Search

Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p

Billion-dollar investment has been made globally in different industries, and hence, protection of organisational assets against theft or leakage of confidential information becomes a prime objecti...

This study presents online-tuning approach using the moth flame optimization (MFO) algorithm to optimize the parameters of PID and modified PID (I-PD) controllers used in the split-range scheme to control the temperature of the mixing process. The performance of these controllers is investigated for the individual temperature setpoints in terms of settling time and compared with performances obtained using offline-tuning approach with the MFO algorithm. The simulation results show a significant improvement with online-tuning approach as compared to offline approach. To further improve the performance, this study proposes modifications in the original MFO algorithm in three phases: by changing the spiral path, by changing the initial population based on the opposition theory, and by a change in the selection of the flames for the updating mechanism. A new version of MFO algorithm is obtained by combining the above-mentioned modifications and used to tune the PID and I-PD controllers in both offline and online modes. Further, the new algorithm is tested for both the controllers with respect to the effect of system dynamics and the effect of process disturbance. The results obtained after validation show that the use of the new version of the MFO algorithm further improves the online tuning of both the controllers. The simulation results also clearly establish the superior performance of the modified PID (I-PD) controller over the PID controller under all conditions.

With the recent advancement in the field of information technology, online frauds have also been increased and hence password-based systems are no more secure. Therefore need for a more reliable au...

This article presents a nature-inspired optimization technique, namely the moth flame optimization (MFO) algorithm, for tuning the PID controller parameters in the variable range of split-range control scheme to regulate the temperature of the mixing process. Further, the performance of the controller using Ziegler–Nichols (Z–N) tuning method and MFO algorithm is compared for the same. In this study, the controller parameters are tuned using both the methods for various temperature setpoints, and the performance of the controller for the individual temperature setpoints is analyzed in terms of settling time and demand of utilities. Investigations are conducted on the basis of the effect of dead time in the valve and the effect of process disturbance. The performance of the controller is also investigated on the basis of utility consumption using a valve with no dead time. The results show that MFO-based tuning approach provides a significant improvement as compared to Z–N tuning method.

Background The brain performs a very significant job in our body by processing the information associated with human critical inclinations, intentions, sensory attention and awareness, execution, and mental state sustenance during a specific task. The attention of every human being gets altered while undergoing two or more cognitive tasks collaterally. Methods In this research, subjects were asked to perform two tasks collaterally in which one task was considered as primary task whereas the other task as secondary. The EEG (Electroencephalography) signals of the subjects undergoing those collateral cognitive tasks were recorded using RMS EEG-32 Super Spec machine. Result The relative band powers ratio (Theta to Beta band power ratio) helped in tracing the point of time when the attention devoted to the primary task got deviated by the secondary task. An auditory P300 peak generation validated the deviation in this research. Conclusion This research track could pave the way for designing a battery that can analyze the subject’s performance during multi-tasking. Other than this, such an investigation will help in avoiding the disasters caused by the attention deviation of the operator.

Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

A novel improved meta heuristic method, Hybrid salp swarm using sine cosine algorithm (Hybrid SS-SC) for maximum power point tracking (MPPT) under partial shading condition is proposed in this paper. The proposed algorithm envisages finding the best GMPP. The simulation studies clearly demonstrate that the method compares favourably with the conventional P&O method as well as with some other nature inspired algorithms in tracking GMPP in less time.

Temperature sensing performance of Er3 +/Yb3 + doped CaMoO4 phosphor prepared via polyol method is reported herein. The X-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy are done to confirm the phase, structure and purity of the synthesized phosphor. The infrared to green upconversion emission is investigated using 980 nm diode laser excitation along with its dependence on input pump power and external temperature. The temperature dependent fluorescence intensity ratio of two upconversion emission bands assigned to 2H11/2 → 4I15/2 (530 nm) and 4S3/2 → 4I15/2 (552 nm) transitions has shown two distinct slopes in the studied temperature range - 300 to 760 K and therefore, dual nature of temperature sensitivity is observed in this phosphor. This phenomenon in rare earth doped materials is either scarcely reported or overlooked. The material has shown higher sensitivity in the high temperature region (535 K

The brain plays an important role in our body as it manages majority human judgmental tendencies, intentions, resolution towards particular situation, performance and mental state sustenance during any particular task. The psychologists have been trying to create a link between the observed pattern with respect to these factors and the person’s brain. In this study, participants were supposed to pinpoint ten spots between sought of analogous pictures that make them different from each other within a given time. The neural response to the particular cognitive task was acquired by RMS EEG-32 Super Spec machine. Further, frequency bands were extracted from the signals obtained employing EEG (Electroencephalography). They were analysed to know the point in time when the brain settles at a steady state after the completion of the task. The beginning of the steady state was validated by the manual signaling. This study can be used for tracking the recovery of many neurologically impaired patients.

