Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Electrically excited synchronous motor (EESM) is widely used in many large equipment drives because of its strong overload capacity, high efficiency, and adjustable power factor. The research and development of a high-performance EESM control system can realize the high combination of energy-saving speed regulation and green environmental protection and has a high social effect and economic value. In this paper, the signal injection method is used to obtain the initial rotor position information of EESM. Sliding Fourier transform is used to improve the initial position angle detection method based on the rotor signal injection method, and the improved method is compared with the traditional voltage integration method. Rotor high-frequency signal injection method was used to detect the rotor position information of the motor during operation, and the influence of the damping winding on the rotor signal injection method was analyzed. On the premise that the damping winding had no influence on the method, a method of obtaining the rotor position information of EESM without a speed sensor was designed. Finally, the speed sensorless regulation system using the initial rotor position detection method is simulated, which verifies the accuracy of the proposed speed sensorless control scheme.

Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks

The laser-based audio signal injection can be used for attacking voice controllable systems. An attacker can aim an amplitude-modulated light at the microphone's aperture, and the signal injection acts as a remote voice-command attack on voice-controllable systems. Attackers are using vulnerabilities to steal things that are in the form of physical devices or the form of virtual using making orders, withdrawal of money, etc. Therefore, detection of these signals is important because almost every device can be attacked using these amplitude-modulated laser signals. In this project, the authors use deep learning to detect the incoming signals as normal voice commands or laser-based audio signals. Mel frequency cepstral coefficients (MFCC) are derived from the audio signals to classify the input audio signals. If the audio signals are identified as laser signals, the voice command can be disabled, and an alert can be displayed to the victim. The maximum accuracy of the machine learning model was 100%, and in the real world, it's around 95%.

ADS-B Signal Verification Using a Coherent Receiver

Automatic Dependent Surveillance-Broadcast (ADS-B) enables aircraft to periodically broadcast their flight states such as position and velocity. Compared to classical radar surveillance, it increases update rate and accuracy. Currently, Mode S Extended Squitter is the most common implementation for ADS-B. Due to the simplicity of Mode S design, ADS-B signals are prone to injections. This study proposes a cost-effective solution that verifies the integrity of ADS-B signals using coherent receivers. We design the verification approach by combining the signal’s direction of arrival, estimated from the multi-channel data, with the target bearing calculated from ADS-B messages. By using another high-performance software-defined radio transceiver, we also conduct real signal injection experiments to validate our approaches.

Sensorless Control of Asynchronous Motor at Low speed

The subject of the submitted paper is to provide a detailed description of simulation control of rotor position sensing of an asynchronous motor by an injection method of high-frequency analogue signal on supply signal frequency of up to 5 Hz, i.e., at zero and low speed. In general, contrary to discrete signal injection, the implementation of the method appears to be simpler for continual signal injection aimed at monitoring of asymmetry of rotary electric machines, however, the process of information gathering related to position of monitored asymmetries during signal reaction processing is more complicated. Genuine verification of the method requires designing a mathematical model of a motor including asymmetries caused by rotor grooving and by magnetic core saturation. The asymmetries occurring in asynchronous motors considerably influence the instant value of a stator induction LS. Asymmetries caused by magnetic circuit saturation were identified and eliminated because of inducing the measured signal distortion. The elimination method LMDEM is the method proposed for repressing the asymmetries. The asymmetries caused by rotor grooving are intended to detect the rotor position. In final part, mathematical functions will be used for converting the signal to rotor position.