Nadia Saad Noori

• Sechi, Fabien; Noori, Nadia; Charu, Sharma; Sharma, Pawan; Aidoo, Isaac Kweku & Gonzalez-Longatt, Francisco [Vis alle 7 forfattere av denne artikkelen] (2026). Towards distinguishing cybersecurity attacks and safety faults in distributed energy resources-rich smart grids: a systematic literature review. International Journal of Information Security. ISSN 1615-5262. 25. doi: 10.1007/s10207-026-01308-z. Fulltekst i vitenarkiv Vis sammendrag

  This study presents a Systematic Literature Review (SLR) of methods and challenges related to distinguishing cybersecurity attacks and safety faults in Distributed Energy Resources (DER)-rich smart grids. The increasing integration of DERs has improved grid flexibility and sustainability, but has also introduced greater operational complexity and expanded the attack surface of smart grid infrastructures. In such environments, cyber-attacks and safety faults may produce similar anomalies, making accurate distinction a critical requirement for resilient and secure grid operation. This review examines the literature on anomaly detection and event classification in DER-rich smart grids towards distinguishing these anomalies and events, with a particular focus on approaches that could support the differentiation of malicious and non-malicious events. It analyzes the main categories of data used in the literature, including sensor data, smart meter data, network traffic, phasor measurements, and weather-related information. It also reviews machine learning and artificial intelligence techniques, including supervised, unsupervised, and deep learning approaches, and discusses their applicability, strengths, and limitations within different contexts. Furthermore, the review synthesizes evaluation practices, operational application domains, and key open challenges, including limited dataset realism, data quality issues, explainability, scalability, and model generalization. Existing testbeds are highlighted as essential for reproducing realistic grid conditions and validating approaches; however, in their current state, no testbed framework is directly applicable to distinguishing cybersecurity attacks from operational safety faults in an informed manner. Importantly, only 6 of the 25 included primary studies explicitly attempt to distinguish cyber-attacks from physical safety faults within a single evaluation setting. The review is therefore explicitly positioned as a combined systematic mapping and gap analysis: rather than demonstrating a mature solution space, it documents how sparsely the central distinction problem is addressed, characterizes the few existing attempts in depth, and derives a research agenda from this gap. Overall, the review identifies major research gaps and outlines directions for developing more safe and secure solutions for DER-rich smart grids.

• Østebø, Håvard; Varma, Varun Ravi & Noori, Nadia (2026). Agent-based model exploring impact of social traits and emerging collective patterns during an emergency evacuation operation. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, s. 794–803. doi: 10.7148/2026-0794. Fulltekst i vitenarkiv Vis sammendrag

  This study presents an agent-based simulation framework for analysing social response during crisis situations, using a fire evacuation scenario as a controlled experimental setting in which the hazard is represented through the spread of smoke. Individuals are modelled as agents with different behavioural strategies, including altruistic, selfish, herding, and rational, interacting in a confined environment affected by smoke, crowding, and exit-capacity constraints. Agents update their behaviour based on local conditions, internal states such as panic and health, and interactions with nearby agents. Three controlled experiments explores how social behaviour, evacuation infrastructure, and hazard severity influences outcomes. The results reveal a non-linear relationship between altruism and survival, identify exit capacity as a critical structural constraint, and indicate that faster smoke spread increases panic, injury rates, and evacuation time. Overall, the findings demonstrate how simple individual decision rules generate complex collective evacuation dynamics under emergency evacuation.

