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Found 15 papers, 7 papers with code
An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations
Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification.
Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems
Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as collision avoidance, search and rescue, and object manipulation.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations
The artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision.
Model-Driven Requirements for Humans-on-the-Loop Multi-UAV Missions
We introduce the meta-model and the requirements elicitation process with examples drawn from a search-and-rescue mission in which multiple UAVs collaborate with humans to respond to the emergency.
Software Engineering Human-Computer Interaction
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques.
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening
In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets.
Intrinsic Optical and Electronic Properties from Quantitative Analysis of Plasmonic Semiconductor Nanocrystal Ensemble Optical Extinction
It captures individual NC optical properties and their contributions to the ensemble spectra thereby enabling the analysis of intrinsic NC properties from an ensemble measurement.
Optics Mesoscale and Nanoscale Physics
CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMS
Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics.
A New Parallel Algorithm for Two-Pass Connected Component Labeling
In the second phase, we assign the final labels to the pixels.
Qualitative Decision Methods for Multi-Attribute Decision Making
The general objective of MCDA is to enable the DM to order all alternatives in order of the stated preferences, and choose the ones that are best, i. e., optimal with respect to the preferences over the criteria.
