How to cite this paper
Aladwani, H., Ariffin, M & Mustapha, F. (2022). A supervised machine-learning method for optimizing the automatic transmission system of wind turbines.Engineering Solid Mechanics, 10(1), 35-56.
Refrences
Abbasi, W. A., Wang, Z., Zhou, Y., & Hassan, S. (2019). Research on measurement of supply chain finance credit risk based on Internet of Things. International Journal of Distributed Sensor Networks, 15(9), 1550147719874002.
Abecassis-Moedas, C. (2006). Integrating design and retail in the clothing value chain: An empirical study of the organisation of design. International Journal of Operations & Production Management, 26(4), 412-428.
Al-Bahadly, I. (2009). Building a wind turbine for rural home. Energy for sustainable development, 13(3), 159-165.
Ariffin, M., Yee Lee, T., & Mohamed, S. B. (2014). Design Improvement of Automatic Transmission System for Remote Controlled Car. Paper presented at the Applied Mechanics and Materials.
Asrol, M., Papilo, P., & Gunawan, F. E. (2021). Support Vector Machine with K-fold Validation to Improve the Industry’s Sustainability Performance Classification. Procedia Computer Science, 179, 854-862.
Bhutta, M. M. A., Hayat, N., Farooq, A. U., Ali, Z., Jamil, S. R., & Hussain, Z. (2012). Vertical axis wind turbine–A review of various configurations and design techniques. Renewable and Sustainable Energy Reviews, 16(4), 1926-1939.
Brito, P. Q., Soares, C., Almeida, S., Monte, A., & Byvoet, M. (2015). Customer segmentation in a large database of an online customized fashion business. Robotics and Computer-Integrated Manufacturing, 36, 93-100.
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Bozdogan, H. (2018). Supply chain integrity: A key to sustainable supply chain management. Journal of Business Logistics, 39(1), 38-56.
Cheng, Y., Peng, J., Gu, X., Zhang, X., Liu, W., Zhou, Z., . . . Huang, Z. (2020). An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain. Computers & Industrial Engineering, 139, 105834.
Cho, J., Jeong, K., Park, M., & Park, N. (2015). Dynamic response analysis of wind turbine gearbox using simplified local tooth stiffness of internal gear system. Journal of Computational and Nonlinear Dynamics, 10(4), 041001.
Chong, W., Fazlizan, A., Poh, S., Pan, K., & Ping, H. (2012). Early development of an innovative building integrated wind, solar and rain water harvester for urban high rise application. Energy and Buildings, 47, 201-207.
Chong, W., Naghavi, M., Poh, S., Mahlia, T., & Pan, K. (2011). Techno-economic analysis of a wind–solar hybrid renewable energy system with rainwater collection feature for urban high-rise application. Applied Energy, 88(11), 4067-4077.
Chong, W., Pan, K., Poh, S., Fazlizan, A., Oon, C., Badarudin, A., & Nik-Ghazali, N. (2013). Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application. Renewable Energy, 51, 388-397.
Chong, W., Poh, S., Fazlizan, A., & Pan, K. (2012). Vertical axis wind turbine with omni-directional-guide-vane for urban high-rise buildings. Journal of Central South University, 19(3), 727-732.
Dabiri, J. O. (2011). Potential order-of-magnitude enhancement of wind farm power density via counter-rotating vertical-axis wind turbine arrays. Journal of renewable and sustainable energy, 3(4), 043104.
Daneshmand-Mehr, M., Najafi, M., & Sadeghian, R. (2020). Determining the optimal forecasting combination of the four-level supply chain to minimize the bullwhip effect. مدیریت صنعتی, 15(51), 13-30.
Delgoshaei, A., Ariffin, M. K. A., & Baharudin, B. (2016). Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach. Journal of Industrial Engineering and Management (JIEM), 9(3), 732-785.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Delgoshaei, A., & Gomes, C. (2019). A new method for minimizing cell underutilization in the process of dynamic cell forming and scheduling using artificial neural networks. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 13(1), JAMDSM0021-JAMDSM0021.
Eriksson, S., Bernhoff, H., & Leijon, M. (2008). Evaluation of different turbine concepts for wind power. Renewable and Sustainable Energy Reviews, 12(5), 1419-1434.
Fallahpour, A., Kazemi, N., Molani, M., Nayyeri, S., & Ehsani, M. (2018). An intelligence-based model for supplier selection integrating data envelopment analysis and support vector machine. Iranian Journal of Management Studies, 11(2), 209-241.
