scholarly journals Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb3Sn wires

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
T. Bagni ◽  
G. Bovone ◽  
A. Rack ◽  
D. Mauro ◽  
C. Barth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Particle Physics ◽  
High Performance ◽  
Mechanical Performance ◽  
Learning Algorithm ◽  
Copper Matrix ◽  
Analysis Tool ◽  
Key Factors ◽  
X Ray ◽  
Micro Tomography

AbstractThe electro-mechanical and electro-thermal properties of high-performance Restacked-Rod-Process (RRP) Nb3Sn wires are key factors in the realization of compact magnets above 15 T for the future particle physics experiments. Combining X-ray micro-tomography with unsupervised machine learning algorithm, we provide a new tool capable to study the internal features of RRP wires and unlock different approaches to enhance their performances. Such tool is ideal to characterize the distribution and morphology of the voids that are generated during the heat treatment necessary to form the Nb3Sn superconducting phase. Two different types of voids can be detected in this type of wires: one inside the copper matrix and the other inside the Nb3Sn sub-elements. The former type can be related to Sn leaking from sub-elements to the copper matrix which leads to poor electro-thermal stability of the whole wire. The second type is detrimental for the electro-mechanical performance of the wires as superconducting wires experience large electromagnetic stresses in high field and high current conditions. We analyze these aspects thoroughly and discuss the potential of the X-ray tomography analysis tool to help modeling and predicting electro-mechanical and electro-thermal behavior of RRP wires and optimize their design.

scholarly journals Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jiayi Ji ◽  
Liangyuan Hu ◽  
Bian Liu ◽  
Yan Li
Keyword(s):  
Machine Learning ◽  
Multilevel Modeling ◽  
High Performance ◽  
Learning Algorithm ◽  
Key Factors ◽  
Data Set ◽  
Modeling Approach ◽  
Stroke Prevalence ◽  
The Impact ◽  
Neighborhood Level

Abstract Background Stroke is a chronic cardiovascular disease that puts major stresses on U.S. health and economy. The prevalence of stroke exhibits a strong geographical pattern at the state-level, where a cluster of southern states with a substantially higher prevalence of stroke has been called the stroke belt of the nation. Despite this recognition, the extent to which key neighborhood characteristics affect stroke prevalence remains to be further clarified. Methods We generated a new neighborhood health data set at the census tract level on nearly 27,000 tracts by pooling information from multiple data sources including the CDC’s 500 Cities Project 2017 data release. We employed a two-stage modeling approach to understand how key neighborhood-level risk factors affect the neighborhood-level stroke prevalence in each state of the US. The first stage used a state-of-the-art Bayesian machine learning algorithm to identify key neighborhood-level determinants. The second stage applied a Bayesian multilevel modeling approach to describe how these key determinants explain the variability in stroke prevalence in each state. Results Neighborhoods with a larger proportion of older adults and non-Hispanic blacks were associated with neighborhoods with a higher prevalence of stroke. Higher median household income was linked to lower stroke prevalence. Ozone was found to be positively associated with stroke prevalence in 10 states, while negatively associated with stroke in five states. There was substantial variation in both the direction and magnitude of the associations between these four key factors with stroke prevalence across the states. Conclusions When used in a principled variable selection framework, high-performance machine learning can identify key factors of neighborhood-level prevalence of stroke from wide-ranging information in a data-driven way. The Bayesian multilevel modeling approach provides a detailed view of the impact of key factors across the states. The identified major factors and their effect mechanisms can potentially aid policy makers in developing area-based stroke prevention strategies.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

SMART X-RAY BAGGAGE SECURITY SYSTEM USING MACHINE LEARNING ALGORITHM

2020 ◽  
Vol 8 (2) ◽  
pp. 8
Author(s):  
M. SUDHA ◽  
R. HARINI ◽  
D. JAYASHREE ◽  
K. KEERTHI NISHA ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Security System ◽  
Machine Learning Algorithm ◽  
X Ray

