Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal parameters found that elicit a steady-state visually evoked potential (SSVEP) in certain parts of the cerebral cortex where it can be reliably perceived by the electrode of the EEG device. After that, we continue with a description of the advanced machine learning pipeline model that can perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise (about 85% validation score) and (b) reliably distinguish between EEG records collected from different human participants (about 80% validation score). Finally, we demonstrate that the proposed method works reliably even with an inexpensive (less than $100) consumer-grade EEG sensing device and with participants who do not have previous experience with EEG technology (EEG illiterate). All this in combination opens up broad prospects for the development of new types of consumer devices, [e.g.] based on virtual reality helmets or augmented reality glasses where EEG sensor can be easily integrated. The proposed method can be used to improve an online user experience by providing [e.g.] password-less user identification for VR / AR applications. It can also find a more advanced application in intensive care units where collected EEG data can be used to classify the level of conscious awareness of patients during anesthesia or to automatically detect hardware failures by classifying the input signal as noise

A simple predictive method of critical flicker detection for human healthy precaution

Interharmonics and flickers have an interrelationship between each other. Based on International Electrotechnical Commission (IEC) flicker standard, the critical flicker frequency for a human eye is located at 8.8 Hz. Additionally, eye strains, headaches, and in the worst case seizures may happen due to the critical flicker. Therefore, this paper introduces a worthwhile research gap on the investigation of interrelationship between the amplitudes of the interharmonics and the critical flicker for 50 Hz power system. Consequently, the significant findings obtained in this paper are the amplitudes of two particular interharmonics are able to detect the critical flicker. In this paper, the aforementioned amplitudes are detected by adaptive linear neuron (ADALINE). After that, the critical flicker is detected by substituting the aforesaid amplitudes to the formulas that have been generated in this paper accordingly. Simulation and experimental works are conducted and the accuracy of the proposed algorithm which utilizes ADALINE is similar, as compared to typical Fluke power analyzer. In a nutshell, this simple predictive method for critical flicker detection has strong potential to be applied in any human crowded places (such as offices, shopping complexes, and stadiums) for human healthy precaution purpose due to its simplicity

Power Quality

Electrical power is becoming one of the most dominant factors in our society. Power generation, transmission, distribution and usage are undergoing signifi cant changes that will aff ect the electrical quality and performance needs of our 21st century industry. One major aspect of electrical power is its quality and stability – or so called Power Quality. The view on Power Quality did change over the past few years. It seems that Power Quality is becoming a more important term in the academic world dealing with electrical power, and it is becoming more visible in all areas of commerce and industry, because of the ever increasing industry automation using sensitive electrical equipment on one hand and due to the dramatic change of our global electrical infrastructure on the other. For the past century, grid stability was maintained with a limited amount of major generators that have a large amount of rotational inertia. And the rate of change of phase angle is slow. Unfortunately, this does not work anymore with renewable energy sources adding their share to the grid like wind turbines or PV modules. Although the basic idea to use renewable energies is great and will be our path into the next century, it comes with a curse for the power grid as power fl ow stability will suff er. It is not only the source side that is about to change. We have also seen signifi cant changes on the load side as well. Industry is using machines and electrical products such as AC drives or PLCs that are sensitive to the slightest change of power quality, and we at home use more and more electrical products with switching power supplies or starting to plug in our electric cars to charge batt eries. In addition, many of us have begun installing our own distributed generation systems on our rooft ops using the latest solar panels. So we did look for a way to address this severe impact on our distribution network. To match supply and demand, we are about to create a new, intelligent and self-healing electric power infrastructure. The Smart Grid. The basic idea is to maintain the necessary balance between generators and loads on a grid. In other words, to make sure we have a good grid balance at all times. But the key question that you should ask yourself is: Does it also improve Power Quality? Probably not! Further on, the way how Power Quality is measured is going to be changed. Traditionally, each country had its own Power Quality standards and defi ned its own power quality instrument requirements. But more and more international harmonization efforts can be seen. Such as IEC 61000-4-30, which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce of measurement instruments so that they can also be used in volume applications and even directly embedded into sensitive loads. But work still has to be done. We still use Power Quality standards that have been writt en decades ago and don’t match today’s technology any more, such as fl icker standards that use parameters that have been defi ned by the behavior of 60-watt incandescent light bulbs, which are becoming extinct. Almost all experts are in agreement - although we will see an improvement in metering and control of the power fl ow, Power Quality will suff er. This book will give an overview of how power quality might impact our lives today and tomorrow, introduce new ways to monitor power quality and inform us about interesting possibilities to mitigate power quality problems. Regardless of any enhancements of the power grid, “Power Quality is just compatibility” like my good old friend and teacher Alex McEachern used to say. Power Quality will always remain an economic compromise between supply and load. The power available on the grid must be suffi ciently clean for the loads to operate correctly, and the loads must be suffi ciently strong to tolerate normal disturbances on the grid

Flicker spreading in a transmission network

This paper reports the flicker spreading in the transmission network. Chapter 1 presents introduction containing brief background and key concepts, followed by description of the corresponding instrumentation in Chapter 2. Key contribution of the paper is elaborated in Chapters 3 and 4. Chapter 3 reports measurements of the flicker magnitude along the 400 kV, 220 kV and 110 kV transmission grid for various distances from flicker origin on 400 kV grid, and Chapter 4 gives cost-effective predictive model, enabling estimation of the flicker magnitude for arbitrary selected origin-to-spot distance base on non-linear regression approach. Paper is extension of the work presented at Smagrimet 2019 conference

