A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Non-intrusive algorithm corrects corrupted data in classification models.
A novel algorithm uses Gaussian process regression to interpret non-intrusive ROMs.
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring …
Improves NILM with multi-label SRC, outperforming state-of-the-art.
Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliance-level feedback. This observation has increased the relevance of load monitoring …
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partiall…
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architectur…
The paper introduces metrics to evaluate NILM algorithms' performance on unseen buildings.
Paper proposes a novel optimization method for disaggregating smart meter data.
New method learns low-dimensional models for systems with non-polynomial terms.
Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy…
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combin…
A new deep learning method for energy disaggregation.
Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous ho…
MEVA aggregates model predictions to improve accuracy without needing model details.
Enhances PCE surrogates using transfer learning for expensive simulations.
Traditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric lea…
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
New method uses sparse random features for crashworthiness analysis.
Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various met…
Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability. To cope with demanding analysis such as optimization and reliability, surrogate models (a.k.a meta-models) have been increasingly investigated in the last decade.…
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
This work proposes a novel method for interpolating ROMs without solving FEM models.
Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech. We demonstrate that we can model human raters' mean opinion scores (MOS) of synthesized speech using a deep recurrent neural network whose inputs consist solely of a raw waveform. Our best models …
Method learns latent dynamics of complex systems from noisy data.
Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties…
Sparse Polynomial Chaos expansions improve accuracy and efficiency in simulations.
A new method optimizes complex engineering designs under uncertainty efficiently.
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by…
Paper improves voice trigger detection for privacy-centric smart assistants.
Detects adversarial inputs in deep learning models without modifying the main network.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and their applicability over macroscopic time scales of physical interest is computationally infeasible or impractical. In complex systems, with lim…
Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and elec…
Random feature model approximates PDE solutions efficiently.
Paper proposes personalized climate control for driver comfort.
Improved DeepONet variants using Transformer cross-conditioning enhance PDE solution efficiency.
ARMED models improve deep learning interpretability and generalize better on clustered data.
Study predicts room occupancy using machine learning, achieving high accuracy.
MF-GLaM models improve stochastic simulator emulation with multifidelity data.
Neural Chaos uses neural networks instead of polynomials for stochastic modeling.
Reduced-order model improves LES for atmospheric pollutant dispersion.
Convolutional networks predict turbulence from wall quantities.
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. However, extracting useful information from camera trap images is a cumber…
This work ensures stability in POD basis interpolation for pMOR in hyperelasticity.
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…