Fanoodi, B., Malmir, B., & Jahantigh, F. F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in biology and medicine, 113, 103415.
Foltin, P., Brunclík, M., Ondryhal, V., & Vogal, L. (2018). Usability of Performance Indicators of Logistics Infrastructure Availability in Supply Chain Designing. Business Logistics in Modern Management.
Gavalda, J., Massons, J., & Diaz, F. (1990). Experimental study on a self-adapting Darrieus—Savonius wind machine. Solar & Wind Technology, 7(4), 457-461.
Gipe, P. (2004). Wind power. Wind Engineering, 28(5), 629-631.
Goettsch, D., Castillo-Villar, K. K., & Aranguren, M. (2020). Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing. Energies, 13(24), 6554.
Golkhoo, F., & Moselhi, O. (2019). Optimized material management in construction using multi-layer perceptron. Canadian Journal of Civil Engineering, 46(10), 909-923.
Greenblatt, D., Schulman, M., & Ben-Harav, A. (2012). Vertical axis wind turbine performance enhancement using plasma actuators. Renewable Energy, 37(1), 345-354.
Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978-6985.
Hashim, H., & Ho, W. S. (2011). Renewable energy policies and initiatives for a sustainable energy future in Malaysia. Renewable and Sustainable Energy Reviews, 15(9), 4780-4787.
Herbert, G. J., Iniyan, S., Sreevalsan, E., & Rajapandian, S. (2007). A review of wind energy technologies. Renewable and Sustainable Energy Reviews, 11(6), 1117-1145.
Ho, T. K. (1995). Random decision forests. Paper presented at the Proceedings of 3rd international conference on document analysis and recognition.
Hossain, A., Iqbal, A., Rahman, A., Arifin, M., & Mazian, M. (2007). Design and development of a 1/3 scale vertical axis wind turbine for electrical power generation. Journal of Urban and Environmental Engineering, 1(2), 53-60.
Howell, R., Qin, N., Edwards, J., & Durrani, N. (2010). Wind tunnel and numerical study of a small vertical axis wind turbine. Renewable Energy, 35(2), 412-422.
Ishak, A., & Wijaya, T. (2019). Rubber Spare Parts Supplier Selection Model Using Artificial Neural Network: Multi-Layer Perceptron. Paper presented at the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019).
Islam, M., Amin, M. R., Ting, D. S.-K., & Fartaj, A. (2007). Aerodynamic factors affecting performance of straight-bladed vertical axis wind turbines. Paper presented at the ASME international mechanical engineering congress and exposition.
Islam, M., Ting, D. S.-K., & Fartaj, A. (2008). Aerodynamic models for Darrieus-type straight-bladed vertical axis wind turbines. Renewable and Sustainable Energy Reviews, 12(4), 1087-1109.
Jiang, M. P., Zheng, S. Y., Wang, H., Zhang, S. Y., Yao, D. S., Xie, C. F., & Liu, D. L. (2019). Predictive model of aflatoxin contamination risk associated with granary-stored corn with versicolorin A monitoring and logistic regression. Food Additives & Contaminants: Part A, 36(2), 308-319.
Johari, M. K., Jalil, M., & Shariff, M. F. M. (2018). Comparison of horizontal axis wind turbine (HAWT) and vertical axis wind turbine (VAWT). International Journal of Engineering and Technology, 7(4.13), 74-80.
Kanellos, F., & Hatziargyriou, N. (2008). Control of variable speed wind turbines in islanded mode of operation. IEEE Transactions on Energy Conversion, 23(2), 535-543.
Korobenko, A., Hsu, M.-C., Akkerman, I., & Bazilevs, Y. (2014). Aerodynamic simulation of vertical-axis wind turbines. Journal of Applied Mechanics, 81(2).
Kozłowski, E., Borucka, A., & Świderski, A. (2020). Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodność, 22.
Liu, Y., & Huang, L. (2020). Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks, 16(1), 1550147720903631.
Mahadevan, S., Poornima, S., Tripathi, K., & Pushpalatha, M. (2019). A survey on machine learning algorithms for the blood donation supply chain. Paper presented at the Journal of Physics: Conference Series.
Mehrjoo, M., & Pasek, Z. J. (2014). Impact of product variety on supply chain in fast fashion apparel industry. Procedia CIRP, 17, 296-301.
Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International Journal of Consumer Studies, 45(3), 396-408.
Mertens, S., van Kuik, G., & van Bussel, G. (2003). Performance of an H-Darrieus in the skewed flow on a roof. J. Sol. Energy Eng., 125(4), 433-440.
Mohamed, M. (2012). Performance investigation of H-rotor Darrieus turbine with new airfoil shapes. Energy, 47(1), 522-530.
Nagurney, A., & Yu, M. (2012). Sustainable fashion supply chain management under oligopolistic competition and brand differentiation. International Journal of Production Economics, 135(2), 532-540.
Negrutiu, C., Vasiliu, C., & Enache, C. (2020). Sustainable Entrepreneurship in the Transport and Retail Supply Chain Sector. Journal of Risk and Financial Management, 13(11), 267.
Nivedh, B. (2014). Major failures in the wind turbine components and the importance of periodic inspections. Wind Insid, 2014, 5.
Oh, Y., Busogi, M., Ransikarbum, K., Shin, D., Kwon, D., & Kim, N. (2019). Real-time quality monitoring and control system using an integrated cost effective support vector machine. Journal of Mechanical Science and Technology, 33(12), 6009-6020.
Park, K.-s., Asim, T., & Mishra, R. (2012). Computational fluid dynamics based fault simulations of a vertical axis wind turbines. Paper presented at the Journal of Physics: Conference Series.
Polinder, H., Van Bussel, G., & Dubois, M. (2004). Design of a PM generator for the Turby, a wind turbine for the built environment. Paper presented at the Proceedings of ICEM 2004.
Raut, S. S., & Mali, D. (2014). Automatic transmission gearbox with centrifugal clutches. International Journal of Research in Engineering and Technology, 3(10), 307-309.
Rezanoori, A., Ariffin, M., Delgoshaei, A., Jalil, N., & Zulkefli, Z. (2019). A new method to improve passenger vehicle safety using intelligent functions in active suspension system. Engineering Solid Mechanics, 7(4), 313-330.
Shi, Y., Song, X., & Song, G. (2021). Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. Applied Energy, 282, 116046.
Silva, N., Ferreira, L. M. D., Silva, C., Magalhães, V., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083-2090.
Stein, P., Hsu, M.-C., Bazilevs, Y., & Beucke, K. (2012). Operator-and template-based modeling of solid geometry for Isogeometric Analysis with application to Vertical Axis Wind Turbine simulation. Computer methods in applied mechanics and engineering, 213, 71-83.
Taghiyeh, S., Lengacher, D. C., & Handfield, R. B. (2020). A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc. arXiv preprint arXiv:2006.08931.
Takao, M., Kuma, H., Maeda, T., Kamada, Y., Oki, M., & Minoda, A. (2009). A straight-bladed vertical axis wind turbine with a directed guide vane row—Effect of guide vane geometry on the performance—. Journal of thermal Science, 18(1), 54-57.
Update, A. M. (2017). Global wind report. Global Wind Energy Council.
Vandenberghe, D., & Dick, E. (1987). A free vortex simulation method for the straight bladed vertical axis wind turbine. Journal of Wind Engineering and Industrial Aerodynamics, 26(3), 307-324.
Verma, N., & Pachori, A. (2015). Theoretical Approach for Comparison of Various Types of Wind Generator Systems. International Journal of Recent Resource Electrical and Electronic Engineering. IJRREEE, 2, 29-35.
Villegas, M. A., Pedregal, D. J., & Trapero, J. R. (2018). A support vector machine for model selection in demand forecasting applications. Computers & Industrial Engineering, 121, 1-7.
Wan, X.-l., Zhang, Z., Rong, X.-x., & Meng, Q.-c. (2016). Exploring an interactive value-adding data-driven model of consumer electronics supply chain based on least squares support vector machine. Scientific Programming, 2016.
Wu, Q. (2010). Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system. Journal of computational and applied mathematics, 233(10), 2481-2491.
Yeh, T.-H., & Wang, L. (2008). A study on generator capacity for wind turbines under various tower heights and rated wind speeds using Weibull distribution. IEEE Transactions on Energy Conversion, 23(2), 592-602.
Yue, L., Yafeng, Y., Junjun, G., & Chongli, T. (2007). Demand forecasting by using support vector machine. Paper presented at the Third International Conference on Natural Computation (ICNC 2007).