A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY Sepsis): A Proof-of-Concept Study (Preprint)

2021 ◽  
Author(s):  
Inger Persson ◽  
Andreas Östling ◽  
Martin Arlbrandt ◽  
Joakim Söderberg ◽  
David Becedas
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Scoring Systems ◽  
High Accuracy ◽  
Prediction Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Operating Characteristics ◽  
Mortality And Morbidity ◽  
Institute Of Technology

BACKGROUND Despite decades of research, sepsis remains a leading cause of mortality and morbidity in ICUs worldwide. The key to effective management and patient outcome is early detection, where no prospectively validated machine learning prediction algorithm is available for clinical use in Europe today. OBJECTIVE To develop a high-performance machine learning sepsis prediction algorithm based on routinely collected ICU data, designed to be implemented in Europe. METHODS The machine learning algorithm is developed using Convolutional Neural Network, based on the Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III Clinical Database, focusing on ICU patients aged 18 years or older. Twenty variables are used for prediction, on an hourly basis. Onset of sepsis is defined in accordance with the international Sepsis-3 criteria. RESULTS The developed algorithm NAVOY Sepsis uses 4 hours of input and can with high accuracy predict patients with high risk of developing sepsis in the coming hours. The prediction performance is superior to that of existing sepsis early warning scoring systems, and competes well with previously published prediction algorithms designed to predict sepsis onset in accordance with the Sepsis-3 criteria, as measured by the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC). NAVOY Sepsis yields AUROC = 0.90 and AUPRC = 0.62 for predictions up to 3 hours before sepsis onset. The predictive performance is externally validated on hold-out test data, where NAVOY Sepsis is confirmed to predict sepsis with high accuracy. CONCLUSIONS An algorithm with excellent predictive properties has been developed, based on variables routinely collected at ICUs. This algorithm is to be further validated in an ongoing prospective randomized clinical trial and will be CE marked as Software as a Medical Device, designed for commercial use in European ICUs.

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

2021 ◽  
Author(s):  
Olfa Hamdi-Larbi ◽  
Ichrak Mehrez ◽  
Thomas Dufaud
Keyword(s):  
Machine Learning ◽  
Numerical Method ◽  
High Performance ◽  
Programming Model ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Matrix Compression ◽  
Target Architecture ◽  
Parallel Programming Model

Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

scholarly journals Characterisation of a High-Performance Al–Zn–Mg–Cu Alloy Designed for Wire Arc Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1610 ◽  
Author(s):  
Paulo J. Morais ◽  
Bianca Gomes ◽  
Pedro Santos ◽  
Manuel Gomes ◽  
Rudolf Gradinger ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Additive Manufacturing ◽  
High Performance ◽  
Mechanical Performance ◽  
Fluorescence Analysis ◽  
Cold Metal Transfer ◽  
Direction Parallel ◽  
X Ray ◽  
Wire Arc Additive Manufacturing

Ever-increasing demands of industrial manufacturing regarding mechanical properties require the development of novel alloys designed towards the respective manufacturing process. Here, we consider wire arc additive manufacturing. To this end, Al alloys with additions of Zn, Mg and Cu have been designed considering the requirements of good mechanical properties and limited hot cracking susceptibility. The samples were produced using the cold metal transfer pulse advanced (CMT-PADV) technique, known for its ability to produce lower porosity parts with smaller grain size. After material simulations to determine the optimal heat treatment, the samples were solution heat treated, quenched and aged to enhance their mechanical performance. Chemical analysis, mechanical properties and microstructure evolution were evaluated using optical light microscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray fluorescence analysis and X-ray radiography, as well as tensile, fatigue and hardness tests. The objective of this research was to evaluate in detail the mechanical properties and microstructure of the newly designed high-performance Al–Zn-based alloy before and after ageing heat treatment. The only defects found in the parts built under optimised conditions were small dispersed porosities, without any visible cracks or lack of fusion. Furthermore, the mechanical properties are superior to those of commercial 7xxx alloys and remarkably independent of the testing direction (parallel or perpendicular to the deposit beads). The presented analyses are very promising regarding additive manufacturing of high-strength aluminium alloys.

