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Found 17 papers, 2 papers with code
Optimizing Two-Truck Platooning with Deadlines
The FPTAS can achieve a fuel consumption within a ratio of $(1+\epsilon)$ to the optimal (for any $\epsilon>0$) with a time complexity polynomial in the size of the transportation network and $1/\epsilon$.
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition.
DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes.
Competitive Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids
In this paper, we exploit the structure of information in the prediction window to design a novel prediction-aware online algorithm for energy generation scheduling in microgrids.
Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads
Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under active research in recent years.
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
We systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible.
Principle-driven Fiber Transmission Model based on PINN Neural Network
Instead of adopting input signals and output signals which are calculated by SSFM algorithm in advance before training, this principle-driven PINN based fiber model adopts frames of time and distance as its inputs and the corresponding real and imaginary parts of NLSE solutions as its outputs.
Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage
In this paper, we propose an optimal online algorithm that achieves the best competitive ratio, following the idea of maintaining a constant ratio between the online and the optimal offline peak-reduction performance.
DeepOPF-V: Solving AC-OPF Problems Efficiently
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation.
The energy technique for the six-step BDF method
In combination with the Grenander--Szeg\"o theorem, we observe that a relaxed positivity condition on multipliers, milder than the basic %fundamental requirement of the Nevanlinna--Odeh multipliers that the sum of the absolute values of their components is strictly less than $1$, makes the energy technique applicable to the stability analysis of BDF methods for parabolic equations with selfadjoint elliptic part.
Numerical Analysis Numerical Analysis
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems.
DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow
We then directly reconstruct the phase angles from the generations and loads by using the power flow equations.
Electro-optical Neural Networks based on Time-stretch Method
In this paper, a novel architecture of electro-optical neural networks based on the time-stretch method is proposed and numerically simulated.
Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty
We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration.
DeepOPF: Deep Neural Network for DC Optimal Power Flow
DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions.
Systems and Control
Device-to-Device Load Balancing for Cellular Networks
The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency.
Networking and Internet Architecture
High-speed real-time single-pixel microscopy based on Fourier sampling
However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time.
