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17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Wanner, Johannes Singh, Soumya Nagel, Tobias Patel, Arpit Baazouzi, Sabri Grimm, Julian Birke, Kai Peter Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstrasse 12 Stuttgart70569 Germany Electrical Energy Storage Systems Institute for Photovoltaics University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany

The realization of a circular economy for lithium-ion batteries demands technological advancements capable of performing diagnosis on end-of-life batteries. This will ensure improvement of the life cycle performance of battery systems, considering the most diverse value chains. A key challenge is considering the variety of battery conditions at their end-of-life and the multiple reuse options in second-life applications. This paper presents the different approaches of software-based State of Health (SOH) determination and remaining useful life (RUL) prediction on battery cells. This includes the use and performance comparison of classical regression methods, machine learning regression methods and physical battery models in determining the SOH and degradation process of battery cells. In addition, we consider the resulting RUL conditions of typical second-life applications of electric vehicles (EVs), forklifts, grid stabilisation storage systems, and home storage systems. These results enable the selection and use of electrical and software characterisation methods for efficient battery management diagnostics and end-of-life characterisation. © 2024 Elsevier B.V.. All rights reserved.

关键词： BMS Diagnosis Degradation modelling Li-Ion-Battery Remaining Useful Life Second-life Application

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Control of Composite Manufacturing Processes Through Deep Reinforcement Learning 22

Control of Composite Manufacturing Processes Through Deep Re...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Stieber, Simon Heber, Leonard Obertscheider, Christof Reif, Wolfgang Institute for Software & Systems Engineering University of Augsburg Augsburg Germany University of Applied Sciences Wiener Neustadt Wiener Neustadt Austria

ISBN: (纸本)9798350345346

Resin transfer molding (RTM) is a composite manufacturing process that uses a liquid polymer matrix to create complex-shaped parts. There are several challenges associated with RTM. One of the main challenges is ensuring that the liquid polymer matrix is properly distributed throughout the composite material during the molding process. If the matrix is not evenly distributed, the resulting part may have weak or inconsistent properties. This is the challenge we tackle with the approach presented in this work. We implement an online control using deep reinforcement learning (RL) to ensure a complete impregnation of the reinforcing fibers during the injection phase, by controlling the input pressure on different inlets. This work uses this self-learning paradigm to actively control the injection of an RTM process, which has the advantage of depending on a reward function instead of a mathematical model, which would be the case for model predictive control. A reward function is more straightforward to model and can be applied and adapted to more complex problems. RL algorithms have to be trained through many iterations, for which we developed a simulation environment with a distributed and parallel architecture. We show that the presented approach decreases the failure rate from 54 % to 27 %, by 50 % compared to the same setup with steady parameters. © 2023 IEEE.

关键词： Control Reinforcement Learning Resin Transfer Molding

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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A flexible operation-based infrastructure for collaborative model-driven engineering

A flexible operation-based infrastructure for collaborative ...

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Modellierung 2024

作者： Herac, Edvin Marchezan, Luciano Assunção, Wesley Haas, Rainer Egyed, Alexander Johannes Kepler University Linz Institute of Software Systems Engineering Altenberger Straße 69 Linz4040 Austria Linz Center of Mechatronics GmbH Austria

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Analysis of Patterns in Recorded Signals of software systems With a Variance Based Segmentation Algorithm

arXiv

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arXiv 2025年

作者： Lukić, Bojan Knust, Thorben Rausch, Andreas Clausthal University of Technology Institute for Software and Systems Engineering Germany

Due to the increasing complexity and interconnectedness of different components in modern automotive software systems there is a great number of interactions between these system components and their environment. These interactions result in unique temporal behaviors we call underlying scenarios. The signal data from all system components, which is recorded during runtime, can be processed and analyzed by observing changes in their runtime. Different system behaviors can be characterized by dividing the whole data spectrum into appropriate segments with consistent behavior, classifying these segments, and mapping them to different scenarios. These disjunctive scenarios can be analyzed for their specific behavior which may divert from the expected average system behavior. We state the emerging problem of data segmentation as follows: divide a multivariate data set into a suitable amount of segments with consistent internal behavior. The problem can be divided into 2 subproblems:"How many segments are present in the data set?", and"What are good segmentation indices for the underlying segments?". The complexity of the problem still needs to be assessed, however, at this point we expect it to be NP-hard, as both the number of segments and the segmentation points are unknown. We are in search of appropriate metrics to quantify the quality of a given segmentation of a whole data set. In this paper, we discuss the segmentation of multivariate data, but not the classification of segments into scenario classes. In the following, we investigate segmentation algorithms for solving the subproblem of finding suitable segmentation indices by constant amount of segments. The algorithms are investigated towards effectivity and efficiency by applying them to a data set taken out of a real system trace provided by our automotive partners. The supplementary resources that include the data set and custom algorithms can be found in the GitHub repository1 © 2025, CC BY-SA.

