Values Of Control Parameters Research Articles

A two-step algorithm for acoustic emission event discrimination based on recurrent neural networks

We present an algorithm for seismic event discrimination and event approximate location based on multi-station seismograms. A deep learning approach was applied using a two-step algorithm: (i) signal onsets were identified in individual tracks based on the use of long-short-term memory neural network layers; (ii) if a sufficient number of onsets were reliably identified, a preliminary location was determined. We adopted a “reverse location approach” where the time sense of a seismogram is reverted and the origin time is predicted using a neural network approach based on previously determined onsets. Successful location or origin time prediction also served as a feedback for confirming previous onset identification.The method was tested using a data set of Acoustic Emission generated from the uniaxial loading of a Westerly Granite specimen. Accuracy of the method was better than 97%. Discriminated events were automatically located and their seismic moment tensor was determined. Both types of results were in good agreement with the baseline data set.With respect to the particular nature of processed data, we provide a demo code which shows examples presented in the article. In addition, a detailed description of the algorithm, including the control parameter values, is provided in the text. Based on this information the method can be applied on any data.

An intelligent adaptive control of DC–DC power buck converters

Buck DC–DC converters are broadly used in DC microgrids to provide a constant dc voltage for generation and storage components. Changing of load condition affects the quality of voltage in the buck DC–DC converters. When constant power loads (CPLs) are used, the stability of these power electronic devices is at risk due to negative impedance characteristics of the CPLs. In such condition, an efficient control method is required to ensure the proper operation of the converter. For this purpose, development of an adaptive control methodology is essential to evaluate the accurate values of controller parameters in the shortest time to damp the ripples quickly. This paper develops a backstepping controller with nonlinear disturbance observer to regulate the output voltage of a dc/dc converter feeding a CPL. An artificial neural network (ANN) methodology is used to estimate the backstepping control parameters of the buck converter. The training ability of the ANN technique prevents the existing controller from depending on the working point of the microgrid. The ANN methodology adapts the controller with various changes and reflections of uncertainties in the microgrid. Case studies are conducted on a dc/dc buck converter in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.

Two-Stage Task Offloading Optimization With Large Deviation Delay Analysis in IoT Networks

In the edge computing Internet of Things network, we minimize the offloading overhead (caused by the bandwidth cost for data transmission and computation resource consumption for task remote processing) while providing the end-to-end (E2E) delay provisioning. Under the scenario, a tandem queue consisting of a transmission queue and a computing process queue is formed by the tasks offloaded to the edge server via wireless link and then processed through the computing resource. Due to the tandem queue, the offloading decision and computing resource allocation are coupled over the tandem queue. To make the problem tractable, we decouple the above two operations and propose a two-stage offloading filtering and computing resource allocation policy. After decouple, we then investigate the delay bound violation probability of the tandem queue by leveraging large deviation analysis. Further, we reveal that under the same E2E delay provisioning, the offloading overhead under the proposed decoupled policy can approach to the non-decoupled optimum by selecting an appropriate value of control parameter. Simulation results verify the theoretical analysis and show the efficiency of the proposed policy.

Effect of time-delay feedback control on vehicle seat vibration reduction and suspension performance

To reduce seat vibration caused by uneven road surfaces, the time-delay feedback control into the seat suspension system was introduced and an active seat suspension control method based on time-delay feedback was proposed in this paper. A three-degree-of-freedom (3-DOF) suspension model with time-delay feedback control was established. The time-delay independent stability region and critical stability curve of the system were derived using the method of characteristic root and stability switching. The effect of feedback control parameters on system vibration was investigated in the stability region. The seat acceleration (SA), body acceleration (BA), suspension dynamic deflection (SDD), and tire dynamic displacement (TDD) were used as multi-objective optimization functions and the optimal values of feedback control parameters were obtained based on the particle swarm algorithm (PSO) with above optimization functions. The numerical simulation was conducted to validate the proposed model. The simulated results show that the time-delay feedback control can significantly suppress the vibration response of the seat and effectively improve the suspension performance under different road excitation compared with the passive suspension. It can be seen that the active seat suspension with time-delay control significantly improve ride comfort and handling stability of the vehicle, which can be used as a reference for the active control technology of vehicle suspension.

