05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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    A muscle model for injury simulation
    (2023) Millard, Matthew; Kempter, Fabian; Fehr, Jörg; Stutzig, Norman; Siebert, Tobias
    Car accidents frequently cause neck injuries that are painful, expensive, and difficult to simulate. The movements that lead to neck injury include phases in which the neck muscles are actively lengthened. Actively lengthened muscle can develop large forces that greatly exceed the maximum isometric force. Although Hill-type models are often used to simulate human movement, this model has no mechanism to develop large tensions during active lengthening. When used to simulate neck injury, a Hill model will underestimate the risk of injury to the muscles but may overestimate the risk of injury to the structures that the muscles protect. We have developed a musculotendon model that includes the viscoelasticity of attached crossbridges and has an active titin element. In this work we evaluate the proposed model to a Hill model by simulating the experiments of Leonard et al. [1] that feature extreme active lengthening.
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    Deep learning based prediction and visual analytics for temporal environmental data
    (2022) Harbola, Shubhi; Coors, Volker (Prof. Dr.)
    The objective of this thesis is to focus on developing Machine Learning methods and their visualisation for environmental data. The presented approaches primarily focus on devising an accurate Machine Learning framework that supports the user in understanding and comparing the model accuracy in relation to essential aspects of the respective parameter selection, trends, time frame, and correlating together with considered meteorological and pollution parameters. Later, this thesis develops approaches for the interactive visualisation of environmental data that are wrapped over the time series prediction as an application. Moreover, these approaches provide an interactive application that supports: 1. a Visual Analytics platform to interact with the sensors data and enhance the representation of the environmental data visually by identifying patterns that mostly go unnoticed in large temporal datasets, 2. a seasonality deduction platform presenting analyses of the results that clearly demonstrate the relationship between these parameters in a combined temporal activities frame, and 3. air quality analyses that successfully discovers spatio-temporal relationships among complex air quality data interactively in different time frames by harnessing the user’s knowledge of factors influencing the past, present, and future behaviour with Machine Learning models' aid. Some of the above pieces of work contribute to the field of Explainable Artificial Intelligence which is an area concerned with the development of methods that help understand, explain and interpret Machine Learning algorithms. In summary, this thesis describes Machine Learning prediction algorithms together with several visualisation approaches for visually analysing the temporal relationships among complex environmental data in different time frames interactively in a robust web platform. The developed interactive visualisation system for environmental data assimilates visual prediction, sensors’ spatial locations, measurements of the parameters, detailed patterns analyses, and change in conditions over time. This provides a new combined approach to the existing visual analytics research. The algorithms developed in this thesis can be used to infer spatio-temporal environmental data, enabling the interactive exploration processes, thus helping manage the cities smartly.
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    Efficient modeling and computation methods for robust AMS system design
    (2018) Gil, Leandro; Radetzki, Martin (Prof. Dr.-Ing.)
    This dissertation copes with the challenge regarding the development of model based design tools that better support the mixed analog and digital parts design of embedded systems. It focuses on the conception of efficient modeling and simulation methods that adequately support emerging system level design methodologies. Starting with a deep analysis of the design activities, many weak points of today’s system level design tools were captured. After considering the modeling and simulation of power electronic circuits for designing low energy embedded systems, a novel signal model that efficiently captures the dynamic behavior of analog and digital circuits is proposed and utilized for the development of computation methods that enable the fast and accurate system level simulation of AMS systems. In order to support a stepwise system design refinement which is based on the essential system properties, behavior computation methods for linear and nonlinear analog circuits based on the novel signal model are presented and compared regarding the performance, accuracy and stability with existing numerical and analytical methods for circuit simulation. The novel signal model in combination with the method proposed to efficiently cope with the interaction of analog and digital circuits as well as the new method for digital circuit simulation are the key contributions of this dissertation because they allow the concurrent state and event based simulation of analog and digital circuits. Using a synchronous data flow model of computation for scheduling the execution of the analog and digital model parts, very fast AMS system simulations are carried out. As the best behavior abstraction for analog and digital circuits may be selected without the need of changing component interfaces, the implementation, validation and verification of AMS systems take advantage of the novel mixed signal representation. Changes on the modeling abstraction level do not affect the experiment setup. The second part of this work deals with the robust design of AMS systems and its verification. After defining a mixed sensitivity based robustness evaluation index for AMS control systems, a general robust design method leading to optimal controller tuning is presented. To avoid over-conservative AMS system designs, the proposed robust design optimization method considers parametric uncertainty and nonlinear model characteristics. The system properties in the frequency domain needed to evaluate the system robustness during parameter optimization are obtained from the proposed signal model. Further advantages of the presented signal model for the computation of control system performance evaluation indexes in the time domain are also investigated in combination with range arithmetic. A novel approach for capturing parameter correlations in range arithmetic based circuit behavior computation is proposed as a step towards a holistic modeling method for the robust design of AMS systems. The several modeling and computation methods proposed to improve the support of design methodologies and tools for AMS system are validated and evaluated in the course of this dissertation considering many aspects of the modeling, simulation, design and verification of a low power embedded system implementing Adaptive Voltage and Frequency Scaling (AVFS) for energy saving.
