Abstract

This article proposes an adaptive-robust maneuvering control framework for a planar snake robot under the influence of parameter uncertainties. The entire control objective of maneuvering control can be viewed as the simultaneous establishments of two goals: one to maintain a time-varying body shape of the snake robot for consistent motion (called the outer layer) and the other dealing with the velocity and head-angle tracking of the same (called the inner layer). Unknown variations in the ground friction coefficients have been considered to be the primary source of time-varying uncertainties which affects the control performance in both the layers. Accordingly, an artificial time delay-based adaptive-robust control (ARC) framework, dual adaptive-robust time-delayed control (ARTDC), is proposed. The term dual signifies simultaneous application of ARTDC for the outer as well as the inner layer. ARTDC comprises of two segments: an artificial time delay-based time-delayed estimation (TDE) part and an ARC part. While TDE approximates the completely unknown friction forces, the ARC tackles the approximation error arising from the TDE. More importantly, compared with the existing ARC methodologies, the proposed ARTDC neither presumes the overall uncertainty to be upper bounded by a constant nor requires any prior knowledge of the bound of uncertainty to implement the controller. A Lyapunov function-based method has been adopted for analyzing the stability of the closed-loop system. Simulation studies affirm the improved performance of the ARTDC in contrast to the classical artificial delay-based methodology.

Abstract

Position control for heavy-lift construction vessels is crucial for safe operation during offshore construction. During the various phases of a typical offshore construction assignment, considerable changes in the dynamics of the crane-vessel system occur. Operational hazard was reported if such interchanging dynamics are not properly handled. However, to date and the best of our knowledge, no systematic control solution is reported considering multiphase offshore construction scenarios. This article proposes a switched dynamical framework to model the interchanging phases and to formulate a comprehensive position control solution for heavy-lift vessels. Stability and robustness against modeling imperfections and environmental disturbances are analytically assessed. The effectiveness of the solution is verified on a realistic heavy-lift vessel simulation platform; it is shown that the proposed switched framework sensibly improves accuracy and reduces hazard compared with a nonswitched solution designed for only one phase of the construction scenario. Index Terms— Dynamic positioning (DP) system, heavy-lift construction vessel, observer-based control, switched systems.

Abstract

By increasingly relying on network-based operation for control, monitoring, and protection functionalities, modern wide-area power systems have also become vulnerable to cyber- attacks aiming to damage system performance and/or stability. Resilience in state-of-the-art methods mostly relies on known characteristics of the attacks and static control loops (i.e., with fixed input/output channels). This work proposes a ‘dynamic loop’ wide-area damping strategy, where input/output channel pairs are changed dynamically. We study ‘reactive’ dynamic switching in case of detectable attack and ‘pro-active’ dynamical switching, in case of undetectable (stealth) attacks. Stability of the dynamic loop is presented via Lyapunov theory, under para- metric perturbations, average dwell time switching and external perturbations. Using two- and five-area IEEE benchmarks, it is shown that the proposed strategy provides effective damping and robustness under various detectable (e.g., false data injection, denial-of-service) and stealth (replay, bias injection) attacks. Index Terms—Cyber-attack, dynamic loop, resilience, switched controller, wide-area damping control.

Abstract

In this study we carried out the early distribution of clades and subclades state-wise based on shared mutations in Indian SARS-CoV-2 isolates collected (27th Jan – 27th May 2020). Phylogenetic analysis of these isolates indicates multiple independent sources of introduction of the virus in the country, while principal component analysis revealed some state-specific clusters. It is observed that clade 20A defining mutations C241T (ORF1ab: 5’ UTR), C3037T (ORF1ab: F924F), C14408T (ORF1ab: P4715L), and A23403G (S: D614G) are predominant in Indian isolates during this period. Higher number of coronavirus cases were observed in certain states, viz., Delhi, Tamil Nadu, and Telangana. Genetic analysis of isolates from these states revealed a cluster with shared mutations, C6312A (ORF1ab: T2016K), C13730T (ORF1ab: A4489V), C23929T, and C28311T (N: P13L). Analysis of region-specific shared mutations carried out to understand the large number of deaths in Gujarat and Maharashtra identified shared mutations defining subclade, I/GJ-20A (C18877T, C22444T, G25563T (ORF3a: H57Q), C26735T, C28854T (N: S194L), C2836T) in Gujarat and two sets of co-occurring mutations C313T, C5700A (ORF1ab: A1812D) and A29827T, G29830T in Maharashtra. From the genetic analysis of mutation spectra of Indian isolates, the insights gained in its transmission, geographic distribution, containment, and impact are discussed.