On Deep Machine Learning Based Techniques for Electric Power Systems

This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. The the delays originate from software (response time delay) and hardware (reaction time delay). To reduce the response time delays of APFs, this thesis propose and investigate several different techniques. First a technique based on multiple synchronous reference frame (MSRF) and order-optimized exponential smoothing (ES) to decrease the settling time delay of lowpass filtering steps. To reduce the computational time, this method is implemented in a parallel processing using a graphics processing unit (GPU) to estimate the time-varying harmonics and interharmonics of currents. Furthermore, the MSRF and three machine learning-based solutions are developed to predict future values of voltage and current in electric power systems which can mitigate the effects of the response and reaction time delays of the APFs. In the first and second solutions, a Butterworth filter is used to lowpass filter the\ua0 dq\ua0 components, and linear prediction and long short-term memory (LSTM) are used to predict the filtered\ua0 dq\ua0 components. The third solution is an end-to-end ML-based method developed based on a combination of convolutional neural networks (CNN) and LSTM. The Simulink implementation of the proposed ML-based APF is carried out to compensate for the current waveform harmonics, voltage dips, and flicker in Simulink environment embedded AI computing system Jetson TX2.\ua0In another study, we propose Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) method to replace the controller loops and estimation blocks such as PID, MSRF, and lowpass filters in grid-forming inverters. In a conventional approach it is well recognized that the controller tuning in the differen loops are difficult as the tuning of one loop influence the performance in other parts due to interdependencies.In DDPG the control policy is derived by optimizing a reward function which measure the performance in a data-driven fashion based on extensive experiments of the inverter in a simulation environment.\ua0Compared to a PID-based control architecture, the DDPG derived control policy leads to a solution where the response and reaction time delays are decreased by a factor of five in the investigated example.\ua0Classification of voltage dips originating from cable faults is another topic addressed in this thesis work. The Root Mean Square (RMS) of the voltage dips is proposed as preprocessing step to ease the feature learning for the developed\ua0 LSTM based classifier. Once a cable faults occur, it need to be located and repaired/replaced in order to restore the grid operation. Due to the high importance of stability in the power generation of renewable energy sources, we aim to locate high impedance cable faults in DC microgrid clusters which is a challenging case among different types of faults. The developed Support Vector Machine (SVM) algorithm process the maximum amplitude and\ua0 di/dt\ua0 of the current waveform of the fault as features, and the localization task is carried out with\ua0 95 %\ua0 accuracy.\ua0Two ML-based solutions together with a two-step feature engineering method are proposed to classify Partial Discharges (PD) originating from pulse width modulation (PWM) excitation in high voltage power electronic devices. As a first step, maximum amplitude, time of occurrence, area under PD curve, and time distance of each PD are extracted as features of interest. The extracted features are concatenated to form patterns for the ML algorithms as a second step. The suggested feature classification using the proposed ML algorithms resulted in\ua0 95.5 %\ua0 and\ua0 98.3 %\ua0\ua0 accuracy on a test data set using ensemble bagged decision trees and LSTM networks

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided

Altered states phenomena induced by visual flicker light stimulation

Flicker light stimulation can induce short-term alterations in consciousness including hallucinatory color perception and geometric patterns. In the study at hand, the subjective experiences during 3 Hz and 10 Hz stroboscopic light stimulation of the closed eyes were assessed. In a within-subjects design (N = 24), we applied the Positive and Negative Affect Schedule (mood state), time perception ratings, the Altered State of Consciousness Rating Scale, and the Phenomenology of Consciousness Inventory. Furthermore, we tested for effects of personality traits (NEO Five-Factor Inventory-2 and Tellegen Absorption Scale) on subjective experiences. Such systematic quantification improves replicability, facilitates comparisons between pharmacological and non-pharmacological techniques to induce altered states of consciousness, and is the prerequisite to study their underlying neuronal mechanisms. The resulting data showed that flicker light stimulation-induced states were characterized by vivid visual hallucinations of simple types, with effects strongest in the 10 Hz condition. Additionally, participants’ personality trait of Absorption scores highly correlated with the experienced alterations in consciousness. Our data demonstrate that flicker light stimulation is capable of inducing visual effects with an intensity rated to be similar in strength to effects induced by psychedelic substances and thereby support the investigation of potentially shared underlying neuronal mechanisms

Atmospheric turbulence profiling with SLODAR using multiple adaptive optics wavefront sensors

The slope detection and ranging (SLODAR) method recovers atmospheric turbulence profiles from time averaged spatial cross correlations of wavefront slopes measured by Shack-Hartmann wavefront sensors. The Palomar multiple guide star unit (MGSU) was set up to test tomographic multiple guide star adaptive optics and provided an ideal test bed for SLODAR turbulence altitude profiling. We present the data reduction methods and SLODAR results from MGSU observations made in 2006. Wind profiling is also performed using delayed wavefront cross correlations along with SLODAR analysis. The wind profiling analysis is shown to improve the height resolution of the SLODAR method and in addition gives the wind velocities of the turbulent layers

The eye gaze direction of an observed person can bias perception, memory, and attention in adolescents with and without autism spectrum disorder

The reported experiments aimed to investigate whether a person and his or her gaze direction presented in the context of a naturalistic scene cause perception, memory, and attention to be biased in typically developing adolescents and high-functioning adolescents with autism spectrum disorder (ASD). A novel computerized image manipulation program presented a series of photographic scenes, each containing a person. The program enabled participants to laterally maneuver the scenes behind a static window, the borders of which partially occluded the scenes. The gaze direction of the person in the scenes spontaneously cued attention of both groups in the direction of gaze, affecting judgments of preference (Experiment 1a) and causing memory biases (Experiment 1b). Experiment 2 showed that the gaze direction of a person cues visual search accurately to the exact location of gaze in both groups. These findings suggest that biases in preference, memory, and attention are caused by another person's gaze direction when viewed in a complex scene in adolescents with and without ASD (C) 2009 Elsevier Inc. All rights reserved