Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.
This essay examines Don Ihde’s postphenomological philosophy of technology through the lens of philosophical anthropology, that sub-discipline of philosophy concerned with the nature and place of the human being. While Ihde’s philosophical corpus and its reception in Postphenomenology: A Critical Companion to Ihde indicate rich resources for thinking about human nature, several themes receive too little attention in both, including the nature of the human being, the emergence of the posthuman, and the place of the human being in our contemporary pluriculture.
Critical posthumanists have observed that technoscientific developments are in the process of rewriting human ontology, fundamentally changing what it means to be human. While they argue that the posthuman breaks with the Cartesian liberal subject and embraces a more decentered ontology, their analyses remain firmly situated in a Cartesian world that marginalizes if not completely ignores questions about natality. This essay examines two filmic texts, Blade Runner 2049 and the AMC television show Humans, that are situated firmly in a posthuman environment in which technoscience is seemingly rewriting the conditions of being human and blurring the boundary between human and machine, but which focus on natality and childhood and emphasize themes of parenting and growth and development. In doing so, they disclose shortcomings in critical posthumanism that can only be addressed when we give more serious attention to how natality shapes being human.
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