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Towards Self-Learning Energy Management for Optimal PHEV Operation Around Zero Emission Zones

Kupper, F. (author), Mentink, P. (author), Avramis, N. (author), Meima, N. (author), Lazovik, E. (author), Wilkins, S. (author), Willems, F. (author)

Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great...

conference paper 2022

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Deep Learning Augmented Realistic Avatars for Social VR Human Representation

van der Boon, M. (author), Fermoselle, L. (author), ter Haar, F. (author), Dijkstra-Soudarissanane, S. (author), Niamut, O. (author)

conference paper 2022

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Adapting technology learning curves for prospective techno-economic and life cycle assessments of emerging carbon and utilization pathways

Faber, G. (author), Ruttinger, A. (author), Strunge, T. (author), Langhorst, T. (author), van der Hulst, M.K. (author), Bensebaa, F. (author), Moni, S. (author), Tao, L. (author)

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their...

article 2022

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The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

Selberherr, E. (author), Grau, J.H. (author), Meixner, M. (author), Liere, K. (author), Ercolini, D. (author), de Filippo, C. (author), Caderni, G. (author), Brigidi, P. (author), Turroni, S. (author)

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining...

article 2022

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Constructive Model Inference: Model Learning for Component-based Software Architectures

Hooimeijer, B. (author), Geilen, M. (author), Groote, J. (author), Hendriks, D. (author), Schiffelers, R. (author)

Model learning, learning a state machine from software, can be an effective model-based engineering technique, especially to understand legacy software. However, so far the applicability is limited as models that can be learned are quite small, often insufficient to represent the software behavior of large industrial systems. We introduce a...

conference paper 2022

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Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds

van der Ploeg, C. (author), Alirezaei, M. (author), van de Wouw, N. (author), Esfahani, P.M. (author)

In this article, we propose a tractable nonlinear fault estimation filter along with explicit performance bounds for a class of linear dynamical systems in the presence of both additive and nonlinear multiplicative faults. We consider the case, where both faults may occur simultaneously and through an identical dynamical relationship, a setting...

article 2022

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Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

Pingen, G.L.J. (author), van Ommeren, C.R. (author), van Leeuwen, C.J. (author), Fransen, R.W. (author), Elfrink, T. (author), de Vries, Y.C. (author), Karunakaran, J. (author), Demirovic, E. (author), Yorke-Smith, N. (author)

Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central...

conference paper 2022

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A Compression and Simulation-Based Approach to Fraud Discovery

Fratric, P. (author), Sileno, G. (author), Van Engers, T. (author), Klous, S. (author)

With the uptake of digital services in public and private sectors, the formalization of laws is attracting increasing attention. Yet, non-compliant fraudulent behaviours (money laundering, tax evasion, etc.) - practical realizations of violations of law - remain very difficult to formalize, as one does not know the exact formal rules that define...

conference paper 2022

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Demonstrating fluid connectivity for Computing Anywhere with 6G Cloud Networks

Dimitrovski, T. (author), Chiscop, I. (author), Pileggi, P. (author), Panneman, J. (author)

Anticipating a highly customized and flexible virtual service portfolio of 6G networks, unprecedented complexity will challenge network manageability if left to human operators. Automation and autonomy are key enablers of next generation cloud networks. With this in mind, we present a demonstrator of ML-assisted user plane resource management...

conference paper 2022

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What employees do today because of their experience yesterday: Previous exposure to yellow:number aspects as a cause for SPAD incidents

Burggraaf, J. (author), Groeneweg, J. (author), Sillem, S. (author), van Gelder, P. (author)

When a train passes a red aspect, this is called a Signal Passed at Danger event or SPAD. Sometimes it is easy to identify the SPAD cause but in other cases it is unclear why the incident occurred, especially if the system operated as usual and the train driver was trained and experienced just like his or her colleagues. In previous research,...

article 2022

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Human-Robot Co-Learning for Fluent Collaborations

van Zoelen, E.M. (author), van den Bosch, K. (author), Neerincx, M.A. (author)

A team develops competency by progressive mutual adaptation and learning, a process we call co-learning. In human teams, partners naturally adapt to each other and learn while collaborating. This is not self-evident in human-robot teams. There is a need for methods and models for describing and enabling co-learning in human-robot partnerships....

conference paper 2021

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Temporal transfer learning for ozone preddiction based on CNN-LSTM model

Deng, T. (author), Manders-Groot, A.M.M. (author), Segers, A.J. (author), Bai, Y. (author), Lin, H.Q. (author)

Tropospheric ozone is a secondary pollutant which can affect human health and plant growth. In this paper, we investigated transferred convolutional neural network long short-term memory (TL-CNN-LSTM) model to predict ozone concentration. Hourly CNN-LSTM model is used to extract features and predict ozone for next hour, which is superior to...

conference paper 2021

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ZERO – Detect objects without training examples by knowing their parts

Burghouts, G.J. (author), Hillerström, F.H.J. (author)

Current object recognition techniques are based on deep learning and require substantial training samples in order to achieve a good performance. Nonetheless, there are many applications in which no (or only a few) training images of the targets are available, whilst they are well-known by domain experts. Zero-shot learning is used in use cases...

conference paper 2021

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Reliability Engineering and Computational Intelligence

van Gulijk, C. (author), Zaitseva, E. (author)

Computational intelligence is rapidly becoming an essential part of reliability engineering. This book offers a wide spectrum of viewpoints on the merger of technologies. Leading scientists share their insights and progress on reliability engineering techniques, suitable mathematical methods, and practical applications. Thought-provoking ideas...

book 2021

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Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Schoonderwoerd, T.A.J. (author), Jorritsma, W. (author), Neerincx, M.A. (author), van den Bosch, K. (author)

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated...

article 2021

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Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

Veugen, P.J.M. (author), Kamphorst, B (author), van de L'Isle, N. (author), van Egmond, M.B. (author)

We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a...

conference paper 2021

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Classification Using a Two-Qubit Quantum Chip

Neumann, N.M.P. (author)

conference paper 2021

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Machine learning-based slice management in 5G networks for emergency scenarios

Arora, A. (author), Dimitrovski, T. (author), Litjens, R. (author), Zhang, H. (author)

conference paper 2021

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Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional Networks

Chiscop, I. (author), Gazdag, A. (author), Bosman, J. (author), Biczók, G. (author)

Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect...

conference paper 2021

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Improving the analytical framework for quantifying technological progress in energy technologies

Santhakumar, S. (author), Meerman, J.C. (author), Faaij, A.P.C. (author)

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key...

article 2021