Adaptive two-step method for providing the desired visual quality for SPIHT

Lossy compression has been widely used in various applications due to its variable compression ratio. However, distortions are introduced unavoidably, and this decreases the image quality. Therefore, it is often required to control the quality of the compressed images. A two-step method has been proposed recently to provide the desired visual quality. The average rate-distortion curve was used to determine the proper parameter value that controls compression. However, its performance for the wavelet-based coder Set Partitioning in Hierarchical Trees (SPIHT) is insufficient because there are very wide limits of visual quality variation for different images for a given value of the compression control parameter (CCP). Additionally, previous work has demonstrated that the level of errors, which is the subject of our study relates to texture features of an image to be compressed, where texture presence is an inherent property of remote sensing images. In this paper, our goal is to develop an adaptive two-step method for SPIHT to improve accuracy. The following tasks were solved. First, a prediction of visual quality for a particular parameter value is conducted. The prediction scheme is based on the information extraction from a certain number of image blocks to perform a visual quality calculation of the image compressed for a given CCP value. A threshold is adopted as the complexity grouping; in this paper, images are divided into two groups: simple and complex images. Second, the results of the grouping determine the adaptive curve model adopted. Finally, a two-step compression method is applied according to this curve. The classical metric Peak signal-to-noise ratio (PSNR) is employed to evaluate the image quality. The research method is based on a validation experiment that is conducted for an image set covering different image complexity and texture features. The comparison results of four typical desired values prove that the accuracy has been generally improved, the variances of both the first and second steps have been reduced sufficiently, and the mean absolute error has also been improved. Conclusion: the improvement effects are significant, particularly in the low desired visual quality. A remote sensing image is taken as an example to analyze in detail; the quality of the decompressed images meets the user’s visual requirement, and the errors are acceptable.

Research on Vibration Reduction of Half-Vehicle Active Suspension Sys-tem Based on Time-delayed Feedback Control with Wheel Displacement

This paper proposes parameter controlling optimization of the time-delay feedback that is based on "equivalent harmonic excitation" in effective frequency band towards optimization of delay feedback control parameters for vehicle semi active suspension system. In this way, the optimal values of delay feedback control parameters based on different types of delay feedback control (displacement, velocity and acceleration) of wheels in the effective frequency band are obtained. Finally, through the stability analysis of vehicle semi-active suspension system based on different types of wheel delay feedback control and the numerical simulation analysis of time-domain response of suspension system performance evaluation index, the superiority of the time-delay feedback control strategy based on wheel displacement and the effectiveness and feasibility of the equivalent harmonic excitation optimization strategy are verified. Therefore, it provides a theoretical reference for the selection of time-delay feedback control strategy and the optimal design of time-delay feedback control parameters of vehicle active and semi-active suspension system.

Modeling and simulation of suction-driven flow and heat transfer with temperature-dependent thermal conductivity in a porous channel

In this paper, the heat transfer equation is coupled to the Navier-Stokes equations for a steady flow induced by uniform suction on two porous walls kept at different temperatures. The incompressibility of the fluid and the fact that the velocity field has two components lead to introduce the stream function in the governing equations. A similarity method is used in order to transform the resulting set of partial differential equations satisfied by the stream function and temperature into two ordinary differential equations for the same problem. The analysis is focused on the description of the behaviors of the normal and axial velocities, the streamlines, and temperature through multiple solution branches under different values of the main control parameters of the problem which are the Reynolds number, the Péclet number and the sensitivity of the thermal conductivity to the variations of temperature.

