reproduction of PW have not yet been substantiated. The aim of this work is to study the influence of digital filtering parameters, such as bandwidth and filter order, on the pulse waveform. In the study, a digital bandpass Butterworth filter was used. The lower cutoff frequency of the filter varied from 0.1 to 1 Hz, the upper cutoff frequency varied from 2 to 10 Hz and the filter order – from 2nd to 6th. It was found that an increase in the lower cutoff frequency of the bandpass filtering leads to a decrease in the amplitude of the reflected diastolic wave and distortion of the front of the direct systolic wave. A decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the PW. Increasing the filter order decreases the reflected wave amplitude. The minimal distortions of the PPG signal were observed at the lower cutoff frequency of 0.1 Hz, the upper one at 10 Hz and the filter order equal to 2. Thus, these parameters of a bandpass filtering can be recommended for processing PPG signals for a more accurate morphological analysis of PW. The obtained results make it possible to create devices for PW analysis with substantiated medical and technical requirements for filtration parameters.

Control of inverters in standalone andgrid-connected microgrid using different control strategies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vikash Gurugubelli ◽  
Arnab Ghosh
Keyword(s):  
Transient Response ◽  
Control Strategy ◽  
Control Strategies ◽  
Power Balance ◽  
Droop Control ◽  
Content Type ◽  
Simulink Simulation ◽  
Time Simulation ◽  
Simulation Results ◽  
First Time

Purpose The share of renewable energy sources (RESs) in the power system is increasing day by day. The RESs are intermittent, therefore maintaining the grid stability and power balance is very difficult. The purpose of this paper is to control the inverters in microgrid using different control strategies to maintain the system stability and power balance. Design/methodology/approach In this paper, different control strategies are implemented to the voltage source converter (VSC) to get the desired performance. The DQ control is a basic control strategy that is inherently present in the droop and virtual synchronous machine (VSM) control strategies. The droop and VSM control strategies are inspired by the conventional synchronous machine (SM). The main objective of this work is to design and implement the three aforementioned control strategies in microgrid. Findings The significant contributions of this work are: the detailed implementation of DQ control, droop control and VSM control strategies for VSC in both grid-connected mode and standalone mode is presented; the MATLAB/Simulink simulation results and comparative studies of the three aforementioned controllers are introduced first time in the proposed work; and the opal-RT digital real-time simulation results of the proposed VSM control show the superiority in transient response compared to the droop control strategy. Research limitations/implications In the power system, the power electronic-based power allowed by VSM is dominated by the conventional power which is generated from the traditional SM, and then the issues related to stability still need advance study. There are some differences between the SM and VSM characteristics, so the integration of VSM with the existing system still needs further study. Economical operation of VSM with hybrid storage is also one of the future scopes of this work. Originality/value The significant contributions of this work are: the detailed implementation of DQ control, droop control and VSM control strategies for VSC in both grid-connected mode and standalone mode is presented; the MATLAB/Simulink simulation results and comparative studies of the three aforementioned controllers are introduced first time in the proposed work; and the opal-RT digital real-time simulation results of the proposed VSM control show the superiority in transient response compared to the droop control strategy.

scholarly journals Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen
Keyword(s):  
Signal Processing ◽  
Fourier Transformation ◽  
Filter Design ◽  
Real Life ◽  
Digital Filtering ◽  
Data Preprocessing ◽  
Sensor Data ◽  
Discrete Fourier Transformation ◽  
Filtering Algorithms ◽  
Multiple Sensors

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

Improved Electronically-Tunable Current-Mode Square-Root-Domain First-Order Multifunction Filter

2021 ◽  
pp. 2150190
Author(s):  
Manoj Kumar Jain
Keyword(s):  
Transient Response ◽  
Current Mode ◽  
Square Root ◽  
Spice Simulation ◽  
First Order ◽  
Low Pass ◽  
Multifunction Filter ◽  
Simulation Results ◽  
Electronically Tunable ◽  
High Pass

Some time back, Kircay reported an electronically-tunable current-mode square-root-domain first-order filter capable of realizing low-pass (LP), high-pass (HP) and all-pass (AP) filter functions. When simulated in SPICE, Kircay’s circuit has been found to exhibit DC offsets in case of LP and AP responses and incorrect transient response in case of HP response. In this paper, an improved circuit overcoming these difficulties/deficiencies has been suggested and its workability of the improved circuit as well as its capability in meeting the intended objectives has been demonstrated by SPICE simulation results.

scholarly journals Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4642
Author(s):  
Li Dai ◽  
Dahai You ◽  
Xianggen Yin
Keyword(s):  
Robust Optimization ◽  
Dirichlet Process ◽  
Forecast Error ◽  
Optimization Methods ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Uncertainty Sets ◽  
Uncertainty Set ◽  
Simulation Results

Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

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