Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalised Prediction Methods for Flow-generated Noise Produced by Multiple In-duct Elements

In recent years, several studies have sought to develop a generalised prediction method for flow-generated noise that is produced by the interaction of multiple elements in the air flow duct of an air-conditioning system. The prediction methods for single or multiple in-duct discontinuities are based on the assumption that there is a constant of proportionality between the fluctuating drag force and the steady drag force that acts on the flow spoilers. To develop a prediction method for multiple in-duct discontinuities, the concept of partially coherent sound fields is adopted to formulate the sound powers that are produced by the interaction of multiple in-duct elements at frequencies both below and above the cut-on frequency of the lowest transverse duct mode. An interaction factor is defined as the result of a simple relationship between the sound power that is due to the interaction of multiple in-duct elements and the sound power that is due to a single in-duct element. The method has been found to agree well for two in-duct elements with experimental studies. This paper introduces the recent developments in the prediction methods, and discusses the problems that are encountered in the development of the technique.
Least Squares with Simulated Means for a Problem in Fibre Strength Testing

Some data on the strength of carbon fibres are tackled. A simple stochastic–physical model for the data proves to give rise to an intractable likelihood function but can be used straightforwardly to produce simulated realizations. This suggests a method of estimation that is alternative to maximum likelihood based on simulation. A version of least squares with simulated means is employed.
Finite-dimensional approximation and control of shear flows

Dynamical systems theory can significantly contribute to the understanding and control of fluid flows. Fluid dynamical systems are governed by the Navier-Stokes equations, which are continuous in both time and space, resulting in a state space of infinite dimension. To incorporate tools from systems theory it has become common practise to approximate the infinite-dimensional system by a finite-dimensional lumped system. Current techniques for this reduction step are data driven and produce models which are sensitive to the simulation or experimental conditions. This dissertation proposes a rigorous and practical methodology for the derivation of accurate finite-dimensional approximations and output feedback controllers directly from the governing equations. The approach combines state-space discretisation of the linearised Navier-Stokes equations with balanced truncation to design experimentally feasible low-order controllers. The approximation techniques can be used to design any suitable linear controller. In this study the reduced-order controllers are designed within an H2 optimal control framework to account for external disturbances and measurement noise. Application is focused on control of laminar wall-bounded shear flows to delay the classical transition process initially governed by two-dimensional convective perturbations, to extend laminar flow and reduce skin friction drag. The controllers are successfully tested in the vertical wind tunnel at the TU Delft.
Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias-Variance Trade-off

Vector embedding is a foundational building block of many deep learning models, especially in natural language processing. In this paper, we present a theoretical framework for understanding the effect of dimensionality on vector embeddings. We observe that the distributional hypothesis, a governing principle of statistical semantics, requires a natural unitary-invariance for vector embeddings. Motivated by the unitary-invariance observation, we propose the Pairwise Inner Product (PIP) loss, a unitary-invariant metric on the similarity between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that the PIP loss is tightly connect with two basic properties of vector embeddings, namely similarity and compositionality. By formulating the embedding training process as matrix factorization with noise, we reveal a fundamental bias-variance trade-off between the signal spectrum and noise power in the dimensionality selection process. This bias-variance trade-off sheds light on many empirical observations which have not been thoroughly explained, for example the existence of an optimal dimensionality. Moreover, we discover two new results about vector embeddings, namely their robustness against over-parametrization and their forward stability. The bias-variance trade-off of the PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings.
Fabrication of Multilayer GDP Targets for Foil Thickness Experiments