• Sechi, Fabien & Noori, Nadia Saad (2025). Conceptual framework for testbed design: specific to cybersecurity and operational safety anomalies in DER-rich Smart Grid. I Nasreddine, Belkacem Abdelkader (Red.), 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331533588. s. 1371–1378. doi: 10.1109/smc58881.2025.11343373. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, the digitalisation of power grid systems and the integration of Distributed Energy Resources (DERs) into smart grids (SGs) have increased the complexity of the electrical power ecosystem in Europe, thereby making it more vulnerable to cyberattacks. Despite the development of AI-based anomaly detection systems aimed at improving monitoring and attack identification, a significant limitation persists: such systems often fail to distinguish between cybersecurity attacks and operational safety faults or anomalies. Consequently, there is a critical need to develop more effective methods for detecting and categorising different operational conditions and failures within the power grid. However, the development of such complex systems is not feasible without adequate data and testbeds capable of simulating real-world baseline operational conditions before deployment in production environments.This paper presents a conceptual Cyber-Physical System (CPS) testbed tailored for Europe’s DER-rich smart grids. The testbed integrates human operators, Artificial Intelligence (AI), safety protocols, and robust security measures. It connects a simulated SG electrical energy distribution network based on the CIGRE standard, a Human-Machine Interface (HMI) Supervisory Control and Data Acquisition (SCADA) control room (CR), and a Security Operations Center (SOC) at the Institute for Energy Technology. The objective is to simulate real-world conditions for electrical energy distribution and control systems under complex critical scenarios caused by operational failures and cyber vulnerabilities.First, we evaluated existing machine learning(ML)/AI-based anomaly detection methods and associated datasets in terms of accuracy and explainability in CPS environments. This evaluation revealed the absence of datasets that capture both safety-related and security-related anomalous operational conditions. Second, we conducted a systematic literature review of existing testbeds to extract key requirements necessary to simulate disruptions resulting from operational failures and cyber vulnerabilities. Identified gaps included inadequate simulation of real-world conditions, insufficient modeling of human-AI interaction, and the absence of structured root cause analysis and anomaly categorization methods. We validated the proposed conceptual framework through expert consultation with specialists in cybersecurity and power systems engineering.The proposed conceptual testbed design was made to meet the minimum requirements identified in this research work, to help create a realistic simulation of cyber vulnerabilities and operational failures, facilitating the development of anomaly detection systems that distinguish between cyber threats and operational faults. We detail the architecture, defined scenarios, and implementation, and propose a blueprint and evaluation framework using performance metrics such as realism, scalability, flexibility, and security robustness. The resulting conceptual framework demonstrates the testbed’s potential to meet baseline requirements for cybersecurity and operational safety assessment in SGs. This work contributes to advancing the security and reliability of critical energy infrastructure, with global implications.

• Havås, Robin; Noori, Nadia Saad; Auby, Christian & Cenkeramaddi, Linga Reddy (2024). Optimized Unmanned Aerial Vehicle (UAV) Localization and Autonomous Navigation Stack for Tightly Closed Industrial Spaces, 2024 IEEE International Systems Conference (SysCon). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350358803. doi: 10.1109/SysCon61195.2024.10553582. Fulltekst i vitenarkiv
• Birkeland, Simen; Fjeldvik, Lin Julie; Noori, Nadia Saad; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Thermal video-based hand gestures recognition using lightweight CNN. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137. doi: 10.1007/s12652-024-04851-6. Fulltekst i vitenarkiv
• Birkeland, Simen; Fjeldvik, Lin Julie; Noori, Nadia Saad; Yeduri, Sreenivasa Reddy & Cenkeramaddi, Linga Reddy (2024). Video based hand gesture recognition dataset using thermal camera. Data in Brief. doi: 10.1016/j.dib.2024.110299. Fulltekst i vitenarkiv
• Andreassen, Jarl; Eileraas, Martin; Herrera, Lucia Castro & Noori, Nadia Saad (2023). InCReASE: A dynamic framework towards enhancing situational awareness in cyber incidence response. I Gjøsæter, Terje; Radianti, Jaziar & Murayama, Yuko (Red.), Information Technology in Disaster Risk Reduction. Springer. ISSN 9783031342073. s. 230–243. doi: 10.1007/978-3-031-34207-3_15. Fulltekst i vitenarkiv
• Evensen, Vetle Nesland; Henriksen, Gabriel Bergman; Melhus, Sondre; Olsen, Ole Steine; Haugen, Kristina & Dolmen, Dag [Vis alle 11 forfattere av denne artikkelen] (2023). ReFrogID: Pattern Recognition for Pool Frog Identification Using Deep Learning and Feature Matching. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, s. 365–376. doi: 10.1007/978-3-031-47994-6_33. Fulltekst i vitenarkiv Vis sammendrag

