Proposes ML-RBM for non-intrusive load monitoring without appliance-level data.
problem Non-intrusive load monitoring without appliance-level data.
method Multi-label Restricted Boltzmann Machine (ML-RBM)
result Experimental evaluation of proposed and state-of-the-art techniques.
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator c…
Paper proposes a novel optimization method for disaggregating smart meter data.
problem Energy disaggregation, inferring appliance-specific energy consumption from aggregate meter data.
method Two-stage optimization approach: first phase uses mixed integer programming, second phase binary quadratic optimization with penalty terms and appliance constraints.
result Proposed method successfully reconstructs appliance signatures, overcoming previous optimization-based methods' limitations.
REST improves robustness and efficiency of sleep monitoring models.
problem Noise and energy efficiency in deep learning models for home health monitoring.
method Adversarial training and spectral/sparsity regularization.
result REST models achieve 19x parameter reduction and 15x MFLOPS reduction with 17x energy reduction and 9x faster inference.
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…
Machine learning predicts exercise load from heart rate data post-exercise.
problem Monitoring energy expenditure in real life.
method Machine learning methods (linear regression, etc.) applied to heart rate data.
result Random forest and k-nearest neighbors classifiers predict load levels accurately.
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
problem Limited datasets and high computational power for NILM deployment.
method Developed an interoperable data collection framework and introduced model compression techniques.
result Efficient edge deployment of NILM models for global scalability and sustainability.
Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustain…
This study uses smartphone data to predict when mood interventions are needed for bipolar disorder.
problem Chronic mental illness with extreme mood changes that lead to personal or social consequences.
method Anomaly detection framework using Temporal Normalization to predict mood anomalies from natural speech data.
result A framework for real-world speech-focused mood monitoring using deep learning.
A new deep learning method for energy disaggregation.
problem Energy disaggregation or non-intrusive load monitoring (NILM) to identify individual appliance power usage.
method Sequence to Point Learning based on Bidirectional Dilated Residual Network (BRDN).
result Our method outperforms state-of-the-art approaches in all appliances on REDD and UK-DALE datasets.
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the d…
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
The paper introduces metrics to evaluate NILM algorithms' performance on unseen buildings.
problem Assessing NILM algorithms' performance on new, unseen buildings.
method Developed several metrics to evaluate NILM algorithms' generalization ability.
result Demonstrated the utility of the proposed metrics through two case studies.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Deep neural network improves NILM with attention mechanism.
problem Energy disaggregation of individual appliance power demands from aggregate meter readings.
method Regression and classification subnetworks with attention mechanism.
result Proposed model outperforms state-of-the-art on REDD and UK-DALE datasets.
As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which…
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
This work relaxes energy constraints in self-attention layers for a more general analysis.
problem Understanding inherent biases and dynamics in self-attention layers without energy functions.
method Dynamical systems analysis and Jacobian matrix examination.
result Normalized dynamics are close to a critical state, indicating high inference performance.
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
Paper proposes a new KPI for early fault detection in hydropower plants.
problem Early detection and maintenance of faults in hydropower plants.
method Developed and tested a novel Key Performance Indicator (KPI).
result The KPI outperforms conventional multivariable process control charts.
This paper reviews low voltage load forecasting methods and applications.
problem Reliable forecasting for low voltage networks is needed for decarbonization.
method Comprehensive survey of current approaches, challenges, and trends.
result Established an open list of low voltage datasets for further research.
CRBMs improve financial regime detection with PCD and free energy analysis.
problem Detecting systemic risk regimes in financial time series.
method Extended RBM to CRBM with autoregressive conditioning and PCD. Decomposed free energy into magnitude and correlation components.
result CRBM's free energy metric distinguishes between magnitude shocks and market regimes.
Paper presents ADEPOS framework for energy-efficient anomaly detection.
problem Energy and bandwidth limitations in IoT systems.
method Low precision computing and adaptive neural networks.
result 8.95X energy savings with no loss in detection accuracy.
