Description :

A self-adaptive system (SAS) is capable of adjusting its own behavior in response to any change in its operating environment. It is able to automatically adapt at runtime by adjusting its own parameters or components of the system in order to cope with the dynamic surrounding environment. However, adaptation actions should be identified as per the particular change in the environment, i.e. “What kind of change is required?”. The aim of this literature study is to address the scope of dynamic integration of the new parameter or new component at runtime in SAS. The following set of questions is essential to fulfilling this literature study.

•  Which adaptation techniques are better?
•  How much reliable and safe it is?
•  Is it feasible to implement combination of techniques in SAS?
•  What are the impacts and cost factors?
•  How to increase the performance of the SAS with adaptation techniques?

Literature :

1. Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS), 4(2), 14.
2. Krupitzer, C., Breitbach, M., Roth, F. M., VanSyckel, S., Schiele, G., & Becker, C. (2018). A survey on engineering approaches for self-adaptive systems (extended version).
3. Handte, M., Schiele, G., Matjuntke, V., Becker, C., & Marrón, P. J. (2012). 3PC: System support for adaptive peer-to-peer pervasive computing. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 7(1), 1-19.
4. McKinley, P. K., Sadjadi, S. M., Kasten, E. P., & Cheng, B. H. (2004). Composing adaptive software. Computer, 37(7), 56-64.
5. Epifani, I., Ghezzi, C., Mirandola, R., & Tamburrelli, G. (2009, May). Model evolution by run-time parameter adaptation. In 2009 IEEE 31st International Conference on Software Engineering (pp. 111-121). IEEE.
6. Weyns, D., Schmerl, B., Grassi, V., Malek, S., Mirandola, R., Prehofer, C., & Göschka, K. M. (2013). On patterns for decentralized control in self-adaptive systems. In Software Engineering for Self-Adaptive Systems II (pp. 76-107). Springer, Berlin, Heidelberg.

Supervisor :

Anil Patel

Description :

Advancement of technology and their impact on the real-world can be assessed by how safe it is. It is a primary role of a safety engineer to assess the risk via a process relevant data and learning from past lessons. Due to advanced and autonomous technology, a study for continuous and dynamic risk assessment is required. Aim of this literature study is to address the dynamics of risk and AI-based assessment techniques. The following set of questions is essential to fulfilling this literature study.

•   What are the dynamics of risk?
•   Which quantitative/qualitative risk assessment techniques are available?
•   How AI-based risk analysis is feasible?
•   What is the impact of human factors on AI-based risk assessment?
•   Which AI-techniques can be useful for risk assessment?

Literature :

1. Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment–A review. Safety science, 122, 104492.
2. Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety science, 118, 475-486.
3. Apostolakis, G. E. (2004). How useful is quantitative risk assessment? Risk Analysis: An International Journal, 24(3), 515-520.
4. Aven, T., & Krohn, B. S. (2014). A new perspective on how to understand, assess and manage risk and the unforeseen. Reliability Engineering & System Safety, 121, 1-10.
5. Villa, V., Paltrinieri, N., Khan, F., & Cozzani, V. (2016). Towards dynamic risk analysis: A review of the risk assessment approach and its limitations in the chemical process industry. Safety science, 89, 77-93.
6. Adedigba, S. A., Khan, F., & Yang, M. (2017). Dynamic failure analysis of process systems using neural networks. Process Safety and Environmental Protection, 111, 529-543.
7. Rausand, M. (2013). Risk assessment: theory, methods, and applications (Vol. 115). John Wiley & Sons.

Supervisor:

Anil Patel

Description :

The aim is to investigate which methods exist to evaluate the end-user acceptance of Digital Farming solutions and what the results of these methods are.

Literature :

Literature will be provided

Supervisor:

Felix Möhrle

Description :

The aim is to research what systems exist to connect different Digital Farming solutions (e.g. from different manufacturers) in arable farming. Examples are Agrirouter and Nevonex.

Literature :

Literature will be provided

Supervisor:

Felix Möhrle

Description :

The aim is to research which software engineering standards exist for the evaluation of management information systems (e.g. in medicine and agriculture). Examples are ISO 25010 (software quality) and Quamoco.

Literature :

Literature will be provided

Supervisor:

Felix Möhrle

Description :

The aim is to investigate what communities of practice exist in the agricultural sector that can be used, for example, to evaluate the end-user acceptance of products.

Literature :

Literature will be provided

Supervisor:

Marc Favier

Description :

The aim is to research what systems exist to connect different Digital Farming solutions (e.g. from different manufacturers) in livestock farming. Examples are Herde+ and ISOAGRINET.

Literature :

Literature will be provided

Supervisor:

Marc Favier

Description :

The goal is to investigate what data management solutions exist to ensure product traceability in the food and wine industry. Blockchain is one example, but others will be explored as well.

Literature :

Literature will be provided

Supervisor:

Marc Favier

Description :

We hear today a lot of confusing terms such as digitization, digitalization, and digital transformation. All of them emphasize the importance of bringing technology into the business, health, and education sectors. But what should be done? When? and how? The difference between these terms is not clear.

In this topic, you need to sketch a proper definition for these seemingly identical terms and to find the fine thread that draws the boundaries between them. Examples or use cases from different application domains would enrich your claims!.

Literature :

1.  Digitalization, Digitization, and Innovation
2.  Digitization, Digitalization, And Digital Transformation: Confuse Them At Your Peril
3. Automation, digitization and digitalization and their implications for manufacturing processes

Supervisor :

Rasha Abu Qasem

Description :

Multi-criteria Decision Analysis (MCDA) is a method developed in operations research that is used often in the context of decision making. The Decision-making process is complex in nature. It uses a set of evaluation criteria and constraints to generate a set of optimized alternative decisions. It is required that the evaluation criteria and constraints are well defined from the beginning and have a clear expected range of values. But in real-life application, a lot of uncertainty is involved in the process. 

Your work on this topic is to:

1. investigate how the uncertainty in defining the evaluation criteria and constraints would affect the quality of the set of the generated alternative decisions.
2. show how uncertainty can be integrated into MCDA method.

Literature :

Dealing with Uncertainties in multi-criteria decision analysis

Supervisor :

Rasha Abu Qasem

Description :

For testing autonomous vehicles, it is required to capture driving situations and maneuver in real world. This seminar work should examine how situations and maneuvers are identified automatically. The focus should be on the unsupervised learning by using auto-encoders.

Literature :

1. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8594386
2. https://ieeexplore.ieee.org/abstract/document/8995532
3. https://ieeexplore.ieee.org/abstract/document/8500529
4. https://link.springer.com/chapter/10.1007/978-3-030-22734-0_23
5. https://www.researchgate.net/profile/Loic-Giraldi/publication/349560644_Autonomous_Driving_Validation_with_Model-Based_Dictionary_Clustering/links/606c4cee299bf13f5d5e3c7c/Autonomous-Driving-Validation-with-Model-Based-Dictionary-Clustering.pdf
6. https://ieeexplore.ieee.org/abstract/document/8917326
7. https://arxiv.org/abs/2103.16101
8. https://ieeexplore.ieee.org/abstract/document/9286482
9. ieeexplore.ieee.org/abstract/document/9512929
10. https://www.mdpi.com/2076-3417/10/19/6739
11. https://journals.sagepub.com/doi/full/10.1177/0361198120922210

- General (not autonomous driving related):

https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Learning_Deep_Classifiers_Consistent_With_Fine-Grained_Novelty_Detection_CVPR_2021_paper.pdf

Supervisor :

Christian Wolschke

Description :

Reasoning is the act of thinking about something in a logical and sensible way. Such reasoning aspects are of highly employed during design stages of systems engineering., however with the ever-changing topology of cyber physical systems, the need for runtime reasoning is slowly gathering pace and especially in the field of runtime safety assurance. It will be interesting to know different perspectives of runtime reasoning and their respective goals.

The work will comprise of SLR of runtime reasoning methods presently being researched upon, followed by a qualitative evaluation of how they can influence runtime safety assurance.

Literature :

Literature will be provided

Supervisor :

Nishanth Laxman

Description :

Machine learning (ML) algorithms are increasingly being used in data-driven approaches for systems development. Their venture into safety-critical application domains like autonomous driving, Industry 4.0, smart grid, etc. is of particular relevance. Unlike established safety-critical components, outcomes of components containing models based on ML algorithms, can neither be proven nor assumed to be correct in any situation. ML based components which are being used for runtime applications like detection and decision making are often subject to various uncertainties which in-turn might affect safety assurance. It will be vital to gather information on different possible uncertainties which arise because of using these ML based components.

The work will comprise of SLR of uncertainties in ML algorithms used in safety critical applications, followed by creation of a classification of these uncertainties.

Literature :

Literature will be provided

Supervisor :

Nishanth Laxman