Lumped-Circuits Model of Lossless Transmission Lines and Its Numerical Characteristics

Aiming at the lumped-circuits model of the lossless transmission line in the digital simulation, the article discusses and analyzes the unit step response generation of the lumped-circuits model by comparing the numerical simulation results of the implicit trapezoidal method, the implicit Euler method, and a multi-step formula. The root cause of numerical oscillations pointed out that using the L-stable numerical algorithm to indirectly simulate the dynamic response of the lumped-circuits model is a numerical method that does not truly reflect the original model, but it can directly reflect the true dynamic response of the lossless transmission line. In this study, a method for determining the chained number in the digital simulation of a lumped-circuits model is given. The simulation results prove the effectiveness of the method.

Influence of Novel Redirector with Bypass Damping on the Performance of Load-Sensing Steering System

Load-sensing steering systems for articulated loaders are prone to large pressure shocks and oscillations during steering operations, affecting the system stability. An optimized structure of the redirector with bypass damping is proposed to improve this phenomenon. In this structure, orifices and throttle grooves are added to the traditional redirector. To control the steering load and working conditions, the steering load of the loader is replaced by a pressure regulating valve. Simulation and experimental results reveal that the redirector with bypass damping has better load-sensing characteristics than the traditional redirector. The peak output pressure shock caused by the load unit step signal decreases from 6.50 to 5.64 MPa, which means the pressure oscillation of the hydraulic system is reduced by 13.4%. The pressure fluctuation time can be reduced from 2.09 to 1.6 s, with a decrease rate of 23.4%. The output pressure oscillation decays swiftly, and the smoothness of the steering operation is improved significantly.

PID speed control of DC motor using meta-heuristic algorithms

This paper presents archimedes optimization algorithm(AOA) and dispersive flies optimization(DFO) to optimally tune gain parameters of PID control scheme in order to regulate DC motor’s speed. These suggested techniques tune the controller by the minimization of the fitness function represented by the integral of time multiplied by absolute error (ITAE). The modelling and simulation are carried out in MATLAB/Simulink. The transient response of unit step input obtained from AOA-PID-ITAE andDFO-PID-ITAE controllers were compared to those obtained from Ziegler-Nichols (ZN) method and particle swarm optimization(PSO). The results indicate that AOA-PID-ITAE and DFO-PID-ITAE are more efficient than ZN method and PSO in reducing rise time and settling time. Likewise, DFOconverge faster to the optimal solution with lower overshoot than AOA and PSO.

Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control.

Convolution-based time-domain simulation for fluidelastic instability in tube arrays

A convolution-based numerical algorithm is presented for the time-domain analysis of fluidelastic instability in tube arrays, emphasizing in detail some key numerical issues involved in the time-domain simulation. The unit-step and unit-impulse response functions, as two elementary building blocks for the time-domain analysis, are interpreted systematically. An amplitude-dependent unit-step or unit-impulse response function is introduced to capture the main features of the nonlinear fluidelastic (FE) forces. Connections of these elementary functions with conventional frequency-domain unsteady FE force coefficients are discussed to facilitate the identification of model parameters. Due to the lack of a reliable method to directly identify the unit-step or unit-impulse response function, the response function is indirectly identified based on the unsteady FE force coefficients. However, the transient feature captured by the indirectly identified response function may not be consistent with the physical fluid-memory effects. A recursive function is derived for FE force simulation to reduce the computational cost of the convolution operation. Numerical examples of two tube arrays, containing both a single flexible tube and multiple flexible tubes, are provided to validate the fidelity of the time-domain simulation. It is proven that the present time-domain simulation can achieve the same level of accuracy as the frequency-domain simulation based on the unsteady FE force coefficients. The convolution-based time-domain simulation can be used to more accurately evaluate the integrity of tube arrays by considering various nonlinear effects and non-uniform flow conditions. However, the indirectly identified unit-step or unit-impulse response function may fail to capture the underlying discontinuity in the stability curve due to the prespecified expression for fluid-memory effects.

Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.

Point of Care Ultrasound during the Medical Teaching Rounds in the COVID-19 Era -The Hospitalist Perspective-

In Internal Medicine, POCUS is gaining significant favorability. An increasing number of clinicians are interested in being trained for POCUS. The newer portable ultrasounds are small and can be transported easily during rounds. Their design is now for a more intuitive use. Training of Internists now involves assessing patients utilizing POCUS technology in residency. Here at Danbury Hospital, we have formal POCUS training. Attending internists are now attempting to incorporate POCUS training as a part of continuing medical education. POCUS in the hospitalist or general practitioner world has not been completely defined. Generally, the patient seen in the medical ward is not ill as the patients seen in intensive care units (ICU), Emergency Department (ED), and other high acute-care settings. However, from time to time, internists need to treat high acuity patients on the medical floors before transferring them to a higher level of care or when they are required to cover an open ICU or Progressive Care Unit (step-down unit). The role of POCUS while managing stable patients may differ significantly compared to the role in more acute patients. A well-defined spectrum for the use of POCUS does not currently exist. However, there are efforts in this regard. POCUS is an emerging and exciting diagnostic modality in the medical ward. We believe that the pandemic has given it a new meaning for the hospitalist and general practitioner, and we expect that its use and significance will only grow in the few years ahead.

Imperialist competitive algorithm for determining the parameters of a Sugeno fuzzy controller

We used an imperialist competitive algorithm to determine the parameters of a fuzzy controller of type Sugeno that would ensure a good unit step response of a second-order single-input and single-output automatic system.

Influence of Methods Approximating Fractional-Order Differentiation on the Output Signal Illustrated by Three Variants of Oustaloup Filter

Fractional-order (FO) differential equations are more and more frequently applied to describe real-world applications or models of phenomena. Despite such models exhibiting high flexibility and good fits to experimental data, they introduce their inherent inaccuracy related to the order of approximation. This article shows that the chosen model influences the dynamic properties of signals. First, we calculated symbolically the steady-state values of an FO inertia using three variants of the Oustaloup filter approximation. Then, we showed how the models influence the Nyquist plots in the frequency domain. The unit step responses calculated using different models also have different plots. An example of FO control system evidenced different trajectories dependent on applied models. We concluded that publicized parameters of FO models should also consist of the name of the model used in calculations in order to correctly reproduce described phenomena. For this reason, the inappropriate use of FO models may lead to drawing incorrect conclusions about the described system.