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Constructing Health Indicators for Systems with Few Failure Instances Using Unsupervised Learning

de Pater, I.I. (author), Mitici, M.A. (author)

Health indicators are crucial to assess the health of complex systems. In recent years, several studies have developed data-driven health indicators using supervised learning methods. However, due to preventive maintenance, there are often not enough failure instances to train a supervised learning model, i.e., the data is unlabelled with an...

conference paper 2023

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Color Equivariant Convolutional Networks

Lengyel, A. (author), Strafforello, O. (author), Bruintjes, R. (author), Gielisse, A.S. (author), van Gemert, J.C. (author)

Color is a crucial visual cue readily exploited by Convolutional Neural Networks (CNNs) for object recognition. However, CNNs struggle if there is data imbalance between color variations introduced by accidental recording conditions. Color invariance addresses this issue but does so at the cost of removing all color information, which sacrifices...

conference paper 2023

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Predictive Maintenance Planning Using Renewal Reward Processes and Probabilistic RUL Prognostics: Analyzing the Influence of Accuracy and Sharpness of Prognostics

Mitici, M.A. (author), de Pater, I.I. (author), Zeng, Zhiguo (author), Barros, Anne (author)

We pose the maintenance planning for systems using probabilistic Remaining Useful Life (RUL) prognostics as a renewal reward process. Data-driven probabilistic RUL prognostics are obtained using a Convolutional Neural Network with Monte Carlo dropout. The maintenance planning model is illustrated for aircraft turbofan engines. The results show...

conference paper 2023

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Jumping Shift: A Logarithmic Quantization Method for Low-Power CNN Acceleration

Jiang, Longxing (author), Aledo Ortega, D. (author), van Leuken, T.G.R.M. (author)

Logarithmic quantization for Convolutional Neural Networks (CNN): a) fits well typical weights and activation distributions, and b) allows the replacement of the multiplication operation by a shift operation that can be implemented with fewer hardware resources. We propose a new quantization method named Jumping Log Quantization (JLQ). The key...

conference paper 2023

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MNEMOSENE++: Scalable Multi-Tile Design with Enhanced Buffering and VGSOT-MRAM based Compute-in-Memory Crossbar Array

Escuin, Carlos (author), García-Redondo, Fernando (author), Zahedi, M.Z. (author), Ibáñez, Pablo (author), Monreal, Teresa (author), Viñals, Victor (author), Llabería, José María (author), Myers, James (author), Ryckaert, Julien (author), Biswas, Dwaipayan (author), Catthoor, Francky (author)

This paper optimizes the MNEMOSENE architecture, a compute-in-memory (CiM) tile design integrating computation and storage for increased efficiency. We identify and address bottlenecks in the Row Data (RD) buffer that cause losses in performance. Our proposed approach includes mitigating these buffering bottlenecks and extending MNEMOSENE’s...

conference paper 2023

document

Simplicial Vector Autoregressive Model For Streaming Edge Flows

Krishnan, Joshin (author), Money, Rohan (author), Beferull-Lozano, Baltasar (author), Isufi, E. (author)

Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network...

conference paper 2023

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Analyzing Components of a Transformer under Different Dataset Scales in 3D Prostate CT Segmentation

Tan, Yicong (author), Mody, P. (author), van der Valk, Viktor (author), Staring, M. (author), van Gemert, J.C. (author)

Literature on medical imaging segmentation claims that hybrid UNet models containing both Transformer and convolutional blocks perform better than purely convolutional UNet models. This recently touted success of hybrid Transformers warrants an investigation into which of its components contribute to its performance. Also, previous work has a...

conference paper 2023

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Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Moradi, M. (author), Ghorbani, R. (author), Sfarra, Stefano (author), Tax, D.M.J. (author), Zarouchas, D. (author)

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be...

conference paper 2022

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Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang, Xinyu (author), Abbasi, Qammer H. (author), Fioranelli, F. (author), Romain, Olivier (author), Le Kernec, Julien (author)

Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler...

conference paper 2022

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BackboneAnalysis: Structured Insights into Compute Platforms from CNN Inference Latency

Hafner, Frank M. (author), Zeller, Matthias (author), Schutera, Mark (author), Abhau, Jochen (author), Kooij, J.F.P. (author)

Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many...

conference paper 2022

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Long-term behaviour recognition in videos with actor-focused region attention

Ballan, Luca (author), Strafforello, O. (author), Schutte, Klamer (author)

Long-Term activities involve humans performing complex, minutes-long actions. Differently than in traditional action recognition, complex activities are normally composed of a set of sub-actions, that can appear in different order, duration, and quantity. These aspects introduce a large intra-class variability, that can be hard to model. Our...

conference paper 2021

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An Attention Module for Convolutional Neural Networks

Zhu, B. (author), Hofstee, H.P. (author), Lee, Jinho (author), Al-Ars, Z. (author)

Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: the approximation problem and the insufficient capacity problem of the...

conference paper 2021

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Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions

Li, Z. (author), Mancini, Maria Elisabetta (author), Monizzi, Giovanni (author), Andreini, Daniele (author), Ferrigno, Giancarlo (author), Dankelman, J. (author), De Momi, Elena (author)

Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach...

conference paper 2021

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On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

Wu, L. (author), Perin, G. (author)

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural...

conference paper 2021

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Towards Finite-Time Consensus with Graph Convolutional Neural Networks

Iancu, A. (author), Isufi, E. (author)

Atrial electrograms are often used to gain understanding on the development of atrial fibrillation (AF). Using such electrograms, cardiologists can reconstruct how the depolarization wave-front propagates across the atrium. Knowing the exact moment at which the depolarization wavefront in the tissue reaches each electrode is an important aspect...

conference paper 2020

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Probabilistic wind power forecasting combining deep learning architectures

Arends, Eric Lacoa (author), Watson, S.J. (author), Basu, S. (author), Cheneka, B.R. (author)

A series of probabilistic models were bench-marked during the European Energy Markets forecasting Competition 2020 to assess their relative accuracy in predicting aggregated Swedish wind power generation using as input historic weather forecasts from a numerical weather prediction model. In this paper, we report the results of one of these...

conference paper 2020

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Detecting, classifying, and mapping retail storefronts using street-level imagery

Sharifi Noorian, S. (author), Qiu, S. (author), Psyllidis, A. (author), Bozzon, A. (author), Houben, G.J.P.M. (author)

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep...

conference paper 2020

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Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network

Basu, S. (author), Watson, S.J. (author), Lacoa Arends, Eric (author), Cheneka, B.R. (author)

A hybrid neural network model, comprising of a convolutional neural network and a multilayer perceptron network, has been developed for day-ahead forecasting of regional scale wind power production. This model requires operational weather forecasts as input and also has the capability to ingest data from ensemble forecasts. Even though the...

conference paper 2020

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ST-Sem: A Multimodal Method for Points-of-Interest Classification Using Street-Level Imagery

Sharifi Noorian, S. (author), Psyllidis, A. (author), Bozzon, A. (author)

Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general mor- phological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and iden- tity of a POI. Recent advancements in computer vision could...

conference paper 2019

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Convolutional Graph Neural Networks

Gama, Fernando (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)

Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images. The use of convolutions, however, makes them unsuitable for...

conference paper 2019