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  • 1
    Monograph available for loan
    Monograph available for loan
    Wien, Austria : Institute for Applied and Numerical Mathematics, Technical University Vienna
    Call number: M 93.0873
    Type of Medium: Monograph available for loan
    Pages: 304 Seiten
    ISBN: 3901363009
    Language: English
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 315-322 
    ISSN: 1572-8145
    Keywords: Wavelet networks ; flank wear assessment ; feedforward neural networks ; signal classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2020-12-09
    Description: Estimation of the postmortem interval in advanced postmortem stages is a challenging task. Although there are several approaches available for addressing postmortem changes of a (human) body or its environment (ecologically and/or biochemically), most are restricted to specific timeframes and/or individual and environmental conditions. It is well known, for instance, that buried bodies decompose in a remarkably different manner than on the ground surface. However, data on how established methods for PMI estimation perform under these conditions are scarce. It is important to understand whether and how postmortem changes are affected under burial conditions, if corrective factors could be conceived, or if methods have to be excluded for respective cases. We present the first multi-methodological assessment of human postmortem decomposition carried out on buried body donors in Europe, at the Amsterdam Research Initiative for Sub-surface Taphonomy and Anthropology (ARISTA) in the Netherlands. We used a multidisciplinary approach to investigate postmortem changes of morphology, skeletal muscle protein decomposition, presence of insects and other necrophilous animals as well as microbial communities (i.e., microbiomes) from August to November 2018 associated with two complete body exhumations and eight partial exhumations. Our results clearly display the current possibilities and limitations of methods for PMI estimation in buried remains and provide a baseline for future research and application.
    Electronic ISSN: 1932-6203
    Topics: Medicine , Natural Sciences in General
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