arXiv subjects

Shubham Singh

Publications and source records attributed to Shubham Singh.

45 records · Page 3Linked to original sources

arXiv2022

Efficient Analytical Derivatives of Rigid-Body Dynamics using Spatial Vector Algebra

An essential need for many model-based robot control algorithms is the ability to quickly and accurately compute partial derivatives of the equations of motion. State of the art approaches to this problem often use analytical methods based on the chain rule applied to existing dynamics algorithms. Although these methods are an improvement over finite differences in terms of accuracy, they are not always the most efficient. In this paper, we contribute new closed-form expressions for the first-order partial derivatives of inverse dynamics, leading to a recursive algorithm. The algorithm is benchmarked against chain-rule approaches in Fortran and against an existing algorithm from the Pinocchio library in C++. Tests consider computing the partial derivatives of inverse and forward dynamics for robots ranging from kinematic chains to humanoids and quadrupeds. Compared to the previous open-source Pinocchio implementation, our new analytical results uncover a key computational restructuring that enables efficiency gains. Speedups of up to 1.4x are reported for calculating the partial derivatives of inverse dynamics for the 50-dof Talos humanoid.

cs.RO↗

arXiv2021

A New Robust Network Slack Based Measure Model

In real-life challenges, unforeseen and unknown occurrences commonly influence the data values, which may affect the performance of the problems. The performance of decision-making units (DMUs) is determined using the slack-based measure (SBM) model, which considers only crisp data values without uncertainty and is a black-box model. Many authors have used fuzzy SBM and stochastic SBM to deal with ambiguity and uncertainty, and many have used these approaches to deal with ambiguous and uncertain data. However, some ambiguous and uncertain data can not be taken as fuzzy logic and stochastic data due to the huge set of rules which must be given to construct a conceptual method. So, this paper tackles the uncertain data using robust optimization in which the input-output data are limited to remain within an uncertainty set, with extra constraints depending on the worst-case output for the uncertainty set and black-box is tackle with network SBM model. Thus, this paper proposes a robust network SBM model with negative, missing, and undesirable data that aims to maximize the efficiency same as traditional network SBM, and the constraint for a worst-case efficiency calculated by the uncertainty set its sustaining constraint while determining the efficiencies. Finally, the performances of Indian banks are computed, and then the results of the proposed models are compared to the crisp network SBM models and demonstrating how decision-makers can magnify the bank's performance of Indian banks.

math.OC↗

arXiv2021

Multi-Attributed and Structured Text-to-Face Synthesis

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Frechet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset.

cs.CV↗

arXiv2021

Selective Enhancement in Phonon Scattering leads to High Thermoelectric Figure of Merit in ZnO -- Graphene Oxide Core-shell Nanohybrids

ZnO is a promising candidate as an environment friendly thermoelectric (TE) material. However, the poor TE figure of merit (zT) needs to be addressed to achieve significant TE efficiency for commercial applications. Here we demonstrate that selective enhancement in phonon scattering leads to increase in zT of RGO encapsulated Al-doped ZnO core shell nanohybrids, synthesized via a facile and scalable method. The incorporation of 1 at% Al with 1.5 wt% RGO into ZnO (AGZO) has been found to show significant enhancement in zT (=0.52 at 1100 K) which is an order of magnitude larger compared to that of bare undoped ZnO. Photoluminescence and X-ray photoelectron spectroscopy measurements confirm that RGO encapsulation significantly quenches surface oxygen vacancies in ZnO along with nucleation of new interstitial Zn donor states. Tunneling spectroscopy reveals that the band gap of ~ 3.4 eV for bare ZnO reduces effectively to ~ 0.5 eV upon RGO encapsulation, facilitating charge transport. The electrical conductivity enhancement also benefits from the more than 95% densification achieved, using the spark plasma sintering method, which aids reduction of GO into RGO. The same Al doping and RGO capping synergistically brings about drastic reduction of thermal conductivity, through enhanced phonon-phonon and point defect-phonon scatterings. These opposing effects on electrical and thermal conductivities enhances the power factors as well as the zT value. Overall, a practically viable route for synthesis of oxide - RGO TE material which could find its practical applications for the high-temperature TE power generation.

cond-mat.mtrl-sci↗

arXiv2020

Real-time Collaboration Between Mixed Reality Users in Geo-referenced Virtual Environment

Collaboration using mixed reality technology is an active area of research, where significant research is done to virtually bridge physical distances. There exist a diverse set of platforms and devices that can be used for a mixed-reality collaboration, and is largely focused for indoor scenarios, where, a stable tracking can be assumed. We focus on supporting collaboration between VR and AR users, where AR user is mobile outdoors, and VR user is immersed in true-sized digital twin. This cross-platform solution requires new user experiences for interaction, accurate modelling of the real-world, and working with noisy outdoor tracking sensor such as GPS. In this paper, we present our results and observations of real-time collaboration between cross-platform users, in the context of a geo-referenced virtual environment. We propose a solution for using GPS measurement in VSLAM to localize the AR user in an outdoor environment. The client applications enable VR and AR user to collaborate across the heterogeneous platforms seamlessly. The user can place or load dynamic contents tagged to a geolocation and share their experience with remote users in real-time.

