Arman Sargolzaei

Preventing Time-Delay Switch Attack on Load Frequency Control in Distributed Power Systems

A time-delay switch (TDS) attack on a control system is caused by adversaries that strategically imbed time delays into such systems. TDS attacks can make a control system, or more specifically a distributed power control system, unstable. Time delays can be introduced in the sensing loop (SL) or control lines. This paper describes a novel, simple, and effective method to thwart TDS attacks on SL. The proposed method works by augmenting the controller with a time-delay estimator to estimate any time delays. The modified controller controls the system under TDS attack. Also, the time-delay estimator will track time delays introduced by an adversary using a modified model reference control with an indirect supervisor and a modified least mean square minimization technique.

Delayed inputs attack on load frequency control in smart grid

Current smart power grids have open communication infrastructures to improve efficiency, reliability and sustainability of supply. However, their open communication architecture makes them vulnerable to cyber-attacks with potentially catastrophic consequences. Here for the first time, we propose a time-delay-switch (TDS) attack by introducing time delays in the dynamics of power systems. Such an attack will have devastating consequences on smart grids if no prevention measures are considered in the design of these power systems. We considered how a TDS attack affects the dynamic performance of a power system. To do this, we first formulated a state space model of a smart power grid system under TDS attack using a hybrid systems approach. Second, we prove by analysis and demonstrate by simulation examples how a TDS attack can be used to sabotage and destabilize a smart grid.

A new robust wavelet based algorithm for baseline wandering cancellation in ECG signals

Wavelet transform has been emerged over recent years as a powerful time-frequency analysis and signal coding tool favored for the interrogation of complex non stationary signals. Its application to bio-signal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the Electrocardiogram (ECG). In this paper, the emerging roles of the wavelet transform in the ECG preprocessing and noise removing step is discussed in detail. One of the most important noise sources, baseline wandering, which can be affected ECG signal analysis is introduced and a new method based on wavelet transform is being proposed. The proposed method construct a model of baseline wander with multiresolution analysis of the signal using discrete wavelet transform and then remove the baseline wander from the ECG signal using the constructed model. Simulations were carried out to show the performance of the algorithm using the MIT-BIH noise stress test database and PTB diagnosis database. The quality of the results by the proposed technique is found to meet or exceed that of published results using other conventional methods such as kalman filtering and conventional digital filters.

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

BackgroundThe lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.MethodsA new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.ResultsThe study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.ConclusionsThe current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.

Active/Reactive Power Control of Three Phase Grid Connected Current Source Boost Inverter Using Particle Swarm Optimization

PID control is the most common and simplest control method used for system control. However, selecting PID control coefficients for an optimal performance in power system applications is a central problem that few have addressed. Here, we propose the use of the particle swarm optimization (PSO) method to search for the optimal parameters of a PI controller. We applied this method to design an optimal PI controller for active and reactive power in a three phase grid connected current source boot inverter (CSBI). Simulation results show that our PSO-PI parameters selection method leads to better performance.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease

BackgroundIntracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure.MethodsTwo groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups.ResultsAnalysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study.ConclusionsThis study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

Signal Processing Based for Fetal Electrocardiogram Extraction

In this paper, we describe four important indirect methods which be used to extract the fetal Electrocardiogram (FECG) signal from an ECG recorded on the mothers abdomen. These methods include the following ones: singular value decomposition (SVD) method, independent component analysis (ICA) method, wavelet based methods and adaptive filtering method. The mentioned methods use signal processing techniques for extracting FECG from abdominal electrocardiogram (AECG). We have explained advantages and disadvantages of each method. The methods have also applied on both synthetic and real ECG signals. Efficiencies of the methods compared together based on three important criterions and results are stated and best method based on three criterions is selected.

Model predictive power control approach for three-phase single-stage grid-tied PV module-integrated converter

This paper presents the concept of the three-phase module-integrated converters (MICs) incorporated in grid-tied large-scale photovoltaic (PV) systems. The current-source converter (CSC) with dc voltage boost capability, namely single-stage power conversion system, is proposed for three-phase PV MIC system. A model predictive scheme with low switching frequency is designed to control the proposed topology in such a way that provides a certain amount of active and reactive power in steady-state operation and also provides a proper ratio of reactive power under transient conditions to meet the low voltage ride through (LVRT) regulations. To predict the future behavior of current control values and switching states, a discrete-time model of the MIC is developed in synchronous reference frame. It is demonstrated that the injected active and reactive power can be controlled using minimizing the cost function introduced in the predictive switching algorithm. The proposed structure is simulated in MATLAB/SIMULINK software. The results verify the desired performance of the proposed control scheme for exchanging of both active and reactive powers between the PV MIC and the grid within different operating conditions.

Sensorimotor control: computing the immediate future from the delayed present

BackgroundThe predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few theoretical models have tried to explain these facts comprehensively. Here, we propose a sensorimotor learning and control model that can be used to (1) predict the dynamics of variable time delays and current and future sensory states from delayed sensory information; (2) learn new sensorimotor realities; and (3) control a motor system in real time.ResultsThis paper proposed a new time-delay estimation method and developed a computational model for a predictive control solution of a sensorimotor control system under time delay. Simulation experiments are used to demonstrate how the proposed model can explain a sensorimotor system’s ability to compensate for delays during online learning and control. To further illustrate the benefits of the proposed time-delay estimation method and predictive control in sensorimotor systems a simulation of the horizontal Vestibulo-Ocular Reflex (hVOR) system is presented.Without the proposed time-delay estimation and prediction, the hVOR can be unstable and could be affected by high frequency oscillations. These oscillations are reminiscent of a fast correction mechanism, e.g., a saccade to compensate for the hVOR delays. Comparing results of the proposed model with those in literature, it is clear that the hVOR system with impaired time-delay estimation or impaired sensory state predictor can mimic certain outcomes of sensorimotor diseases. Even more, if the control of hVOR is augmented with the proposed time-delay estimator and the predictor for eye position relative to the head, then hVOR control system can be stabilized.ConclusionsThree claims with varying degrees of experimental support are proposed in this paper. Firstly, the brain or any sensorimotor system has time-delay estimation circuits for the various sensorimotor control systems. Secondly, the brain continuously estimates current/future sensory states from the previously sensed states. Thirdly, the brain uses predicted sensory states to perform optimal motor control.

Time-delay analysis on grid-connected three-phase current source inverter based on SVPWM switching pattern

Time delays exist in most of the electronic components, digital controllers and DSPs. Certain values of time delay can easily corrupt the performance of a power control system. This time delay can strictly disturb the system dynamic in power control applications with low to medium switching frequency. In this paper, we overcome the effect of time delay in an SVPWM based switching pattern for a grid connected three-phase current source inverter. The time delay is tracked in real time and the states of the system are estimated. Our experimental results clearly show that the proposed approach can compensate the effect of the time delay and improve the quality of the performance.