Abstract

This paper proposes a novel Gap Reduced Minimum Error Robust Simultaneous (GRMERS) estimator for resource-constrained Nano Aerial Vehicle (NAV) that enables a single estimator to provide simultaneous and robust estimation for a given N unstable and uncertain NAV plant models. The estimated full state feedback enables a stable flight for NAV. The GRMERS estimator is implemented utilizing a Minimum Error Robust Simultaneous (MERS) estimator and Gap Reducing (GR) compensators. The MERS estimator provides robust simultaneous estimation with minimal largest worst-case estimation error even in the presence of a bounded energy exogenous disturbance signal. The GR compensators reduce the gap between the graphs of N linear plant models to decrease the estimation error generated by the MERS estimator. A sufficient condition for the existence of a simultaneous estimator is established using LMIs and robust estimation theory. Further, MERS estimator and GR compensator design are formulated as non-convex tractable optimization problems and are solved using the population-based genetic algorithms. The performance of the GRMERS estimator consisting of MERS estimator and GR compensators from the population-based genetic algorithms is validated through simulation studies. The study results indicate that a single GRMERS estimator can produce state estimates with reduced errors for all flight conditions. The results indicate that the single GRMERS estimator is robust than the individually designed H inifinity filters.

Abstract

he performance various academic and commercial text recognition solutions for many languages world-wide has been satisfactory. Many projects now use the ocr as a reliable module. As of now, Indian languages are far away from this state, which is unfortunate. Beyond many challenges due to script and language, this space is adversely affected by the scattered nature of research, lack of systematic evaluation, and poor resource dissemination. In this work, we aim to design and implement a web-based system that could indirectly address some of these aspects that hinder the development of ocr for Indian languages. We hope that such an attempt will help in (i) providing and establishing a consolidated view of state-of-the-art performances for character and word recognition at one place (ii) sharing resources and practices (iii) establishing standard benchmarks that clearly explain the capabilities and limitations …

Abstract

Text recognition systems of today (aka OCRs) are mostly based on supervised learning of deep neural networks. Performance of these is limited by the type of data that is used for training. In the presence of diverse style in the document images (eg. fonts, print, writer, imaging process), creating a large amount of training data is impossible. In this paper, we explore the problem of adapting an existing OCR, already trained for a specific collection to a new collection, with minimal supervision or human effort. We explore three popular strategies for this: (i) Fine Tuning (ii) Self Training (ii) Fine Tuning + Self Training. We discuss details on how these popular approaches in Machine Learning can be adapted to the text recognition problem of our interest. We hope, our empirical observations on two different languages will be of relevance to wider use cases in text recognition.

Abstract

Control of fixed-wing Unmanned Aerial Vehicles (UAVs) is typically organized according to two layers: the low-level control or autopilot, and the high-level control or guidance. The disadvantage of this modular design is that an intelligent guidance layer may become ineffective if the autopilot layer cannot deal with uncertainty. In fact, the required knowledge derived from linearization of equations of motion (trimming points) makes most autopilots sensitive to uncertainty. In this work, we study an autopilot framework where the knowledge of the UAV dynamics and of trimming points is not required. The proposed design, tested with complex UAV dynamics, can emulate the behavior of a carefully tuned off-the-shelf autopilot, without using its a priori knowledge.

Abstract

Multi-area load frequency control (LFC) selects and controls a few generators in each area of the power system in an effort to dampen inter-area frequency oscillations. To effectively dampen such oscillations, it is required to enhance and lower the control activity dynamically during operation, so as to adapt to changing circumstances. Changing circumstances should cover not only parametric uncertainties and unmodelled dynamics (e.g. aggregated area dynamics and bus dynamics), but also the increasing structural flexibility of modern power systems (e.g. protection mechanisms against faults and cyber-attacks, or topology reconfiguration mechanisms for demand response). As formal stability guarantees around such an attractive adaptive multi-area LFC concept are still lacking, this work proposes framework in which adaptation and switching are combined in a provably stable way to handle parametric uncertainty, unmodelled dynamics, and dynamical interconnections of the power system. Stability is studied in the Lyapunov theory sense using the standard structure-preserving modelling approach, and the resulting adaptive multi-area LFC design is validated using an IEEE 39-bus benchmark.

Abstract

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention based feedback mechanism named ‘Attentional Feedback Network’ (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically . We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.

Abstract

With the advent of intelligent transport, quadrotors are becoming an attractive solu-tion while lifting or dropping of payloads during emergency evacuations, construc-tion works etc. During such operations, dynamic variations in (possibly unknown)payload cause considerable changes in the system dynamics. However, a systematic control solution to tackle such interchanging dynamical behaviour is still missing.This paper proposes a switched dynamical framework to capture the interchanging dynamics of a quadrotor during vertical operations and a robust adaptive control solution to tackle such dynamics when it is unknown. The stability of the closed-loop system is studied analytically and the effectiveness of the proposed solution is verified via simulations

Abstract

In the presence of unmodelled dynamics and uncertainties with no a priori constant bounds, conventional robust adaptation strategies for switched systems cannot allow the control gains of inactive subsystems to remain constant during inactive intervals: vanishing gains are typically required in order to prove bounded stability. As a consequence, these strategies, known in literature as leakage-based adaptive methods, might introduce poor transients at each switching instant. Leakage-based adaptive control becomes even more problematic in the switched nonlinear case, where non-conservative state-dependent upper bounds for uncertainties and unmodelled dynamics are required. This work shows that, for a class of switched Euler–Lagrange systems, such difficulties can be mitigated: a novel leakage-based stable mechanism is introduced which allows the gains of inactive subsystems to remain constant. At the same time, unmodelled dynamics and uncertainties with no a priori bounds can be handled by a quadratic state-dependent upper bound structure that reduces conservativeness as compared to state-of-the-art structures. The proposed design is validated analytically and its performance is studied in simulation with a pick-and-place robotic manipulator example.

Abstract

Automated vision-based score estimation models can be used to provide an alternate opinion to avoid judgment bias. Existing works have learned score estimation models by regressing the video representation to ground truth score provided by judges. However, such regression-based solutions lack interpretability in terms of giving reasons for the awarded score. One solution to make the scores more explicable is to compare the given action video with a reference video, which would capture the temporal variations vis-á-vis the reference video and map those variations to the final score. In this work, we propose a new action scoring system termed as Reference Guided Regression (RGR), which comprises (1) a Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges, and (2) a Score Estimation Module that uses the first module to find

Abstract

Mutual information (MI) is an information-theoretic measure of dependency between two random variables. Several methods to estimate MI, from samples of two random variables with unknown underlying probability distributions have been proposed in the literature. Recent methods realize parametric probability distributions or critic as a neural network to approximate unknown density ratios. The approximated density ratios are used to estimate different variational lower bounds of MI. While these methods provide reliable estimation when the true MI is low, they produce high variance estimates in cases of high MI. We argue that the high variance characteristic is due to the uncontrolled complexity of the critic's hypothesis space. In support of this argument, we use the data-driven Rademacher complexity of the hypothesis space associated with the critic's architecture to analyse generalization error bound of variational lower bound estimates of MI. In the proposed work, we show that it is possible to negate the high variance characteristics of these estimators by constraining the critic's hypothesis space to Reproducing Hilbert Kernel Space (RKHS), which corresponds to a kernel learned using Automated Spectral Kernel Learning (ASKL). By analysing the aforementioned generalization error bounds, we augment the overall optimisation objective with effective regularisation term. We empirically demonstrate the efficacy of this regularization in enforcing proper bias variance tradeoff on four variational lower bounds, namely NWJ, MINE, JS and SMILE.