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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Ghasemi, Golsa Kharde, Mandar Müller, Manuel Jazdi, Nasser Weyrich, Michael University of Stuttgart Stuttgart70569 Germany Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

ISBN: (纸本)9798350320695

Regarding the requirement of continuous development, and adaptation to new changes in the industrial automation field, systems are becoming progressively more complex. In order to cope with the increasing complexity, systems are developed model-based using libraries of predefined models. Various modeling processes assist in simplifying the understanding of the systems deployed in heterogeneous and dynamic environments. On the other hand, the models are growing in complexity to represent the assets precisely. The complexity affects various attributes of the models such as understandability, ease of use, scalability, performance, testing, and verification. Therefore, one of the main challenges of analyzing simulation models for different use cases in different development life cycle phases is complexity assessment. A comprehensive complexity analysis approach enables achieving the proper selection of the models among all available alternatives. This paper presents several approaches for models' complexity assessment from the literature and proposes an approach to select the appropriate simulation models among all alternatives. This approach is realized with a framework to depict the process of how to achieve the required models according to the complexity value and the estimated complexity budget for various use cases. To validate the proposed approach, representative industry models of a Robot Vacuum Gripper are used to select the model based on the complexity metric in simulation. © 2023 IEEE.

关键词： Life cycle

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Siamese Neural Network-based Quality-Related Abnormality Detection for Sintering Process

Siamese Neural Network-based Quality-Related Abnormality Det...

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2024 China automation Congress, CAC 2024

作者： Fan, Weiwei Li, Shiqi Jia, Chao Xiong, Jiatao Zhang, Hanwen Zhang, Haifeng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Software Engineering and Appraisal Center China Electronics Standardization Institute Beijing China

ISBN: (纸本)9798350368604

The sintering process serves as a crucial pretreatment unit in ironmaking, supplying the primary raw materials for the blast furnace. Accurately and real-time estimating the quality of the final sintered ore holds significant importance. Nonetheless, the sintering process data is marred by complexities such as nonlinearity, nonstationarity, non-Gaussianity, and dynamic behaviors, which complicate the application of traditional fault detection methods. In this paper, we introduce a novel approach employing Siamese neural networks for quality-related abnormality detection (SNN-QAD) for the sintering process, focusing specifically on detecting abnormalities in the ferrous oxide (FeO) content of sintered ore. Siamese neural networks are initially used to isolate quality-related stationary features, adeptly navigating the data's nonlinear properties. The Wasserstein distance is then applied to gauge the dissimilarity between distributions, tackling the non-Gaussian distribution challenge. These stationary features are subsequently leveraged as input for a regression model, with the FeO content acting as the output variable, to facilitate training. Hotelling's (T2) statistic is employed to identify deviations and pinpoint fault occurrences. The paper culminates with the experimental validation of our method using actual sintering process data. © 2024 IEEE.

关键词： Blast furnaces

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of Computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Research on obstacle avoidance algorithm for wheeled robot based on Raspberry Pi 4

Research on obstacle avoidance algorithm for wheeled robot b...

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4th International Conference on Mechanical, Electronics, and Electrical and automation Control, METMS 2024

作者： Liu, Qianqian Li, Ningsi Li, Dong Yan, Jiamin Li, Jialu Department of Automation Taiyuan Institute of Technology Taiyuan China Department of Electrical Engineering Taiyuan Institute of Technology Taiyuan China School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Raspberry Pi is a credit card sized microcomputer based on the Linux operating system. It has widely application prospects due to its powerful functionality and scalability which can be applied to multiple scenarios by expanding hardware and software. This paper focuses on designing a wheeled robot based on Raspberry Pi and conducting research on robot obstacle avoidance algorithm on this robot. The paper firstly describes the core hardware design of the main control and movement control parts of the robot, then discusses the design and implementation of Bluetooth communication and obstacle avoidance algorithms. Finally, the obstacle avoidance algorithm is tested and optimal control parameters are given. The experiment also proves the effectiveness of the obstacle avoidance algorithm. © 2024 SPIE.

