scholarly journals Outlier detection for PIV statistics based on turbulence transport

2021 ◽  
Vol 63 (1) ◽  
Author(s):  
E. Saredi ◽  
A. Sciacchitano ◽  
F. Scarano
Keyword(s):  
Kinetic Energy ◽  
Outlier Detection ◽  
State Of The Art ◽  
Turbulent Transport ◽  
Three Dimensional ◽  
Measured Data ◽  
Piv Measurements ◽  
Novel Approach ◽  
Problematic Issue ◽  
Turbulence Transport

AbstractThe occurrence of data outliers in PIV measurements remains nowadays a problematic issue; their effective detection is relevant to the reliability of PIV experiments. This study proposes a novel approach to outliers detection from time-averaged three-dimensional PIV data. The principle is based on the agreement of the measured data to the turbulent kinetic energy (TKE) transport equation. The ratio between the local advection and production terms of the TKE along the streamline determines the admissibility of the inquired datapoint. Planar and 3D PIV experimental datasets are used to demonstrate that in the presence of outliers, the turbulent transport (TT) criterion yields a large separation between correct and erroneous vectors. The comparison between the TT criterion and the state-of-the-art universal outlier detection from Westerweel and Scarano (Exp Fluids 39:1096–1100, 2005) shows that the proposed criterion yields a larger percentage of detected outliers along with a lower fraction of false positives for a wider range of possible values chosen for the threshold. Graphical abstract

scholarly journals QUBIC: Exploring the Primordial Universe with the Q&U Bolometric Interferometer

Universe ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 42 ◽  
Author(s):  
Aniello Mennella ◽  
Peter Ade ◽  
Giorgio Amico ◽  
Didier Auguste ◽  
Jonathan Aumont ◽  
...  
Keyword(s):  
Frequency Band ◽  
State Of The Art ◽  
Spectral Imaging ◽  
Measured Data ◽  
Instrumental Effects ◽  
Main Frequency ◽  
Bolometric Detectors ◽  
Novel Approach ◽  
Systematic Effects ◽  
Final Instrument

In this paper, we describe QUBIC, an experiment that will observe the polarized microwave sky with a novel approach, which combines the sensitivity of state-of-the-art bolometric detectors with the systematic effects control typical of interferometers. QUBIC’s unique features are the so-called “self-calibration”, a technique that allows us to clean the measured data from instrumental effects, and its spectral imaging power, i.e., the ability to separate the signal into various sub-bands within each frequency band. QUBIC will observe the sky in two main frequency bands: 150 GHz and 220 GHz. A technological demonstrator is currently under testing and will be deployed in Argentina during 2019, while the final instrument is expected to be installed during 2020.

scholarly journals Dealing with Incompatibilities among Procedural Goals under Uncertainty

2019 ◽  
Vol 22 (64) ◽  
pp. 47-62
Author(s):  
Mariela Morveli Espinoza ◽  
Juan Carlos Nieves ◽  
Ayslan Possebom ◽  
Cesar Augusto Tacla
Keyword(s):  
State Of The Art ◽  
Three Dimensional ◽  
Dimensional Vector ◽  
Theoretical Evaluation ◽  
Rational Agents ◽  
Abstract Argumentation ◽  
Novel Approach ◽  
Logical Strength ◽  
The Cost ◽  
Selection Of

By considering rational agents, we focus on the problem of selecting goals out of a set of incompatible ones. We consider three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal, the instrumental (or based on resources), and the superfluity. We represent the agent's plans by means of structured arguments whose premises are pervaded with uncertainty. We measure the strength of these arguments in order to determine the set of compatible goals. We propose two novel ways for calculating the strength of these arguments, depending on the kind of incompatibility thatexists between them. The first one is the logical strength value, it is denoted by a three-dimensional vector, which is calculated from a probabilistic interval associated with each argument. The vector represents the precision of the interval, the location of it, and the combination of precision and location. This type of representation and treatment of the strength of a structured argument has not been defined before by the state of the art. The second way for calculating the strength of the argument is based on the cost of the plans (regarding the necessary resources) and the preference of the goals associated with the plans. Considering our novel approach for measuring the strength of structured arguments, we propose a semantics for the selection of plans and goals that is based on Dung's abstract argumentation theory. Finally, we make a theoretical evaluation of our proposal.

scholarly journals A Three-Dimensional Navier-Stokes Calculation Applied to an Axial Compressor Rotor and Stator

1993 ◽  
Author(s):  
Robert P. Dring ◽  
Ricky J. VanSeters ◽  
Robert M. Zacharias
Keyword(s):  
Total Pressure ◽  
Large Scale ◽  
State Of The Art ◽  
Three Dimensional ◽  
Axial Compressor ◽  
Measured Data ◽  
Navier Stokes ◽  
Second Stage ◽  
Compressor Rotor ◽  
Pressure Distributions