In the recent past, the web (internet) has emerged as the most interactive authentication system for all of us (i.e. Internet banking passwords, system or building access, and e-payment platforms, etc.) and as a result, traditional authentication systems (like passwords or token-based) are never again more secure i.e. they are vulnerable to attacks. As a result, the security of individual information and safe access to a system winds up prime necessities. Therefore, the EEG-based authentication system has recently become a reasonable key for high-level security. This study centers upon P300 evoked potential-based authentication system designing. In this paper, a new visual stimulus paradigm (i.e. [Formula: see text] P300 speller) using pictures of different objects as stimuli for a person authentication system is designed instead of the conventional character-based paradigm (i.e. [Formula: see text] speller) for increasing the classification accuracy and Information Transfer Rate (ITR). The trial begins by exhibiting a collection of pictures of various objects on four corners of the PC screen comprising of random object pictures (non-target) alongside password pictures (target) that trigger P300 reactions. The P300 reaction’s rightness then checks the identity of the subject concerning the focused pictures (Target). The proposed investigation model achieves higher classification accuracy of 96.78%, along with 0.03075 False Rejection Rate (FRR), 0.03297 False Acceptation Rate (FAR), and ITR of [Formula: see text]. This study has shown that P300-based authentication system has an advantage over conventional methods (Password, Token, etc.) as EEG-based systems cannot be mimicked or forged (like Shoulder surfing in case of password) and can still be used for disabled users with a brain in good running condition. The classification results revealed that the performance of the QDA classifier outperformed other classifiers based on accuracy and ITR.

The conventional perturb and observe algorithm used for maximum power point tracking of PV system suffers from major drawbacks such as steady state oscillation around final maximum power point and loss of tracking direction i.e. divergence from actual maximum power point for rapid change in insolation. Both these problems result in significant power loss as the PV system operating point is getting diverted from actual maximum power point. As Buck-Boost converter plays an important role while changing the operating point of PV panel, it contributes to significant amount of power loss in the form of switching loss, conduction loss etc. This paper proposes a method to avoid the above given shortcomings of perturb & observe algorithm. Major focus is given on the designing of Buck-Boost converter as to date nobody has taken into consideration the losses occurring in the converter even though converter forms an integral part of the MPPT set up. The losses occurring in the conventional buck boost converter have been computed and its effect on the efficiency of MPPT system is investigated. The soft-switched buck boost converter for PV applications is designed so as to minimize the losses taking place in converter.

The synthesis and spectroscopy of the upconverting nanoparticles, cubic NaYF4:Er3+/Yb3+ phosphor is developed for latent fingermark detection. The cubic phase of NaYF4: Er3+/Yb3+ phosphor is synthesized by thermal decomposition method using trifluoroacetate precursor with coordinating ligand octadecene and oleic acid in a mixture of technical grade. The synthesized samples showed intense green emission using 976 nm diode laser as an excitation source. Because of excellent property of luminescence in green regime the sample is used to detect the latent fingermark on a porous glass surface.

Upconversion emission and temperature sensing of the Er 3+ /Yb 3+ doped ZnO–CaTiO 3 nano-composite phosphor is studied by varying the ZnO concentration. The XRD and EDX studies reveal the formation of composite phase when ZnO doping exceeds above 10 mw%. Five prominent upconversion emission bands at 410, 492, 524, 545 and 662 nm corresponding to 2 H 9/2 → 4 I 15/2 , 4 F 3/2 → 4 I 15/2 , 2 H 11/2 → 4 I 15/2 , 4 S 3/2 → 4 I 15/2 and 4 F 9/2 → 4 I 15/2 transitions, respectively are found under 980 nm excitation from a diode laser. On the basis of rise time analysis it was found that energy transfer process is responsible for the intense upconversion emission. Large reduction in decay time of 4 S 3/2 level is observed on the ZnO incorporation in host matrix. Moreover, the absolute sensor sensitivity, relative sensor sensitivity and calculated color coordinates of the samples are also determined. These results indicate the potentiality of this composite phosphor for various applications.

This paper explores the application of artificial neural networksfor online identification of a multimachine power system. A recurrent neural networkhas been proposedas the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions . The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.

