scholarly journals Packet Loss Differentiation Over Manet Based on a BP Neural Network

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Dimitris Kanellopoulos ◽  
Pratik Gite
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Packet Loss ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Backpropagation Neural Network ◽  
Packet Losses ◽  
Simulation Experiments ◽  
Data Packets ◽  
Simulation Results

An adaptive distributed routing algorithm is essential in MANETs, since there is no central routing system. Actually, there is no central point of coordination; each node is responsible for forwarding data packets to other nodes, thereby acting as router and host. A packet might travel through multiple intermediary ad hoc nodes in order to arrive to its destination, while the nature of wireless multi-hop channel is bringing in various types of packet losses. This paper focuses on three main reasons of online packet losses in MANETs: (1) losses due to wireless link errors; (2) losses due to congestion; and (3) losses due to route alteration. It proposes a deep learning-based algorithm for packet loss discrimination. The algorithm uses the backpropagation neural network (BPNN) concept. We performed simulation experiments for evaluating the performance of the proposed loss discrimination algorithm under different network configurations. Through simulation results, we confirmed that the proposed algorithm improves packet loss discrimination and route alteration in the network. It also reduces congestion and increases network throughput.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

scholarly journals A modified NK algorithm based on BP neural network and DEMATEL for evolution path optimization of urban innovation ecosystem

2021 ◽  
Author(s):  
Ruijian Liu ◽  
Fangcheng Tang ◽  
Yuhan Wang ◽  
Shaofang Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Governance ◽  
Innovation Network ◽  
Governance System ◽  
Innovation Ecosystem ◽  
Governance Capacity ◽  
New Era ◽  
Evolution Path ◽  
Simulation Results

AbstractIn the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of “how the urban innovation ecosystem evolves”, this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects.

scholarly journals Reducing Packet Losses in Mobile Ad Hoc Network Using the Warning Message Generated from a Routing Node

2015 ◽  
Vol 62 (2) ◽  
pp. 141-145 ◽  
Author(s):  
Rezvi Shahariar ◽  
Abu Naser
Keyword(s):  
Energy Level ◽  
Total Energy ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Critical Energy ◽  
Packet Losses ◽  
Warning Message ◽  
Mobile Ad Hoc

In mobile ad hoc network communication is performed usually by using only send and receive messages and every node is powered by limited energy from low capacity battery. Every send or receive message takes particular amount of energy from the node. So node’s total energy level gradually decreases each time while it is sending or receiving something. In this way node will die out and packets coming from the source will be dropped since one of the routing node on the current route is no longer functioning. These packet loss events are observed and minimized in this paper. In the proposed approach, when source receives Warning Message from any routing node on the ongoing route then it will stop sending packets on the ongoing route. Critical energy level of routing node has been defined to generate a Warning Message when routing node’s energy level reduces to critical energy level. DOI: http://dx.doi.org/10.3329/dujs.v62i2.21979 Dhaka Univ. J. Sci. 62(2): 141-145, 2014 (July)

scholarly journals Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

2022 ◽  
Author(s):  
Yangbing Zheng ◽  
Xiao Xue ◽  
Jisong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Fuzzy Relation ◽  
Analysis Procedure ◽  
Fuzzy Neural ◽  
Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

Investigating the Performance of the TSS Scheme in Noisy MANETs

2013 ◽  
pp. 1038-1058
Author(s):  
Hussein Al-Bahadili ◽  
Shakir M. Hussain ◽  
Ghassan F. Issa ◽  
Khaled El-Zayyat
Keyword(s):  
Packet Loss ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Node Mobility ◽  
Noisy Environment ◽  
Transmission Impairments ◽  
Threshold Secret Sharing ◽  
Success Ratio ◽  
Wireless Signal ◽  
Simulation Results

