Motion Modeling using Motion Concepts of Fuzzy Artificial Potential Fields

Motlagh, Omid; Ramli, Abdul Rahman; Motlagh, Fateme Navvab; Tang, Sai Hong; Ismail, Napsiah

doi:10.15282/ijame.2.2010.6.0014

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…As an alternative, a simulation model can be developed to predict the actual condition of engine performance by using the artificial neural network approach. ANN modelling has been chosen for its capability to solve complex and difficult problems where conventional modelling methods fail [11][12][13][14]. A well-trained ANN can be used as a predictive model for a specific application, and is a data-processing system inspired by biological neural systems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of marine diesel engine performance by using artificial neural network model

Noor¹,

Mamat²,

Najafi³

et al. 2016

J. Mech, Eng, Sci.

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This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R 2 ) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Prediction of marine diesel engine performance by using artificial neural network model

Noor¹,

Mamat²,

Najafi³

et al. 2016

J. Mech, Eng, Sci.

View full text Add to dashboard Cite

show abstract

“…For a long time, new product development has been considered as an essential element for organizational competitiveness and success. Product development also plays a critical role in the survival and success of manufacturing enterprises, and many researchers have improved their cognition of the need to manage it strategically (Ayağ & Özdemir, 2009;Brown & Eisenhardt, 1995;Chesbrough & Teece, 2002;Griffin & Hauser, 1996;Krishnan & Ulrich, 2001;Motlagh, Ramli, Motlagh, Tang, & Ismail, 2010). However, truly effective product development remains difficult (Lee & Santiago, 2008).…”

Section: Introductionmentioning

confidence: 99%

Application of House of Quality, Fuzzy-Analytical Hierarchy Process and Rough-Grey Analysis in Design Concept Evaluation – A Case Study

Turan¹,

Omar²

2013

J MECH ENG SCI

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Design concept evaluation plays a critical role in the early phases of product development as it has a significant impact on the downstream development processes, as well as on the success of the product developed. In this work, a novel three-stage methodology has been developed. The preliminary stage screens all the criteria from different viewpoints using House of Quality (HoQ). The second stage uses a Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) to obtain the alternatives weighting, and the final stage verifies the ranking of the alternatives by a Rough-Grey Analysis. This method will enable designers to make better informed decisions before finalizing their choice. A case example from industry is presented to demonstrate the efficacy of the proposed methodology. The prospective benefit of this new method is that it can help designers to reduce the risk of late design changes or corrections. The result of the example shows that the HoQ, Fuzzy-AHP and Rough-Grey Analysis approach provided a novel alternative to existing methods of performing design concept evaluation. The proposed framework has successfully helped the designers to reduce the product development time.

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Optimal accuracy and runtime trade-off in wavelet based single-trial P300 detection

Motlagh

Tang

Motlagh

2013

2013 IEEE International Conference on Signal and Image Processing Applications

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Single-trail detection of P300 from EEG signals is the main challenge of diagnostic purposes and research applications. In this article, Wavelet Transform is used for feature extraction from EEG signals. The goal is to prove the capability of wavelet transform in P300 feature extraction. A number of established wavelet feature extraction methods were evaluated from accuracy and computation speed perspectives. To conduct uniform evaluation, Support Vector Machine (SVM) was used for classification of all methods. The results show that DWT can be fast in computing signal features with lower accuracy, while a combination of DWT and T-CWT is proven to be more accurate when real-time computation is concerned.

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Motion Modeling using Motion Concepts of Fuzzy Artificial Potential Fields

Cited by 3 publications

References 14 publications

Prediction of marine diesel engine performance by using artificial neural network model

Prediction of marine diesel engine performance by using artificial neural network model

Application of House of Quality, Fuzzy-Analytical Hierarchy Process and Rough-Grey Analysis in Design Concept Evaluation – A Case Study

Optimal accuracy and runtime trade-off in wavelet based single-trial P300 detection

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