This paper presents a neural network predictive controller for the UPFC to improve the transient stability performance of the power system. A neural network model for the power system is trained using the backpropagation learning method employing the Levenberg-Marquardt algorithm for faster convergence. This neural identifier is then utilized during predictive control of the UPFC. The damped Gauss-Newton method employing 'backtracking' as the line search method for step selection is used by the predictive controller to predict the future control inputs. The 4- machine 2-area power system which is a benchmark power system is used to demonstrate the performance of the proposed controller. The system under consideration is simulated for different transients over a range of operating conditions using Matlab/Simulink. The proposed neural network predictive controller exhibits superior damping performance in comparison to the conventional PI controller. The simulation results also establish convergence of the minimization algorithm to an acceptable solution within single iteration.

The purpose of this study was to investigate bacterial load in ready-to-sale sheep meat with special reference to Salmonella. Samples were collected from 100 sheep carcasses from retail meat shops in domestic markets. On carcasses, where bacterial counts were obtained, the mean of the log 10 aerobic plate count was 7.26 cfu g -1 , and that of total coliform count and total Escherichia coli count was 4.11 log 10 cfu g -1 and 3.03 log 10 cfu g -1 , respectively. All the samples (100) were found positive for coliforms, 49.0% were positive for E. coli and 3.0% were positive for Salmonella. The isolates were serotyped as Salmonella infantis having antigenic structure 6, 7: r: 1, 5. Antibiogram revealed highest (100.0%) sensitivity towards amikacin, ceftriaxone, ciprofloxacin, chloramphenicol, colistin sulphate, gentamicin and nalidixic acid followed by cefuroxime and tetracycline (66.67% each) and cotrimoxazole (33.33%). All the strains were resistant to ampicillin.

This paper proposes the design of a feedback controller for a voltage controlled Induction Motor (IM) drive by using the Ant Colony Optimization (ACO) technique. ACO is an evolutionary stochastic combinatorial computational discipline inspired by the behavior of ant colonies. A model of the induction motor drive is developed and its performance is evaluated while using a conventional PI controller. However it can well be seen that the system works satisfactorily for a particular operating point while its performance deteriorates for other operating points. This facilitates the need for online tuning. To accomplish this task a feedback controller is designed that uses ACO for online tuning. Comparison between two variants of ACO is also presented in this paper.

The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller., {"references":["P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. V. Cutsem, and V. Vittal, \"Definition and classification of power system stability,\" IEEE Trans. Power Systems, vol. 19, no. 2, pp. 1387-1401, 2004.","N. G. Hingorani, and L. Gyuvgi, Understanding FACTS concepts & technology of flexible AC transmission systems. Delhi: IEEE Press, 2001, ch. 1.","M. Vilathgamuwa, X. Zhu, and S. S. Choi, \"A robust control method to improve the performance of a unified power flow controller,\" Electrical Power System Research, vol. 55, pp. 103-111, 2000.","B. C. Pal, \"Robust damping of interarea oscillations with unified power flow controller,\" IEE Proceedings on Generation, Transmission, Distribution, vol. 149, no. 6, pp. 733-738, 2002.","A. H. M. A. Rahim, and S. A. Al-Baiyat, \"A robust damping controller design for a unified power flow controller,\" in Proc. 39th IEEE Universities Power Engineering Conference, (UPEC), IEEE, Bristol, 2004, pp. 265-269.","A.H M.A. Rahim, J. M. Bakhashwain, and S. A. Al-Baiyat, \"Robust damping controls for a unified power flow controller,\" International Journal of Emerging Electric Power Systems, vol. 6, no. 2, pp. 1-21, 2006.","S. A. Taher, S. Akbari, A. Abdolalipour, and R. Hematti, \"Robust decentralized controller design for UPFC using µ-synthesis,\" Communications in Nonlinear Science and Numerical Simulation, vol. 15, pp. 2149-2161, 2010.","A.T. Al-Awami, Y. L. Abdel-Magid, and M. A. Abido, \"Simultaneous stabilization of power system using UPFC-based controllers,\" Electric Power Components and Systems, vol. 34, no. 9, pp. 941-959, 2006.","K. Wang, B. Yan, M. L. Crow, and D. Gan, \"A feedback linearization baed unified power flow controller internal controller for power flow control,\" Electric Power Components and Systems, vol. 40, no. 6, pp. 628-647, 2012.\n[10]\tV. Azbe, U. Gabrijel, D. Povh, and R. Mihalic, \"The energy function of a general multimachine system with a unified power flow controller,\" IEEE Trans. Power Syst. vol. 20, no. 3, pp. 1478-1485, 2005.\n[11]\tA. Zangeneh, A. Kazemi, M. Hajatipour, and S. Jadid, \"A Lyapunov theory based UPFC controller for power flow control,\" International Journal of Electric Power and Energy Systems, vol. 31, pp. 302-308, 2009.\n[12]\tSheela Tiwari, Ram Naresh, R. Jha, \"Neural network predictive control of UPFC for improving transient stability performance of power system,\" Applied Soft Computing, vol.11, no. 8, pp.4581-4590, 2011.\n[13]\tSheela Tiwari, Ram Naresh, R. Jha, \"Neural network predictive control of UPFC for enhancing transient stability performance of a single machine infinite bus system,\" International Journal of Advancements in Electronics and Electrical Engineering, vol. 1, no. 2, pp.100-104, 2012.\n[14]\tM. Klein, G. J. Rogers, and P. Kundur, \"A fundamental study of inter-area oscillations in power systems,\" IEEE Transactions on Power Systems, vol. 6, no. 3, pp.914-921, 1991.\n[15]\tS. Mishra, \"Neural network based adaptive UPFC for improving transient stability performance of power system,\" IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 461-470, 2006.\n[16]\tD. W. Clarke, C. Mohtadi, and P. C. Tuffs, \"Generalized Predictive Control – Part I: The Basic Algorithm,\" Automatica, vol. 23, no. 2, pp. 137-148, 1987.\n[17]\tD. W. Clarke, C. Mohtadi, and P. C. Tuffs, \"Generalized Predictive Control – Part II: Extensions and Interpretations,\" Automatica, vol. 23, no. 2, pp. 149-160, 1987.\n[18]\tD. W. Clarke, and C. Mohtadi, \"Properties of Generalized predictive Control,\" Automatica, vol. 25, no. 6, pp. 859-875, 1989.\n[19]\tD. Soloway, and P.J. Haley, \"Neural generalized predictive control- A Newton-Raphson implementation,\" in: Proc. IEEE International Symposium on Intelligent Control, Michigan, USA, September 15-18, 1996, pp. 277-282.\n[20]\tE. F. Camacho, and C. Bordons, Model Predictive Control. London: Springer-Veralag GmbH, 2004.\n[21]\tJ. E. Dennis, and R. B. Schnabel, Numerical methods for unconstrained optimization and nonlinear equations. Philadelphia: SIAM, 1996."]}