关键词： Robots

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Tackling Erosion in Variant-Rich software Systems: Challenges and Approaches

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Procedia CIRP 2024年 128卷 633-637页

作者： Johannes Stümpfle Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

software product lines (SPL) have emerged as a pivotal paradigm in software engineering, enabling the efficient development of variant-rich software systems. Consistently updating these systems, often through over-the-air updates, enables the continuous integration of new features and bug fixes, ensuring the system remains up to date throughout its entire lifecycle. However, evolving such complex systems is an error prone task, leading to a phenomenon known as erosion. This phenomenon significantly impacts the efficiency and longevity of software systems, presenting a formidable challenge for manufacturers of variant-rich software systems, such as in the automotive domain. While existing studies concentrate on the evolutionary planning of variant-rich software systems, there is a noticeable lack of research addressing the problem of erosion. In this paper, we conduct an in-depth exploration of the erosion phenomena within variant-rich software systems. We begin by highlighting the significance of controlling erosion in extensive variant-rich software systems. Subsequently, we address the current challenges regarding tackling erosion, including issues such as the lack of a consensus on understanding and defining erosion, as well as the early detection and elimination. Finally, we outline a first approach aimed at tackling erosion in variant-rich software systems.

关键词： software Product Line Erosion Design: challenges & Innovation

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6DFLRNet: 6D rotation representation for head pose estimation based on facial landmarks and regression

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Multimedia Tools and Applications 2024年第26期83卷 68605-68624页

作者： Zhao, Na Ma, Yaofei Li, Xiaopeng Lee, Shin-Jye Wang, Jian Engineering Research Center of Cyberspace School of Software Yunnan University Yunnan Kunming650504 China Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China Institute of Management of Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China

Head pose estimation methods can be generally classified into two categories: model-based and appearance-based methods. The model-based approach relies on facial landmarks for three-dimensional reconstruction, aiming to achieve high-precision results. However, this method is heavily dependent on the accuracy of these landmarks. The appearance-based approach utilizes images as input and employs feature extraction and calculations to generate outcomes. While the appearance-based method boasts greater robustness, its accuracy falls short of the former. In this paper, a new and effective hybrid method is proposed. This hybrid approach combines the strengths of both methods. Unlike the conventional model-based methods, the proposed method regards the facial landmarks in 2D images as a sequence of neural network inputs and then obtains the head pose estimation results for users by neural network regression. The proposed method solves the fuzzy rotation labeling problem by using a rotation matrix representation, introducing a 6D rotation matrix representation as an intermediate state of the rotation matrix to achieve effective direct regression. Introducing face processing enhances the robustness of the model in cross-dataset scenarios. The proposed method achieves remarkable results based on imprecise face recognition and a simplistic model. The proposed method can be divided into three parts. First, the proposed method applies face processing on the input image;second, the method detects facial landmarks;and third, it converts these facial landmarks into sequences and obtains the 6D rotation representation of the head pose by regression. Extensive experiments on the publicly available BIWI, PRIMA, and DrivFace datasets show that this method is functional and performs better than other state-of-the-art methods. Compared to other methods, this approach demonstrates an average performance improvement of at least 10% across the dataset. © The Author(s), under exclusive licen

关键词： Rotation

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Qualitative and quantitative evaluation of a methodology for the Digital Twin creation of brownfield production systems 28

Qualitative and quantitative evaluation of a methodology for...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Braun, Dominik Jazdi, Nasser Schlogl, Wolfgang Weyrich, Michael Stuttgart Germany University of Stuttgart Institute of Industrial Automation and Software Engineering Stuttgart Germany Siemens AG Digital Engineering Nuremberg Germany

ISBN: (纸本)9798350339918

The Digital Twin is a well-known concept of Industry 4.0 and is the cyber part of a cyber-physical production system providing several benefits such as virtual commissioning or predictive maintenance. The existing production systems are lacking a Digital Twin which has to be created manually in a time-consuming and error-prone process. Therefore, methods to create digital models of existing production systems and their relations between them were developed. This paper presents the implementation of the methodology for the creation of multi-disciplinary relations and a quantitative and qualitative evaluation of the benefits of the methodology. © 2023 IEEE.

