arXiv subjects

Nishant Kumar

Publications and source records attributed to Nishant Kumar.

43 records · Page 3Linked to original sources

arXiv2019

HAUAR: Home Automation Using Action Recognition

Today, many of the home automation systems deployed are mostly controlled by humans. This control by humans restricts the automation of home appliances to an extent. Also, most of the deployed home automation systems use the Internet of Things technology to control the appliances. In this paper, we propose a system developed using action recognition to fully automate the home appliances. We recognize the three actions of a person (sitting, standing and lying) along with the recognition of an empty room. The accuracy of the system was 90% in the real-life test experiments. With this system, we remove the human intervention in home automation systems for controlling the home appliances and at the same time we ensure the data privacy and reduce the energy consumption by efficiently and optimally using home appliances.

cs.HC↗

arXiv2019

Drishtikon: An advanced navigational aid system for visually impaired people

Today, many of the aid systems deployed for visually impaired people are mostly made for a single purpose. Be it navigation, object detection, or distance perceiving. Also, most of the deployed aid systems use indoor navigation which requires a pre-knowledge of the environment. These aid systems often fail to help visually impaired people in the unfamiliar scenario. In this paper, we propose an aid system developed using object detection and depth perceivement to navigate a person without dashing into an object. The prototype developed detects 90 different types of objects and compute their distances from the user. We also, implemented a navigation feature to get input from the user about the target destination and hence, navigate the impaired person to his/her destination using Google Directions API. With this system, we built a multi-feature, high accuracy navigational aid system which can be deployed in the wild and help the visually impaired people in their daily life by navigating them effortlessly to their desired destination.

cs.HC↗

arXiv2016

Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge

This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.

cs.LG↗

arXiv2016

Tuning the bulk properties of bidisperse granular mixtures by small amount of fines

We study the bulk properties of isotropic bidisperse granular mixtures using discrete element simulations. The focus is on the influence of the size (radius) ratio of the two constituents and volume fraction on the mixture properties. We show that the effective bulk modulus of a dense granular (base) assembly can be enhanced by up to 20% by substituting as little as 5% of its volume with smaller sized particles. Particles of similar sizes barely affect the macroscopic properties of the mixture. On the other extreme, when a huge number of fine particles are included, most of them lie in the voids of the base material, acting as rattlers, leading to an overall weakening effect. In between the limits, an optimum size ratio that maximizes the bulk modulus of the mixture is found. For loose systems, the bulk modulus decreases monotonically with addition of fines regardless of the size ratio. Finally, we relate the mixture properties to the 'typical' pore size in a disordered structure as induced by the combined effect of operating volume fraction (consolidation) and size ratio.

cond-mat.soft↗

arXiv2016

Memory of jamming - multiscale models for soft and granular matter

Soft, disordered, micro-structured materials are ubiquitous in nature and industry, and are different from ordinary fluids or solids, with unusual, interesting static and flow properties. The transition from fluid to solid -at the so-called jamming density- features a multitude of complex mechanisms, but there is no unified theoretical framework that explains them all. In this study, a simple yet quantitative and predictive model is presented, which allows for a variable, changing jamming density, encompassing the memory of the deformation history and explaining a multitude of phenomena at and around jamming. The jamming density, now introduced as a new state-variable, changes due to the deformation history and relates the system's macroscopic response to its microstructure. The packing efficiency can increase logarithmically slow under gentle repeated (isotropic) compression, leading to an increase of the jamming density. In contrast, shear deformations cause anisotropy, changing the packing efficiency exponentially fast with either dilatancy or compactancy. The memory of the system near jamming can be explained by a microstatistical model that involves a multiscale, fractal energy landscape and links the microscopic particle picture to the macroscopic continuum description, providing a unified explanation for the qualitatively different flow-behavior for different deformation modes. To complement our work, a recipe to extract the history-dependent jamming density from experimentally accessible data is proposed, and alternative state-variables are compared. The proposed simple macroscopic constitutive model is calibrated with the memory of microstructure. Such approach can help understanding predicting and mitigating failure of structures or geophysical hazards, and will bring forward industrial process design/optimization, and help solving scientific challenges in fundamental research.

cond-mat.soft↗

arXiv2015

Macroscopic model with anisotropy based on micro-macro informations

Physical experiments can characterize the elastic response of granular materials in terms of macroscopic state-variables, namely volume (packing) fraction and stress, while the microstructure is not accessible and thus neglected. Here, by means of numerical simulations, we analyze dense, frictionless, granular assemblies with the final goal to relate the elastic moduli to the fabric state, i.e., to micro-structural averaged contact network features as contact number density and anisotropy. The particle samples are first isotropically compressed and later quasi-statically sheared under constant volume (undrained conditions). From various static, relaxed configurations at different shear strains, now infinitesimal strain steps are applied to "measure" the effective elastic response; we quantify the strain needed so that plasticity in the sample develops as soon as contact and structure rearrangements happen. Because of the anisotropy induced by shear, volumetric and deviatoric stresses and strains are cross-coupled via a single anisotropy modulus, which is proportional to the product of deviatoric fabric and bulk modulus (i.e. the isotropic fabric). Interestingly, the shear modulus of the material depends also on the actual stress state, along with the contact configuration anisotropy. Finally, a constitutive model based on incremental evolution equations for stress and fabric is introduced. By using the previously measured dependence of the stiffness tensor (elastic moduli) on the microstructure, the theory is able to predict with good agreement the evolution of pressure, shear stress and deviatoric fabric (anisotropy) for an independent undrained cyclic shear test, including the response to reversal of strain.

cond-mat.soft↗

arXiv2008

Rheological Behavior of Aqueous Suspensions of Laponite: New Insights into the Ageing Phenomena

In this paper, ageing behavior of suspensions of laponite with varying salt concentration is investigated using rheological tools. It is observed that the ageing is accompanied by an increase in the complex viscosity. The succeeding creep experiments performed at various ages showed damped oscillations in the strain. The characteristic time-scale of the damped oscillations, retardation time, showed a prominent decrease with the age of the system. However, this dependence weakens with an increase in the salt concentration, which is known to change microstructure of the system from glass-like to gel-like. We postulate that a decrease in the retardation time can be represented as a decrease in the viscosity (friction) of the dissipative environment surrounding the arrested entities that opposes elastic deformation of the system. We believe that ageing in colloidal glass leads to a greater ordering that enhances relative spacing between the constituents thereby reducing the frictional resistance. However, since a gel state is inherently different in structure (fractal network) than that of a glass (disordered), ageing in the same does not induce ordering. Consequently, we observe inverse dependence of retardation time on age becoming weaker with an increase in the salt concentration. We analyze these results from a perspective of ageing dynamics of both glass state and gel state of laponite suspensions.

cond-mat.soft↗