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Found 14 papers, 4 papers with code
Extended Impedance Modal Analysis of Internal Dynamics in Grid-Following Inverters
The EMAI method enables the analysis of the dynamic characteristics of each control loop in the GFL based on the impedance model.
Data-Driven Subsynchronous Oscillation Suppression for Renewable Energy Integrated Power Systems Based on Koopman Operator
Using Koopman operator, the system dynamics are accurately extracted from data and utilized to the linear model predictive control (MPC).
Generalized Modal Analysis in Power System with High CIG Penetration: Concept and Quantitative Assessment
This paper presents a Generalized Modal Analysis (GMA) concept for the small-signal stability analysis of power systems with high penetration of Converter-Interfaced Generation (CIG).
Explainable Bayesian Recurrent Neural Smoother to Capture Global State Evolutionary Correlations
Through integrating the evolutionary correlations across global states in the bidirectional recursion, an explainable Bayesian recurrent neural smoother (EBRNS) is proposed for offline data-assisted fixed-interval state smoothing.
Modal Analysis of Power System with High CIG Penetration Based on Impedance Models
By examining the interplay between different elements and system modes in high CIG environments, this study offers insights and a foundational framework for delineating the oscillatory modes' participation and stability characteristics of power systems with substantial CIG integration.
Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO
Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels.
Explainable Gated Bayesian Recurrent Neural Network for Non-Markov State Estimation
The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data.
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
After formulating the system model with the mutual coupling effect and the reflection phase/amplitude errors of the RIS, a novel DNNDANM method is proposed for the DOA estimation by combining the deep neural network (DNN) and the decoupling atomic norm minimization (DANM).
Detection of Ghost Targets for Automotive Radar in the Presence of Multipath
Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas.
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution
The model incorporates cross-spatial pixel integration attention (CSPIA) to introduce contextual information into a local window, while cross-stage feature fusion attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage.
URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwanted signals in received data.
Seventy Years of Radar and Communications: The Road from Separation to Integration
Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing
Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, sharing a majority of hardware and signal processing modules and, in a typical case, sharing a single transmitted signal.
