Heat losses in Ceramic Coated Diesel Engine

The energy produced due to combustion of fuel in an engine is partly converted into work and the rest is lost. The knowledge of how the energy is lost will help in finding means to reduce the same to improve the performance of the engine in terms of efficiency and power output. The elimination of, in cylinder heat transfer to either the coolant or the environment does not violet the second law of the thermodynamics and moreover according to the first law, has the potential of producing more work. Another advantage of this concept is that great reduction losses in parasitic losses due to the simplicity of the cooling system, thus increasing the brake power of the engine. The main purposes of the study are to calculate the heat losses at different engine loads and speeds with and without ceramic coated diesel engine. The result showed a reduction in heat losses to the coolant and increases in exhaust energy at all the loads levels. Keywords: Ceramic coating, Diesel engine, Heat losse

On Saliency Maps and Adversarial Robustness

A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior can be quantified by considering the alignment between input image and saliency map. In this work, we provide a different perspective to this coupling, and provide a method, Saliency based Adversarial training (SAT), to use saliency maps to improve adversarial robustness of a model. In particular, we show that using annotations such as bounding boxes and segmentation masks, already provided with a dataset, as weak saliency maps, suffices to improve adversarial robustness with no additional effort to generate the perturbations themselves. Our empirical results on CIFAR-10, CIFAR-100, Tiny ImageNet and Flower-17 datasets consistently corroborate our claim, by showing improved adversarial robustness using our method. saliency maps. We also show how using finer and stronger saliency maps leads to more robust models, and how integrating SAT with existing adversarial training methods, further boosts performance of these existing methods.Comment: Accepted at ECML-PKDD 2020, Acknowledgements adde

Identification of Dimensions of the Optimization of Fuel Consumption in Air Transport Industry: A Literature Review

The objective of this paper is to identify the parameters regarding the fuel consumption optimization in air transport industry. Today due to high oil prices, energy crises, boomed competition, and traffic growth, fuel consumption is becoming a critical aspect in aviation industry. Therefore in such a highly competitive environment optimization of fuel consumption is essential in the airline industry for the survival of an airline. This literature reviews the various journals, articles, conference papers, and thesis work etc. related to fuel consumption in aviation industry after the Arab oil embargo in 1973 to 2010. Literature review identifies the several parameters which effect the fuel consumption in airline. This paper also suggests the importance of research gaps and bridging of research gaps for the optimization of fuel consumption. Current research effort for the optimization of fuel consumption needs a holistic model for customized optimization of fuel consumption. For developing the holistic model the study identifies parameters which effect the fuel consumption in aviation industry. This work develops informational framework of optimization of fuel consumption. The evaluation of informational framework will be done in term of reliability and validity checking. The extracted information will further be subjected to factor analysis. This study would form an input for aviation sector to achieve optimal fuel consumption. Keywords:  Aviation industry, Fuel consumption, Optimizatio

Modeling the Civil Aircraft Operations for the Optimization of Fuel Consumption in Indian Air Transport Industry

This paper presents a model for the optimization of fuel consumption during the operations of civil aircrafts. Several parameters were identified which effect the aircraft fuel consumption during its operations from the literature. An informational framework was developed. It was evaluated, validated and refined. Average importance rating of decision variables was done. The unimportant variables were discarded and information refinement is done for the important and moderate variables. The information refinement is done by applying the Principal Component Analysis with the help of MS excels and a fuel optimization model was developed. Aviation model specific variables can be selectively simulated to obtain the fuel consumption optimization model for that particular product. Further we hope our work will serve its intended purpose by its industrial implementation and will actually help reduce aviation fuel consumption.   Keyword: Air transport industry, Aircraft operations, Aviation turbine fuel (ATF), Fuel consumption, Fuel optimizatio

Factors affecting adoption intention of electric vehicle: a cross-cultural study

In recent years, the surge in the adoption of electric vehicles has played a vital role in reducing fossil fuel consumption and greenhouse gas emissions. However, limited cross-national research has been conducted on the determinants of electric vehicle adoption in developing and developed countries. This study examines the factors influencing the intention to adopt electric vehicles in India (378 participants) and Spain (265 participants). This study develops an integrated model that combines the unified theory of acceptance and use of technology (UTAUT2) and the value-belief-norm (VBN) model while accounting for the impact of national culture. The model is tested using structural equation modeling. The results indicate the integrated UTAUT2-VBN model is a valuable tool for explaining the differences in adoption intention across cultures. Moreover, the national cultural system plays a significant moderating role in most relationships within the model. This study offers valuable insights into the factors influencing electric vehicle adoption in different cultural contexts, which can inform policies and strategies to promote sustainable transportation.Funding for open access publishing: Universidad de Granada/CBU