Self-tuning control of parametrically excited active magnetic bearing system due to harmonic base motion using fuzzy logic

Using active magnetic bearings for vibration control of flexible rotors subject to large base motion is both interesting and challenging. Rotors on ships, airplanes, and space-crafts fall in this category. These applications pose challenge for the active magnetic bearing designer, as the large motion of base renders the rotor-shaft-AMB system, time varying in nature, and also causes parametric excitation to the rotor system. Apart from stability concerns in such systems, it is difficult to design and choose optimal values of controller parameters because the system is subject to different base motions at different times during operation. In order to address this issue, this work applies fuzzy logic and proposes a simple yet effective selftuning control (STC) to control vibrations of flexible rotors supported by active magnetic bearings, which is subject to excitations due to base motion, in addition to unbalance excitation, usually present in rotors. To this end, first, the controller parameters are optimized considering the levitation performance of active magnetic bearings based on the transient response. Next, the fuzzy logic-based self-tuning algorithm is presented. Detailed comparison of performance between optimal and self-tuning control for different base motion conditions show that the proposed self-tuning controller outperforms the optimal control.

Modelling and control of static synchronous series compensator interfaced with DFIG-based wind farm using PSO for SSR alleviation

Subsynchronous resonance (SSR) in wind turbine generators interfaced with series compensated transmission line has been explored in recent few years. Instead of torsional interaction (TI), the induction generation effect (IGE) prompts SSR in such systems. The mitigation of SSR using flexible ac transmission system (FACTS) devices like a thyristor-controlled series capacitor (TCSC), gate-controlled series capacitor (GCSC), static var compensator (SVC) and static synchronous compensator (STATCOM) has been researched in the literature. However, in wind turbine generators, the efficacy of static synchronous series compensator (SSSC) to mitigate SSR is relatively less explored. This paper presents mathematical modelling of SSSC and a study of hybrid series compensation of transmission line using SSSC and fixed series capacitor (FSC) to diminish the SSR effect in doubly-fed induction generator (DFIG)-based wind farm. The values of controller parameters of SSSC for SSR mitigation are identified using particle swarm optimisation (PSO) and compared with the Ziegler and Nichols (Z–N) method. Results obtained through PSO are more precise and finer than the conventional approach. The eigenvalue analysis and fast Fourier transform (FFT) analysis are shown to demonstrate the damping offered by SSSC to SSR oscillations along with transient simulations. The impact of SSR at different compensation levels and wind speed is examined, and the effectiveness of SSSC for SSR mitigation is illustrated using the hybrid compensation method. The risk of SSR is high at low wind speeds and a high compensation level. In such instances, it is shown that SSSC is capable of effectively alleviating SSR.

Parameters Optimization of Taguchi Method Integrated Hybrid Harmony Search Algorithm for Engineering Design Problems

Performance of convergence to the optimum value is not completely a known process due to characteristics of the considered design problem and floating values of optimization algorithm control parameters. However, increasing robustness and effectiveness of an optimization algorithm may be possible statistically by estimating proper algorithm parameters values. Not only the algorithm which utilizes these estimated-proper algorithm parameter values may enable to find the best fitness in a shorter time, but also it may supply the optimum searching process with a pragmatical manner. This study focuses on the statistical investigation of the optimum values for the control parameters of the harmony search algorithm and their effects on the best solution. For this purpose, the Taguchi method integrated hybrid harmony search algorithm has been presented as an alternative method for optimization analyses instead of sensitivity analyses which are generally used for the investigation of the proper algorithm parameters. The harmony memory size, the harmony memory considering rate, the pitch adjustment rate, the maximum iteration number, and the independent run number of entire iterations have been debated as the algorithm control parameters of the harmony search algorithm. To observe the effects of design problem characteristics on control parameters, the new hybrid method has been applied to different engineering optimization problems including several engineering-optimization examples and a real-size engineering optimization design. End of extensive optimization and statistical analyses to achieve optimum values of control parameters providing rapid convergence to optimum fitness value and handling constraints have been estimated with reasonable relative errors. Employing the Taguchi method integrated hybrid harmony search algorithm in parameter optimization has been demonstrated as it is a reliable and efficient manner to obtain the optimum results with fewer numbers of run and iteration.

