Nitrogen-rich salts based on the combination of 1,2,4-triazole and 1,2,3-triazole rings: a facile strategy for fine tuning energetic properties

2018 ◽  
Vol 6 (5) ◽  
pp. 2239-2248 ◽  
Author(s):  
Zhen Xu ◽  
Guangbin Cheng ◽  
Shunguan Zhu ◽  
Qiuhan Lin ◽  
Hongwei Yang
Keyword(s):  
High Performance ◽  
Fine Tuning ◽  
Energetic Properties ◽  
Energetic Salts

Twenty-one high performance monoanionic and dianionic energetic salts based on the combination of 1,2,4-triazole and 1,2,3-triazole rings were studied.

3,4-Diamino-1,2,4-triazole based energetic salts: synthesis, characterization, and energetic properties

2015 ◽  
Vol 39 (7) ◽  
pp. 5265-5271 ◽  
Author(s):  
Jin-Ting Wu ◽  
Jian-Guo Zhang ◽  
Xin Yin ◽  
Zi-Yuan Cheng ◽  
Cai-Xia Xu
Keyword(s):  
Metathesis Reaction ◽  
Reaction Method ◽  
Energetic Properties ◽  
Protonation Reaction ◽  
Energetic Salts

A series of energetic salts based on 3,4-diamino-1,2,4-triazole with promising detonation performances have been synthesized using a metathesis reaction method or a protonation reaction method.

scholarly journals Deep Learning of COVID-19 Chest X-Rays: New Models or Fine Tuning?

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Dominant Role ◽  
Care Center ◽  
Characteristic Curve ◽  
Fine Tuning ◽  
Urgent Care ◽  
X Rays ◽  
Training Time

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

Design, synthesis and investigations of a series of energetic salts through the variation of amines and concentration of picrate anions

CrystEngComm ◽  
2020 ◽  
Vol 22 (29) ◽  
pp. 4842-4852 ◽  
Author(s):  
Ramling S. Mathpati ◽  
Srinivas Dharavath ◽  
Nikhil Kumar ◽  
Vikas D. Ghule ◽  
Avijit Kumar Paul ◽  
...  
Keyword(s):  
Picric Acid ◽  
Design Synthesis ◽  
Energetic Properties ◽  
Energetic Salts ◽  
High Yields

Eight energetic salts combining N-bases with picrate ion were synthesized in high yields. The structures and energetic properties were regulated by the variation of N-base molecules and the concentration of the picric acid in the reaction mixture.

HIERARCHICAL MAPPING FOR HPC APPLICATIONS

2011 ◽  
Vol 21 (03) ◽  
pp. 279-299 ◽  
Author(s):  
I-HSIN CHUNG ◽  
CHE-RUNG LEE ◽  
JIAZHENG ZHOU ◽  
YEH-CHING CHUNG
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Scale Up ◽  
Matrix Multiplication ◽  
Spectral Graph Theory ◽  
Communication Patterns ◽  
Fine Tuning ◽  
Mapping Algorithm ◽  
Communication Time ◽  
Run Time

As the high performance computing systems scale up, mapping the tasks of a parallel application onto physical processors to allow efficient communication becomes one of the critical performance issues. Existing algorithms were usually designed to map applications with regular communication patterns. Their mapping criterion usually overlooks the size of communicated messages, which is the primary factor of communication time. In addition, most of their time complexities are too high to process large scale problems. In this paper, we present a hierarchical mapping algorithm (HMA), which is capable of mapping applications with irregular communication patterns. It first partitions tasks according to their run-time communication information. The tasks that communicate with each other more frequently are regarded as strongly connected. Based on their connectivity strength, the tasks are partitioned into supernodes based on the algorithms in spectral graph theory. The hierarchical partitioning reduces the mapping algorithm complexity to achieve scalability. Finally, the run-time communication information will be used again in fine tuning to explore better mappings. With the experiments, we show how the mapping algorithm helps to reduce the point-to-point communication time for the PDGEMM, a ScaLAPACK matrix multiplication computation kernel, up to 20% and the AMG2006, a tier 1 application of the Sequoia benchmark, up to 7%.