cs.HC↗

arXiv2020

Visible Insights of the Invisible Pandemic: A Scientometric, Altmetric and Topic Trend Analysis

The recent SARS-COV-2 virus outbreak has created an unprecedented global health crisis! The disease is showing alarming trends with the number of people getting infected with this disease, new cases and death rate are all highlighting the need to control this disease at the earliest. The strategy now for the governments around the globe is how to limit the spread of the virus until the research community develops treatment/drug or vaccination against the virus. The outbreak of this disease has unsurprisingly led to huge volume of research within a short period of time surrounding this disease. It has also led to aggressive social media activity on twitter, Facebook, dedicated blogs, news reports and other online sites actively involved in discussing about the various aspects of and related to this disease. It becomes a useful and challenging exercise to draw from this huge volume of research, the key papers that form the research front, its influence in the research community, and other important research insights. Similarly, it becomes important to discern the key issues that influence the society concerning this disease. The paper is motivated by this. It attempts to distinguish which are the most influential papers, the key knowledge base and major topics surrounding the research covered by COVID-19. Further it attempts to capture the society's perception by discerning key topics that are trending online. The study concludes by highlighting the implications of this study.

cs.DL↗

arXiv2019

Investigation of Thermoelectric properties of Magnetic Insulator FeRuTiSi Using First Principle Calculation

In this work, we have investigated the electronic structure and thermoelectric properties of quaternary heusler alloy, FeRuTiSi, using first principle DFT tools implemented in WIEN2k and BoltzTraP code. Electronic structure calculations using TB-mBJ potential shows appearance of flat band at the conduction band edge, thus electron in conduction band have the large effective mass (me*), and therefore mainly contribute for negatively large value of Seebeck coefficient (S). This alloy has indirect band gap of 0.59 eV, and shows the n-type transport behavior. Under the constant relaxation time approximation (tau = 10 -14 s), temperature dependent Seebeck coefficient, electrical conductivity (sigma), and electronic thermal conductivity (ke) were also estimated. The maximum figure-of-merit (ZT), for the FeRuTiSi compound is found to be ~0.86 at 840 K, with n-type doping, which suggests that this quaternary alloy can be a good candidate among the n-type material for thermoelectric applications in high-temperature reg

cond-mat.str-el↗

arXiv2019

Thermoelectric properties of Half-Metallic FeMnScGa Using First Principle Calculation

Here, we have investigated the thermoelectric properties of FeMnScGa alloy by combined use of full potential linearized augmented plane-wave (FP-LAPW) method and Boltzmann transport theory implemented in Wien2K and BoltzTraP code, respectively. Using the TB-mBJ potential, half-metallic nature is clearly observed with energy band gap of ~0.41 eV for down spin channel. Transport coefficients in 200-1000 K temperature range are investigated under the constant relaxation time approximations (tau=10-14 s). Total contributions to the Seebeck coefficients from up and dn-spin channels are estimated by using two-current model. The calculated values of Seebeck coefficients are found to be negative in the entire temperature region under present study, which suggests the n-type characteristic of this alloy. The magnitude of Seebeck coefficient is found to be increasing with temperature and exhibit the large value ~ -87 micro-V/K in 700-1000 K temperature range. The maximum in magnitude of total electrical conductivity value is found to be ~2.0 x 105 ohm-1m-1 at 1000 K. The present study shows, with half-metallic electronic structure, FeMnScGa have very good thermoelectric behavior, and can be suitable for thermoelectric application in high temperature region.

cond-mat.str-el↗

arXiv2018

Competitive electrohydrodynamic and electrosolutal advection arrests evaporation kinetics of droplets

The present article reports the hitherto unreported phenomenon of arrested evaporation dynamics in pendent droplets in an electric field ambience. The evaporation kinetics of pendant droplets of electrically conducting saline solutions in the presence of a transverse, alternating electric field is investigated experimentally. It has been observed that while increase of field strength arrests the evaporation, increment in field frequency has the opposite effect. The same has been explained on the solvation kinetics of the ions in the polar water. Theoretical analysis reveals that change in surface tension and diffusion driven evaporation model cannot predict the arrested or decelerated evaporation. With the aid of Particle Image Velocimetry, suppression of internal circulation velocity within the droplet is observed under electric field stimulus, and this affects the evaporation rate directly. A mathematical scaling model is proposed to quantify the effects of electrohydrodynamic circulation, electrothermal and electro-solutal advection on the evaporation kinetics of the droplet. The analysis encompasses major governing parameters, viz. the thermal and solutal Marangoni numbers, the Electrohydrodynamic number, the electro Prandtl and electro Schmidt numbers and their respective contributions. It has been shown that the electrothermal Marangoni effect is supressed by the electric field, leading to deteriorated evaporation rates. Additionally, the electrosolutal Marangoni effect further supresses the internal advection, which again arrests the evaporation rate by a larger proportion. Stability analysis reveals that the electric body force retards the stable internal circulation within such droplets and arrests advection.

physics.flu-dyn↗