Delay-dependent anti-windup synthesis for stability of constrained state delay systems using pole-constraints

This paper proposes the design of anti-windup compensator gain for stability of actuator input constrained state delay systems using constrained pole-position of the closed-loop. Based on Delay-Dependent Lyapunov-Krasovskii functionals and local sector conditions, a new LMI characterization is derived that ensures closed-loop asymptotic stability of constrained state delay systems while accounting upper bound fixed state delay and largest lower bound of the system’s pole-position in the formulation of anti-windup gain. Besides, at saturation, the method significantly nullifies the inherent slow dynamics present in the system. It is shown in the comparative numerical examples that the LMI formulation draws stability with improved time-domain performance.     

Carbon nanotubes based strain sensors

This paper presents the design, fabrication and experimental results of carbon nanotubes (CNTs) based strain sensors. The sensors were fabricated by pressed CNTs tablets and elastic polymer beam. The diameter of multiwalled nanotubes (MWNTs) varied between 10 and 30 nm. The tablets of multiwalled CNTs having nominal thickness of 1.0 mm were pressed at a pressure of 200 and 300 MPa. The inter-electrodes distance (length) and width of the surface-type samples were in the range of 10–12 mm and 5–6 mm respectively. The DC resistance of the sensors having the strain sensitivity in the range of 50–80 in average increased under tension and decreased under compression.     

Searching surface orientation of microscopic objects for accurate 3D shape recovery

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.     

Prediction of cellular structure in free expansion of viscoelastic media

A systematic model is presented for a free expansion polymer foaming process that includes simultaneous nucleation and bubble growth. An influence volume approach, which couples nucleation and bubble growth, is used to account for the limited supply of dissolved gas. The melt rheology is described using the Larson viscoelastic model. The initial conditions are obtained at the upper bound of critical cluster size under conditions of elastic deformation. The resulting set of equations are solved using a combination of numerical techniques. A parametric study is conducted to examine the effects of key process variables on bubble growth, nucleation, and final bubble size distribution. It shows that the factors influencing nucleation and growth affect the ultimate bubble sizes and their distribution. The Gibbs number, a dimensionless measure of the barrier to overcome for nucleation, has the strongest impact on the cellular structure of the foam. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 67:1353–1368, 1998     

Multilayer heuristics based clustering framework (MHCF) for author name disambiguation

Scientometrics - Author name ambiguity is a nontrivial problem currently faced by digital libraries and scholarly data search engines affecting their findings related to the authorship data...     

Selection of solar tracking system using extended TOPSIS technique with interval type-2 pythagorean fuzzy numbers

Optimization and Engineering - The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group...     

A First-Principle Study of Monolayer Transition Metal Carbon Trichalcogenides

Journal of Superconductivity and Novel Magnetism - Monolayer MnCX3 metal–carbon trichalcogenides have been investigated by using the first-principle calculations. The compounds show...     

Information extraction from scientific articles: a survey

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.