Optimization of Noise Transfer Path Based on the Composite Panel Acoustic and Modal Contribution Analysis

The noise of a cab directly affects the comfort and labor efficiency of the operators. The optimization of the structure-borne transmission path can obviously reduce the cab noise. The method of panel acoustic contribution analysis (PACA) is used to reduce structure noise. However, most studies only consider the panel acoustic contribution of a single frequency, without considering the contribution of major frequencies synthesis to confirm the optimized panels. In this paper, a novel method is proposed based on composite panel acoustic and modal contribution analysis and noise transfer path optimization in a vibro-acoustic model. First, the finite element model (FEM) and the acoustic model are established. Based on the acoustic transfer vector (ATV) method, a composite panel acoustic contribution analysis method is proposed to identify the panels affecting the noise of the field point. Combined with the modal acoustic contribution of the modal acoustic transfer vector (MATV) method, the noise field point is confirmed in the area which has the most significant influence. Second, the optimization algorithm NLOPT which is a nonlinear optimization is applied to design the areas. The noise transfer path optimization with vibroacoustic coupling response can quickly determine the optimal thickness of the panels and reduce low-frequency noise. The effectiveness of the proposed method is applied and verified in an excavator cab. The sound pressure level (SPL) the driver’s right ear (DRE) decreased obviously. The acoustic analysis of the composite panel acoustic contribution and modal acoustic contribution can more accurately recognize an optimized area than the traditional PACA. This method can be applied in the optimization of the structure-borne transmission path for construction machinery cab and vehicle body.

Computing Radiated Sound Power using Quadratic Power Transfer Vector (QPTV)

Pressure Acoustic Transfer Functions or Vectors (PATVs) relate the surface velocity of a structure to the sound pressure level at a field point in the surrounding fluid. These functions depend only on the structure geometry, properties of the fluid medium (sound speed and characteristic density), the excitation frequency and the location of the field point, but are independent of the surface velocity values themselves. Once the pressure acoustic transfer function is computed between a structure and a specified field point, we can compute pressure at this point for any boundary velocity distribution by simply multiplying the forcing function (surface velocity) with the acoustic transfer function. These PATVs are usually computed by application of the Reciprocity Principle, and their computation is well understood. In this work, we present a novel way to compute the Velocity Acoustic Transfer Vector (VATV) which is a relation between the surface velocity of the structure and fluid particle velocity at a field point. To our knowledge, the computation of the VATV is completely new and has not been published in earlier works. By combining the PATVs and VATVs at a number of field points surrounding the structure, we obtain the Quadratic Power Transfer Vector (QPTV) that allows us to compute the sound power radiated by a structure for ANY surface velocity distribution. This allows rapid computation of the sound power for an arbitrary surface velocity distributions and is useful in designing quiet structures by minimizing the sound power radiated.

Geometry of k-Yamabe Solitons on Euclidean Spaces and Its Applications to Concurrent Vector Fields

In this paper, we give some classifications of the k-Yamabe solitons on the hypersurfaces of the Euclidean spaces from the vector field point of view. In several results on k-Yamabe solitons with a concurrent vector field on submanifolds in Riemannian manifolds, is proved that a k-Yamabe soliton (Mn,g,vT,λ) on a hypersurface in the Euclidean space Rn+1 is contained either in a hypersphere or a hyperplane. We provide an example to support this study and all of the results in this paper can be implemented to Yamabe solitons for k-curvature with k=1.

Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System

Step/stare imaging with focal plane arrays (FPAs) has become the main approach to achieve wide area coverage and high resolution imaging for long range targets. A fast steering mirror (FSM) is usually utilized to provide back-scanned motion to compensate for the image motion. However, the traditional optical design can just hold one field point relatively stable, typically the central or on-axis field point, on the FPA during back-scanning; all other field points may wander during the exposure due to imaging distortion characteristics of the optical system, which reduces the signal to noise ratio (SNR) of the target. Aiming toward this problem, this paper proposes a non-rotationally symmetric field mapping method for the back-scanned step/stare imaging system, which can make all field points stable on the FPA during back-scanning. First of all, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established. Then, a back-scanned step/stare imaging system based on the model is designed, in which this non-rotationally symmetric mapping can be implemented with an afocal telescope including freeform lenses. Freeform lenses can produce an anamorphic aberration to adjust distortion characteristics of the optical system to control image wander on an FPA. Furthermore, the simulations verify the effectiveness of the method.

Neural Network Recognition of Marine Benthos and Corals

We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016–2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored the current applications of deep learning for corals and benthic image classification by discussing the most recent studies conducted by researchers. We review the developments in the field, point out their current limitations, and outline their timelines and unique potential. We also discussed a few future research directions in the fields of deep learning. Future needs are the age detection of single species, in order to track trends in their population recruitment, decline, and recovery. Fine resolution, at the polyp level, is still to be developed, in order to allow separation of species with similar macroscopic features. That refinement of DL will allow such comparisons and their analyses. We conclude that the usefulness of future, more refined automatic identification will allow reef comparison, and tracking long term changes in species diversity. The hitherto unused addition of intraspecific coral color parameters, will add the inclusion of physiological coral responses to environmental conditions and change thereof. The core aim of this review was to underscore the strength and reliability of the DL approach for documenting coral reef features based on an evaluation of the currently available published uses of this method. We expect that this review will encourage researchers from computer vision and marine societies to collaborate on similar long-term joint ventures.

CALCULATION OF THE INTEGRAL NOISE LEVEL AT A FIELD POINT OF A FREE SUPERSONIC JET OF A ROCKET ENGINE

The paper considers an acoustic field created by a supersonic jet (CES) of a rocket engine freely flowing into flooded space. The acoustic field was presented in the form of a diagram of noise isobars, from which it can be seen that the acoustic field is formed by two effective noise sources: the nearest one, lying at a distance of 5-10 calibers from the nozzle cut and the far one, lying at a distance of 15-30 calibers from the nozzle cut.

NEXT-GENERATION ALZHEIMER’S THERAPEUTICS: LEVERAGING DEEP BIOLOGY

A recent EU/US CTAD Task Force Report focused on non-amyloid approaches to Alzheimer’s disease (AD) modification (1). While the broad range of targets and therapies highlighted is in some ways sobering, several themes and advances in the field point to principles and technologies that are encouraging and will likely accelerate progress. These themes include: the view that amyloid and non-amyloid approaches might ultimately be complementary or synergistic; the biological diversity of approaches; emerging -omics strategies that might help guide such options; and finally, the incorporation of aging biology into perspectives of target prioritization and disease modification.