Modeling and performance analysis of an SMD assembly station using stochastic Petri nets and artificial neural networks

2002 ◽  
Author(s):  
M.A. Arslan ◽  
I. Fidan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Analysis ◽  
Petri Nets ◽  
Stochastic Petri Nets ◽  
Artificial Neural ◽  
And Performance

scholarly journals Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets

2021 ◽  
Vol 13 (7) ◽  
pp. 1262
Author(s):  
Leyi Shi ◽  
Shanshan Du ◽  
Yifan Miao ◽  
Songbai Lan
Keyword(s):  
Performance Analysis ◽  
Petri Nets ◽  
Performance Optimization ◽  
Stochastic Petri Nets ◽  
Satellite Network ◽  
Moving Target ◽  
Defense System ◽  
Moving Target Defense ◽  
Active Defense ◽  
And Performance

With the development of satellite communication networks and the increase of satellite services, security problems have gradually become some of the most concerning issues. Researchers have made great efforts, including conventional safety methods such as secure transmission, anti-jamming, secure access, and especially the new generation of active defense technology represented by MTD. However, few scholars have theoretically studied the influence of active defense technique on the performance of satellite networks. Formal modeling and performance analysis have not been given sufficient attention. In this paper, we focus on the performance evaluation of satellite network moving target defense system. Firstly, two Stochastic Petri Nets (SPN) models are constructed to analyze the performance of satellite network in traditional and active defense states, respectively. Secondly, the steady-state probability of each marking in SPN models is obtained by using the isomorphism relation between SPN and Markov Chains (MC), and further key performance indicators such as average time delay, throughput, and the utilization of bandwidth are reasoned theoretically. Finally, the proposed two SPN models are simulated based on the PIPE platform. In addition, the effect of parameters on the selected performance indexes is analyzed by varying the values of different parameters. The simulation results prove the correctness of the theoretical reasoning and draw the key factors affecting the performance of satellite network, which can provide an important theoretical basis for the design and performance optimization of the satellite network moving target defense system.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data

2020 ◽  
pp. 845-864
Author(s):  
Sara Moridpour ◽  
Ehsan Mazloumi ◽  
Reyhaneh Hesami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asset Management ◽  
Management Strategies ◽  
Operating Cost ◽  
Light Rail ◽  
Ann Model ◽  
Track Geometry ◽  
Artificial Neural ◽  
And Performance

The increase in number of passengers and tramcars will wear down existing rail structures faster. This is forcing the rail infrastructure asset owners to incorporate asset management strategies to reduce total operating cost of maintenance whilst improving safety and performance. Analysing track geometry defects is critical to plan a proactive maintenance strategy in short and long term. Repairing and maintaining the correctly selected tram tracks can effectively reduce the cost of maintenance operations. The main contribution of this chapter is to explore the factors influencing the degradation of tram tracks (light rail tracks) using existing geometric data, inspection data, load data and repair data. This chapter also presents an Artificial Neural Networks (ANN) model to predict the degradation of tram tracks. Predicting the degradation of tram tracks will assist in understanding the maintenance needs of tram system and reduce the operating costs of the system.

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