NIPPF versus ANIPPF controller outcomes on semi-direct drive cutting transmission system in a shearer

Nonlinear integral positive position feedback (NIPPF) and adaptive nonlinear integral positive position feedback (ANIPPF) controllers are used in a shearer's semi-direct drive cutting transmission system, which is powered by a low speed and high torque permanent magnet synchronous motor (PMSM). The transmission system's torsional vibration characteristics were investigated with internal parameter and external cutting load excitation changes caused by harmonic force. In the case of primary and 1:1 internal resonance, an analytic solution is obtained by employing the averaging technique to solve the nonlinear differential equations and modeling the system with NIPPF and ANIPPF controllers. The Poincare portraits were created to imagine how the PMSM would behave around their fixed points. A good match is found when the approximate solutions are compared to the numerical simulations via the Runge–Kutta method fourth-order (RK4). The linearized stability strategy is used to ensure stability near fixed points in the autonomous system. Before and after control, the steady-state amplitude and stability of nonlinear systems were investigated and analyzed. Optimal system operation conditions and frequency response curves (FRCs) were examined at various controller and system parameter values. Numerous system characteristics were found to have an effect after being combined with NIPPF and ANIPPF. The numerical and analytical solutions at time-history and FRCs were compared using the MATLAB program to confirm their comparability. This study's findings were compared to those found in the literature. We conclude from this analysis that the ANIPPF controller provides the best system control. The calculations also revealed that ANIPPF is the most effective controller. Finally, the controls suppress the system's vibration amplitude and chaos motion. The outcomes of this study were used to develop a theoretical foundation for the shearer semi-direct drive cutting transmission system's design and construction.

A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation.

Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions. It is analytically intractable to compute the necessary control parameter values of a droplet-generating device that produces optimized droplets. Furthermore, as the length scale of the fluid flow changes, the formation physics and optimized conditions that induce flow decomposition into droplets also change. Hence, a single proportional integral derivative controller is too inflexible to optimize devices of different length scales or different control parameters, while classification machine learning techniques take days to train and require millions of droplet images. Therefore, the question is posed, can a single method be created that universally optimizes multiple length-scale droplets using only a few data points and is faster than previous approaches? In this paper, a Bayesian optimization and computer vision feedback loop is designed to quickly and reliably discover the control parameter values that generate optimized droplets within different length-scale devices. This method is demonstrated to converge on optimum parameter values using 60 images in only 2.3 h, 30× faster than previous approaches. Model implementation is demonstrated for two different length-scale devices: a milliscale inkjet device and a microfluidics device.

An analytical solution for nonlinear vibration of functionally graded porous micropipes conveying fluid in damping medium

This paper presents an analytical solution for nonlinear free vibration of functionally graded porous micropipes conveying fluid embedded in viscous damping medium. By applying Von-Kármán's nonlinear strain relations, strain gradient theory and Hamilton's principle, the nonlinear governing equations of motion are derived in the presence of viscous damping medium. The nonlinear governing equations of motion are discretized and converted to a set of nonlinear ordinary differential equations by using Galerkin's method. For the first time, closed-form expressions are obtained for nonlinear frequency, critical fluid velocity and damped time response of the functionally graded porous micropipe conveying fluid by employing the homotopy analysis method. In addition, the convergence control parameter is considered to diminish the error of the damped vibration response. The effects of the geometrical properties of the micropipe and the viscous damping medium on the vibration of the functionally graded porous micropipe conveying fluid are investigated. The results show that the optimum value of the convergence control parameter dramatically decreases the error of the homotopy solution. Also, the vibration period is increased by the increment of the fluid velocity, damping coefficient, porosity volume fraction, power-law exponent and the length of the functionally graded porous micropipe conveying fluid.