Algorithm Certainty Analysis of Spatial Data for Terrain Model

The terrain survey techniques of photogrammetry, LIDAR, Sonar or seismic studies are subject to limitation of shadow zones. It is not possible to capture the terrain pattern and requires interpolation and extrapolation for conformal mapping of spatial coordinates for generation of terrain model. The discrete data is mapped through a function set whose domain returns the analytic test in Riemann map. The algorithm adopted in analysis for such mapping does not have a certainty score or probability of degree of correctness conforming to the physical landscape of shadow zones. The aim of the paper is to establish a generator of certainty degree of the mapping along with a continuous terrain model generator. The confirmed mapping of terrain presents a continuous spatial coordinate set which form the boundary of the shadow zone with discrete spatial coordinates. The discrete set is normalized in Gaussian distribution through a Poisson distribution transition. The continuous data set is represented by Laurentian series in which the function will be analytic and can be mapped to Riemann surface with singularities within the annulus and outside the annulus of approximate space sub set (Euclidean space).The singularities will be discarded through Picard's theorem and analytic test at poles with Cauchy's residual theorem is done. The resulting set of spatial coordinates will restructure within Riemann number sphere which will be mapped on the plane as stereographic projection. The Gaussian distribution which forms the basis of analysis will provide with the tool for generating the probability of certainty of every terrain model idealised to conform to the physical landscape

Fuel consumption optimization in air transport: a review, classification, critique, simple meta-analysis, and future research implications

This paper presents a review, classification schemes, critique, a simple meta-analysis and future research implication of fuel consumption optimization (FCO) literature in the air transport sector. This review is based on 277 articles published in various publication outlets between 1973 and 2014. A review of 277 articles related to the FCO in air transport was carried out. It provides an academic database of literature between the periods of 1973– 2014 covering 69 journals and proposes a classification scheme to classify the articles. Twelve hundred of articles were identified and reviewed for their direct relevance to the FCO in air transport. Two hundred seventy seven articles were subsequently selected, reviewed and classified. Each of the 277 selected articles was categorized on four FCO dimensions (Aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternate fuels & fuel properties). The articles were further classified into six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical- experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). In addition, a simple meta-analysis was also carried out to enhance understanding of the development and evolution of research in the FCO. This has resulted in the identification of 277 articles from 69 journals by year of publication, journal, and topic area based on the two classification schemes related to FCO research, published between, 1973 to December- 2014. In addition, the study has identified the 4 dimensions and 98 decision variables affecting the fuel consumption. Also, this study has explained the six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). The findings of this study indicate that the analytical-mathematical research methodologies represent the 47 % of FCO research. The results show that there is an increasing trend in research of the FCO. It is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), so we can say that there are 187 articles which appeared in various journals and other publication sources in the area of FCO since 2000. Furthermore there is increased trend in research on FCO from 2000 onward. This is due to the fact that continuously new researchers are commencing their research activities in FCO research. This shows clearly that FCO research is a current research area among many research groups across the world. Lastly, the prices of jet fuel have significantly increased since the 2005. The aviation sector’s fuel efficiency improvements have slowed down since the 1970s–1980s due to the slower pace of technological development in engine and aerodynamic designs and airframe materials. We conclude that FCO models need to address the composite fuel consumption problem by extending models to include all the dimensions, i.e. aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternative fuels & fuel properties. FCO models typically comprise all the four dimensions and this reality need to be taken into account in global FCO models. In addition, these models should have objectives or constraints to evaluate the aircraft sizes according to market structure, impact of various policy measures on fuel burn, and near term potential alternative fuel options in the global FCO problem. In the models reviewed, we evaluated that, only the few authors considered these factors. The literature identifies 98 decision variables affecting the fuel consumption related to various dimensions in air transport. So we can conclude that this analysis could represent the informational framework for FCO research in air transport. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of optimization techniques in fuel consumption of air transport. The addressed dimensions & decision variables could be of potential value to future researchers on the aviation fuel consumption optimization research and is also capable of further refinements. In future, for fuel consumption optimization the explored decision variables could be checked for their reliability and validity and a statistically significant model with minimum number of decision variable could be developed. Further, on the basis of this statistical significant model and with the best market requirement for transport aircraft, the researchers can frame the objective function for fuel consumption minimization problem & decide their dependent variables, independent variables, constant, and constraints. Furthermore, this study will also provide the base for fuel conservation, energy efficiency, and emission reduction in the aviation sector. Document type: Articl

On the benefits of defining vicinal distributions in latent space

The vicinal risk minimization (VRM) principle is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions. There is strong numerical and theoretical evidence showing that VRM outperforms ERM in terms of generalization if appropriate vicinal functions are chosen. Mixup Training (MT), a popular choice of vicinal distribution, improves the generalization performance of models by introducing globally linear behavior in between training examples. Apart from generalization, recent works have shown that mixup trained models are relatively robust to input perturbations/corruptions and at the same time are calibrated better than their non-mixup counterparts. In this work, we investigate the benefits of defining these vicinal distributions like mixup in latent space of generative models rather than in input space itself. We propose a new approach - \textit{VarMixup (Variational Mixup)} - to better sample mixup images by using the latent manifold underlying the data. Our empirical studies on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that models trained by performing mixup in the latent manifold learned by VAEs are inherently more robust to various input corruptions/perturbations, are significantly better calibrated, and exhibit more local-linear loss landscapes.Comment: Accepted at Elsevier Pattern Recognition Letters (2021), Best Paper Award at CVPR 2021 Workshop on Adversarial Machine Learning in Real-World Computer Vision (AML-CV), Also accepted at ICLR 2021 Workshops on Robust-Reliable Machine Learning (Oral) and Generalization beyond the training distribution (Abstract

An Empirical Analysis of Facebook’s Free Basics Program

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