A literature review of fuel effects on performance and emission characteristics of low-temperature combustion strategies

Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Experimental assessment on performance and combustion behaviors of reactivity-controlled compression ignition engine operated by n-pentanol and cottonseed biodiesel

Low Temperature Combustion (LTC) strategies are most promising to simultaneously reduce oxides of nitrogen (NOx) and soot emissions from diesel engines along with offering higher thermal efficiency...

Prospective fuels for diesel low temperature combustion engine applications: A critical review:

Effect of ply orientation and through-thickness position of delamination on the reflection of fundamental symmetric S0 Lamb mode in GFRP composite plate structures.

Experimental investigations to reduce unburned emissions in reactivity controlled compression ignition through fuel modifications

Conversational Recommender Systems belong to a class of knowledge based systems which simulate a customer’s interaction with a shopkeeper with the help of repeated user feedback till the user settles on a product. One of the modes for getting user feedback is Preference Based Feedback, which is especially suited for novice users(having little domain knowledge), who find it easy to express preferences across products as a whole, rather than specific product features. Such kind of novice users might not be aware of the specific characteristics of the items that they may be interested in, hence, the shopkeeper/system should show them a set of products during each interaction, which can constructively stimulate their preferences, leading them to a desirable product in subsequent interactions. We propose a novel approach to conversational recommendation, UtilSim, where utilities corresponding to products get continually updated as a user iteratively interacts with the system, helping her discover her hidden preferences in the process. We show that UtilSim, which combines domain-specific “dominance” knowledge with SimRank based similarity, significantly outperforms the existing conversational approaches using Preference Based Feedback in terms of recommendation efficiency.

Saurabh Gupta

Papers

Prospective fuels for diesel low temperature combustion engine applications: A critical review:

Effect of ply orientation and through-thickness position of delamination on the reflection of fundamental symmetric S0 Lamb mode in GFRP composite plate structures.

Experimental investigations to reduce unburned emissions in reactivity controlled compression ignition through fuel modifications

UtilSim: Iteratively Helping Users Discover Their Preferences

Experimental Investigations to Extend the Load Range of Premixed Charge Compression Ignited Light Duty Diesel Engine through Fuel Modifications