Cabreranavarro5273

Extending the previous work done by the authors, this paper develops a time domain synthesis method for the classical guitar based on substructuring concepts and using the Udwadia-Kalaba modeling strategy. Adopting a modal description of the dynamics of the separate flexible subsystems in terms of their unconstrained modes and enforcing coupling constraint conditions for the assembly, the result is an explicit dynamical modal formulation for the coupled system that directly lends itself to time-stepping methods for simulation. The guitar model couples six strings through an experimentally based body model of an actual instrument, includes two string polarizations, and the string geometrical nonlinear effects, as well as the string/fret interaction as the instrument is played. Details are given for putting all the vibrating components together in a satisfying manner, and a specific strategy is explored to allow for a nonrigid fret using flexible-dissipative-inertial constraints. Linsitinib concentration The reliability of the approach is demonstrated with simulation examples that confirm the features one would expect regarding the dynamical behavior of classical guitars. Finally, a pragmatic approach is made to calculate the radiated sound by convolution, combining the computed bridge force with a measured vibro-acoustic impulse response of the instrument, which proved to give satisfactory sounding results.When a transducer is placed on aural cartilage, relatively loud sound becomes audible in a conduction form termed cartilage conduction (CC). Previous studies have revealed the acoustical differences between CC and conventional air or bone conduction. This study elucidates the working principle of CC through measurements of threshold shifts by water injection into the ear canal under various fixation place conditions. Seven volunteers with normal hearing participated. A lightweight transducer was fixed for three CC conductions (on the tragus, antitragus, and intertragal incisure), and two non-CC conditions (on the pre-tragus and mastoid). Thresholds were measured at 500, 1000, and 2000 Hz in the 0%-, 40%-, and 80%-water injection conditions. Results for the three CC conditions revealed unique features different from those for the non-CC conditions. For the CC conditions, the thresholds increased by the 40%-water injection at all frequencies. However, with additional water injection (80%-water injection), the thresholds decreased at 500 and 1000 Hz; in particular, dramatically at 500 Hz. The results suggest that a direct vibration of the aural cartilage is important to obtaining the significant contribution of airborne sound to hearing above 1000 Hz. Fixation place results in no significant difference in acoustic features among CC conditions.A multi-task learning (MTL) method with adaptively weighted losses applied to a convolutional neural network (CNN) is proposed to estimate the range and depth of an acoustic source in deep ocean. The network input is the normalized sample covariance matrices of the broadband data received by a vertical line array. To handle the environmental uncertainty, both the training and validation data are generated by an acoustic propagation model based on multiple possible sets of environmental parameters. The sensitivity analysis is investigated to examine the effect of mismatched environmental parameters on the localization performance in the South China Sea environment. Among the environmental parameters, the array tilt is found to be the most important factor on the localization. Simulation results demonstrate that, compared with the conventional matched field processing (MFP), the CNN with MTL performs better and is more robust to array tilt in the deep-ocean environment. Tests on real data from the South China Sea also validate the method. In the specific ranges where the MFP fails, the method reliably estimates the ranges and depths of the underwater acoustic source.Ultrasound atomic force microscopy (AFM) has received considerable interest due to its subsurface imaging capabilities, particularly for nanostructure imaging. The local contact stiffness variation due to the presence of a subsurface feature is the origin of the imaging contrast. Several research studies have demonstrated subsurface imaging capabilities with promising resolution. However, there is limited literature available about the definition of spatial resolution in subsurface AFM. The changes in contact stiffness and their link to the subsurface resolution are not well understood. We propose a quantitative approach to assess the resolution in subsurface AFM imaging. We have investigated the influences of several parameters of interest on the lateral resolution. The quantification of the subsurface feature size can be based on threshold criteria (full width at half maximum and Rayleigh criteria). Simulations and experimental measurements were compared, revealing that the optimal choice of parameter settings for surface topography AFM is suboptimal for subsurface AFM imaging.We propose a test method to study the effects of strain on the thermal conductivity of thin films. First, a strain setup was designed to apply stress to a thin film, and a test system was built to measure its thermal conductivity by combining the strain setup with the 3-ω method. The strain setup can apply stress to the specimen by adjusting load weights, while the strain of a thin film was obtained by measuring the applied stress with a force sensor. Second, the effects of strain on the resistance and temperature coefficients of a metal thin film were studied using the strain setup and the four-wire resistance measurement method; the results show that the resistance and temperature coefficients of metal thin films decrease with strain. Finally, the effects of strain on the thermal conductivity of a silicon dioxide thin film and silicon substrate were studied using the proposed method and test system. As the strain increased from 0% to 0.072%, the thermal conductivity of the 300-nm thick silicon dioxide thin film decreased from 0.907 W/(m K) to 0.817 W/(m K). The thermal conductivity of the 0.5-mm thick silicon substrate fluctuated in the range of 130.6 W/(m K) to 118.8 W/(m K) and then tended to stabilize around 126.4 W/(m K).