References
- X.S. Lin, Y.F. Chen, C.F. Yang, “Development and Implement of a Cane Sugar Automated System”, Guangxi Sugarcane, China, No. 2, 2008, pp. 39-43.
- T. Lu, F. Luo, Z.Y. Mao, C.C. Wen, “Parameters Soft-sensing Based on Neural Network in Crystallizing Process of Cane Sugar”, Proceedings of the 4th World Congress on Intelligent Control and Automation, Vol. 3, 2002, pp. 1944-19448.
- Y.M. Meng, H.P. He, F.N. Lu, “Research of a Real-time Soft Sensor System of Syrup Supersaturation in Sugar Crystallization Process”, Journal of Guangxi University (Natural Science Edition), China, Vol. 38, No. 1, 2012, pp. 1-4.
- H.P. He, “Research and Application of an Expert System in Sugar Process Based on Rough Set and Support Vector Machine”, Master of Academic Thesis, Faculty of Mechanical Engineering, Guangxi University, China, 2013.
- P.F. Bordui, G.M. Loiacono, “In-line bulk supersaturation measurement by electrical conductometry in KDP crystal growth from aqueous solution”, Journal of Crystal Growth, Vol. 67, No. 2, 1984, pp. 168-172.10.1016/0022-0248(84)90175-1
- D.D. Dunuwila, K.A. Berglund, “ATR FTIR spectroscopy for in situ measurement of supersaturation”, Journal of Crystal Growth, Vol. 179, No. 1–2, 1997, pp. 185-193.10.1016/S0022-0248(97)00119-X
- A. Markande, J. Fitzpatrick, A. Nezzal, L. Aerts, A. Redl, “Application of in-line monitoring for aiding interpretation and control of dextrose monohydrate crystallization”, Journal of Food Engineering, Vol. 114, No. 1, 2013, pp. 8-13.10.1016/j.jfoodeng.2012.07.029
- P. Barrett, B. Glennon, “Characterizing the Metastable Zone Width and Solubility Curve Using Lasentec FBRM and PVM”, Chemical Engineering Research and Design, Vol. 80, No. 7, 2002, pp. 799-805.10.1205/026387602320776876
- C. Damour, M. Benne, B.G. Perez, J.P. Chabriat, “Nonlinear predictive control based on artificial neural network model for industrial crystallization”, Journal of Food Engineering, Vol. 99, No. 2, 2010, pp. 225-231.10.1016/j.jfoodeng.2010.02.027
- Z.L. Sha, M.L. Kultanen, S. Palosaari, “Neural network simulation for non-MSMPR crystallization”, Chemical Engineering Journal, Vol. 81, No. 1–3, 2001, pp. 101-107.10.1016/S1385-8947(00)00238-2
- W. Paengjuntuek, L. Thanasinthana, A. Arpornwichanop, “Neural network-based optimal control of a batch crystallizer”, Neurocomputing, Vol. 83, 2012, pp. 158-164.10.1016/j.neucom.2011.12.008
- A. Mesbah, J. Landlust, A.E.M. Huesman, H.J.M. Kramer, P.J. Jansens, P.M.J. Van den Hof, “A model-based control framework for industrial batch crystallization processes”, Chemical Engineering Research and Design, Vol. 88, No. 9, 2010, pp. 1223-1233.10.1016/j.cherd.2009.09.010
- G.M. Agusta1, K. Hulliyah, Arini, R.B. Bahaweres, “Applying merging convetional marker and backpropagation neural network in QR code augmented reality tracking”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 5, 2013, pp. 1918-1948.10.21307/ijssis-2017-620
- W.L. Li, P. Fu, W.Q. Cao, “Study on feature selection and identification method of tool wear states based on SVM”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 2, 2013, pp. 448-465.10.21307/ijssis-2017-549
- Y.M. Huang, Y.S. Duan, “The Design and Implementation of a Distributed Object-oriented Knowledge-based System for Hierarchical Simulation Modeling”, Proceedings. Fourth Annual Conference on AI, Simulation, and Planning in High Autonomy Systems, 1993, pp. 164-17.
- G. Li, “Knowledge acquisition method by rough set in the expert system”, Master of Academic Thesis, Xi’an University of Architecture and Technology, China, 2004.
- Y.L. Jiang, C.F. Xu, J. Gou, Z.X. Li, “Research on Rough Set Theory Extension and Rough Reasoning”, IEEE International Conference on Systems, Man and Cybernetics, 2004.
- J.Y. Wang, C. Gao, “Fast and Complete Algorithm for Reduction Based on Discernable Matrix”, Computer Engineering and Applications, vol. 44, No. 8, 2008, pp. 92-94.
