Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

Efendioglu, Hasan Seckin; Yıldırım, Tülay; Fidanboylu, Kemal

doi:10.3390/s90907167

Cited by 31 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the second component, given in the (6), should be expressed in more simple form. To simply the γ S,Ri,D = γ S,R i γ R i ,D (γ S,R i + γ R i ,D + 1 ⁄ ), a tight upper bound is obtained [12] as:…”

Section: Fig 2 Relay Selection Modelmentioning

confidence: 99%

“…The BEP (Bit Error Probability) of system is obtained by substituting (11) and (12) into (10) and evaluating the integration with a similar way in [12], ( ) can be obtained in a closed form as:…”

Section: Fig 2 Relay Selection Modelmentioning

confidence: 99%

“…Nevertheless, the performance of AF Cooperative Diversity with an Nth best relay is also presented in [12]. However, the total transmitted power is equally allocated at the source and the selected relay in [11][12].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimum Power Allocation in Amplify-and-Forward Relay Selection Systems By Using ANNs

2016

IJCCIE

View full text Add to dashboard Cite

Abstract-Relay selection scheme in cooperative communication systems has been known as an effective technique that provides full diversity with efficient use of bandwidth in literature. Generally, the total power is shared equally between the source and the selected relay in relay selection schemes. However, the power allocation between the source and the selected relay is critical to further improve the error performance. In this study, we utilize Artificial Neural Networks (ANNs) to determine the optimum power allocation in Amplified-and Forward (AF) relay selection schemes for a minimum Bit Error Probability (BEP) performance over various channel gains. Since the analytical solution for an optimum power allocation in relay selection schemes is very complicated, the use of ANNs is an efficient and quick solution to further improve the performance.

show abstract

Section: Fig 2 Relay Selection Modelmentioning

confidence: 99%

Section: Fig 2 Relay Selection Modelmentioning

confidence: 99%

See 1 more Smart Citation

Optimum Power Allocation in Amplify-and-Forward Relay Selection Systems By Using ANNs

2016

IJCCIE

View full text Add to dashboard Cite

show abstract

“…Considering that the MFR signals intercepted by ELINT systems can be used as training samples, the data-driven predicting methods [24,25,26,27] should be proper alternatives. Predictive state representation (PSR) [28] is a data-driven dynamic system model, which has the advantages of being more generic, more powerful in representation, and easier to learn.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Ou¹,

Yong-guang²,

Zhao³

et al. 2017

Sensors

View full text Add to dashboard Cite

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

show abstract

“…Various studies on the recovery of lost data have mostly been focused on the failure tolerance of the wireless sensor networks [19,20]. The concept of large-scale neuron sensor networks or a carbon nanotubes (CNT)-based artificial neural system (ANS) [11,12,21] or the use of artificial neural networks in management of strain data in a sensor network [22,23] may be used in estimation of strain data without recovery of abnormal sensors.…”

Section: Introductionmentioning

confidence: 99%

A Practical Data Recovery Technique for Long-Term Strain Monitoring of Mega Columns during Construction

Choi

Kwon

Kim

et al. 2013

Sensors

View full text Add to dashboard Cite

A practical data recovery method is proposed for the strain data lost during the safety monitoring of mega columns. The analytical relations among the measured strains are derived to recover the data lost due to unexpected errors in long-term measurement during construction. The proposed technique is applied to recovery of axial strain data of a mega column in an irregular building structure during construction. The axial strain monitoring using the wireless strain sensing system was carried out for one year and five months between 23 July 2010 and 22 February 2012. During the long-term strain sensing, three different types of measurement errors occurred. Using the recovery technique, the strain data that could not be measured at different intervals in the measurement were successfully recovered. It is confirmed that the problems that may occur during long-term wireless strain sensing of mega columns during construction could be resolved through the proposed recovery method.

show abstract

Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

Cited by 31 publications

References 18 publications

Optimum Power Allocation in Amplify-and-Forward Relay Selection Systems By Using ANNs

Optimum Power Allocation in Amplify-and-Forward Relay Selection Systems By Using ANNs

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

A Practical Data Recovery Technique for Long-Term Strain Monitoring of Mega Columns during Construction

Contact Info

Product

Resources

About