scholarly journals Fabrication and testing of high-performance all-metal neutron guides and axisymmetric mirrors by electrochemical replication

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1513-1528
Author(s):  
B. Khaykovich ◽  
S. Romaine ◽  
A. Ames ◽  
R. Bruni ◽  
H. A. Ambaye ◽  
...  
Keyword(s):  
Neutron Scattering ◽  
High Performance ◽  
State Of The Art ◽  
Neutron Transport ◽  
Optical Devices ◽  
High Fidelity ◽  
Single Piece ◽  
Critical Components ◽  
Structure Of Matter ◽  
Neutron Guides

ABSTRACTNeutron scattering is one of the most useful methods of studying the structure of matter, with applications to biomedical, structural, magnetic and energy-related materials. Neutron-scattering instruments are installed around research reactors or accelerator-based neutron sources, and neutron guides are critical components of these facilities. They are neutron-transport optical devices consisting of state-of-the-art mirrors often tens of meters long. Here we demonstrate a novel fabrication method of all-metallic neutron guides and axisymmetric mirrors by electroplating from precision mandrels. The process allows for the fabrication of single-piece all-metal guides of prismatic and axisymmetric shapes. We also demonstrate supermirror guides and axisymmetric focusing supermirrors produced with the same technology. We present the fabrication and tests of the multilayer-coated replicated guides and optic and show that the mandrel is reproduced with high fidelity and reliability. Such supermirror optics will provide game-changing improvements in neutron techniques.

scholarly journals SERPENT/SUBCHANFLOW COUPLED CALCULATIONS FOR A VVER CORE AT HOT FULL POWER

2021 ◽  
Vol 247 ◽  
pp. 04006
Author(s):  
Diego Ferraro ◽  
Manuel García ◽  
Uwe Imke ◽  
Ville Valtavirta ◽  
Riku Tuominen ◽  
...  
Keyword(s):  
High Performance ◽  
Neutron Transport ◽  
Coupling Scheme ◽  
High Fidelity ◽  
Reactor Physics ◽  
Coupled Calculation ◽  
Final Goal ◽  
Wide Range ◽  
Computational Resources ◽  
Full Core

An increasing interest on the development of highly accurate methodologies in reactor physics is nowadays observed, mainly stimulated by the availability of vast computational resources. As a result, an on-going development of a wide range of coupled calculation tools is observed within diverse projects worldwide. Under this framework, the McSAFE European Union project is a coordinated effort aimed to develop multiphysics tools based on Monte Carlo neutron transport and subchannel thermal-hydraulics codes. These tools are aimed to be suitable for high-fidelity calculations both for PWR and VVER reactors, with the final goal of performing pin-by-pin coupled calculations at full core scope including burnup. Several intermediate steps are to be analyzed in-depth before jumping into this final goal in order to provide insights and to identify resources requirements. As part of this process, this work presents the results for a pin-by-pin coupling calculation using the Serpent 2 code (developed by VTT, Finland) and the subchannel code SUBCHANFLOW (SCF, developed by KIT, Germany) for a full-core VVER model. For such purpose, a recently refurbished master-slave coupling scheme is used within a High Performance Computing architecture. A full-core benchmark for a VVER-1000 that provides experimental data is considered, where the first burnup step (i.e. fresh core at hot-full rated power state) is calculated. For such purpose a detailed (i.e. pin-by-pin) coupled Serpent-SCF model is developed, including a simplified equilibrium xenon distribution (i.e. by fuel assembly). Comparisons with main global reported results are presented and briefly discussed, together with a raw estimation of resources requirements and a brief demonstration of the inherent capabilities of the proposed approach. The results presented here provide valuable insights and pave the way to tackle the final goals of the on-going high-fidelity project.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Communication Failure Resilient Distributed Neural Network for Edge Devices

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1614
Author(s):  
Jonghun Jeong ◽  
Jong Sung Park ◽  
Hoeseok Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
State Of The Art ◽  
Wearable Devices ◽  
Communication Failure ◽  
Canadian Institute ◽  
Multiple Devices ◽  
Knowledge Distillation ◽  
Partitioning Technique

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

Development of high-fidelity neutron transport code STREAM

2021 ◽  
pp. 107915
Author(s):  
Sooyoung Choi ◽  
Wonkyeong Kim ◽  
Jiwon Choe ◽  
Woonghee Lee ◽  
Hanjoo Kim ◽  
...  
Keyword(s):  
Neutron Transport ◽  
High Fidelity ◽  
Transport Code

French Translation and Validation of the Mayo High Performance Teamwork Scale for Nursing Students in a High-Fidelity Simulation Context

2019 ◽  
Vol 30 ◽  
pp. 25-33 ◽  
Author(s):  
Émilie Gosselin ◽  
Mélanie Marceau ◽  
Christian Vincelette ◽  
Charles-Olivier Daneau ◽  
Stéphan Lavoie ◽  
...  
Keyword(s):  
Nursing Students ◽  
High Performance ◽  
High Fidelity ◽  
French Translation ◽  
High Fidelity Simulation ◽  
Translation And Validation
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