Search Results for author:
Found 11 papers, 3 papers with code
Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model
This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation.
Maximising Quantum-Computing Expressive Power through Randomised Circuits
This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost, in terms of the sampling cost of quantum circuits.
A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control
In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis.
Prompt-Learning for Cross-Lingual Relation Extraction
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
MT-SNN: Enhance Spiking Neural Network with Multiple Thresholds
Spiking neural networks (SNNs), as a biology-inspired method mimicking the spiking nature of brain neurons, is a promising energy-efficient alternative to the traditional artificial neural networks (ANNs).
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement
This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis.
A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch
This letter proposes a data-driven sparse polynomial chaos expansion-based surrogate model for the stochastic economic dispatch problem considering uncertainty from wind power.
Quantum reinforcement learning in continuous action space
As an application, our method can solve the quantum state-generation problem in a single shot: it only requires a one-shot optimization to generate a model that outputs the desired control sequence for arbitrary target state.
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC).