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    Maschinelles Lernen für intelligente Automatisierungssysteme mit dezentraler Datenhaltung am Anwendungsfall Predictive Maintenance
    (2019) Maschler, Benjamin; Jazdi, Nasser; Weyrich, Michael
    Für eine hohe Ergebnisqualität sind Machine Learning Algorithmen auf eine breite Datenbasis angewiesen. Studien zeigen jedoch, dass viele Unternehmen nicht bereit sind, ihre Daten mit anderen Unternehmen, beispielsweise in Form einer gemeinsamen Daten-Cloud, zu teilen. Ziel sollte es daher sein, effizientes maschinelles Lernen mit einer dezentralen Datenhaltung, die den Verbleib vertraulicher Daten im jeweiligen Ursprungs-Unternehmen ermöglicht, zu ermöglichen. In diesem Artikel wird diesbezüglich ein neuartiges Konzept vorgestellt und hinsichtlich seiner Potentiale für intelligente Automatisierungssysteme am Beispiel des Anwendungsfalls Predictive Maintenance analysiert. Die Umsetzbarkeit des Konzepts unter Nutzung verschiedener bestehender Ansätze wird diskutiert, bevor schließlich auf potentielle Mehrwerte für Anlagenbetreiber sowie -hersteller unter besonderer Berücksichtigung der Perspektive kleiner und mittlerer Unternehmen eingegangen wird.
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    Distributed cooperative deep transfer learning for industrial image recognition
    (2020) Maschler, Benjamin; Kamm, Simon; Nasser, Jazdi; Weyrich, Michael
    In this paper, a novel light-weight incremental class learning algorithm for live image recognition is presented. It features a dual memory architecture and is capable of learning formerly unknown classes as well as conducting its learning across multiple instances at multiple locations without storing any images. In addition to tests on the ImageNet dataset, a prototype based upon a Raspberry Pi and a webcam is used for further evaluation: The proposed algorithm successfully allows for the performant execution of image classification tasks while learning new classes at several sites simultaneously, thereby enabling its application to various industry use cases, e.g. predictive maintenance or self-optimization.
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    Improving the accuracy of musculotendon models for the simulation of active lengthening
    (2023) Millard, Matthew; Kempter, Fabian; Stutzig, Norman; Siebert, Tobias; Fehr, Jörg
    Vehicle accidents can cause neck injuries which are costly for individuals and society. Safety systems could be designed to reduce the risk of neck injury if it were possible to accurately simulate the tissue-level injuries that later lead to chronic pain. During a crash, reflexes cause the muscles of the neck to be actively lengthened. Although the muscles of the neck are often only mildly injured, the forces developed by the neck’s musculature affect the tissues that are more severely injured. In this work, we compare the forces developed by MAT_156, LS-DYNA’s Hill-type model, and the newly proposed VEXAT muscle model during active lengthening. The results show that Hill-type muscle models underestimate forces developed during active lengthening, while the VEXAT model can more faithfully reproduce experimental measurements.