Hamiltonian energy computation of a novel memristive mega-stable oscillator (MMO) with dissipative, conservative and repelled dynamics

Mega-stable oscillators with memristor have not been investigated in the literature up to now. In this contribution, a novel memristive mega-stable oscillator (MMO) with a plethora of properties is introduced. The originality of the introduced systems is that the 3D model exhibits mega-stability without external perturbation, which was not the case in most existing mega-stable models considered in the literature. The investigation of the volume contraction rate of the oscillator revealed that its processes dissipative, conservative, and repelled dynamics depending on the values of the bifurcation control parameter. The mega-stable nature of the investigated oscillator is characterized by the coexistence of attractors in a nested structure. In addition, the well-known Helmholtz theorem is used to determine the Hamiltonian energy used during the mechanism of the megastability. Finally, multiple coexisting mega-stable attractors are captured in Pspice simulations by configuring the initial conditions, further supporting the numerical results.

Optimal Control for Cleaner Hybrid Vehicles: A Backward Approach

This work presents an application of the optimal control theory to find trade offs between fuel consumption and pollutant emissions (CO, HC, NOx) of sustaining hybrid vehicles. Both cold start and normal operations are considered. The problem formulation includes two state variables: battery state of energy and catalyst temperature; and three control variables: torque repartition between engine and motor, spark advance, and equivalence ratio. Optimal results were obtained by delaying the first engine crank after the urban part of the mission. The results show that a quick catalyst light off is performed. Once the catalyst is primed, special control parameters values are adopted to operate the engine.

Relationship and influences of behavioral and psychological factors on metabolic control of patients with type 2 diabetes mellitus

Background/Aim. Achieving good metabolic control, which plays a key role in reducing or preventing macrovascular and microvascular complications of diabetes mellitus (DM), requires continuous patient involvement in the self-management of DM. This continued engagement, which makes type 2 DM (T2DM) one of the most physically and emotionally demanding diseases, can become, at certain periods of life, extremely severe and lead to emotional distress (symptoms of depression and DM-related distress) and d-terioration of metabolic control. The aim of this study was to examine the association and influence of behavioral and psychological factors on the metabolic control of patients with T2DM. Methods. The research was conducted as a descriptive-analytic cross-sectional study. The method of random sampling included 324 subjects with T2DM in the research. The values of biochemical parameters of metabolic control were measured by standard laboratory methods. Blood pressure was measured two times, and the arithmetic mean was calculated. Anthropometric measurement was performed, and body mass index (BMI) was calculated. Attitudes toward medication adherence, adherence to dietary recommendations, level of physical activity, presence of depressive symptoms, and level of DM-related distress were examined using standardized questionnaires. Results. The target values of metabolic control parameters were reached by 21.6% of respondents. Multivariate analysis as predictors of poor metabolic control identified obesity, non-adherence toward dietary recommendations, insulin therapy, low level of physical activity, and clinically significant DM-related distress. Conclusion. Routine application of the questionnaire used in this study in the initial stages or critical moments of the disease can assess patients? attitudes and knowledge about behavioral determinants of DM self-management and timely detect psychological conditions that affect them. It would be realistic to expect that such a comprehensive holistic approach would contribute to a lower incidence of complications and better metabolic control of T2DM.

Numerical solution of system of fuzzy fractional order Volterra integro-differential equation using optimal homotopy asymptotic method

<abstract> <p>In this paper, an efficient technique called Optimal Homotopy Asymptotic Method has been extended for the first time to the solution of the system of fuzzy integro-differential equations of fractional order. This approach however, does not depend upon any small/large parameters in comparison to other perturbation method. This method provides a convenient way to control the convergence of approximation series and allows adjustment of convergence regions where necessary. The series solution has been developed and the recurrence relations are given explicitly. The fuzzy fractional derivatives are defined in Caputo sense. It is followed by suggesting a new result from Optimal Homotopy Asymptotic Method for Caputo fuzzy fractional derivative. We then construct a detailed procedure on finding the solutions of system of fuzzy integro-differential equations of fractional order and finally, we demonstrate a numerical example. The validity and efficiency of the proposed technique are demonstrated via these numerical examples which depend upon the parametric form of the fuzzy number. The optimum values of convergence control parameters are calculated using the well-known method of least squares, obtained results are compared with fractional residual power series method. It is observed from the results that the suggested method is accurate, straightforward and convenient for solving system of fuzzy Volterra integrodifferential equations of fractional order.</p> </abstract>

