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Found 14 papers, 1 papers with code
Image-Text Matching with Multi-View Attention
Existing two-stream models for image-text matching show good performance while ensuring retrieval speed and have received extensive attention from industry and academia.
Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Service Transformers
This proposed SDNet OPF model can be easily embedded into existing primary distribution network (PDNet) OPF models, resulting in a holistic power system decision-making solution for integrated primary-secondary distribution networks.
Robust Model Predictive Techno-Economic Control of Active Distribution Networks
Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables.
Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks
Hence, this paper enhances the scope of analytical estimation of sensitivity matrices for unbalanced networks with 1-phase, 2-phase, and 3-phase Delta/Wye-connected loads, DERs with smart inverter functionality, and substation/line step-voltage regulators (SVR).
Data-Driven Affinely Adjustable Robust Volt/VAr Control
Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power.
Automatic Self-Adaptive Local Voltage Control Under Limited Reactive Power
To this end, this paper proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of distributed energy resources.
Online Voltage Control for Unbalanced Distribution Networks Using Projected Newton Method
It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm.
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles
This paper develops a data-driven approach to accurately predict the restoration time of outages under different scales and factors.
Progressive Graph Convolution Network for EEG Emotion Recognition
Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion.
Analyzing Photovoltaic's Impact on Conservation Voltage Reduction in Distribution Networks
The results show that the allocations of solar PV have the most significant effect on the CVR performance, where a dispersed allocation of solar PV will help flatten voltage profile and achieve deeper voltage reductions at the substation, less energy consumption and line losses.
A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data
In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from the known hourly net demand data recorded by smart meters: 1) At the aggregate level, the proposed approach separates the total PV generation and native demand time series from the total net demand time series for customers with PVs.
An Online Feedback-Based Linearized Power Flow Model for Unbalanced Distribution Networks
The non-linearity and non-convexity of power flow models and the phase coupling challenge the analysis and optimization of unbalanced distribution networks.
Multistage Model for Robust Face Alignment Using Deep Neural Networks
First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment.
Exploiting Contextual Information via Dynamic Memory Network for Event Detection
Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks.