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Mobile Health from Developers’ Perspective

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SN Computer Science 2024年第1期5卷 1-23页

作者： Oliveira, Pedro Almir M. Junior, Evilasio Costa Andrade, Rossana M. C. Santos, Ismayle S. Neto, Pedro A. Santos Group of Computer Networks Software Engineering and Systems (GREat) Federal University of Ceará Ceará Brazil Federal Institute of Maranhão (IFMA) Maranhão Brazil Laboratory of Software Optimization and Testing (LOST) Federal University of Piauí Piauí Brazil

This paper extends an initial investigation of eHealth from the developers’ perspective. In this extension, our focus is on mobile health data. Despite the significant potential of this development area, few studies try to understand the challenges faced by these professionals. This perspective is relevant to identify the most used technologies and future perspectives for research investigation. Using a KDD-based process, this work analyzed eHealth and mHealth discussions from Stack Overflow (SO) to comprehend this developers’ community. We got and processed 6082 eHealth and 1832 mHealth questions. The most discussed topics include manipulating medical images, electronic health records with the HL7 standard, and frameworks to support mobile health (mHealth) development. Concerning the challenges faced by these developers, there is a lack of understanding of the DICOM and HL7 standards, the absence of data repositories for testing, and the monitoring of health data in the background using mobile and wearable devices. Our results also indicate that discussions have grown mainly on mHealth, primarily due to monitoring health data through wearables and about how to optimize resource consumption during health-monitoring. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： eHealth mHealth Mining software repositories Stack overflow

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Towards Specification Completion for systems with Emergent Behavior based on DevOps

Towards Specification Completion for Systems with Emergent B...

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Ailane, Mohamed Toufik Aniculaesei, Adina Knieke, Christoph Rausch, Andreas Sholichin, Fauzi Clausthal University of Technology Institute for Software and Systems Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350320282

software systems may experience multiple emergent behaviors during their operation time. These emergent system behaviors occur when system engineers develop their system under the closed-world assumption, but this assumption is not met during its operation. This means that system engineers work on the basis that they have complete knowledge of the system and its environment during its design, when the system specification that has been created is actually incomplete. In this paper, an observation of an emergent behavior is considered to be a solid proof that the system model specification is still incomplete. A conceptual framework is proposed to harness the emergent behavior and complete the system specification that is provided during the its design. The framework consists of two parts, system development and system operations. It is built on a model-driven approach in order to provide a clear definition of the emergent behavior and a concrete development scheme. The framework exploits the DevOps paradigm as a successful paradigm to achieve the ultimate goal of developing complete system models through the continuous specification completion based on the observed emergent behavior. The goal of this framework is to help develop high-quality and reliable emergent systems based on the specification derived from the emergent behavior that occurred at run time. © 2022 IEEE.

关键词： Specifications

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Leveraging Chat-GPT to Generate Educational Assessment Materials for software engineering 20th

Leveraging Chat-GPT to Generate Educational Assessment Mate...

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20th International Conference on Foundations of Computer Science, FCS 2024, and 20th International Conference on Frontiers in Education, FECS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Seibert, Vasiliy Geger, Tobias Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9783031859298

This paper presents an educational use-case for creating assessment materials with Chat-GPT 3.5 turbo in the domain of software engineering. We attempt to (a) alter specific aspects of a generic domain model (UML class diagram) and (b) generate requirement texts (problem scenarios) that creatively describe the previously altered model. The challenge that comes with these two approaches is (1) to change only the variables contained in the generic domain model in such a way, that the resulting domain model makes sense and (2) to generate natural language requirement texts (scenarios) that creatively and correctly describe the underlying domain model. Results: The first approach (a) yielded satisfactory results. After providing Chat-GPT with examples, it was able to alter all variables in a coherent way according to the topic specified in the prompt. This result indicates potential for other educational use cases and ongoing software engineering research with LLMs. Chat-GPT’s generated requirements texts were useful but required caution from the lecturers before handing them out to students. We reflected on the contribution of this paper (and papers similar to this) and argued, that the provided value lies not only in the quality of the yielded results but much rather in describing educational use cases that are worth following up on with future versions of large language models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： software design

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories Computer architecture Threat assessment Real-time systems Resource management Reliability Computer security Protection Standards software engineering

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