Zhang, Y. (2019). Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume. Cognitive Systems Research, 57, 228-235.
Zhou, E., Zhang, J., Gou, Q., & Liang, L. (2015). A two period pricing model for new fashion style launching strategy. International Journal of Production Economics, 160, 144-156.
Abecassis-Moedas, C. (2006). Integrating design and retail in the clothing value chain: An empirical study of the organisation of design. International Journal of Operations & Production Management, 26(4), 412-428.
Al-Bahadly, I. (2009). Building a wind turbine for rural home. Energy for sustainable development, 13(3), 159-165.
Ariffin, M., Yee Lee, T., & Mohamed, S. B. (2014). Design Improvement of Automatic Transmission System for Remote Controlled Car. Paper presented at the Applied Mechanics and Materials.
Asrol, M., Papilo, P., & Gunawan, F. E. (2021). Support Vector Machine with K-fold Validation to Improve the Industry’s Sustainability Performance Classification. Procedia Computer Science, 179, 854-862.
Bhutta, M. M. A., Hayat, N., Farooq, A. U., Ali, Z., Jamil, S. R., & Hussain, Z. (2012). Vertical axis wind turbine–A review of various configurations and design techniques. Renewable and Sustainable Energy Reviews, 16(4), 1926-1939.
Brito, P. Q., Soares, C., Almeida, S., Monte, A., & Byvoet, M. (2015). Customer segmentation in a large database of an online customized fashion business. Robotics and Computer-Integrated Manufacturing, 36, 93-100.
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Bozdogan, H. (2018). Supply chain integrity: A key to sustainable supply chain management. Journal of Business Logistics, 39(1), 38-56.
Cheng, Y., Peng, J., Gu, X., Zhang, X., Liu, W., Zhou, Z., . . . Huang, Z. (2020). An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain. Computers & Industrial Engineering, 139, 105834.
Cho, J., Jeong, K., Park, M., & Park, N. (2015). Dynamic response analysis of wind turbine gearbox using simplified local tooth stiffness of internal gear system. Journal of Computational and Nonlinear Dynamics, 10(4), 041001.
Chong, W., Fazlizan, A., Poh, S., Pan, K., & Ping, H. (2012). Early development of an innovative building integrated wind, solar and rain water harvester for urban high rise application. Energy and Buildings, 47, 201-207.
Chong, W., Naghavi, M., Poh, S., Mahlia, T., & Pan, K. (2011). Techno-economic analysis of a wind–solar hybrid renewable energy system with rainwater collection feature for urban high-rise application. Applied Energy, 88(11), 4067-4077.
Chong, W., Pan, K., Poh, S., Fazlizan, A., Oon, C., Badarudin, A., & Nik-Ghazali, N. (2013). Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application. Renewable Energy, 51, 388-397.
Chong, W., Poh, S., Fazlizan, A., & Pan, K. (2012). Vertical axis wind turbine with omni-directional-guide-vane for urban high-rise buildings. Journal of Central South University, 19(3), 727-732.
Dabiri, J. O. (2011). Potential order-of-magnitude enhancement of wind farm power density via counter-rotating vertical-axis wind turbine arrays. Journal of renewable and sustainable energy, 3(4), 043104.
Daneshmand-Mehr, M., Najafi, M., & Sadeghian, R. (2020). Determining the optimal forecasting combination of the four-level supply chain to minimize the bullwhip effect. مدیریت صنعتی, 15(51), 13-30.
Delgoshaei, A., Ariffin, M. K. A., & Baharudin, B. (2016). Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach. Journal of Industrial Engineering and Management (JIEM), 9(3), 732-785.
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55.
Delgoshaei, A., & Gomes, C. (2019). A new method for minimizing cell underutilization in the process of dynamic cell forming and scheduling using artificial neural networks. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 13(1), JAMDSM0021-JAMDSM0021.
Eriksson, S., Bernhoff, H., & Leijon, M. (2008). Evaluation of different turbine concepts for wind power. Renewable and Sustainable Energy Reviews, 12(5), 1419-1434.
Fallahpour, A., Kazemi, N., Molani, M., Nayyeri, S., & Ehsani, M. (2018). An intelligence-based model for supplier selection integrating data envelopment analysis and support vector machine. Iranian Journal of Management Studies, 11(2), 209-241.
Fanoodi, B., Malmir, B., & Jahantigh, F. F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in biology and medicine, 113, 103415.