  The global decline in amphibian populations is a pressing issue, with numerous species facing the threat of extinction. One such species is the pool frog, Pelophylax lessonae, whose Norwegian population has experienced a significant long-term decline since monitoring began in 1996. This decline has pushed the species to the verge of local extinction. A substantial knowledge gap in the species’ biology hinders the proposal and evaluation of effective management actions. Consequently, there is a pressing need for efficient techniques to gather data on population size and composition. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in various domains, including ecology and evolution. Current research in these fields primarily focuses on species modeling, behavior detection, and identity recognition. The progress in mobile technology, ML, and DL has equipped researchers across numerous disciplines, including ecology, with innovative data collection methods for building knowledge bases on species and ecosystems. This study addresses the need for systematic field data collection for monitoring endangered species like the pool frog by employing deep learning and image processing techniques. In this research, a multidisciplinary team developed a technique, termed ReFrogID, to identify individual frogs using their unique abdominal patterns. Utilizing RGB images, the system operates on two main principles: (1) a DL algorithm for automatic segmentation achieving AP@89.147, AP50@99.123, and AP75@98.942, and (2) pattern matching via local feature detection and matching methods. A new dataset, pelophylax_lessonae, addresses the identity recognition problem in pool frogs. The effectiveness of ReFrogIDis validated by its ability to identify frogs even when human experts fail. Source code is available at here.

• Noori, Nadia; Shanbhag, Vignesh Vishnudas; Kandukuri, Surya Teja & Schlanbusch, Rune (2022). Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 7(1), s. 368–375. doi: 10.36001/phme.2022.v7i1.3327. Fulltekst i vitenarkiv Vis sammendrag

  The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hardware & software) required for implementing a real-time soft sensor unit for monitoring seal wear condition. We used a feedforward multilayer perceptron ANN (Scaled Conjugate Gradient- SCG algorithm) that is trained with the back propagation algorithm, which is a popular network architecture for a multitude of applications (automotive, oil and gas, electronics). We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders by applying it to both raw (full frequency spectrum) and down sampled frequency measurements. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions like that of hydraulic cylinders. The test cases were generated with seals of three different conditions (unworn, semi-worn, worn). From the AE spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X down sampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A general set of 16 time and frequency domain features were designed. Then a training set was developed using relevant set of features (4, 5, and 16 features). The data was used to train the ANN (70% training – 30% test & validation) and SVM (60 % training - 40% test and validation). Classification of down sampled measurements, both ANN and SVM were able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99% with execution time less than seconds. Therefore, the proposed approach can be applied as part of an automated seal wear classification technique based on AE and ANN/SVM and can be used for real-time monitoring of seal wear in hydraulic cylinders.

• Anderson, Abraham; Olafarson, Einar Julius; Andersen, Per-Arne & Noori, Nadia Saad (2022). The ODeLIndA Dataset for Field-of-View Obstruction Detection Using Transfer Learning for Real-Time Industrial Applications. I Bramer, Max & Stahl, Frederic (Red.), Artificial Intelligence XXXIX 42nd SGAI International Conference on Artificial Intelligence, AI 2022. Springer. ISSN 9783031214417. s. 197–210. doi: 10.1007/978-3-031-21441-7_14. Fulltekst i vitenarkiv Vis sammendrag