The objective of this paper is to apply a three-dimensional Navier-Stokes calculation to the second stage rotor and stator of a large scale compressor and to compare the computed results with measured data. The comparisons include: (1) airfoil and endwall surface streamlines, (2) radial-circumferential distributions of exit total pressure, (3) spanwise distributions of circumferentially averaged inlet and exit flow quantities, and (4) fullspan airfoil pressure distributions. This assessment demonstrates that the computational state-of-the-art has advanced to a point where calculations such as this can be applied with reasonable confidence in both design and analysis situations.

scholarly journals Physics-based universal outlier detector for flow statistics

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Edoardo Saredi ◽  
Andrea Sciacchitano ◽  
Fulvio Scarano
Keyword(s):  
Measurement Uncertainty ◽  
Outlier Detection ◽  
Velocity Fields ◽  
Test Cases ◽  
Piv Measurements ◽  
Detection Approach ◽  
Dynamic Velocity ◽  
Order Of Magnitude ◽  
Turbulence Transport ◽  
Detection Strategies

Outlier detection for PIV velocity fields is still nowadays an active field of research. In the last decades, several image pre-processing and processing algorithms have been developed aiming at increasing the dynamic velocity range of PIV measurements and reducing the measurement uncertainty. Nevertheless, PIV velocity fields are still often characterised by the presence of outliers, which potentially hamper the correct interpretation of the flow physics and negatively affect the evaluation of the flow statistics. The outlier detection strategies presented in literature are mainly based on the statistical analysis of the velocity vector with its immediate neighbour. Most of these algorithms have been demonstrated to be effective for instantaneous flow fields, where the errors associated with the outliers are order of magnitude larger than the expected measurement uncertainty. However, these approaches are not as effective for the flow statistics, where the outliers yield errors of the same order of the measurement uncertainty. To overcome this limitation, this paper proposed an outlier detection approach based on the agreement of the flow statistics to the constitutive equations, more specifically to the turbulent kinetic energy (TKE) transport equation. The focus is posed on the ratio between the local advection terms of TKE and a robust estimation of the TKE production along the local streamline. It is demonstrated that, in presence of outliers, the proposed principle yields a clear separation between the correct and the erroneous vectors. In order to assess the performance of the proposed principle, three different test cases are considered. For all of them, the results are compared with a reference outlier detection methodology, namely the universal outlier detection method proposed by Westerweel and Scarano (2005). The proposed turbulence transport-based approach exhibits higher performance in terms of percentage of outliers correctly identified in the flow statistics.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonas Albers ◽  
Angelika Svetlove ◽  
Justus Alves ◽  
Alexander Kraupner ◽  
Francesca di Lillo ◽  
...  
Keyword(s):  
Three Dimensional ◽  
High Specificity ◽  
Histopathological Analysis ◽  
Specific Information ◽  
Data Sets ◽  
Virtual Histology ◽  
Novel Approach ◽  
Cell Tissue ◽  
Immuno Histochemistry ◽  
3D Information

AbstractAlthough X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

scholarly journals Artificial Tumor Microenvironments in Neuroblastoma

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1629
Author(s):  
Colin H. Quinn ◽  
Andee M. Beierle ◽  
Elizabeth A. Beierle
Keyword(s):  
Extracellular Matrix ◽  
Three Dimensional ◽  
Therapeutic Interventions ◽  
3D Bioprinting ◽  
Culture Studies ◽  
In Vivo Models ◽  
Novel Treatments ◽  
Tumor Microenvironments ◽  
Novel Approach

In the quest to advance neuroblastoma therapeutics, there is a need to have a deeper understanding of the tumor microenvironment (TME). From extracellular matrix proteins to tumor associated macrophages, the TME is a robust and diverse network functioning in symbiosis with the solid tumor. Herein, we review the major components of the TME including the extracellular matrix, cytokines, immune cells, and vasculature that support a more aggressive neuroblastoma phenotype and encumber current therapeutic interventions. Contemporary treatments for neuroblastoma are the result of traditional two-dimensional culture studies and in vivo models that have been translated to clinical trials. These pre-clinical studies are costly, time consuming, and neglect the study of cofounding factors such as the contributions of the TME. Three-dimensional (3D) bioprinting has become a novel approach to studying adult cancers and is just now incorporating portions of the TME and advancing to study pediatric solid. We review the methods of 3D bioprinting, how researchers have included TME pieces into the prints, and highlight present studies using neuroblastoma. Ultimately, incorporating the elements of the TME that affect neuroblastoma responses to therapy will improve the development of innovative and novel treatments. The use of 3D bioprinting to achieve this aim will prove useful in developing optimal therapies for children with neuroblastoma.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

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