scholarly journals Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229041 ◽  
Author(s):  
Lucas Encarnacion-Rivera ◽  
Steven Foltz ◽  
H. Criss Hartzell ◽  
Hyojung Choo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Analysis Tool ◽  
Machine Learning Algorithm ◽  
Muscle Histology ◽  
Imagej Software

scholarly journals Printability and Tensile Performance of 3D Printed Polyethylene Terephthalate Glycol Using Fused Deposition Modelling

Polymers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1220 ◽  
Author(s):  
Sofiane Guessasma ◽  
Sofiane Belhabib ◽  
Hedi Nouri
Keyword(s):  
Polyethylene Terephthalate ◽  
Mechanical Performance ◽  
Substantial Reduction ◽  
Fused Deposition Modelling ◽  
X Ray ◽  
Fused Deposition ◽  
Wide Range ◽  
Tensile Performance ◽  
3D Printed ◽  
Micro Tomography

Polyethylene terephthalate glycol (PETG) is a thermoplastic formed by polyethylene terephthalate (PET) and ethylene glycol and known for his high impact resistance and ductility. The printability of PETG for fused deposition modelling (FDM) is studied by monitoring the filament temperature using an infra-red camera. The microstructural arrangement of 3D printed PETG is analysed by means of X-ray micro-tomography and tensile performance is investigated in a wide range of printing temperatures from 210 °C to 255 °C. A finite element model is implemented based on 3D microstructure of the printed material to reveal the deformation mechanisms and the role of the microstructural defects on the mechanical performance. The results show that PETG can be printed within a limited range of printing temperatures. The results suggest a significant loss of the mechanical performance due to the FDM processing and particularly a substantial reduction of the elongation at break is observed. The loss of this property is explained by the inhomogeneous deformation of the PETG filament. X-ray micro-tomography results reveal a limited amount of process-induced porosity, which only extends through the sample thickness. The FE predictions point out the combination of local shearing and inhomogeneous stretching that are correlated to the filament arrangement within the plane of construction.

The Impact of Interface Characteristics on Mechanical Performance of a Hot-Forged Cu/Ti-Coated-Diamond Composite

2021 ◽  
Vol 1016 ◽  
pp. 1682-1689
Author(s):  
Lei Lei ◽  
Leandro Bolzoni ◽  
Fei Yang
Keyword(s):  
High Performance ◽  
Mechanical Performance ◽  
Hot Forging ◽  
Copper Matrix ◽  
Diamond Surface ◽  
Copper Powders ◽  
Tic Particle ◽  
Elemental Copper ◽  
Cold Pressed ◽  
The Impact

The Cu/55vol.%diamond (Ti) composites were fabricated by hot forging of the cold-pressed powder preforms, consisted of elemental copper powders and Ti-coated diamond particles, at 800 °C (800C-Cu/55Dia composite) and 1050 °C (1050C-Cu55Dia composite), respectively. Well bonded interface was achieved between the diamond and the copper matrix for the 800C-Cu/55Dia composite, and the coverage of diamond by interface was about 96%, attributed to homogeneously distributed nanospherical TiC interface formed on the diamond surface. However, obvious coarse TiC particle size and spallation of the formed interface were observed in the 1050C-Cu55Dia composite, implying that the composite had a relatively low bonding strength. The formed chemical bonding, good wettability and strong mechanical interlocking between the diamond and the copper matrix enable the 800C-Cu/55Dia composite having a high tensile strength of 145 MPa and a strain at fracture of 0.35%, which are about 260% and 170% higher than those of the 1050C-Cu55Dia composite, suggesting that the 800C-Cu/55Dia composite has the potential to have a high thermal conductivity and use as high-performance heat sink materials.

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