  Several challenges arise in industrial applications when using machine vision in safety-critical operations, such as field of view (FoV) obstruction, limited visibility of the equipment’s operational perimeter, difficulty obtaining datasets, and high accuracy requirement. This research shows how machine learning methods such as convolutional neural networks (CNN) and transfer learning can tackle these challenges. Transfer learning works well when the source and target domain images are similar, which is the case for identifying the operational conditions and FoV status in a fixed industrial environment where heavy equipment is operating with the aid of a machine vision module. In this work, eight categories describing FoV status and environmental conditions were selected as follows: clean lens/clear view, cracked lens, dirty lens, foggy, frost, dark view, flares, and rainy images. We used 21 616 images for the training set and 9450 for the test and validation. We benchmark the performance of several transfer learning models and networks (i.e., MobileNet, VGG16, VGG19, DenseNet, Inceptionv3, ResNet50, and Xception) against a CNN model/network trained from scratch. The experiments show that MobileNet combined with transfer learning performs best, with 98% accuracy compared to 93% for the traditional CNN model. Furthermore, the model can detect obstructions in real-time using commodity hardware when tested with a video stream. Finally, we provide a novel dataset for multi-class obstruction detection, which can be found here.

• Imenes, Anne Gerd; Noori, Nadia Saad; Uthaug, Ole Andreas Nesvåg; Kröni, Robert; Bianchi, Filippo Maria & Belbachir, Nabil (2021). A Deep Learning Approach for Automated Fault Detection on Solar Modules Using Image Composites. I Angus, Rockett (Red.), 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665419222. doi: 10.1109/PVSC43889.2021.9518540. Fulltekst i vitenarkiv Vis sammendrag

  Aerial inspection of solar modules is becoming increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. In this paper, we explore and evaluate the use of computer vision and deep learning methods for automating the analysis of fault detection and classification in large scale photovoltaic module installations. We use convolutional neural networks to analyze thermal and visible color images acquired by cameras mounted on unmanned aerial vehicles. We generate composite images by overlaying the thermal and visible images to investigate improvements in detection accuracy of faint features related to faults on modules. Our main goal is to evaluate whether image processing with multi-wavelength composite images can improve both the detection and the classification performance compared to using thermal images alone. The hypothesis is that fusion of images acquired at different wavelengths (i.e., thermal infrared, red, green, and blue visible ranges) would enhance the multi-wavelength representation of faults and thus their histogram feature signatures. The results showed a successful detection and localization of faint fault features using composite images. However, the classification of the fault categories did not show significant improvements and needs continued investigation. This research represents a step towards the design of robust automated methods to improve fault detection from airborne images. Further work is still necessary to reach a classification accuracy comparable with the performance of human experts.

• Kandukuri, Surya Teja; Shanbhag, Vignesh Vishnudas; Meyer, Thomas; Caspers, Leo; Noori, Nadia & Schlanbusch, Rune (2021). Automated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 6(1), s. 203–210. doi: 10.36001/phme.2021.v6i1.2942. Fulltekst i vitenarkiv Vis sammendrag

  Seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In literature, successful attempts have been made using acoustic emission-based condition monitoring to classify the seal wear. However, limited attempts have been made to implement automated approaches to classify seal wear using acoustic emission features. Therefore, this article presents an automated approach for rapid and computationally economical diagnosis of seal wear using acoustic emission. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions similar to that of hydraulic cylinders. From the acoustic emission spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X downsampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A set of time and frequency domain features were designed, and the Support Vector Machine (SVM) was trained on 60 % of the test cases generated with seals of three different conditions. The SVM was able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99 %. Therefore, the proposed automated seal wear classification technique based on acoustic emission and SVM can be used for real-time monitoring of seal wear in hydraulic cylinders.