关键词： brownfield Digital Twin evaluation

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Automated Configuration of Behavior Models in Digital Twins based on a Knowledge-Graph 57

Automated Configuration of Behavior Models in Digital Twins ...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Dittler, Daniel Bodenstein, Frederike Hildebrandt, Gary Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Institute of Smart Systems and Services Pforzheim University of Applied Sciences Pforzheim Germany

A modular production plant can be simulated with different behavior models with varying modeling depths, thus enabling a wide variety of applications. However, the plant operator faces the challenge of linking the right models for his application. This contribution describes the need to adapt model configurations and associated challenges. Related approaches from the literature and standards are discussed. Subsequently, a novel modeling approach is presented that combines and extends the information from the analyzed approaches to enable automatic linking of model configurations. The approach is realized with the help of a knowledge-graph and evaluated using a material flow simulation scenario. © 2024 The Authors.

关键词： Knowledge graph

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Extending the Intelligent Digital Twin with a context modeling service: A decision support use case 55

Extending the Intelligent Digital Twin with a context modeli...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Sahlab, Nada Braun, Dominik Köhler, Christian Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart70569 Germany

Considering the surrounding context of manufacturing systems at runtime enhances their adaptability, which is advantageous given the increasing demand for flexible production. Context-awareness presents a viable approach to continuously model the surrounding context and relate it to the system's operation. As the surrounding context is both acquirable from heterogeneous data sources and is dynamic in nature, a coherent and evolving context model is necessary. The context modeling approach needs to be both: system-centric to enable re-usability of the model by different applications as well as continuously and efficiently extendable to continuously represent the environment. Some approaches to model context within the manufacturing domain exist, but are mostly tailored to specific applications or do not consider an extension at runtime. To include the surrounding context of a system effectively and adapt it accordingly, a relation between the modeled context and the system during runtime is necessary. The intelligent Digital Twin provides essential interdisciplinary models of a physical system, which can be used for its analysis and monitoring. Adding a further service in the form of a model to represent the surrounding context enhances the intelligent Digital Twin to be used by various applications, e.g., for reconfiguration or decision-support. In our previous work, we have presented a tier-based model to realize context-awareness for intelligent Digital Twins. The presented tier model has different levels corresponding to different context scopes. In this contribution, we present an approach to realize an extendible and evolving tier-based context model with a generic ontology and model transformation to a labeled property graph with focus on the external context. To show the applicability and added value of the context model, a use case is presented, which addresses an intelligent warehouse's Digital Twin and its context-model supported decision-making. © 2022 Th

关键词： context-awareness cyber-physical systems Digital Twin intelligent warehouse labeled property graph ontology

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InteLiv: An Architecture for Graph-Based Dynamic Context Modeling for Smart Living 28

InteLiv: An Architecture for Graph-Based Dynamic Context Mod...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Stümpfle, Johannes Sahlab, Nada Kamm, Simon Grimmeisen, Philipp Jazdi, Nasser Weyrich, Michael University of Stuttgart Institute of Industrial Automation and Software Engineering Stuttgart Germany Corporate Research Center ABB AG Ladenburg Germany

ISBN: (纸本)9798350339918

Advancements in the Internet of Things (IoT) applied by automation systems lead to an increasing and flexible interconnection of various devices and the generation of voluminous data. In the domain of home automation for instance, having a multitude of interconnected data leveraged to knowledge could enhance user-centeredness and energy efficiency, among other use cases. However, the data as well as data sources are dynamic and heterogeneous, posing a great challenge to unfold their full capabilities. Thus, a unifying and correspondingly dynamic architecture is needed for supporting the flexibility and extensibility of such systems and data sources. In this paper, we present the concept and implementation of a context-aware INTElligent LIVing (inteLiv) architecture. Consisting of a data layer, a context layer and a service layer, the architecture abstracts the heterogeneous data of the linked devices via a middleware. The resulting system enables the collection of data across networked heterogeneous devices and models the collected data by using a unified dynamic context model. A hybrid approach was chosen for the designed context model: A combination of an ontology-based and property graph-based model represent a core aspect of this work as opposed to common static ontological representations and pre-defined context-based use cases. In the following contribution, the inteLiv ontology is first defined to describe the concepts of the intelligent living system. Based on the modeled context, reasoning algorithms also enable the derivation of new context information. The context is stored as a knowledge graph and can be used for context-aware applications, which can be designed during runtime. Furthermore, a user interaction with the system is possible via a web interface. © 2023 IEEE.

关键词： Middleware

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