arXiv subjects

Suresh Sundaram

Publications and source records attributed to Suresh Sundaram.

78 records · Page 5Linked to original sources

arXiv2020

Robust simultaneous stabilization and decoupling of unstable adversely coupled uncertain resource constraints plants of a nano air vehicle

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as Robust Simultaneously Stabilizing Decoupling (RSSD) output feedback controller that achieves simultaneous stabilization, desired decoupling, robustness, and performance for a finite set of unstable multi-input-multi-output adversely coupled uncertain plants. To synthesize a RSSD output feedback controller, a new method that is based on a central plant is proposed in this paper. Given a finite set of plants for simultaneous stabilization, we considered a plant in this set that has the smallest maximum $v-$gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of a RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of a RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating a RSSD controller for a fixed-wing nano air vehicle. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

eess.SY↗

arXiv2020

A Scenario-based Branch-and-Bound Approach for MES Scheduling in Urban Buildings

This paper presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a non-convex NPhard problem with uncertainties due to renewable generation and demand. A model predictive control approach is used to handle the uncertainties and price variations. This in-turn requires solving a time-coupled multi-time step MINLP during each time-epoch which is computationally intensive. This investigation proposes an approach called the Scenario-Based Branch-and-Bound (SB3), a light-weight solver to reduce the computational complexity. It combines the simplicity of convex programs with the ability of meta-heuristic techniques to handle complex nonlinear problems. The performance of the SB3 solver is validated in the Cleantech building, Singapore and the results demonstrate that the proposed algorithm reduces energy cost by about 17.26% and 22.46% as against solving a multi-time step heuristic optimization model.

eess.SY↗

arXiv2019

Conduction and valence band offsets of Ga2O3/h-BN heterojunction

h-BN and Ga2O3 are two promising semiconductor materials. However, the band alignment of the Ga2O3/h-BN heterojunction has not been identified, hindering device development. In this study, the heterojunction was prepared by metalorganic chemical vapor deposition and pulsed laser deposition. Transmission electron microscopy confirmed sharp heterointerface and revealed structural evolution as amorphous-Ga2O3 grew thicker on lattice mismatched h-BN. The valence and conduction band offsets were determined by high-resolution X-ray photoemission spectroscopy to be 1.75 and 3.35-3.65 eV, respectively, corresponding to a type-II heterojunction. The extremely large type-II band offsets along with indirect bandgap of Ga2O3 may be leveraged for exceptional electron confinement and storage.

physics.app-ph↗

arXiv2019

Efficient single input-output layer spiking neural classifier with time-varying weight model

This paper presents a supervised learning algorithm, namely, the Synaptic Efficacy Function with Meta-neuron based learning algorithm (SEF-M) for a spiking neural network with a time-varying weight model. For a given pattern, SEF-M uses the learning algorithm derived from meta-neuron based learning algorithm to determine the change in weights corresponding to each presynaptic spike times. The changes in weights modulate the amplitude of a Gaussian function centred at the same presynaptic spike times. The sum of amplitude modulated Gaussian functions represents the synaptic efficacy functions (or time-varying weight models). The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository. Performance studies show superior generalization ability of SEF-M. An ablation study on time-varying weight model is conducted using JAFFE dataset. The results of the ablation study indicate that using a time-varying weight model instead of single weight model improves the classification accuracy by 14%. Thus, it can be inferred that a single input-output layer spiking neural network with time-varying weight model is computationally more efficient than a multi-layer spiking neural network with long-term or short-term weight model.

cs.NE↗

arXiv2019

A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights

This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme to encode the real-valued input data into spike patterns. MC-SEFRON is trained using the same supervised learning rule used in the SEFRON. After training, the proposed method extracts the knowledge for a given class stored in the classifier by mapping the weighted postsynaptic potential in the time domain to the feature domain as Feature Strength Functions (FSFs). A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier. This knowledge encoding method is derived to maintain consistency between the classification in the time domain and the feature domain. The correctness of the FSF is quantitatively measured by using FSF directly for classification tasks. For a given input, each FSF is sampled at the input value to obtain the corresponding feature strength value (FSV). Then the aggregated FSVs obtained for each class are used to determine the output class labels during classification. FSVs are also used to interpret the predictions during the classification task. Using ten UCI datasets and the MNIST dataset, the knowledge extraction method, interpretation and the reliability of the FSF are demonstrated. Based on the studies, it can be seen that on an average, the difference in the classification accuracies using the FSF directly and those obtained by MC-SEFRON is only around 0.9% & 0.1\% for the UCI datasets and the MNIST dataset respectively. This clearly shows that the knowledge represented by the FSFs has acceptable reliability and the interpretability of classification using the classifier's knowledge has been justified.

cs.NE↗

arXiv2015

Nano selective area growth of GaN by MOVPE on 4H-SiC using epitaxial graphene as a mask: towards integrated III-nitride / graphene / SiC electronics and optoelectronics

We report the growth of high-quality triangular GaN nanomesas, 30-nm thick, on the C-face of 4H-SiC using nano selective area growth (NSAG) with patterned epitaxial graphene grown on SiC as an embedded mask. NSAG alleviates the problems of defective crystals in the heteroepitaxial growth of nitrides, and the high mobility graphene film can readily provide the back low-dissipative electrode in GaN-based optoelectronic devices. The process consists in first growing a 5-8 graphene layers film on the C-face of 4H- SiC by confinement-controlled sublimation of silicon carbide. The graphene film is then patterned and arrays of 75-nanometer-wide openings are etched in graphene revealing the SiC substrate. 30-nanometer-thick GaN is subsequently grown by metal organic vapor phase epitaxy. GaN nanomesas grow epitaxially with perfect selectivity on SiC, in openings patterned through graphene, with no nucleation on graphene. The up-or-down orientation of the mesas on SiC, their triangular faceting, and cross-sectional scanning transmission electron microscopy show that they are biphasic. The core is a zinc blende monocrystal surrounded with single-crystal hexagonal wurtzite. The GaN crystalline nanomesas have no threading dislocations, and do not show any V-pit. This NSAG process potentially leads to integration of high-quality III-nitrides on the wafer scalable epitaxial graphene / silicon carbide platform.

cond-mat.mtrl-sci↗