- X.D. Miao, S.M. Li, H. Shen, “On-board lane detection system for intelligent vehicle based on monocular vision”, International Journal on Smart Sensing and Intelligent Systems, Vol. 5, No. 4, 2012, pp. 957-972.10.21307/ijssis-2017-517
- S. Amin, Wirawan, H. Gamantyo, “A new unequal clustering algorithm using energy-balanced area partitioning for wireless sensor networks”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 5, 2013, pp. 1808-1829.10.21307/ijssis-2017-616
- A.F. Salami, H. Bello-Salau, F. Anwar, A.M. Aibinu, “A novel biased energy distribution (BED) technique for cluster-based routing in wireless sensor networks”, International Journal on Smart Sensing and Intelligent Systems, Vol. 4, No. 2, 2011, pp. 161-173.10.21307/ijssis-2017-433
- Y.Q. Wang, L. Liu, “New intelligent classification method based on improved meb algorithm”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 72-95.10.21307/ijssis-2017-646
- S. Ramathilaga, J. jiunn, Y. Leu, Y.M. Huang, “Adapted Mean Variable Distance to Fuzzy-C means for Effective Image Clustering”, Proceedings. First International conference on Robot, Vision and Signal Processing, Kaohsiung, Taiwan, 2011, pp. 48-51.10.1109/RVSP.2011.58
- X.W. Kang, X.S. Sun, S. Wang, Y.Q. Liu, Y. Xia, R. Zhou, Z.X. Wu, Y.J. Jin, “A Fast Accuracy Crystal Identification Method Based on Fuzzy C-Means (FCM) Clustering Algorithm for MicroPET”, Proceedings. First International Conference on BioMedical Engineering and Informatics, Sanya, Hainan, China, 2008, pp. 779-782.10.1109/BMEI.2008.351
- H.T. Zhang, H.P. Mao, “Rough Sets Weights Application in the Extension Classification of the Stored-grain Pests Based on Fuzzy C-means Discretization”, Transactions of the Chinese Society for Agricultural Machinery, Vol. 39, No. 7, 2007, pp. 124-128.
- Y.M. Luo, P.Z. Liu, M.H. Liao, “An artificial immune network clustering algorithm for mangroves remote sensing image”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 116-134.10.21307/ijssis-2017-648
- F. Calabrese, A. Corallo, A. Margherita, A.A. Zizzari, “A Knowledge-based Decision Support System for Shipboard Damage Control”, Expert Systems with Applications, Vol. 39, No. 9, 2012, pp. 8204-8211.10.1016/j.eswa.2012.01.146
- M. Liu, T. Quan, S. Luan, “An Attribute Recognition System Based on Rough Set Theory-Fuzzy Neural Network and Fuzzy Expert System”, Fifth World Congress on intelligent Control and Automation, Vol. 3, 2004, pp. 2355-2359.
- Y.M. Huang, S.H. Lin, “An Efficient Inductive Learning Method for Object-oriented Database Using Attribute Entropy”, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 946-951.10.1109/69.553161
- C.Y. Zhang, “Research on Algorithm for Attribute Value Reduction Based on Rough Set”, Computer and Modernization, China, Vol. 7, 2008, pp. 79-81.
- B.B. Qu, “Research on Knowledge Acquisition for Decision Information System Based on Rough Set Theory”, Master of Academic Thesis, Faculty of Computer Science and Technology, Huazhong University of Science and Technology, China, 2006.
- L. Peng, “Research on Classification Algorithm of Support Vector Machine and its Application”, Maste of Academic Thesis, Faculty of Electrical and Information Engineering, Hunan University, China, 2007.
- J.L. An, Z.O. Wang, Q.X. Yang, Z.P. Ma, C.J. Gao, “Study on Method of On-line Identification for Complex Nonlinear Dynamic System Based on SVM”, Proceedings of International Conference on Machine Learning and Cybernetics, Vol. 3, 2005, pp. 1654-1659.
- A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei, “An appropriate prodedure for detection of journal-bearing fault using power spectral density, K-nearest neighbor and support vector machine”, International Journal on Smart Sensing and Intelligent Systems, Vol. 5, No. 3, 2012, pp. 685-700.10.21307/ijssis-2017-502
- F. Ardjani, K. Sadouni, M. Benyettou, “Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)”, Proceedings. Second International Workshop on Database Technology and Application (DBTA), 2010, pp. 1-4.10.1109/DBTA.2010.5658994
- Y.D. Wang, J.X. Liu, “Research of Particle Swarm Optimization and its Improvement”, Information and Computer, China, Vol. 3, 2012, pp. 129-130.
- W. Jatmiko, W. Pambuko, A. Febrian, P. Mursanto, A. Muis, B. Kusumoputro, K. Sekiyama, T. Fukuda, “Ranged subgroup particle swarm optimization for localizing multiple odor sources”, International Journal on Smart Sensing and Intelligent Systems, Vol. 3, No. 3, 2010, pp. 411-442.10.21307/ijssis-2017-401
- J. Manikandan, B. Venkataramani, “Study and Evaluation of a Multiclass SVM Classifier Using Diminishing Learning Technique”, Neurocomputing, Vol. 73, No. 10-12, 2010, pp. 129145.10.1016/j.neucom.2009.11.042
- N.K.Suryadevara, A. Gaddam, R.K.Rayudu and S.C. Mukhopadhyay, “Wireless Sensors Network based safe Home to care Elderly People: Behaviour Detection”, Sens. Actuators A: Phys. (2012), doi:10.1016/j.sna.2012.03.020, Volume 186, 2012, pp. 277 – 283.10.1016/j.sna.2012.03.020
- J. Luts, F. Ojeda, “A Tutorial on Support Vector Machine-based Methods for Classification Problems in Chemometrics”, Analytica Chimica Acta, Vol. 665, No. 2, 2010, pp. 129-145.10.1016/j.aca.2010.03.03020417323