Jonne van Dreven

PhD student, Computer Science, Blekinge Institute of Technology

PhD student, Unit Water and Energy Transition, VITO

Joined

July 2025

Names

Jonne van Dreven (Preferred)

Emails

****@bth.se (Confirmed)

Personal Links

Google Scholar

DBLP

ORCID

Career & Education History

PhD student

Computer Science, Blekinge Institute of Technology (bth.se)

Present

PhD student

Unit Water and Energy Transition, VITO (vito.be)

Present

Advisors, Relations & Conflicts

No relations added

Expertise

Computer Science

Present

Artificial Intelligence

Present

Publications

Federated Multi-Source Data Fusion for Semi-Supervised Fault Detection in District Heating Substations
Jonne van Dreven, Sadi Alawadi, Abbas Cheddad, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- Wiley Expert Systems
- Readers: Everyone
From bearings to substations: Transfer Learning for fault detection in district heating
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- Energy
- Readers: Everyone
HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations
Jonne van Dreven, Abbas Cheddad, Ahmad Nauman Ghazi, Sadi Alawadi, Jad Al Koussa, Dirk Vanhoudt
- Energy and AI
- Readers: Everyone
SCENTS: Multi-Source Streaming Consensus Embedding for Time Series Data Fusion
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- Information Fusion
- Readers: Everyone
A Learnable Cross-Modal Adapter for Industrial Fault Detection Using Pretrained Vision Models
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- IEEE Transactions on Industrial Informatics
- Readers: Everyone
From data scarcity to diagnostic precision: A novel data augmentation and fault diagnosis framework for district heating substations
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- Eng. Appl. Artif. Intell. 2025
- Readers: Everyone
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
- CoRR 2024
- Readers: Everyone

Jonne van Dreven

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Federated Multi-Source Data Fusion for Semi-Supervised Fault Detection in District Heating Substations

From bearings to substations: Transfer Learning for fault detection in district heating

HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations

SCENTS: Multi-Source Streaming Consensus Embedding for Time Series Data Fusion

A Learnable Cross-Modal Adapter for Industrial Fault Detection Using Pretrained Vision Models

From data scarcity to diagnostic precision: A novel data augmentation and fault diagnosis framework for district heating substations

SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations

Co-Authors