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Found 14 papers, 4 papers with code
A Computational Efficient Pumped Storage Hydro Optimization in the Look-ahead Unit Commitment and Real-time Market Dispatch Under Uncertainty
To provide a practical solution to the daily operation of a PSHU in a single day look-ahead commitment (LAC) and real-time market, this paper proposes two pumped storage hydro (PSH) models that only use probabilistic price forecast to incorporate uncertainties and manage risks in the LAC and real-time market operation.
Conflict Detection in IoT-based Smart Homes
Conflicts may arise during interactions between the resident and IoT services in smart homes.
Fermi softness: a local perspective on surface reactivity
Understanding how electronic structure determines the reactivity of solid surface, is a central topic of modern surface science.
Chemical Physics
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal.
Chemical Physics
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL).
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
The advantages of the proposed framework in convergence rate and control performance are illuminated.
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities.
Cognitive Amplifier for Internet of Things
We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience.
Service mining for Internet of Things
A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up.
Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models
Quantum machine learning models have been gaining significant traction within atomistic simulation communities.
Data Analysis, Statistics and Probability Chemical Physics Computational Physics
Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited
Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approximations made when calculating molecular energies within quantum chemistry.
Chemical Physics
The "DNA" of chemistry: Scalable quantum machine learning with "amons"
In analogy to the DNA sequence in a gene encoding its function, constituting amons encode a query molecule's properties.
Chemical Physics
Machine learning prediction errors better than DFT accuracy
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.
Ranked #18 on
Formation Energy
on QM9
Understanding molecular representations in machine learning: The role of uniqueness and target similarity
The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation.
Chemical Physics