Abstract Four different variations of doped, planar targets were fabricated using multilayer glow discharge polymerization for the foil thickness campaign at the Extended Performance Facility at the University of Rochester. The planar film targets consisted of from one to four layers of CH, CHGe, and CHSi. The composition of Ge and Si was controlled by the flow of dopant gas (either tetramethyl germane or tetramethyl silane) and measured with X-ray florescence. After laser cutting the 200 × 900 × 80-µm film targets out of the larger film, the targets were released from the substrate. Coating nonuniformity when using an inductively coupled discharge device can be a challenge. We improved the uniformity by rotating the substrate. Film thickness was measured with a chromatic confocal sensor system. Thickness measurements were fit to a Gaussian function, which smoothed the thickness data set and allowed accurate interpolation of thickness measurements. A challenge for freestanding, planar glow discharge polymer films is intrinsic stress in the coating. Prior to coating the final targets, the coating stress for various deposition parameters was measured. A series of runs with CH, CHGe, and CHSi were coated on thin silicon wafers. The wafers were characterized for bending before and after coating with a stylus profilometer to determine the coating stress using the Stony equation. In general, higher chamber operating pressures resulted in lower stress coatings.
Keck Planet Imager and Characterizer: status update

Here we report on the status of the The Keck Planet Imager and Characterizer (KPIC), which is an on-going series of upgrades to the W.M. Keck II adaptive optics system and instrument suite focused on exoplanet imaging and spectroscopic characterization. The KPIC infrared pyramid wavefront sensor and fiber injection unit to high-resolution infrared spectrograph NIRSPEC have been assembled, integrated and are under-going tests at the University of Hawaii before installation at the Summit in the Fall of 2018.
Buoyancy-driven flow in a confined aquifer with a vertical gradient of permeability

We examine the injection of fluid of one viscosity and density into a horizontal permeable aquifer initially saturated with a second fluid of different viscosity and density. The novel feature of the analysis is that we allow the permeability to vary vertically across the aquifer. This leads to recognition that the interface may evolve as either a rarefaction wave that spreads at a rate proportional to $t$ , a shock-like front of fixed length or a mixture of shock-like regions and rarefaction-wave-type regions. The classical solutions in which there is no viscosity ratio between the fluids and in which the formation has constant permeability lead to an interface that spreads laterally at a rate proportional to $t^{1/2}$ . However, these solutions are unstable to cross-layer variations in the permeability owing to the vertical shear which develops in the flow, causing the structure of the interface to evolve to the rarefaction wave or shock-like structure. In the case that the viscosities of the two fluids are different, it is possible that the solution involves a mixture of shock-like and rarefaction-type structures as a function of the distance above the lower boundary. Using the theory of characteristics, we develop a regime diagram to delineate the different situations. We consider the implications of such heterogeneity for the prediction of front locations during $\text{CO}_{2}$ sequestration. If we neglect the permeability fluctuations, the model always predicts rarefaction-type solutions, while even modest changes in the permeability across a layer can introduce shocks. This difference may be very significant since it leads to the $\text{CO}_{2}$ plume occupying a greater fraction of the pore space between the injector and the leading edge of the $\text{CO}_{2}$ front in a layer of the same mean permeability. This has important implications for estimates of the fraction of the pore space that the $\text{CO}_{2}$ may access.
Atomistic simulation of femtosecond laser pulse interactions with a copper film: Effect of dependency of penetration depth and reflectivity on electron temperature

The response of copper films to irradiation with laser pulses of fluences in the range of 100–6000 J/m2 is simulated by using a modified combination of a two-temperature model (TTM) and molecular dynamics (MD). In this model, the dependency of the pulse penetration depth and the reflectivity of the target on electron temperature are taken into account. Also, the temperature-dependent electron-phonon coupling factor, electron thermal conductivity, and electron heat capacity are used in the simulations. Based on this model, the dependence of the integral reflectivity on pulse fluence, the changes in the film thickness, and the evolution of density and electron and lattice temperatures are obtained. Moreover, snapshots that show the melting and disintegration processes are presented. The disintegration starts at a fluence of 4200 J/m2, which corresponds with an absorbed fluence of 616 J/m2. The calculated values of integral reflectivity are in good agreement with the experimental data. The inclusion of such .
Accurately estimating PSF with straight lines detected by Hough transform

This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.
Joint Clustering With Correlated Variables

