Search Results for author:
Found 9 papers, 1 papers with code
Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve
The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data.
Computationally Enhanced Approach for Chance-Constrained OPF Considering Voltage Stability
The effective management of stochastic characteristics of renewable power generations is vital for ensuring the stable and secure operation of power systems.
Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network
Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality.
Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach
So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model.
Conservative Sparse Neural Network Embedded Frequency-Constrained Unit Commitment With Distributed Energy Resources
The increasing penetration of distributed energy resources (DERs) will decrease the rotational inertia of the power system and further degrade the system frequency stability.
On Geometric Structure of Activation Spaces in Neural Networks
Based on this new algorithm, four common geometric properties shared by the activation spaces are concluded, which gives a rather clear description of the activation spaces.
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images
A variety of deep neural networks have been applied in medical image segmentation and achieve good performance.
Curvature-based Comparison of Two Neural Networks
In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them.
The Local Dimension of Deep Manifold
Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.
