Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

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The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal process ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size B, and the step size or learning rate n. For small B and large n, SGD corresponds to a stochastic ...

Like many other countries, Switzerland offers various incentives to promote residential solar PV, but not all households have equal access to them. Using a microsimulation approach based on merged data from the Swiss Household Budget Survey and Household E ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Photovoltaic (PV) technology is necessary for global decarbonization. However, one of the challenges of the technology is that its land use may conflict with other space demands. Building-integrated photovoltaic (BIPV) is a solution to efficiently use the ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

Solar photovoltaics (PV) is one of the most competitive renewable energy technologies in order to meet the increasing global energy demand and decrease CO2 emissions by competing effectively with fossil fuels. One of the important applications of PV energy ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Netw ...

Because building-integrated photovoltaic (BIPV) modules are fully integrated into a building envelope, the back of the module can be exposed to little or no ventilation, resulting in increased operating temperatures. As the temperature increases, the perfo ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these ...

Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Chattez avec Graph Search

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Network time series forecasting in photovoltaics power production

On the different regimes of stochastic gradient descent

Distributional justice, effectiveness, and costs of current and alternative solar PV incentive schemes in Switzerland

Aggregating Spatial and Photometric Context for Photometric Stereo

Advanced Manufacturing and Characterization of Building-Integrated Photovoltaic Modules

Digital Twins of Stone Masonry Buildings for Damage Assessment

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Reliability and Long-term Performance of Building Integrated Photovoltaic (BIPV) Modules

Robust time series denoising with learnable wavelet packet transform

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Neural Exponential Stabilization of Control-affine Nonlinear Systems

Correlating long-term performance and aging behaviour of building integrated PV modules

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks