https://www.mdu.se/

mdu.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 17) Show all publications
Al-Dulaimy, A., Hatvani, L., Behnam, M., Fattouh, A. & Chirumalla, K. (2024). An Overview of Cloud-Based Services for Smart Production Plants. In: IFIP Advances in Information and Communication Technology: . Paper presented at IFIP Advances in Information and Communication Technology (pp. 461-475). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>An Overview of Cloud-Based Services for Smart Production Plants
Show others...
2024 (English)In: IFIP Advances in Information and Communication Technology, Springer Science and Business Media Deutschland GmbH , 2024, p. 461-475Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Cloud computing, cloud manufacturing services, digital servitization, digital transformation, manufacturing, Cloud Manufacturing, Cloud manufacturing service, Cloud-based, Cloud-computing, Enabling technologies, Manufacturing service, Production plant, Servitization, Smart manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Systems
Identifiers
urn:nbn:se:mdh:diva-68582 (URN)10.1007/978-3-031-71645-4_31 (DOI)001356142100031 ()2-s2.0-85204615682 (Scopus ID)9783031716447 (ISBN)
Conference
IFIP Advances in Information and Communication Technology
Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2025-01-29
Fattouh, A., Chirumalla, K., Ahlskog, M., Behnam, M., Hatvani, L. & Bruch, J. (2023). Remote integration of advanced manufacturing technologies into production systems: integration processes, key challenges and mitigation actions. Journal of Manufacturing Technology Management
Open this publication in new window or tab >>Remote integration of advanced manufacturing technologies into production systems: integration processes, key challenges and mitigation actions
Show others...
2023 (English)In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786Article in journal (Refereed) Published
Abstract [en]

The study examines the remote integration process of advanced manufacturing technology (AMT) into the production system and identifies key challenges and mitigating actions for a smoother introduction and integration process.

Design/methodology/approach: The study adopts a case study approach to a cyber-physical production system at an industrial technology center using a mobile robot as an AMT.

Findings: By applying the plug-and-produce concept, the study exemplifies an AMT's remote integration process into a cyber-physical production system in nine steps. Eleven key challenges and twelve mitigation actions for remote integration are described based on technology–organization–environment theory. Finally, a remote integration framework is proposed to facilitate AMT integration into production systems.

Practical implications: The study presents results purely from a practical perspective, which could reduce dilemmas in early decision-making related to smart production. The proposed framework can improve flexibility and decrease the time needed to configure new AMTs in existing production systems.

Originality/value: The area of remote integration for AMT has not been addressed in depth before. The consequences of lacking in-depth studies for remote integration imply that current implementation processes do not match the needs and the existing situation in the industry and often underestimate the complexity of considering both technological and organizational issues. The new integrated framework can already be deployed by industry professionals in their efforts to integrate new technologies with shorter time to volume and increased quality but also as a means for training employees in critical competencies required for remote integration.

Keywords
Technology adoption, Industry 4.0 implementation, Cyber-physical production system, Plug and produce, Smart production, Mobile robot, TOE framework, Production system development, Process innovation, Technology integration
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-61541 (URN)10.1108/jmtm-02-2022-0087 (DOI)000916547200001 ()2-s2.0-85146389784 (Scopus ID)
Available from: 2023-01-19 Created: 2023-01-19 Last updated: 2024-01-09Bibliographically approved
Tahvili, S. & Hatvani, L. (2022). Artificial Intelligence Methods for Optimization of the Software Testing Process. Elsevier BV
Open this publication in new window or tab >>Artificial Intelligence Methods for Optimization of the Software Testing Process
2022 (English)Book (Other academic)
Abstract [en]

Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence 

Place, publisher, year, edition, pages
Elsevier BV, 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-61221 (URN)10.1016/C2021-0-00433-8 (DOI)2-s2.0-85143320055 (Scopus ID)9780323919135 (ISBN)9780323912822 (ISBN)
Available from: 2022-12-13 Created: 2022-12-13 Last updated: 2022-12-13Bibliographically approved
Ahmed, M. U., Aslanidou, I., Axelsson, J., Begum, S., Hatvani, L., Olsson, A., . . . Zaccaria, V. (2021). Dilemmas in designing e-learning experiences for professionals. In: Proceedings of the European Conference on e-Learning, ECEL: . Paper presented at 20th European Conference on e-Learning ECEL, 28 Oct 2021, Berlin, Germany (pp. 10-17).
Open this publication in new window or tab >>Dilemmas in designing e-learning experiences for professionals
Show others...
2021 (English)In: Proceedings of the European Conference on e-Learning, ECEL, 2021, p. 10-17Conference paper, Published paper (Refereed)
Abstract [en]

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

Keywords
lifelong learning, higher education, e-learning, online learning, industrial co-production
National Category
Engineering and Technology Educational Sciences
Identifiers
urn:nbn:se:mdh:diva-55701 (URN)10.34190/EEL.21.049 (DOI)000755489500002 ()2-s2.0-85121577831 (Scopus ID)
Conference
20th European Conference on e-Learning ECEL, 28 Oct 2021, Berlin, Germany
Projects
FuturE
Available from: 2021-08-30 Created: 2021-08-30 Last updated: 2022-11-08Bibliographically approved
Tahvili, S., Hatvani, L., Ramentol, E., Pimentel, R., Afzal, W. & Herrera, F. (2020). A Novel Methodology to Classify Test Cases Using Natural Language Processing and Imbalanced Learning. Engineering applications of artificial intelligence, 95, 1-13, Article ID 103878.
Open this publication in new window or tab >>A Novel Methodology to Classify Test Cases Using Natural Language Processing and Imbalanced Learning
Show others...
2020 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 95, p. 1-13, article id 103878Article in journal (Refereed) Published
Abstract [en]

Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes - dependent and independent - can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases' distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases' specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results.

Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-49987 (URN)10.1016/j.engappai.2020.103878 (DOI)000569874100017 ()2-s2.0-85089416670 (Scopus ID)
Projects
TESTOMAT Project - The Next Level of Test Automation
Available from: 2020-09-10 Created: 2020-09-10 Last updated: 2021-01-04Bibliographically approved
Landin, C., Hatvani, L., Tahvili, S., Haggren, H., Längkvist, M., Loutfi, A. & Håkansson, A. (2020). Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases. In: The Fifteenth International Conference on Software Engineering Advances ICSEA 2020: . Paper presented at The Fifteenth International Conference on Software Engineering Advances ICSEA 2020, 18 Oct 2020, Porto, Portugal.
Open this publication in new window or tab >>Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
Show others...
2020 (English)In: The Fifteenth International Conference on Software Engineering Advances ICSEA 2020, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Software testing is still heavily dependent on human judgment since a large portion of testing artifacts such as requirements and test cases are written in a natural text by people. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases should be distinguished based on their properties such as their dependencies and similarities. Knowing the mentioned properties at an early stage of the testing process can be utilized for several test optimization purposes such as test case selection, prioritization, scheduling, and also parallel test execution. In this paper, we apply, evaluate, and compare the performance of two deep learning algorithms to detect the similarities between manual integration test cases. The feasibility of the mentioned algorithms is later examined in a Telecom domain by analyzing the test specifications of five different products in the product development unit at Ericsson AB in Sweden. The empirical evaluation indicates that utilizing deep learning algorithms for finding the similarities between manual integration test cases can lead to outstanding results.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-49989 (URN)
Conference
The Fifteenth International Conference on Software Engineering Advances ICSEA 2020, 18 Oct 2020, Porto, Portugal
Projects
TESTOMAT Project - The Next Level of Test Automation
Available from: 2020-09-29 Created: 2020-09-29 Last updated: 2020-09-29Bibliographically approved
Tahvili, S., Hatvani, L., Felderer, M., Afzal, W. & Bohlin, M. (2019). Automated Functional Dependency Detection Between Test Cases Using Text Semantic Similarity. In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest): . Paper presented at 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 4-9 April 2019, Newark, CA, USA (pp. 19-26). , Article ID 8718215.
Open this publication in new window or tab >>Automated Functional Dependency Detection Between Test Cases Using Text Semantic Similarity
Show others...
2019 (English)In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 2019, p. 19-26, article id 8718215Conference paper, Published paper (Refereed)
Abstract [en]

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.

National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-41272 (URN)10.1109/AITest.2019.00-13 (DOI)000470916100004 ()2-s2.0-85067096441 (Scopus ID)9781728104928 (ISBN)
Conference
2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 4-9 April 2019, Newark, CA, USA
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2020-10-21Bibliographically approved
Sjödin, C., Hatvani, L. & Olsson, A. (2019). Future Challenges for Academic-Industry Value CoCreation Through Lifelong Learning. In: Proceedings of the 18th European Conference on e-Learning ECEL 2019: . Paper presented at Proceedings of the 18th European Conference on e-Learning, ECEL 2019, Hosted By Aalborg University, Copenhagen, Denmark, 7-8 November 2019 (pp. 695-698).
Open this publication in new window or tab >>Future Challenges for Academic-Industry Value CoCreation Through Lifelong Learning
2019 (English)In: Proceedings of the 18th European Conference on e-Learning ECEL 2019, 2019, p. 695-698Conference paper, Published paper (Refereed)
Abstract [en]

The presented research aims to explore future context of e-learning, needs of professionals, and how higher education can respond to those needs. This is an empirical study with a qualitative approach. We interviewed teachers and students about their perceptions of e-learning. The interviews were semi-structured and allowed for reflections. All students interviewed were currently employed in industry and active e-learners. A web-based horizon scanning was made to identify trends in order to understand future context. The study is part of an academic development project with the intention of strengthening academic capacity and company knowledge to stay competitive in an international setting. In this paper, we present several cornerstones for creating courses that are suitable for professionals. Administrative routines and procedures need to be adjusted in order to meet challenges from other actors and the needs of stakeholders.  A seamless experience would be preferred from a consumer-oriented perspective, where flexibility is a key factor. This flexibility is manifested by the need to control their own workload to adjust the work-education balance. Locus of learning needs to be problematized. Students do not identify themselves as co-creators. This is a challenge to overcome in order to design for the work-study situation. Previous studies on distance learning have mostly focused on full time students in today’s context. This study involves foresight and the situation for students in employment. The findings will be relevant for teachers in the design phase of a course intended for any type of student who is not a traditional full-time student, and for university management building a strategy for the future of e-learning.

National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-46624 (URN)10.34190/EEL.19.139 (DOI)000539626900091 ()2-s2.0-85077509138 (Scopus ID)978-1-912764-41-9 (ISBN)978-1-912764-42-6 (ISBN)
Conference
Proceedings of the 18th European Conference on e-Learning, ECEL 2019, Hosted By Aalborg University, Copenhagen, Denmark, 7-8 November 2019
Available from: 2019-12-30 Created: 2019-12-30 Last updated: 2020-09-18Bibliographically approved
Tahvili, S., Hatvani, L., Felderer, M., Afzal, W., Saadatmand, M. & Bohlin, M. (2018). Cluster-Based Test Scheduling Strategies Using Semantic Relationships between Test Specifications. In: 5th International Workshop on Requirements Engineering and Testing RET'18: . Paper presented at 5th International Workshop on Requirements Engineering and Testing RET'18, 02 Jun 2018, Gothenburg, Sweden (pp. 1-4). , F137811
Open this publication in new window or tab >>Cluster-Based Test Scheduling Strategies Using Semantic Relationships between Test Specifications
Show others...
2018 (English)In: 5th International Workshop on Requirements Engineering and Testing RET'18, 2018, Vol. F137811, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

One of the challenging issues in improving the test efficiency is that of achieving a balance between testing goals and testing resources. Test execution scheduling is one way of saving time and budget, where a set of test cases are grouped and tested at the same time. To have an optimal test execution schedule, all related information of a test case (e.g. execution time, functionality to be tested, dependency and similarity with other test cases) need to be analyzed. Test scheduling problem becomes more complicated at high-level testing, such as integration testing and especially in manual testing procedure. Test specifications at high-level are generally written in natural text by humans and usually contain ambiguity and uncertainty. Therefore, analyzing a test specification demands a strong learning algorithm. In this position paper, we propose a natural language processing (NLP) based approach that, given test specifications at the integration level, allows automatic detection of test cases’ semantic dependencies. The proposed approach utilizes the Doc2Vec algorithm and converts each test case into a vector in n-dimensional space. These vectors are then grouped using the HDBSCAN clustering algorithm into semantic clusters. Finally, a set of cluster-based test scheduling strategies are proposed for execution. The proposed approach has been applied in a sub-system from the railway domain by analyzing an ongoing testing project at Bombardier Transportation AB, Sweden.

Keywords
Software testing, Test scheduling, NLP, Dependency, Clustering, Doc2Vec, Optimization, HDBSCAN
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38953 (URN)10.1145/3195538.3195540 (DOI)000890280200001 ()2-s2.0-85051238162 (Scopus ID)9781450357494 (ISBN)
Conference
5th International Workshop on Requirements Engineering and Testing RET'18, 02 Jun 2018, Gothenburg, Sweden
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsTOCSYC - Testing of Critical System Characteristics (KKS)MegaMaRt2 - Megamodelling at Runtime (ECSEL/Vinnova)TESTOMAT Project - The Next Level of Test Automation
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2023-04-12Bibliographically approved
Hatvani, L., Afshar, S. Z. & J. Bril, R. (2018). Optimal Priority and Threshold Assignment for Fixed-priority Preemption Threshold Scheduling. Paper presented at 7th Embedded Operating Systems Workshop EWiLi'17, 06 Oct 2017, Seoul, South Korea. ACM SIGBED Review (1), 43-49
Open this publication in new window or tab >>Optimal Priority and Threshold Assignment for Fixed-priority Preemption Threshold Scheduling
2018 (English)In: ACM SIGBED Review, E-ISSN 1551-3688, no 1, p. 43-49Article in journal (Refereed) Published
Abstract [en]

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold.

This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable.

We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37058 (URN)2-s2.0-84992364765 (Scopus ID)
Conference
7th Embedded Operating Systems Workshop EWiLi'17, 06 Oct 2017, Seoul, South Korea
Projects
PRESS - Predictable Embedded Software Systems
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2019-01-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0073-1674

Search in DiVA

Show all publications