• Salvi, Andrea; Spagnoletti, Paolo & Noori, Nadia (2021). Cyber-resilience of Critical Cyber Infrastructures: integrating digital twins in the electric power ecosystem. Computers & Security. ISSN 0167-4048. doi: 10.1016/j.cose.2021.102507. Fulltekst i vitenarkiv Vis sammendrag

  Here in this work a simple and direct method to synthesize Rubik’s cube-like Linda Type (LTA) zeolite NaA structure is reported. Linda Type (LTA) zeolite was synthesized by hydrothermal method without using any organic template additives. The molar ratio of 3.165Na2O:Al2O3:1.926SiO2:128H2O have been used to prepare the zeolite NaA hydrogel. The thermal stability of the synthesized zeolite NaA was studied through the thermogravimetric analysis (TGA). The results of X-ray diffraction (XRD) analysis showed that the crystallinity of the synthesized zeolite is good and the material is of high purity. Through the FTIR study, functional groups belonging to the zeolite NaA was confirmed. From the FESEM analysis, morphology of the synthesized sample was found to be Rubik’s cube-like structure. The elemental composition of the material was studied using the EDAX analysis and the ratio of Si:Al was found to be ∼ 1. Further from the SAED patterns of the prepared sample, it was observed that the material is polycrystalline in nature. From the nitrogen adsorption/desorption results the zeolite NaA samples was found to be mesoporous in nature. The pore size of the samples have been obtained using the Barrett-Joyner-Halenda (BJH) analysis. The CO2 adsorption isotherm study was carried out at 298 K and the isotherm was fitted by using Langmuir and Freundlich adsorption isotherm.

• Lazreg, Mehdi Ben; Noori, Nadia Saad; Comes, Tina & Goodwin, Morten (2019). Not a Target. A Deep Learning Approach for a Warning and Decision Support System to Improve Safety and Security of Humanitarian Aid Workers. I Barnaghi, Payam; Gottlob, Georg; Manolopoulos, Yannis; Tzouramanis, Theodoros & Vakali, Athena (Red.), 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2019). Association for Computing Machinery (ACM). ISSN 9781450369343. s. 378–382. doi: 10.1145/3350546.3352551. Fulltekst i vitenarkiv
• Radianti, Jaziar; Dokas, Ioannis; Boersma, Kees; Noori, Nadia Saad; Belbachir, Nabil & Stieglitz, Stefan (2019). Enhancing Disaster Response for Hazardous Materials Using Emerging Technologies: The Role of AI and a Research Agenda. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 1000, s. 368–376. doi: 10.1007/978-3-030-20257-6_31. Fulltekst i vitenarkiv
• Lanfranchi, Vitaveska; Noori, Nadia Saad & Sirbu, T (2018). GPS-based solution for tracking and protecting humanitarians in conflict zones. Proceedings of the International ISCRAM Conference. 2018-May, s. 334–349. Fulltekst i vitenarkiv
• Noori, Nadia Saad (2018). Modeling the escalation/de-escalation of response operation levels in disaster response networks using hierarchical Colored Petri Nets (CPN) approach, 2018 Annual IEEE International Systems Conference (SysCon). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538636640. s. 50–58. doi: 10.1109/SYSCON.2018.8369593. Fulltekst i vitenarkiv
• Aboughonim, Ahmed Abdeltawab Abdelgawad S.; Noori, Nadia Saad & Comes, Tina (2018). Automated Scenario Generation for Training of Humanitarian Responders in High-Risk Settings. International Journal of Simulation. Systems, Science and Technology. ISSN 1473-8031. 19(5), s. 23.1–23.9. doi: 10.5013/IJSSST.a.19.05.23. Fulltekst i vitenarkiv Vis sammendrag

  Scenarios are designed to support decision-makers in gaining a better understanding of the consequences of their decisions. Scenario-based simulations, hence, present an ideal way to train decision-makers for complex and high-risk situations. Usually, training scenarios are human-authored which is limiting in terms of the number and diversity of the generated scenarios, in addition to being a time- and resource-consuming process. In this paper, we are introducing a first-order logic rule-based design and implementation of an automated scenario generator system to support designers of virtual, augmented and simulated reality humanitarian training setups. The system is based on knowledge extracted from publicly available humanitarian related databases. Moreover, the system can produce scenarios that adapt to user performance in addition to meeting certain training requirements