Abstract

A fast, robust, parallel, and parameter free version of a frequency filtering preconditioner is proposed for linear systems corresponding to diffusion equation on a structured grid. Proposed solver is faster than the state-of-the-art solvers

Abstract

Datasets play an important role in driving innovation in algorithms and architectures for supervised deep learning tasks. Numerous datasets exist for images, language translation, etc. One of the interesting challenge problems for deep learning is to solve high school problems in mathematics and sciences. To this end, a comprehensive set of dataset containing hundreds of millions of samples, and the generation modules is required that can propel research for these problems. In this paper, a large set of datasets covering mathematics and science problems is proposed, and the dataset generation codes are proposed. Test results on the proposed datasets for character-to-character transformer architecture show promising results with test accuracy above 95%, however, for some datasets it shows test accuracy of below 30%. Dataset will be available at: www.github.com/misterpawan/scimat2.

Abstract

Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircraft

Abstract

Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes. Since data annotation is time-consuming and costly, reducing the label requirement even further is an important goal. To that end, our paper presents a more challenging few-shot setting with almost no class label access. By leveraging self-supervision to learn image representations and similarity for classification at test time, we achieve competitive baselines while using almost zero (0-5) class labels. Compared to existing state-of-the-art approaches which use 60,000 labels, this is a four orders of magnitude (10,000 times) difference. This work is a step towards developing few-shot learning methods that do not depend on annotated data. Our code is publicly released at https: //github.com/adbugger/FewShot. 3

Abstract

Handwritten documents from communities like cultural heritage, judiciary, and modern journals remain largely unexplored even today. To a great extent, this is due to the lack of retrieval tools for such unlabeled document collections. This work considers such collections and presents a simple, robust retrieval framework for easy information access. We achieve retrieval on unlabeled novel collections through invariant features learned for handwritten text. These feature representations enable zero-shot retrieval for novel queries on unlabeled collections. We improve the framework further by supporting search via text and exemplar queries. Four new collections written in English, Malayalam, and Bengali are used to evaluate our text retrieval framework. These collections comprise 2957 handwritten pages and over 300K words. We report promising results on these collections, despite the zero-shot constraint and huge collection size. Our framework allows the addition of new collections without any need for specific finetuning or labeling. Finally, we also present a demonstration of the retrieval framework. [Project Page] Keywords: Document retrieval· Keyword Spotting· Zero-shot retrieval

Abstract

Earthquake-induced landslides can affect people and structures as a result of significant ground shaking or regardless of the intensity. A suitable design ground motion helps to mitigate the impact of such landslides. The most commonly used design ground motion in slope stability analysis is based on probabilistic seismic hazard maps. Uncertainties in the selection of expected ground motion levels have been ignored. The present study is conducted based on improvised fully probabilistic approach, which provides the total probability of slope failure in a particular period under seismic loading by addressing all possible scenarios. This approach is applied to the seismically active Tindharia slope located in Darjeeling, India. The total probability of seismic slope failure obtained in the next 50 years is 30% and the most probable peak-ground acceleration that triggers a landslide is 0.12g. Design peak-ground acceleration predicted from the next 475-year probabilistic seismic hazard map is 1.02g. In the present study, the significant difference in the design peak-ground acceleration from probabilistic seismic-hazard analysis and fully probabilistic approach is observed. The study suggests that the seismic landslide hazard may be overestimated or underestimated when used in the design of ground intensity obtained by conducting PSHA.