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    Sprachassistierter Entwicklungsprozess für automatisierungstechnische Systeme : ein Ansatz zur Strukturierung komplexer Entwicklungsprozesse
    (2020) White, Dustin; Weyrich, Michael
    Der Systementwicklungsprozess nimmt immer mehr an Komplexität zu, da die Systeme selbst immer komplexer werden. Gleichzeitig Vermischen sich die verschiedenen Disziplinen wie Maschinenbau, Elektrotechnik und Softwaretechnik zunehmend, so dass Unternehmen einer Disziplin sprunghafte Komplexitätszuwächse bei ihren Systemen und in ihrer Entwicklung haben. Deshalb wird in dieser Veröffentlichung ein Konzept eines Sprachassistenten erarbeitet, der durch eine Entwicklungsphase führt. Daraus geht hervor, dass die Software zur Unterstützung der Entwicklung ein Informationsmodell benötigt, um die Daten des entwickelten Systems zu speichern und diese mit dem vorhandenen Wissen zu verbinden. Dieses Wissen kann entweder intern oder im Web vorhanden sein. Der Entwicklungsprozess soll daher Kooperation unterstützen, so dass die Assistenzsoftware und Ingenieure miteinander interagieren.
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    Efficient sampling of transition constraints for motion planning under sliding contacts
    (2020) Khoury, Marie Therese
    In contact-based motion planning we consider for humanoid and multiped robots problems like going up a staircase, walking over an uneven surface or climbing a steep hill. Solving such tasks requires finding sequences of fixed and sliding contacts and planning the transition from one contact in the environment to another. However, most existing algorithms do not take sliding contacts into account for navigation problems or consider them only for manipulation scenarios. We propose an approach to contact-based planning that uses sliding contacts and exploits contact transitions. Such transitions are elementary operations required for whole contact sequences. To model sliding contacts, we develop a sliding contact constraint that permits the robot to slide on an object’s surface. To exploit contact transitions, we utilize three constraint modes to enable passage: contact with a start surface, no contact and contact with a goal surface. We develop a sampler that samples these transition modes uniformly. In this thesis we focus on the motion of one robot link’s end from an initial contact point toward a designated goal surface while the other end of the robot remains in sliding contact with the initial surface. Our method is evaluated by testing it on manipulator arms of two, three and seven degrees of freedom with different objects and various sampling-based planning algorithms. From the considered manipulator arm, it would be possible to transfer our concept to more complex robots and scenarios and extend it to a whole sequence of contacts.
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    Stress-aware periodic test of interconnects
    (2022) Sadeghi-Kohan, Somayeh; Hellebrand, Sybille; Wunderlich, Hans-Joachim
    Safety-critical systems have to follow extremely high dependability requirements as specified in the standards for automotive, air, and space applications. The required high fault coverage at runtime is usually obtained by a combination of concurrent error detection or correction and periodic tests within rather short time intervals. The concurrent scheme ensures the integrity of computed results while the periodic test has to identify potential aging problems and to prevent any fault accumulation which may invalidate the concurrent error detection mechanism. Such periodic built-in self-test (BIST) schemes are already commercialized for memories and for random logic. The paper at hand extends this approach to interconnect structures. A BIST scheme is presented which targets interconnect defects before they will actually affect the system functionality at nominal speed. A BIST schedule is developed which significantly reduces aging caused by electromigration during the lifetime application of the periodic test.
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    Design and scaling of exoskeleton power units considering load cycles of humans
    (2022) Waldhof, Marcel; Wochner, Isabell; Stollenmaier, Katrin; Parspour, Nejila; Schmitt, Syn
    Exoskeletons are powerful tools for aiding humans with pathological conditions, in dangerous environments or in manually exhausting tasks. Typically, they are designed for specific maximum scenarios without taking into account the diversity of tasks and the individuality of the user. To address this discrepancy, a framework was developed for personalizing an exoskeleton by scaling the components, especially the electrical machine, based on different simulated human muscle forces. The main idea was to scale a numerical arm model based on body mass and height to predict different movements representing both manual labor and daily activities. The predicted torques necessary to produce these movements were then used to generate a load/performance cycle for the power unit design. Considering these torques, main operation points of this load cycle were defined and a reference power unit was scaled and optimized. Therefore, a scalability model for an electrical machine is introduced. This individual adaptation and scaling of the power unit for different users leads to a better performance and a lighter design.