281,173 research outputs found
Optical Network Virtualisation using Multi-technology Monitoring and SDN-enabled Optical Transceiver
We introduce the real-time multi-technology transport layer monitoring to
facilitate the coordinated virtualisation of optical and Ethernet networks
supported by optical virtualise-able transceivers (V-BVT). A monitoring and
network resource configuration scheme is proposed to include the hardware
monitoring in both Ethernet and Optical layers. The scheme depicts the data and
control interactions among multiple network layers under the software defined
network (SDN) background, as well as the application that analyses the
monitored data obtained from the database. We also present a re-configuration
algorithm to adaptively modify the composition of virtual optical networks
based on two criteria. The proposed monitoring scheme is experimentally
demonstrated with OpenFlow (OF) extensions for a holistic (re-)configuration
across both layers in Ethernet switches and V-BVTs
Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks
In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium matrix
composite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites with
four different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were produced
in different sintering times. The specimens were characterized using metallographic observation,
microhardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned conditions,
a multi layer perceptron back propagation feed forward neural network was constructed to evaluate
and compare the experimental calculated data to predicted values. In neural network training modules,
different composition, sintering time and reinforcement size were used as input (3 inputs), hardness and
Ultimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using the
prepared training set. At the end of training process the test data were used to check the system’s accuracy.
As a result, the comparison of neural network output results with the results from experiments and
empirical relationship has shown good agreement with average error of 2.5%.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3511
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Materials discovery is crucial for making scientific advances in many
domains. Collections of data from experiments and first-principle computations
have spurred interest in applying machine learning methods to create predictive
models capable of mapping from composition and crystal structures to materials
properties. Generally, these are regression problems with the input being a 1D
vector composed of numerical attributes representing the material composition
and/or crystal structure. While neural networks consisting of fully connected
layers have been applied to such problems, their performance often suffers from
the vanishing gradient problem when network depth is increased. In this paper,
we study and propose design principles for building deep regression networks
composed of fully connected layers with numerical vectors as input. We
introduce a novel deep regression network with individual residual learning,
IRNet, that places shortcut connections after each layer so that each layer
learns the residual mapping between its output and input. We use the problem of
learning properties of inorganic materials from numerical attributes derived
from material composition and/or crystal structure to compare IRNet's
performance against that of other machine learning techniques. Using multiple
datasets from the Open Quantum Materials Database (OQMD) and Materials Project
for training and evaluation, we show that IRNet provides significantly better
prediction performance than the state-of-the-art machine learning approaches
currently used by domain scientists. We also show that IRNet's use of
individual residual learning leads to better convergence during the training
phase than when shortcut connections are between multi-layer stacks while
maintaining the same number of parameters.Comment: 9 pages, under publication at KDD'1
Classification of aerial laser scanning point clouds using machine learning: a comparison between Random Forest and Tensorflow
In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1=0.823 for the 9 classes considered, whereas TF had average F1=0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned
Antimicrobial peptide identification using multi-scale convolutional network
Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem.
Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy.
Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN
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Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures
The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu
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