Fine-Tuning the Energetic Properties of Complexes through Ligand Modification

2018 ◽  
Vol 18 (4) ◽  
pp. 2217-2224 ◽  
Author(s):  
Ji-Chuan Zhang ◽  
Hui Su ◽  
Shu Guo ◽  
Ya-Lu Dong ◽  
Shao-Wen Zhang ◽  
...  
Keyword(s):  
Fine Tuning ◽  
Ligand Modification ◽  
Energetic Properties

Investigation of Solid Solubility, Hardness, and Thermal Properties of Au-Ge-Sb System

2014 ◽  
Vol 1016 ◽  
pp. 336-341
Author(s):  
Kamolchanok Thipayarat ◽  
Ekasit Nisaratanaporn ◽  
Boonrat Lohwongwatana
Keyword(s):  
High Performance ◽  
Alloy System ◽  
Fine Tuning ◽  
Lead Free ◽  
Diagram Method ◽  
Average Value ◽  
Wide Range ◽  
Lead Free Solders ◽  
Hardness Values ◽  
Fine Tune

In recent years, the Au-Ge-Sb system has been studied as a possible alternative alloy for soldering applications [1-4]. The alloy has various fbenefits such as (i) low melting temperature which allows the alloy system to be used as a drop-in solution for high performance lead-free solders, (ii) three distinct phases of different hardness values (100, 150 and 500 HV) which offer the ability to fine tune the composition and microstructure to a wide range of properties, and (iii) limited solute solubility which offers ease of control and fine-tuning of microstructure, mechanical properties and colors. Gold compositions centered around 75wt% gold were modeled and selected using the CALPHAD (CALculation of PHAse Diagram) method. Predictions were later confirmed by experimental results. The alloy solidifies in the range of 242.5-261.7 °C. The overall hardness values were measured and confirmed to be within the volume average value of all the phases combined.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

scholarly journals Relative depth estimation from single monocular images with deep convolutional network

2017 ◽  
Author(s):  
◽  
Alex Yang
Keyword(s):  
High Performance ◽  
Depth Estimation ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Relative Depth ◽  
Optimized Design ◽  
Convolutional Network ◽  
Real Time Processing ◽  
Deep Convolutional Network ◽  
Depth Inference

Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice. This thesis addresses the problem of depth estimation from single monocular images using a deep convolutional neural fields framework; which consists of convolutional feature extraction, superpixel dimensionality reduction, and depth inference. Data were collected using a stereo vision camera, which generated depth maps though triangulation that are paired with visual images. The visual image (input) and computed depth map (desired output) are used to train the model, which has achieved 83 percent test accuracy at the standard 25 percent tolerance. The problem has been formulated as depth regression for superpixels and our technique is superior to existing state-of-the-art approaches based on its demonstrated its generalization ability, high prediction accuracy, and real-time processing capability. We utilize the VGG-16 deep convolutional network as feature extractor and conditional random fields depth inference. We have leveraged a multi-phase training protocol that includes transfer learning and network fine-tuning lead to high performance accuracy. Our framework has a robust modular nature with capability of replacing each component with different implementations for maximum extensibility. Additionally, our GPU-accelerated implementation of superpixel pooling has further facilitated this extensibility by allowing incorporation of feature tensors with exible shapes and has provided both space and time optimization. Based on our novel contributions and high-performance computing methodologies, the model achieves a minimal and optimized design. It is capable of operating at 30 fps; which is a critical step towards empowering real-world applications such as autonomous vehicle with passive relative depth perception using single camera vision-based obstacle avoidance, environment mapping, etc.

scholarly journals Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Wansuk Choi ◽  
Seoyoon Heo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Model Fit ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Video Classification ◽  
Video Clips ◽  
Performance Computing

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

Energetic salts of 4-nitramino-3-(5-dinitromethyl-1,2,4-oxadiazolyl)-furazan: powerful alliance towards good thermal stability and high performance

2018 ◽  
Vol 6 (35) ◽  
pp. 16833-16837 ◽  
Author(s):  
Chunlin He ◽  
Gregory H. Imler ◽  
Damon A. Parrish ◽  
Jean'ne M. Shreeve
Keyword(s):  
Thermal Stability ◽  
High Performance ◽  
Energetic Compounds ◽  
Good Thermal Stability ◽  
Straightforward Method ◽  
Energetic Salts

A new series of 4-nitramino-3-(5-dinitromethyl-1,2,4-oxadiazolyl)-furazan-based energetic compounds which are competitive with HMX was synthesized in four steps with an overall yield of ∼50% by using a straightforward method.

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