Foltin, P., Brunclík, M., Ondryhal, V., & Vogal, L. (2018). Usability of Performance Indicators of Logistics Infrastructure Availability in Supply Chain Designing. Business Logistics in Modern Management.
Gavalda, J., Massons, J., & Diaz, F. (1990). Experimental study on a self-adapting Darrieus—Savonius wind machine. Solar & Wind Technology, 7(4), 457-461.
Gipe, P. (2004). Wind power. Wind Engineering, 28(5), 629-631.
Goettsch, D., Castillo-Villar, K. K., & Aranguren, M. (2020). Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing. Energies, 13(24), 6554.
Golkhoo, F., & Moselhi, O. (2019). Optimized material management in construction using multi-layer perceptron. Canadian Journal of Civil Engineering, 46(10), 909-923.
Greenblatt, D., Schulman, M., & Ben-Harav, A. (2012). Vertical axis wind turbine performance enhancement using plasma actuators. Renewable Energy, 37(1), 345-354.
Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978-6985.
Hashim, H., & Ho, W. S. (2011). Renewable energy policies and initiatives for a sustainable energy future in Malaysia. Renewable and Sustainable Energy Reviews, 15(9), 4780-4787.
Herbert, G. J., Iniyan, S., Sreevalsan, E., & Rajapandian, S. (2007). A review of wind energy technologies. Renewable and Sustainable Energy Reviews, 11(6), 1117-1145.
Ho, T. K. (1995). Random decision forests. Paper presented at the Proceedings of 3rd international conference on document analysis and recognition.
Hossain, A., Iqbal, A., Rahman, A., Arifin, M., & Mazian, M. (2007). Design and development of a 1/3 scale vertical axis wind turbine for electrical power generation. Journal of Urban and Environmental Engineering, 1(2), 53-60.
Howell, R., Qin, N., Edwards, J., & Durrani, N. (2010). Wind tunnel and numerical study of a small vertical axis wind turbine. Renewable Energy, 35(2), 412-422.
Ishak, A., & Wijaya, T. (2019). Rubber Spare Parts Supplier Selection Model Using Artificial Neural Network: Multi-Layer Perceptron. Paper presented at the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019).
Islam, M., Amin, M. R., Ting, D. S.-K., & Fartaj, A. (2007). Aerodynamic factors affecting performance of straight-bladed vertical axis wind turbines. Paper presented at the ASME international mechanical engineering congress and exposition.
Islam, M., Ting, D. S.-K., & Fartaj, A. (2008). Aerodynamic models for Darrieus-type straight-bladed vertical axis wind turbines. Renewable and Sustainable Energy Reviews, 12(4), 1087-1109.
Jiang, M. P., Zheng, S. Y., Wang, H., Zhang, S. Y., Yao, D. S., Xie, C. F., & Liu, D. L. (2019). Predictive model of aflatoxin contamination risk associated with granary-stored corn with versicolorin A monitoring and logistic regression. Food Additives & Contaminants: Part A, 36(2), 308-319.
Johari, M. K., Jalil, M., & Shariff, M. F. M. (2018). Comparison of horizontal axis wind turbine (HAWT) and vertical axis wind turbine (VAWT). International Journal of Engineering and Technology, 7(4.13), 74-80.
Kanellos, F., & Hatziargyriou, N. (2008). Control of variable speed wind turbines in islanded mode of operation. IEEE Transactions on Energy Conversion, 23(2), 535-543.
Korobenko, A., Hsu, M.-C., Akkerman, I., & Bazilevs, Y. (2014). Aerodynamic simulation of vertical-axis wind turbines. Journal of Applied Mechanics, 81(2).
Kozłowski, E., Borucka, A., & Świderski, A. (2020). Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodność, 22.
Liu, Y., & Huang, L. (2020). Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks, 16(1), 1550147720903631.
Mahadevan, S., Poornima, S., Tripathi, K., & Pushpalatha, M. (2019). A survey on machine learning algorithms for the blood donation supply chain. Paper presented at the Journal of Physics: Conference Series.
Mehrjoo, M., & Pasek, Z. J. (2014). Impact of product variety on supply chain in fast fashion apparel industry. Procedia CIRP, 17, 296-301.
Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International Journal of Consumer Studies, 45(3), 396-408.
Mertens, S., van Kuik, G., & van Bussel, G. (2003). Performance of an H-Darrieus in the skewed flow on a roof. J. Sol. Energy Eng., 125(4), 433-440.