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we …
The paper develops methods to reduce deployment risk under dynamic covariate shifts.
problem Reduction of deployment risk under dynamic covariate shifts.
method Time-domain Poincare inequality and Jacobian-velocity theorem to identify and control directional tangent energy.
result Drift-aligned tangent regularization (DTR) reduces risk volatility and directional gain in low-rank drift regimes.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
problem Balancing marine conservation and blue economy management
method Deep Zero-Inflated Bernoulli model
result Improved model adequacy and predictive performance
Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent c…
AI systems that explain their decisions can be monitored for harmful intentions.
problem Monitoring AI systems' decision-making processes for harmful intentions is imperfect and can miss some misbehavior.
method Monitoring the chain of thought (CoT) of AI systems that communicate in human language.
result CoT monitoring is a promising but fragile approach to AI safety.
Tiny Eats GRU detects eating episodes on a microcontroller.
problem Automatic dietary monitoring on low-power devices.
method Shallow gated recurrent unit (GRU) architecture on Arm Cortex M0+.
result Tiny Eats GRU achieves 95.15% accuracy with 4% memory usage and 6 ms latency.
New indicator detects financial strain through smart meter data.
problem Fuel poverty in households, affecting millions.
method Smart meters and machine learning for behavior measurement.
result Early detection of financial strain in households.
TEASER improves early time series classification accuracy and speed.
problem Early and accurate classification of time series data.
method TEASER models eTSC as a two-tier classification problem, using a first-tier classifier to assess class probabilities and a second-tier to decide reliability.
result TEASER is two to three times faster at predictions than competitors while maintaining or improving accuracy.
Improved crude oil price forecasting using multi-dimensional LLM sentiment signals.
problem Challenges in predicting crude oil prices due to unstructured news.
method Extracted five sentiment dimensions from GPT-4o, Llama 3.2-3b, and FinBERT models on energy-sector news articles.
result Combining GPT-4o and FinBERT yields the best predictive performance for weekly WTI crude oil futures returns.
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
This research tackles monitoring machine learning algorithms post-deployment, addressing performativity issues.
problem Monitoring machine learning algorithms after deployment, especially when they affect their own data-generating process.
method Uses causal inference techniques to navigate performativity and compares different monitoring criteria and data sources.
result Different monitoring systems have varying operating characteristics and implications for ML monitoring design.
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…
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
Detects corruption in agentic models during execution.
problem Inconsistent context, retrieval errors, or adversarial inputs corrupt intermediate steps of reasoning chains.
method Analyzes token graphs induced by attention and computes spectral statistics to emit accept/reject signals.
result A single threshold on the high frequency energy ratio optimally detects context inconsistency in agentic models.
PITMonitor monitors model calibration over time with formal error guarantees.
problem Fixed-sample tests applied to models over time can lead to false alarms.
method PITMonitor uses mixture e-processes to detect distributional shifts in probability integral transforms.
result PITMonitor achieves competitive detection rates on river's FriedmanDrift benchmark.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
A new method monitors unstructured 3D shapes without registration.
problem Error-prone registration and mesh reconstruction steps in PCD monitoring.
method Intrinsic geometric properties of shapes, using Laplacian and geodesic distances.
result Effective monitoring of defects without registration and mesh reconstruction.
IDS algorithm optimizes sequential decisions in various monitoring settings.
problem Optimizing sequential decisions in complex monitoring scenarios.
method Information-directed sampling (IDS) algorithm for linear partial monitoring.
result IDS achieves nearly worst-case rate optimality in finite-action games.
Focuses on monitoring and explaining models in real-world applications.
problem Ensuring high quality machine learning services in production environments.
method Statistical techniques for model performance and data monitoring, explanations of predictions.
result Challenges and solutions for implementing monitoring and explanation in production models.
Paper proposes an unsupervised NILM framework using GLDA for diverse utility data.
problem Extract appliance components from aggregate energy signals without labeled data.
method Bayesian hierarchical mixture models, Gaussian Latent Dirichlet Allocation (GLDA), online processing.
result Algorithm finds useful consumption patterns from mixed utility data.
Surveying low-cost sensors for air quality monitoring and calibration.
problem Limited spatial resolution due to expensive environmental monitoring stations.
method Low-cost sensors with machine learning for calibration.
result Machine learning improves sensor accuracy over time.
Neural system optimizes glucose levels in diabetics.
problem Limited research on continuous glucose maintenance devices.
method Differential predictive control with neural policy and differentiable modeling.
result Improves glucose level optimization in real-time.
We present a numerical scheme to calculate fluctuation identities for exponential Lévy processes in the continuous monitoring case. This includes the Spitzer identities for touching a single upper or lower barrier, and the more difficult case of the two-barriers exit problem. These identities are given in the Fourier-L…