This paper demonstrates an effective application of artificial neural networks for online identification of a multimachine power system. The paper presents a recurrent neural network as the identifier of the benchmark two area, four machine system. This neural identifier is trained using the static Backpropagation algorithm. The trained neural identifier is then tested using datasets generated by simulating the system under consideration at different operating points and a different loading condition. The test results clearly establish a satisfactory performance of the trained neural identifier in identification of the power system considered.

This paper presents the model of car triple inverted pendulum system using Lagrange equation in detail. This model is then linearized to design an LQR controller to maintain the triple inverted pendulum on a cart around its unstable equilibrium position using single control input. The controllability of the triple inverted pendulum system is investigated and the choice of weights in LQR is also discussed. The system is simulated in MATLAB environment and the simulation results establish the satisfactory performance of the LQR controller in stabilizing the system.

This paper investigates the effectiveness of Model Predictive Controller and its ability to damp out the low frequency oscillations and improve the small signal stability of single machine infinite bus system. The damping functions of the UPFC with various alternative control signals are investigated on the basis of linearized Heffron Phillips model of SMIB equipped with UPFC. The performance of model predictive controller has been compared to that of a controller based on phase compensation technique and is found to be superior as it uses the optimized control inputs to bring stability to the plant. The performance effectiveness of various control signals is evaluated and the best way of controlling the UPFC is proposed.

The power system is a dynamic system that is constantly being subjected to disturbances. It is important that these disturbances do not drive the system to unstable conditions. The Unified Power Flow Controller (UPFC), which is a FACTS device, simultaneously controls the real and reactive power flows on a transmission line as well as regulates voltage at the bus where it is connected. This device creates a tremendous quality impact on power system stability. In this paper, a systematic approach based on phase compensation technique, Robust control technique and Fuzzy logic control technique for designing Unified Power Flow Controller (UPFC) based damping controllers (based on m B and m E ) have been considered. Investigations on comparing the response of controllers based on all the techniques reveal that the performance of Robust Controller and Fuzzy Logic Controller is superior to the controller based on Lead-Lag compensation technique.

This paper presents comparison of the time specification performance between two controllers for an inverted pendulum system. The objective is to determine the control strategy that delivers better performance with respect to pendulum's angle and cart's position. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. The problem is to balance a pole on a mobile platform that can move in only two directions, to the left or to the right. A Linear-Quadratic-Regulator (LQR) and a robust control technique for controlling the linearized system of inverted pendulum model are presented and compared. Simulation studies conducted in MATLAB environment show that both the controllers are capable of controlling the multi output inverted pendulum system successfully. The result shows that LQR produces better response compared to H8 loop shaping control strategies.