Mohamed, M. (2012). Performance investigation of H-rotor Darrieus turbine with new airfoil shapes. Energy, 47(1), 522-530.
Nagurney, A., & Yu, M. (2012). Sustainable fashion supply chain management under oligopolistic competition and brand differentiation. International Journal of Production Economics, 135(2), 532-540.
Negrutiu, C., Vasiliu, C., & Enache, C. (2020). Sustainable Entrepreneurship in the Transport and Retail Supply Chain Sector. Journal of Risk and Financial Management, 13(11), 267.
Nivedh, B. (2014). Major failures in the wind turbine components and the importance of periodic inspections. Wind Insid, 2014, 5.
Oh, Y., Busogi, M., Ransikarbum, K., Shin, D., Kwon, D., & Kim, N. (2019). Real-time quality monitoring and control system using an integrated cost effective support vector machine. Journal of Mechanical Science and Technology, 33(12), 6009-6020.
Park, K.-s., Asim, T., & Mishra, R. (2012). Computational fluid dynamics based fault simulations of a vertical axis wind turbines. Paper presented at the Journal of Physics: Conference Series.
Polinder, H., Van Bussel, G., & Dubois, M. (2004). Design of a PM generator for the Turby, a wind turbine for the built environment. Paper presented at the Proceedings of ICEM 2004.
Raut, S. S., & Mali, D. (2014). Automatic transmission gearbox with centrifugal clutches. International Journal of Research in Engineering and Technology, 3(10), 307-309.
Rezanoori, A., Ariffin, M., Delgoshaei, A., Jalil, N., & Zulkefli, Z. (2019). A new method to improve passenger vehicle safety using intelligent functions in active suspension system. Engineering Solid Mechanics, 7(4), 313-330.
Shi, Y., Song, X., & Song, G. (2021). Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. Applied Energy, 282, 116046.
Silva, N., Ferreira, L. M. D., Silva, C., Magalhães, V., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083-2090.
Stein, P., Hsu, M.-C., Bazilevs, Y., & Beucke, K. (2012). Operator-and template-based modeling of solid geometry for Isogeometric Analysis with application to Vertical Axis Wind Turbine simulation. Computer methods in applied mechanics and engineering, 213, 71-83.
Taghiyeh, S., Lengacher, D. C., & Handfield, R. B. (2020). A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc. arXiv preprint arXiv:2006.08931.
Takao, M., Kuma, H., Maeda, T., Kamada, Y., Oki, M., & Minoda, A. (2009). A straight-bladed vertical axis wind turbine with a directed guide vane row—Effect of guide vane geometry on the performance—. Journal of thermal Science, 18(1), 54-57.
Update, A. M. (2017). Global wind report. Global Wind Energy Council.
Vandenberghe, D., & Dick, E. (1987). A free vortex simulation method for the straight bladed vertical axis wind turbine. Journal of Wind Engineering and Industrial Aerodynamics, 26(3), 307-324.
Verma, N., & Pachori, A. (2015). Theoretical Approach for Comparison of Various Types of Wind Generator Systems. International Journal of Recent Resource Electrical and Electronic Engineering. IJRREEE, 2, 29-35.
Villegas, M. A., Pedregal, D. J., & Trapero, J. R. (2018). A support vector machine for model selection in demand forecasting applications. Computers & Industrial Engineering, 121, 1-7.
Wan, X.-l., Zhang, Z., Rong, X.-x., & Meng, Q.-c. (2016). Exploring an interactive value-adding data-driven model of consumer electronics supply chain based on least squares support vector machine. Scientific Programming, 2016.
Wu, Q. (2010). Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system. Journal of computational and applied mathematics, 233(10), 2481-2491.
Yeh, T.-H., & Wang, L. (2008). A study on generator capacity for wind turbines under various tower heights and rated wind speeds using Weibull distribution. IEEE Transactions on Energy Conversion, 23(2), 592-602.
Yue, L., Yafeng, Y., Junjun, G., & Chongli, T. (2007). Demand forecasting by using support vector machine. Paper presented at the Third International Conference on Natural Computation (ICNC 2007).
Zhang, Y. (2019). Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume. Cognitive Systems Research, 57, 228-235.
Zhou, E., Zhang, J., Gou, Q., & Liang, L. (2015). A two period pricing model for new fashion style launching strategy. International Journal of Production Economics, 160, 144-156.