Department of Electrical Engineering

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IIT Jodhpur

Department of Mechanical Engineering

Nanotechnology

1T and 2H Heterophase MoS2 for Enhanced Sensitivity of GaN transistor-based Mercury ions Sensor

Stability and stabilization with predefined upper bounded settling time (PUBST) for the nonlinear nonautonomous systems are studied in this paper. The concepts of input to state stability are extended to the control problems exhibiting the property of convergence with PUBST (PUBSTC). The Lyapunov functions for depicting the presence of PUBST are introduced and reffered to as PUBSTC Lyapunov functions. Furthermore, a robust sliding mode control allowing the convergence with PUBST is designed for the nonlinear systems with matched disturbances and referred to as PUBSTC-SMC. These developments are accompanied by several academic examples for better understanding. © 2022 IEEE.

World University root node

Proceedings of IEEE International Workshop on Variable Structure Systems

Stabilization of Continuous Time Uncertain Systems with Predefined Upper Bound of Settling Time

In this paper, implicit systems with uncertain input matrix subjected to matched Lipschitz disturbances are considered. For such kind of systems a Lipschitz continuous control, achieving admissible solutions for implicit systems is proposed. The developed methodology consists of two steps. In the first step a stable sliding surface is designed after transforming the original system into a regular like form. Then a first order sliding mode controller through an integrator is designed which will ensure finite-time convergence to the sliding manifold and the exponential convergence to the origin in spite of the presence of Lipschitz disturbances and uncertain input matrix. © 2022 European Control Association

European Journal of Control

A Lipschitz continuous sliding mode control for implicit systems

Over the past few years, rated convergence has gained importance and has acquired tremendous interest from the research community. In this regard, some nomenclatures have emerged, broadly falling under the spectrum of finite time stability. However, these notions have certain aspects which differentiate them. Therefore it becomes imperative to bring out a comparative study among these notions. Moreover, such recent concepts are being applied to several application problems. Thus it is essential to understand even the small details associated with them. The purpose of the present paper is to address these objectives. In addition, the convergence of states and boundedness of the control in case of arbitrary time convergence has also been discussed. © 2022 IEEE.

IECON Proceedings (Industrial Electronics Conference)

On the Predefined, Prescribed and Arbitrary Time Convergence

In this technical note, free-will arbitrary time terminal sliding mode control design is proposed. With such a design, it is possible to control the time duration of sliding in the sliding phase. Under the assumption that the chosen arbitrary time is greater than the reaching phase time (tr), it is shown that the nth order chain of integrators converges to zero within the selected arbitrary time. An algorithm is also proposed for the case when tr is not known to the designer. Stability analysis based on Lyapunov theory has been provided. © 2004-2012 IEEE.

IEEE Transactions on Circuits and Systems II: Express Briefs

Free-Will Arbitrary Time Terminal Sliding Mode Control

Quantifying image naturalness using differential curvelet features

<a href="https://www.sciencedirect.com/topics/computer-science/machine-learning">Machine learning</a> (ML) in general and deep learning (DL) in particular has become an extremely popular tool in several vision applications (like object detection, super resolution, segmentation, object tracking etc.). Almost in parallel, the issue of explainability in ML (i.e. the ability to explain/elaborate the way a trained ML model arrived at its decision) in vision has also received fairly significant attention from various quarters. However, we argue that the current philosophy behind explainable ML suffers from certain limitations, and the resulting explanations may not meaningfully uncover black box ML models. To elaborate our assertion, we first raise a few fundamental questions which have not been adequately discussed in the corresponding literature. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles in the related areas.

Fulltext

Image and Vision Computing

Does explainable machine learning uncover the black box in vision applications?

The existing power system utilizes communication infrastructure for fast and reliable transfer of control and protection inputs. This dependency of a power system on communication infrastructure for critical applications makes it vulnerable to cyber attacks. Cyber-induced outages trigger both randomized and intentional switchings in a power system producing relatively similar dynamics as natural events making their classification difficult. This article proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system. This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks. The proposed classification method uses PCA to deduce truncated z-score sequences [or principal test sequences (PTSs)] capturing distinct spatio-temporal progression patterns of natural disturbances and cyber outages. The PTSs in the training sets are shuffled and sampled using a stratified random sampling technique and classified using an ensemble long short-term memory network. The proposed method is tested for simulation examples of FSI and FCI attacks in the standard IEEE118-bus test system, where it showed improved accuracy and time performance.

IEEE Systems Journal

PCA-LSTM Learning Networks With Markov Chain Models for Online Classification of Cyber-Induced Outages in Power System

The continuously fluctuating energy output and varying power demands in the renewable energy systems have led to the degradation of power quality. This work presents a model predictive based control for a solar PV system integrated to the grid for optimal management and control of the power transfer. The double stage three-phase configuration is controlled using model predictive control (MPC) strategy, which considers the power converters' switching states to predict the next control variable. The control uses a modified-dual second-order generalized-integrator for estimation of the power requirements based on the continuously varying system parameters. The PCC voltages assist and the ride through operation are performed based on the drops in voltage levels and optimum switching state is selected based on the minimization of the cost function to deliver the required active and reactive powers to the grid. The performance of the controller is validated through simulation and is also shown using hardware implementation. The IEEE-519 standard is followed throughout and a comparative analysis shows the remarkable performance of the presented grid controller. © 1986-2012 IEEE.

IEEE Transactions on Energy Conversion

An MPC Based Algorithm for a Multipurpose Grid Integrated Solar PV System with Enhanced Power Quality and PCC Voltage Assist

A compressive acquisition technique for on-array image compression is proposed in this paper. It capitalizes on representation ability of accumulated spatial gradients of the acquired scene. The local variations inferred from strength of the accumulated gradients are used as cues to vary number of samples read through the image sensor readout. Such sampling enables the reconstruction using traditional interpolation techniques with desired quality. The proposed method is first verified using MATLAB simulations, where on an average, a compression of 87% is achieved, for a threshold of 40 intensity levels. The images are reconstructed using nearest neighbour interpolation (NNI) method which results in a mean peak signal to noise ratio (PSNR) value of 29.09 dB. The reconstructed images are further enhanced using deep convolutional neural network, which improves the PSNR to 32.46 dB. The biggest advantage of the proposed technique is low-complex hardware design. As a proof of concept, a hardware implementation of the technique is performed using discrete components. Pixel intensity values of standard images are converted into analog voltages using a data acquisition system and mapped in the input voltage range of 1.5 V -5.5 V. For a threshold of 3.8 V, the compression of 81% - 83% is observed for the considered images. The proposed technique is simple and effective, and is suitable for low-power complementary metal oxide semiconductor (CMOS) image sensors. © 1991-2012 IEEE.