Optimization of Torque Control Parameters for Wind Turbine Based on Drive Chain Active Damping Control

Considering the mutual coupling effect of wind turbine control parameters, an optimization method of the control parameters for torque system based on drive chain active damping control is proposed, which improves the control accuracy and solves the problem that the control objectives are difficult to coordinate. Firstly, the mathematical model of the torque-speed system and the dynamical model of the drive chain active damping control are established. At the same time, the initial value of active damping gain is calculated according to the designed band-pass filter measurement results. Secondly, to facilitate the setting of proportional-integral (PI) control parameters, the Routh method is adopted to identify the torque-speed system with large inertia characteristic as a low-order inertia system. Subsequently, the Integrated Time and Absolute Error (ITAE) criterion is used to set the initial value of the PI control parameters for the torque system. Furthermore, the PI control parameters and active damping gains at each equilibrium point are optimized based on the Hierarchic Genetic Algorithm, which improves the accuracy of control parameters. At the same time, the control objectives are coordinated by optimizing weight allocation based on the Pareto. Then the adaptive control method is constructed by fitting them with the wind speed to improve the control accuracy far from the equilibrium points. Finally, the effectiveness of the proposed method is verified by comparing the frequency-domain characteristic, control accuracy, twist vibration of the drive chain, tower vibration and load.

Short-term outcome of bariatric surgery on nonalcoholic fatty liver disease: a Korean perspective.

PurposeObesity is associated with nonalcoholic fatty liver disease, one of the most common causes of chronic liver disease. We aimed to demonstrate the effectiveness of bariatric surgery for hepatic steatosis and fibrosis in Korean patients.MethodsA total of 32 consecutive patients were enrolled in this study. Hepatic steatosis and liver fibrosis were assessed before surgery and 6 months after surgery using transient elastography and serologic panels.ResultsThirteen patients (40.6%) were male and 19 (59.4%) were female, with a mean age of 39.3 ± 11.3 years. The body mass index was significant at the 6th month: 39.1 ± 6.7 and 30.3 ± 4.7 kg/m2 (P < 0.001), respectively. The mean preoperative controlled attenuation parameter and liver stiffness measurement values were 325.4 ± 55.9 dB/m and 7.4 ± 4.8 kPa, respectively, before surgery, and they decreased to 267.1 ± 45.1 dB/m and 5.3 ± 2.3 kPa, respectively, 6 months postoperatively (P < 0.001, respectively).ConclusionThese results suggest that bariatric surgery is associated with a significant improvement in liver steatosis and fibrosis. Bariatric surgery has a beneficial effect on nonalcoholic fatty liver disease in patients with morbid obesity in Korea.

Experience in using the methods of catastrophe theory in describing the dynamics of phytoplankton in the Vistula lagoon of the Baltic Sea

Ideas and methods of catastrophe theory are widely used to analyze “regime shifts” in ecosystems. In this work, the analysis of the seasonal dynamics of phytoplankton in the Russian part of the Vistula Lagoon of the Baltic Sea is carried out on the basis of using the simplest possible model of the catastrophe theory − cusp catastrophe. The total phytoplankton biomass is considered as a state variable. The use of one of the methods of multivariate analysis − principal components method − made it possible to carry out the entire analysis depending on a small number of parameters. A calculation of the control parameters values is carried out using the factor loadings of the main components for the data that characterize the hydro-physical and hydro-chemical regimes as well as the level of biogenic loading on the aquatic ecosystem of the lagoon. It was found that, depending on the specific situation, the same factor or different combinations of factors can play both a stabilizing role and be the cause of destabilization.