• Noori, Nadia; Wang, Yan; Comes, Tina; Schwarz, Philipp & Lukosch, Heide (2017). Behind the scenes of scenario-based training: Understanding scenario design and requirements in high-risk and uncertain environments. Proceedings of the International ISCRAM Conference. 2017-May, s. 948–959. Fulltekst i vitenarkiv

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• Misra, Sanjay; Kvalvik, Petter; Gran, Bjørn Axel; Kjølerbakken, Kai Morgan; Omerovic, Aida & Noori, Nadia Saad (2026). Artificial Intelligence in Context of Digital Sovereignty - 1st Editio. Routledge. ISBN 9781032945507. 408 s. Fulltekst i vitenarkiv Vis sammendrag

  In a world increasingly shaped by technology, digital sovereignty is a critical need for nations and organizations to preserve their autonomy. It is more than just control over data and infrastructure; it's about safeguarding values, privacy, and ethics in an era of AI. This concept empowers entities to reduce dependency on foreign tech, ensuring that their digital future reflects their unique priorities and needs. It offers crucial insights for those navigating the shifting technological and ge

• Misra, Sanjay; Kvalvik, Petter; Gran, Bjørn Axel; Kjølerbakken, Kai Morgan; Omerovic, Aida & Noori, Nadia Saad (2026). Artificial Intelligence in Context of Digital Sovereignty. Routledge. ISBN 9781003581581. 408 s. doi: 10.1201/9781003581581. Fulltekst i vitenarkiv Vis sammendrag

  In a world increasingly shaped by technology, digital sovereignty is a critical need for nations and organizations to preserve their autonomy. It is more than just control over data and infrastructure; it's about safeguarding values, privacy, and ethics in an era of AI. This concept empowers entities to reduce dependency on foreign tech, ensuring that their digital future reflects their unique priorities and needs. It offers crucial insights for those navigating the shifting technological and geopolitical landscape, providing both thought-provoking theory and practical strategies for achieving digital independence. The book offers a roadmap for navigating the complexities of the adoption of AI, focusing on privacy, security, and the importance of aligning AI with local values. It's a timely guide for anyone invested in the future of technology and geopolitics. This book is essential reading for policymakers, academics, tech practitioners, and anyone concerned with the intersection of AI, digital sovereignty, and cybersecurity. Key Features: Discusses the improvement of digital sovereignty with reference to AI to meet legal and technological barriers/challenges. Gives the current status of development of Digital sovereignty and its realization. Discusses the improvement of policies regarding digital sovereignty. Covers a broad spectrum of topics that are related to cyber sovereignty, data sovereignty, technological sovereignty, role of AI, industrial applications, data center sovereignty, industrial data space, and others. Functions as a platform for experts in many fields to share their novel insights and showcase the multidisciplinary nature of the field.

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• Havås, Robin; Noori, Nadia Saad; Auby, Christian & Cenkeramaddi, Linga Reddy (2024). Optimized Unmanned Aerial Vehicle (UAV) Localization and Autonomous Navigation Stack for Tightly Closed Industrial Spaces. Fulltekst i vitenarkiv
• Imenes, Anne Gerd; Noori, Nadia Saad; Uthaug, Ole Andreas Nesvåg; Kröni, Robert; Bianchi, Filippo Maria & Belbachir, Nabil (2021). A Deep Learning Approach for Automated Fault Detection on Solar Modules Using Image Composites. Fulltekst i vitenarkiv Vis sammendrag

  Aerial inspection of solar modules is becoming increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. In this paper, we explore and evaluate the use of computer vision and deep learning methods for automating the analysis of fault detection and classification in large scale photovoltaic module installations. We use convolutional neural networks to analyze thermal and visible color images acquired by cameras mounted on unmanned aerial vehicles. We generate composite images by overlaying the thermal and visible images to investigate improvements in detection accuracy of faint features related to faults on modules. Our main goal is to evaluate whether image processing with multi-wavelength composite images can improve both the detection and the classification performance compared to using thermal images alone. The hypothesis is that fusion of images acquired at different wavelengths (i.e., thermal infrared, red, green, and blue visible ranges) would enhance the multi-wavelength representation of faults and thus their histogram feature signatures. The results showed a successful detection and localization of faint fault features using composite images. However, the classification of the fault categories did not show significant improvements and needs continued investigation. This research represents a step towards the design of robust automated methods to improve fault detection from airborne images. Further work is still necessary to reach a classification accuracy comparable with the performance of human experts.