A Mobile Ad Hoc Network (MANET) suffers from high packet-loss due to various transmission impairments, such as: wireless signal attenuation, free space loss, thermal noise, atmospheric absorption, multipath effect, and refraction. All of these impairments are represented by a generic name, noise, and therefore such a network is referred to as a noisy network. For modeling and simulation purposes, the noisy environment is described by introducing a probability function, namely, the probability of reception (pc), which is defined as the probability that transmitted data is successfully delivered to its destination despite the presence of noise. This chapter describes the implementation and investigates the performance of the Threshold Secret Sharing (TSS) node authentication scheme in noisy MANETs. A number of simulations are performed using the MANET Simulator (MANSim) to estimate the authentication success ratio for various threshold secret shares, number of nodes, node speeds, and noise-levels. Simulation results demonstrate that, for a certain threshold secret share, the presence of noise inflicts a significant reduction in the authentication success ratio, while node mobility inflicts no or an insignificant effect. The outcomes of these simulations are important to facilitate efficient network management.

scholarly journals Risk Assessment System Based on Fuzzy Composite Evaluation and a Backpropagation Neural Network for a Shield Tunnel Crossing under a River

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao Liang ◽  
Taiyue Qi ◽  
Zhiyi Jin ◽  
Shaojie Qin ◽  
Pengtao Chen
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Risk Assessment ◽  
Assessment System ◽  
Chamber Pressure ◽  
Shield Tunnel ◽  
Risk Level ◽  
Backpropagation Neural Network ◽  
Grouting Pressure ◽  
Simulation Results

Constructing a shield tunnel that crosses under a river poses considerable safety risks, and risk assessment is essential for guaranteeing the safety of tunnel construction. This paper studies a risk assessment system for a shield tunnel crossing under a river. Risk identification is performed for the shield tunnel, and the risk factors and indicators are determined. The relationship between the two is determined preliminarily by numerical simulation, the numerical simulation results are verified by field measurements, and a sample set is established based on the numerical simulation results. Fuzzy comprehensive evaluation and a backpropagation neural network are then used to evaluate and analyze the risk level. Finally, the risk assessment system is used to evaluate the risk for Line 5 of the Hangzhou Metro in China. Based on the evaluation results, adjustments to the slurry strength, grouting pressure, and soil chamber pressure are proposed, and the risk is mitigated effectively.

Heterogeneous Architecture for Ad Hoc Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1059-1062 ◽  
Author(s):  
Xue Bing Wang
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc Network ◽  
Network Architecture ◽  
Ad Hoc ◽  
Model Simulation ◽  
Routing Algorithm ◽  
Clustering Coefficient ◽  
Heterogeneous Architecture ◽  
Simulation Results ◽  
Hoc Networks

By making small adjustment to general ad hoc network architecture, we build a network topology with short average path length and high clustering coefficient, which are two important metrics of ad hoc networks. Furthermore, an efficient probabilistic flooding routing algorithm is proposed based on this network model. Simulation results show that this architecture behaves better performance than its ordinary counterpart.

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

Deadlock Free Load Balanced Adaptive Routing for Network on Chip (NoC) Systems

2016 ◽  
Vol 13 (10) ◽  
pp. 7592-7598
Author(s):  
J Kalaivani ◽  
B Vinayagasundaram
Keyword(s):  
Packet Loss ◽  
Probability Function ◽  
Adaptive Routing ◽  
Network On Chip ◽  
Communication Architecture ◽  
Data Packets ◽  
Oblivious Routing ◽  
Simulation Results ◽  
On Chip ◽  
Load Balanced

The Network-on-Chip (NoC) systems have emerged in on-chip communication architecture in various fields. To achieve excellent results in Network on Chip (NoC) systems application, the routing must eliminate the deadlock issues from the network. To overcome this issue in the network, in this paper, we propose Deadlock Free Load Balanced Adaptive Routing. In this approach, Oblivious Routing (OR) algorithm is implemented on the channel by using the probability function. The network considers the capacity of the node and tries to maximize the throughput based on the connectivity between the data packets flow and minimize the channel load. A Reconfiguration Protocol is used for the data packets to choose other channel in the network if the deadlock occurs. Simulation results show that this approach reduces the delay and packet loss in the network.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

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