Developing new measuring and ANN prediction method for the longitudinal diameter of intermingled untwisted heat set bulk continues filament yarns

Dadgar, Mehran

doi:10.1016/j.measurement.2020.108727

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…We also review the proximity sensors using IR (light? ), ultrasound, and electromagnetic waves [2,15,29,32,[37][38][39]. First, we consider the principle of distance measurement using optical sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Neural network applications can diagnose faults based on predicting the gearbox's oil level and speed. Dadgar developed an ANN and regression models [15]. These neural networks were utilized to predict the diameter of the heat-treated fiber.…”

Section: Related Workmentioning

confidence: 99%

“…To predict or accurately estimate the required values, it is essential to have a large amount of input data that fully describe the distinct properties [13]. The success of deep learning algorithms depends on selecting suitable neural network structures for applications [14,15]. Therefore, analysis of the system components and selection of the appropriate neural network structure play a decisive role in deep learning applications.…”

Section: Introductionmentioning

confidence: 99%

“…The first solution improves the hardware accuracy of the sensor, including optimizing the design of the sensor's components [1,2,4,5,8,[26][27][28]. The second method uses software to calculate more accurate values from the raw sensor values [11,14,15,19,[29][30][31][32][33]. Most of the research on optical sensors has focused on improving the sensor accuracy by using a highly focused beam and has avoided the influence of environmental factors [8].…”

Section: Introductionmentioning

confidence: 99%

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Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems

Bui¹,

Nguyen²,

Vu³

et al. 2022

Meas. Sci. Technol.

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Optical time of flight (ToF) sensors has potentials in the revolution of distance measurement. These sensors can continously monitor the distance and track the object movement. However, the existed sensing methods for such distance optical sensors mainly calculate the flight time e.g., pulse transit and receive time without considering theenvironment effects. Therefore, the measurement accuracy is severely reduced. There areother technologies with higher accuracy in the distance measurement. Nonetheless, they are too expensive due to the high accurate power supply. In this paper, we innovatievely improve the accuracy in continous distance measurement using the artificial neural network (ANN) technique. The proposed method can be applied for very cheap optical distance sensors with analog output in a real-time system.. Moreover, the propose method can self-calibrate and be miniaturized for such cheap analog sensor applications. The prototype is built with the infrared sensor GP2Y0A02YK0F and Arduino control board (add the name of Adruino board), the ANN is implemented using the deep learning algorithm. The test results show that the distance measurement accurary is significantly improved and the measuring range is increased from 15 to 150 cm. Also, we calculate MSE, MAE, MBE, and R2 for further perfomance evaluation. The experimental results have proved the superior of the proposed ANN method in optical distance measurement. The proposed method can be applied for many types of sensors

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems

Bui¹,

Nguyen²,

Vu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…ANNs are artificial intelligence applications that simulate the biological processes of neurons in the human brain. ANN has become a favorite, massively parallel information processing system (Moradi et al, 2021;Dadgar, 2021;Kaviani and Sohn, 2021). ANNs imitate the information processing structure or functionalities of biological neural networks by utilizing many coupled and simple artificial neurons (Gately, 1996;Hamedi and Jahromi, 2021).…”

Section: The Efficiency Of Canadian Universitiesmentioning

confidence: 99%

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Jauhar

Zolfagharinia²,

Amin³

2022

BIJ

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PurposeThis research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.Design/methodology/approachThe authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.FindingsThe major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.Research limitations/implicationsThis paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.Practical implicationsThe findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.Originality/valueThis research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

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Genetic algorithm-assisted an improved AdaBoost double-layer for oil temperature prediction of TBM

Ren

Wang

Pang

et al. 2022

Advanced Engineering Informatics

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Developing new measuring and ANN prediction method for the longitudinal diameter of intermingled untwisted heat set bulk continues filament yarns

Cited by 6 publications

References 18 publications

Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems

Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Genetic algorithm-assisted an improved AdaBoost double-layer for oil temperature prediction of TBM

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