Data-Driven Control and Its Applications to Microgrids

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Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, w ...

With the rapid development of electric vehicles, photovoltaic-storage-charging stations that supply power to electric vehicles are becoming increasingly important. To optimize the energy scheduling of integrated photovoltaic-storage-charging stations, impr ...

The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimat ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Enzyme-enabled biobatteries are promising green options to power the next-generation of bioelectronics and implantable medical devices. However, existing power sources based on enzymatic biofuel chemistry exhibit limited scale-down feasibility due to the s ...

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their ...

A device (1) and method for processing data comprises at least one input layer (2), at least one modulation layer (3), and at least one output layer (4). The input layer (2) is configured to receive at least one carrier signal (Sc). The modulation layer (3 ...

Biorefineries hold the potential to provide products and energy carriers at reduced environmental impact compared to their fossil-based counterparts. Thus, they can contribute to the decarbonization of sectors in which electrification of demands is challen ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minim ...

The transmission overload after a disturbance poses significant security risk to the power system. Once it happens, an efficient remedial action must be taken to relieve the line overloads as quickly as possible. The line overload mitigation problem become ...

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel r ...

Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

Digitalization is a megatrend that affects and transforms societal, economic, and environmental processes on a global scale. Driven by a combination of technological advances as well as shifting societal demands, digitalization also affects the operation a ...

In this paper, we propose a multi-fidelity approach for parameter estimation problems based on Physics-Informed Neural Networks (PINNs). The proposed methods apply to models expressed by linear or nonlinear differential equations, whose parameters need to ...

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

Data-Driven Control and Its Applications to Microgrids

Chattez avec Graph Search

Proteome-wide prediction of the mode of inheritance and molecular mechanisms underlying genetic diseases using structural interactomics

Research on optimal scheduling of a photovoltaic-storage-charging integrated power station based on intraday two-stage model predictive control

Measurement - Based/Model-Less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Enzyme-Enabled Droplet Biobattery for Powering Synthetic Tissues

Decoding the brain: From neural representations to mechanistic models

Optical data processing using structural non-linearity

Leveraging industrial biorefineries for the energy transition

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Robust Collaborative Learning with Linear Gradient Overhead

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

Distributed Scheme for Line Overload Mitigation With Linearized AC Power Flow

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios*

Bayesian Recurrent Units and the Forward-Backward Algorithm

Democratizing Machine Learning

Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Numerical Analysis - A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes

Automatic Content Curation of Visual Heritage