• Noori, Nadia Saad (2020). Cybersecurity between automation and sensemaking: are we still playing catch up? Fulltekst i vitenarkiv
• Noori, Nadia Saad (2020). Cyber Incident Management Challenges: no one-size fits all. Fulltekst i vitenarkiv Vis sammendrag

  The electrical energy infrastructure is one of the most complex and critical infrastructures as other sectors depend upon it to deliver their essential services. Cyber threat to the electrical grid is real and of grave importance. A large volume of documents exists providing guidance or direction on securing the grid against these threats; however, much of the work so far has been disjointed . Looking at the operational aspects of cyber incident management such as inter-organizational coordination and communication, overall, there is a low level of awareness regarding information infrastructure interdependencies and vulnerabilities for entities outside the organization’s boundaries . Moreover, entities involved in provisioning cybersecurity are not homogenous in terms of their goals, incentives, and disincentives. Each organization own its unique organizational culture, its standard operation procedures, and its internal supervisory controls condition the manner in which they coordinate with other entities. Therefore, this might lead to a conflict and might hinder efforts preventing, detecting and responding to cyber incidents. The absence of a process or a mechanism to coordinate interorganizational responses to a cyber security incident lead to a lack of awareness of vulnerabilities and capabilities of other organizations and how cyber-attacks might affect them. Often, communications channels for disseminating information before and during a cyber security incident can be through small, and informal information exchange networks, based on personal relationships and trust . Consequently, there is a lack of understanding concerning what capabilities and information each organization had access to and how to share it . This is highly problematic due to the interconnectedness of the organizations operating in the energy sector and due to the high risk of spill overs and cascading effects. Thus, there is a need to establish an integrated approach to cyber incident management addressing the specific needs of energy systems beyond to facilitate a collaborative and effective cyber incident response operations.

• Noori, Nadia Saad; Waag, Tor Inge & Bianchi, Filippo Maria (2020). Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches. Fulltekst i vitenarkiv Vis sammendrag

  <p>Offshore oil drilling is a complex process that requires careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring operating conditions of drilling systems are critical to the overall production cycle. In this paper, we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work, we aim to design an intelligent system for monitoring the health of IBOP system using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly. <p>The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.

• Noori, Nadia Saad; Waag, Tor Inge; Viumdal, Håkon; Sharma, Roshan; Jondahl, Morten Hansen & Jinasena, Asanthi (2020). Non-Newtonian Fluid Flow Measurement in Open Venturi Channel Using Shallow Neural Network Time Series and Non-contact Level Measurement Radar Sensors. Fulltekst i vitenarkiv
• Noori, Nadia Saad & Waag, Tor Inge (2019). Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System. Fulltekst i vitenarkiv
• Noori, Nadia Saad (2018). ICT role in supporting humanitarian aid missions in hostile environments: the case of iTRACK project. Fulltekst i vitenarkiv
• Noori, Nadia Saad (2017). Behind the Scenes of Scenario-Based Training: Understanding Scenario Design and Requirements in High-Risk and Uncertain Environments. Fulltekst i vitenarkiv
• Havås, Robin; Cenkeramaddi, Linga Reddy; Noori, Nadia Saad & Auby, Christian (2022). Implementation Of An Autonomous Navigation Stack For UAVs In Tightly Closed Industrial Spaces. Universitetet i Agder. Fulltekst i vitenarkiv

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