Olga Fink

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Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Olga Fink, Florent Evariste Forest, Gaëtan Michel Frusque

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

2025

Continuous Health State Monitoring of High Voltage Circuit Breakers

Olga Fink, Gaëtan Michel Frusque

Circuit breakers (CBs) are renowned for their high reliability and long lifespan. As a result, many CBs installed decades ago are now approaching their predefined end of service life. However, this predefined service life does not always reflect the actual ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

Olga Fink, Ismail Nejjar

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

2025

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-training Strategies and Performance Insights

Olga Fink, Florent Evariste Forest

This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain. Recent research has proposed vario ...

Springer Science and Business Media Deutschland GmbH2025

Exploiting semantic scene reconstruction for estimating building envelope characteristics

Olga Fink, Florent Evariste Forest

Precise assessment of geometric building envelope characteristics is essential for parametric simulation analysis and informed retrofitting decisions. Previous methods for estimating building characteristics, such as window-to-wall ratio and building footp ...

Elsevier BV2025

Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures

Olga Fink

Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing expl ...

2025

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Olga Fink, Han Sun

Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive tra ...

2025

Domain Adaptive Unfolded Graph Neural Networks

Olga Fink

Over the last decade, graph neural networks (GNNs) have made significant progress in numerous graph machine learning tasks. In real-world applications, where domain shifts occur and labels are often unavailable for a new target domain, graph domain adaptat ...

Association for the Advancement of Artificial Intelligence2025

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Olga Fink, Keivan Faghih Niresi

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

2024

Robust time series denoising with learnable wavelet packet transform

Olga Fink, Gaëtan Michel Frusque

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 ...

2024

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