The complexity of future automotive software will lead to an intricate and unforeseen impact of product and project decisions on systems level, even in late development phases. To cope with this fact, the early assess...
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ISBN:
(纸本)9781728151250
The complexity of future automotive software will lead to an intricate and unforeseen impact of product and project decisions on systems level, even in late development phases. To cope with this fact, the early assessment of design decisions is a key factor for success. Within this context, our focus lies in determining if a given hardware platform is capable of fulfilling end-to-end timing requirements such as the reaction latency for a given software and finding a feasible deployment using information that is available in early design phases. For this, we propose an integrated analysis and design space exploration approach based on Eclipse APP4MC that is tailored towards software with self-suspending task sets and heterogeneous commercial off-the-shelf hardware consisting of regular processing units (CPUs) and accelerators represented by integrated GPUs. We address the challenge of searching the design space in order to find a feasible solution that fulfills reaction latency constraints for a given set of task chains in Logical Execution Time based communication and optimize the reaction latency for implicit communication. The applicability of our approach is finally demonstrated on an industrial case study.
This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural network...
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This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations.
The complexity of future automotive software will lead to an intricate and unforeseen impact of product and project decisions on systems level, even in late development phases. To cope with this fact, the early assess...
详细信息
ISBN:
(纸本)9781728151250
The complexity of future automotive software will lead to an intricate and unforeseen impact of product and project decisions on systems level, even in late development phases. To cope with this fact, the early assessment of design decisions is a key factor for success. Within this context, our focus lies in determining if a given hardware platform is capable of fulfilling end-to-end timing requirements such as the reaction latency for a given software and finding a feasible deployment using information that is available in early design phases. For this, we propose an integrated analysis and design space exploration approach based on Eclipse APP4MC that is tailored towards software with self-suspending task sets and heterogeneous commercial off-theshelf hardware consisting of regular processing units (CPUs) and accelerators represented by integrated GPUs. We address the challenge of searching the design space in order to find a feasible solution that fulfills reaction latency constraints for a given set of task chains in Logical Execution Time based communication and optimize the reaction latency for implicit communication. The applicability of our approach is finally demonstrated on an industrial case study.
An important shift in software delivery is the definition of a cloud service as an independently deployable unit by following the microservices architectural style. Container virtualization facilitates development and...
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ISBN:
(数字)9783030291938
ISBN:
(纸本)9783030291921;9783030291938
An important shift in software delivery is the definition of a cloud service as an independently deployable unit by following the microservices architectural style. Container virtualization facilitates development and deployment by ensuring independence from the runtime environment. Thus, cloud services are built as container-based systems - a set of containers that control the lifecycle of software and middle-ware components. However, using containers leads to a new paradigm for service development and operation: Self-service environments enable software developers to deploy and operate container-based systems on their own - you build it, you run it. Following this approach, more and more operational aspects are transferred towards the responsibility of software developers. In this work, we propose a concept for self-adaptive cloud services based on container virtualization in line with the microservices architectural style and present a model-based approach that assists software developers in building these services. based on operational models specified by developers, the mechanisms required for self-adaptation are automatically generated. As a result, each container automatically adapts itself in a reactive, decentralized manner. We evaluate a prototype, which leverages the emerging TOSCA standard to specify operational behavior in a portable manner.
Multicore processors promise to improve the performance of systems, by integrating more and more cores onto a single chip. Existing software systems, such as control software from the automation domain, need adjustmen...
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ISBN:
(纸本)9781479931873
Multicore processors promise to improve the performance of systems, by integrating more and more cores onto a single chip. Existing software systems, such as control software from the automation domain, need adjustments to be adapted on multicores. To exploit the concurrency offered by multicore processors, appropriate algorithms have to be used to divide the control application software into tasks, and tailored task partitioning and scheduling approaches are required to increase the overall performance. In this paper we present a model-driven approach for automatic synthesis and deployment of control applications on multicore processors. The approach is centered around a system model, which describes the control applications, the multicore platform, as well as the mapping between the two. We apply the approach on a number of control applications out of which we synthesize tasks and present their run-time behavior in a real-time operating system.
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