About: Indian Institute of Space Science and Technology is a education organization based out in Thiruvananthapuram, India. It is known for research contribution in the topics: Antenna (radio) & Graphene. The organization has 882 authors who have published 1714 publications receiving 18116 citations. The organization is also known as: IIST.
08 Oct 2016
University of Ljubljana1, University of Birmingham2, Czech Technical University in Prague3, Linköping University4, Austrian Institute of Technology5, Carnegie Mellon University6, Parthenope University of Naples7, University of Isfahan8, Autonomous University of Madrid9, University of Ottawa10, University of Oxford11, Hong Kong Baptist University12, Kyiv Polytechnic Institute13, Middle East Technical University14, Hacettepe University15, King Abdullah University of Science and Technology16, Pohang University of Science and Technology17, University of Nottingham18, University at Albany, SUNY19, Chinese Academy of Sciences20, Dalian University of Technology21, Xi'an Jiaotong University22, Indian Institute of Space Science and Technology23, Hong Kong University of Science and Technology24, ASELSAN25, Australian National University26, Commonwealth Scientific and Industrial Research Organisation27, University of Missouri28, University of Verona29, Universidade Federal de Itajubá30, United States Naval Research Laboratory31, Marquette University32, Graz University of Technology33, Naver Corporation34, Imperial College London35, Electronics and Telecommunications Research Institute36, Zhejiang University37, University of Surrey38, Harbin Institute of Technology39, Lehigh University40
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).
••23 Jan 2019
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Abstract: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
TL;DR: The results of the proposed research could help top management in taking strategic level decision making with respect to selection of suppliers in a resilient supply chain.
Abstract: Suppliers can be considered as inevitable sources of external risks in modern supply chains. In this context, resilience that stands for the adaptive capability to respond to disruptions and recovering from it needs to be considered in supplier selection. But selection of suppliers is a challenging issue that involves the evaluation of both qualitative and quantitative attributes, in usual have imprecise and limited information. Grey relational analysis based on linguistic assessment of supplier rating and attribute weightings could judiciously be used under these situations to obtain a set of possibility values for prioritizing supplier selection. In this research, a supplier to be selected in the context of a resilient supply chain is termed as a resilient supplier. Taking electronic supply chain as a case study, with six alternative suppliers, grey possibility values for supplier selection were calculated and the suppliers were prioritized. Sensitivity analysis was also conducted to identify how far the selection priorities of suppliers change by varying the weightings given to each of the resilience attributes. This helps us in identifying the attributes of resilience where a particular supplier performs well. A comparison of proposed grey methodology with analytic hierarchy process (AHP) and analytic network process (ANP) was also conducted to comprehend extent of out-performance. The results of the proposed research could help top management in taking strategic level decision making with respect to selection of suppliers in a resilient supply chain.
TL;DR: In this article, Salimian et al. examined the mechanical properties such as tensile, flexural and impact strength of fiber-reinforced composites and found that the permanganate treatment caused a reduction in the impact strength.
Abstract: Sisal fibers were subjected to various chemical and physical modifications such as mercerization, heating at 100 °C, permanganate treatment, benzoylation and silanization to improve the interfacial bonding with matrix. Composites were prepared by these fibers as reinforcement, using resin transfer molding (RTM). The mechanical properties such as tensile, flexural and impact strength were examined. Mercerized fiber-reinforced composites showed 36% of increase in tensile strength and 53% in Young’s modulus while the permanganate treated fiber-reinforced composites performed 25% increase in flexural strength. However, in the case of impact strength, the treatment has been found to cause a reduction. The water absorption study of these composites at different temperature revealed that it is less for the treated fiber-reinforced composites at all temperatures compared to the untreated one. SEM studies have been used to complement the results emanated from the evaluation of mechanical properties.
TL;DR: In this article, a brief review of spherical flame propagation method, counterflow/stagnation burner method, heat-flux method, annular stepwise method, externally heated diverging channel method, and Bunsen method is presented.
Abstract: Accurate measurement and prediction of laminar burning velocity is important for characterization of premixed combustion properties of a fuel, development and validation of new kinetic models, and calibration of turbulent combustion models. Understanding the variation of laminar burning velocity with thermodynamic conditions is important from the perspective of practical applications in industrial furnaces, gas turbine combustors and rocket engines as operating temperatures and pressures are significantly higher than ambient conditions. With this perspective, a brief review of spherical flame propagation method, counterflow/stagnation burner method, heat-flux method, annular stepwise method, externally heated diverging channel method, and Bunsen method is presented. A direct comparison of power exponents for temperature (α) and pressure (β) obtained from different experiments and derived from various kinetic mechanisms is reported to provide an independent tool for detailed validation of kinetic schemes. Accurate prediction of laminar burning velocities at higher temperatures and pressures for individual fuels will help in closer scrutiny of the existing experimental data for various uncertainties due to inherent challenges in individual measurement techniques. Laminar burning velocity data for hydrogen (H2), gaseous alkane fuels (methane, ethane, propane, n-butane, n-pentane), liquid alkane fuels (n-heptane, isooctane, n-decane), alcohols (CH3OH, C2H5OH, n-propanol, n-butanol, n-pentanol) and di-methyl ether (DME) are obtained from literature of last three decades for a wide range of pressures (1–10 bar), temperatures (300–700 K), equivalence ratios and mixture dilutions. The available experimental and numerical data for H2 and methane fuels compares well for various pressures and temperatures. However, more experimental and kinetic model development studies are required for other fuels. Comparison of laminar burning velocity data obtained from different measurement techniques at higher initial pressures and temperatures showed significant deviations for all fuels. This suggests to conduct focused measurements at elevated pressure and temperature conditions for different fuels to enable the development of accurate kinetic models for wider range of mixtures and thermodynamic conditions.
Showing all 902 results
|Vinay Kumar Dadhwal||40||322||6217|
|B. S. Manoj||25||159||4172|
|Raghvendra Kumar Mishra||23||102||1411|
|K. B. Jinesh||21||64||1590|
|Jagadheep D. Pandian||21||38||1369|
|Nirmala R. James||20||32||1073|
|P. R. Sinha||19||39||1101|
|Rakesh Kumar Singh||19||123||1392|
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