Spectral Analysis of Electromagnetic Compatibility

The report's main aim was to transmit the frequency signals without any disturbances and compute the time and frequencies of energies. Conduct the data burst system with the help of an anechoic chamber to study the electromagnetic interference and utilise the spectrum analyser to identify the required frequency during the conduction of signal transmission and minimise the noise disturbances.

Closed Loop Performance Analysis of Classical PID and Robust H-infinity Controller for VTOL Unmanned Quad Tiltrotor Aerial Vehicle

Unmanned Aerial Vehicles (UAVs) guidance, control and navigation have directed the attention of many researchers in both aerospace engineering as well as control theory. Due to the unique rotor structure of Tiltrotor hybrid UAVs, they exhibit special application value. Quad Tiltrotor UAVs set up a distinctive platform that satisfies the needs of the varying mission requirements by combining the conventional features of high-speed cruise capabilities of an aircraft and hovering capabilities of a helicopter and by tilting its four rotors. The aim of this research article is to control the attitude and altitude of the UAV in the presence of uncertainty using two different control techniques. This paper addresses the comparative analysis of the robust H-infinity controller with classical PID control designs for the transition manoeuvre of a hybrid UAV: the VTOL Tiltrotor UAV. The proposed controllers achieve hover to cruise mode transition and vice-versa. The main idea behind the design of controller is to model and analyze the UAV’s position and attitude dynamics. The desired flight trajectory and the transition manoeuvre is achieved by controlling the tilt angle in 15° intervals from 90° to 0° and vice-versa. Performance index subjected to IAE is estimated and compared for both the controllers in the presence of noise, disturbances and uncertainties. The results of simulation illustrate that the robust H-infinity controller achieves better transition, good adaptability, robust performance and robust stability for the whole flight envelope when compared with the PID controller.

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Online parameter identification is essential for the accuracy of the battery Equivalent Circuit Model (ECM). The traditional Recursive Least Squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the Recursive Total Least Squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, fast convergence rate, and also robustness against noise corruption.

Disrupting Audio Event Detection Deep Neural Networks with White Noise

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families.

Observational Study of the Effect of Moderate and High-Intensity Exercise on Insomnia

The purpose of this observational study is to determine whether moderate-intensity exercises improve the quality of sleep for subjects with insomnia. The duration of this observational study was four weeks. For the first two weeks of the observational study, subjects served as their own control and completed a sleep log. At the beginning of the third week, subjects were randomly split into (1) Group One with moderate-intensity exercise or (2) Group Two with high-intensity exercise. Two-tailed t-tests displayed significant improvements in quality of sleep with the implementation of both moderate and high-intensity exercise. However, Group One participants were found to feel significantly more well-rested than Group Two. The moderate-intensity exercise group showed improvement in quality of sleep, the number of awakenings, and restfulness. In comparison, the high-intensity exercise group demonstrated improvement in the number of awakenings and daytime sleepiness; although, without reported improvement in sleep quality. There were variables in participants’ sleeping environments, including changes in sleep location, travel, sleep-inducing medication, alcohol use, anxiety, unanticipated noise disturbances, and use of electronic devices before bed that was not controlled for in this observational study.

Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example

In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.

Design and Implementation of PID, GA and Fuzzy logic Controllers for an Electrical Drive with Various Noise Disturbances

It is a great challenge for human being to keep up the constant speed in drive when external Noise disturbances occur due to fluctuations of power supply. In order to avoid these issues, PID controllers are intended using predictable method such as Ziegler Nichols method. But finest level is not obtained in transient and steady state. During the MATLAB Simulation, the error is present transient and steady state behavior in conventional PID controllers. Hence it is necessary to design a PID controller with Novel intelligent technique for speed control of drive like fuzzy and Genetic Algorithm. It considers error as fitness function which is to be minimized using various GA operators such as mutation etc. The Drive will be operated with different external noises like sinusoidal noise, Saw tooth noise and Ramp noise and comparison between PID, GA and Fuzzy PID will be presented and their performances are studied.