ABSTRACT Traditional clustering methods focus on grouping subjects or (dependent) variables assuming independence between the variables. Clusters formed through these approaches can potentially lack homogeneity. This article proposes a joint clustering method by which both variables and subjects are clustered. In each joint cluster (in general composed of a subset of variables and a subset of subjects), there exists a unique association between dependent variables and covariates of interest. To this end, a Bayesian method is designed, in which a semi-parametric model is used to evaluate any unknown relationships between possibly correlated variables and covariates of interest, and a Dirichlet process is used to cluster subjects. Compared to existing clustering techniques, the major novelty of the method exists in its ability to improve the homogeneity of clusters, along with the ability to take the correlations between variables into account. Via simulations, we examine the performance and efficiency of the proposed method. Applying the method to cluster allergens and subjects based on the association of wheal size in reaction to allergens with age, we found that a certain pattern of allergic sensitization to a set of allergens has a potential to reduce the occurrence of asthma.
A Tunable Narrowband Microwave Photonic Bandpass Filter with an Ultra-High-Q Silicon Microring Resonator

We experimentally demonstrate a tunable sub-gigahertz bandwidth microwave photonic bandpass filter based on a silicon microring resonator. A 170 MHz full width at half maximum bandwidth is achieved owing to utilize an ultra-high-Q as 1.14⨯106 of the microring resonator. The corresponding rejection ratio is 26.5 dB. The central frequency of the bandpass filter could be tuned from 2.0 GHz to 18.4 GHz.
\textbf{simode}: \textbf{R} Package for statistical inference of ordinary differential equations using separable integral-matching

In this paper we describe \textbf{simode}: Separable Integral Matching for Ordinary Differential Equations. The statistical methodologies applied in the package focus on several minimization procedures of an integral-matching criterion function, taking advantage of the mathematical structure of the differential equations like separability of parameters from equations. Application of integral based methods to parameter estimation of ordinary differential equations was shown to yield more accurate and stable results comparing to derivative based ones. Linear features such as separability were shown to ease optimization and inference. We demonstrate the functionalities of the package using various systems of ordinary differential equations.
A fuzzy rumor spreading model based on transmission capacity

This paper proposes a rumor spreading model that considers three main factors: the event importance, event ambiguity, and the publics critical sense, each of which are defined by decision makers using linguistic descriptions and then transformed into triangular fuzzy numbers. To calculate the resultant force of these three factors, the transmission capacity and a new parameter category with fuzzy variables are determined. A rumor spreading model is then proposed which has fuzzy parameters rather than the fixed parameters in traditional models. As the proposed model considers the comprehensive factors affecting rumors from three aspects rather than examining special factors from a particular aspect. The proposed rumor spreading model is tested using different parameters for several different conditions on BA networks and three special cases are simulated. The simulation results for all three cases suggested that events of low importance, those that are only clarifying facts, and those that are strongly critical do not result in rumors. Therefore, the model assessment results were proven to be in agreement with reality. Parameters for the model were then determined and applied to an analysis of the 7.23 Yong–Wen line major transportation accident (YWMTA). When the simulated data were compared with the real data from this accident, the results demonstrated that the interval for the rumor spreading key point in the model was accurate, and that the key point for the YWMTA rumor spread fell into the range estimated by the model.
Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.
A novel phase-averaging method based on vortical structure correlation

In this paper we investigate a new method for phase averaging based on the correlation of vortical structures in a (cid:29)ow (cid:28)eld. The method requires the presence of a large scale precessing structure in the (cid:29)ow, such as for instance the precessing vortex core found in swirling (cid:29)ows. The transformation from time to phase is done by correlation of the instantaneous Q (cid:28)elds to determine the phase shift between two instants of the precession. Once the phase shift is determined, the di(cid:27)erent (cid:29)ow(cid:28)elds are rotated back to the zero phase reference and the data are ensemble averaged. The method is tested on tomographic PIV measurements of an annular jet (cid:29)ow and it is shown that it is able to extrude the large scale structures found in turbulent swirling jet (cid:29)ows.
Study About the Despeckling Methods for Retinal Optical Coherence Tomography Images

