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Lightweight floor structures, such as timber or hybrid timber floors, face challenges associated with excessive vibrations and elevated levels of low-frequency impact sound. Especially here, accurate prediction of a floor’s vibration and acoustic behavior is essential. However, typical laboratory testing of building elements is costly and time-consuming. To reduce costs, in this study, adapted simulations are carried out on two types of hybrid steel-timber floor structures to evaluate vibrations and impact sound. The hybrid elements are made of laminated veneer lumber as the top and bottom layers and a trapezoidal steel component as the web. Vibration measurements are used in combination with Bayesian optimization to efficiently calibrate Finite Element models, which are subsequently utilized to quantify and validate the floor structures regarding vibrations and impact sound. The two types of cross-sections, i.e., closed and open, are investigated and compared. The impact sound pressure level computations reveal promising results in predicting the behavior of the hybrid structures. However, further countermeasures are required to fulfill vibration serviceability requirements.

Uncertainties are significant in the early vibroacoustic development, e.g., of a car body, to prevent costly modifications close to the start of production. First, engineers must know which uncertain parameters are sensitive: Our previous work identified 170 uncertain parameters being sensitive out of a complex finite element model with 1,300 uncertain parameters – a parameter reduction of approximately 87%. Second, engineers aim to find reliable distributions of these sensitive input parameters for finite element simulations. Finding these distributions is very demanding in a large-scale vibroacoustic model with several connecting parameters, as research already acknowledges regarding simplified connections. In this paper, we address this challenge with neural networks. For this, we use data in the frequency domain. Due to the curse of dimensionality, it is difficult to determine the parameter set of 170 parameters with a neural network. Therefore, we examine the influence of the number of parameters on the performance of neural networks. Furthermore, we train a fully connected feed-forward neural network and compare this to a one-dimensional convolutional neural network. The latter exhibits a better performance. Finally, we show how to determine distributions of the analyzed parameters based on artificial measurement data. Due to this process, we can significantly improve our finite element simulations and show how to deal with the challenge of determining uncertain parameters in a large-scale vibroacoustic finite element model based on data in the frequency domain. [ABSTRACT FROM AUTHOR]

Automotive ultrasonic sensors come into play for close-range surround sensing in parking and maneuvering situations. In addition to ultrasonic ranging, classifying obstacles based on ultrasonic echoes to improve environmental perception for advanced driver-assistance systems is an ongoing research topic. Related studies consider only magnitude-based features for classification. However, the phase of an echo signal contains relevant information for target discrimination. This study discusses and evaluates the relevance of the target phase in echo signals for object classification in automotive ultrasonic sensing based on lab and field measurements. Several phase-aware features in the time domain and time-frequency features based on the continuous wavelet transform are proposed and processed using a convolutional neural network. Indeed, phase features are found to contain relevant information, producing only 4% less classification accuracy than magnitude features when the phase is appropriately processed. The investigation reveals high redundancy when magnitude and phase features are jointly fed into the neural network, especially when dealing with time-frequency features. However, incorporating the target phase information facilitates the identification quality in high clutter environments, increasing the model's robustness against signals with low signal-to-noise ratios. Ultimately, the presented work takes one further step toward enhanced object discrimination in advanced driver-assistance systems. [ABSTRACT FROM AUTHOR]

In electromagnetics, self-complementary antenna shapes possess a frequency-independent characteristic due to their particular shape. Based on this concept, this paper investigates the potential applications of self-complementary shapes for sound absorption to broaden the bandwidth and enhance the performances of resonators without altering the sizes and resonance frequencies. Self-complementary shapes, such as log-periodic planar tooth and log-spiral shapes, are used to demonstrate the effectiveness of our approach in enhancing the absorption bandwidth and performance of a resonator. Such shapes improve the absorption performance up to 58 % compared to a resonator with an equivalent circular area when the opening area is reduced, whereas they exhibit weak performance for wide opening areas. Numerical and experimental analyses are conducted to verify their performances and to investigate the effect of the materials inside the backing cavity, neck length, opening size, and geometry on absorption. By extending the use of frequency-independent antenna shapes as sound-absorbing structures, this approach overcomes the inherent limitations of resonant metamaterials to achieve an enhanced sound absorption for various applications. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion encoding gradients are known to yield vibrations of the typical clinical MR scanner hardware with a frequency of 20 to 30 Hz, which may lead to signal loss in diffusion‐weighted MR measurements. This work proposes to mitigate vibration‐induced signal loss by introducing a vibration‐matching gradient (VMG) to match vibrational states during the 2 diffusion gradient pulses. Theory and Methods: A theoretical description of displacements induced by gradient switching was introduced and modeled by a 2‐mass‐spring‐damper system. An additional preceding VMG mimicking timing and properties of the diffusion encoding gradients was added to a high b‐value diffusion‐weighted MR spectroscopy sequence. Laser interferometry was employed to measure 3D displacements of a phantom surface. Lipid ADC was assessed in water–fat phantoms and in vivo in the tibial bone marrow of 3 volunteers. Results: The modeling and the laser interferometer measurements revealed that the displacement curves are more similar during the 2 diffusion gradients with the VMG compared to the standard sequence, resulting in less signal loss of the diffusion‐weighted signal. Phantom results showed lipid ADC overestimation up to 119% with the standard sequence and an error of 5.5% with the VMG. An 18% to 35% lower coefficient of variation was obtained for in vivo lipid ADC measurement when employing the VMG. Conclusion: The application of the VMG reduces the signal loss introduced by hardware vibrations in a high b‐value diffusion‐weighted MRS sequence in phantoms and in vivo. Reference measurements based on laser interferometry and mechanical modelling confirmed the findings. [ABSTRACT FROM AUTHOR]