This paper presents study about the different despeckling methods used for quality enhancement of retinal optical coherence tomography (OCT) images. Speckle noise is an inherent property of an OCT images which affects the visual quality of the images, hence difficult to diagnosis the patients. Therefore speckle noise reduction from the OCT images is an important prerequisite, whenever OCT imaging is used for diagnosis. The speckle noise intensity depends on the various imaging system parameters of its systems and the different structure representations used for the image tissues. A despeckling technique is to be designed in such a way that it should be able to reduce the speckle noise from the OCT images while preserving the tissues and fine details of the images.
Output regulation with unknown-input exosystems

In this paper, an output regulation problem with unknown-input driven exosystem is formulated and solved for a class of nonlinear plant with unity relative degree. To handle both bounded and unbounded exosystems, we relaxed the asymptotic convergence requirement on the closed-loop state to its associated steady-state and proposed an approximated regulator-equation condition. By using changing supply functions and dynamic high-gain techniques, an adaptive rule was provided to solve our problem. An example was given to show its effectiveness.
Network flow oriented approaches for vehicle sharing relocation problems

Managing a one-way vehicle sharing system means periodically moving free access vehicles from excess to deficit stations in order to avoid local shortages. We propose and study here several network flow oriented models and algorithms which deal with a static version of this problem while unifying preemption and non preemption as well as carrier riding cost, vehicle riding time and carrier number minimization. Those network flow models are vehicle driven, which means that they focus on the way vehicles are exchanged between excess and deficit stations. We perform a lower bound and approximation analysis which leads us to the design and test of several heuristics. One of them involves implicit dynamic network handling.
Tunable dual-band perfect metamaterial absorber based on a graphene-SiC hybrid system by multiple resonance modes

We propose dynamically tunable dual-band perfect absorption of light in a composite structure, which consists of a graphene ribbon array coated on a dielectric layer, a silicon carbide (SiC) film and a distributed Bragg reflector (DBR). Two absorption peaks reaching 98.8% at 10.87 μm and 97.5% at 12.4 µm are observed in the hybrid structure. The dual-band absorption enhancement can be explained by two different mechanisms: one is attributed to the graphene localized surface plasmons (GLSP), and the other is originated from the excitation of Tamm plasmon polaritons (TPP) at the interface between SiC and DBR. Moreover, the high absorption peaks can be modulated by altering the geometrical parameters or dynamically controlling the chemical potential of graphene ribbons. More significantly, we numerically and theoretically investigate the hybridization between GLSP mode and TPP mode, which is characterized by the anti-crossing behaviour of the two modes. The mode splitting phenomenon is a typical Rabi splitting. In addition, we also study the coupling characteristics based on graphene surface plasmons (GSP) mode and TPP mode. This work offers a new paradigm for enhancing light–matter interaction through multiple resonance modes, and the proposed device could provide potential applications in MIR plasmonic devices, such as absorbers, detectors, and modulators, etc.
Optical Network Technologies for Future Digital Cinema

Digital technology has transformed the information flow and support infrastructure for numerous application domains, such as cellular communications. Cinematography, traditionally, a film based medium, has embraced digital technology leading to innovative transformations in its work flow. Digital cinema supports transmission of high resolution content enabled by the latest advancements in optical communications and video compression. In this paper we provide a survey of the optical network technologies for supporting this bandwidth intensive traffic class. We also highlight the significance and benefits of the state of the art in optical technologies that support the digital cinema work flow.
Parallel noise eliminate: A parallel noise elimination algorithm for massive text categorization

Noise data in text are one of the main factors affecting the quality of text categorization. A parallel noise data elimination algorithm based on principal component analysis method and term frequency-inverse document frequency method for the noise data issue of massive text categorization is proposed. Five types of noise data which may occur during text categorization process are analyzed and summarized in this paper. Before text categorization, a redundant noise elimination algorithm based on key feature selection is presented for redundant noise features. During the process of text categorization, the error noise detection algorithm is given for inaccurate noise features. The proposed method is compared with other four typical noise processing methods in different noise ratios on two common corpora. The results show that the proposed method is feasible and can maintain more stable and excellent classification performance and lower running time.