Destek vektör makineleri kullanarak dinamik yumurta ağırlıklarının sınıflandırılması

ismail yabanova, Mehmet Yumurtacı
1.524 234

Öz


Günümüzde üretim sektöründe hız önemli bir faktör haline gelmiştir. Bundan dolayı üretilen ürünlerin ağırlıklarının tartımlarının da hızlı bir şekilde yapılması gerekmektedir. Hızlı bir şekilde tartım işlemi yapabilmek için dinamik tartım sistemleri geliştirilmiştir. Dinamik tartım sistemlerinde ürünler tartım platformu üzerinden hareket halinde geçerken tartılmaktadırlar ve istenilen tartım hızlarına bu şekilde ulaşılabilmektedir. Ancak dinamik tartım sistemlerinde ürünün hareketli tartılmasından dolayı sistemdeki mekanik titreşimler ölçüm sinyalinde istenmeyen bir bozucu etki oluşturmaktadır. Geleneksel olarak bu sinyal filtrelendikten sonra ürünün stabil olduğu andaki tartım ağırlığı bir metot kullanılarak belirlenmeye çalışılmaktadır. Genellikle ürünler ağılıkları belirlendikten sonra belirli ağırlık sınıflarına göre tasnif edilmektedirler. Bu çalışmada dinamik olarak tartılan yumurtaların Gıda Tarım Hayvancılık Bakanlığı Türk Gıda Kodeksi Yumurta Tebliğinde belirtilen ağırlık sınıflarına göre tasnif edilmesi destek vektör makineleri kullanılarak doğrudan ham veriyle gerçekleştirilmiştir.

Anahtar kelimeler


Dinamik tartım; destek vektör makineleri; yük hücresi

Tam metin:

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Referanslar


Niedźwiecki, M., Wasilewski, A., “Application of Adaptive Filtering to Dynamic Weighing of Vehicles”, Control Engineering Practice, Cilt 4, No 5, 635-644, 1996.

Yamazaki, T., Sakurai, Y., Ohnishi, H., Kobayashi, M., Kurosu, S., “Continuous Mass Measurement in Checkweighers and Conveyor Belt Scales”, Proceedings of the 41st SICE Annual Conference, 470-474, 5-7 Ağustos 2002.

Boschetti, G., Caracciolo, R., Richiedei, D., Trevisani, A., “Model-Based Dynamic Compensation of Load Cell Response in Weighing Machines Affected by Environmental Vibrations”, Mechanical Systems and Signal Processing, Cilt 34, No 1-2, 116-130, 2013.

Pietrzak, P., Meller, M., Niedźwiecki, M., “Dynamic Mass Measurement in Checkweighers Using a Discrete Time-Variant Low-Pass Filter”, Mechanical Systems and Signal Processing, Cilt 48, No 1-2, 67-76, 2014.

Yamazaki, T., Ono, T., “Dynamic Problems in Measurement of Mass-Related Quantities”, SICE Annual Conference, 1183-1188, 17-20 Eylül 2007.

Jafaripanah, M., Al-Hashimi, B.M., White, N.M., “Application of Analog Adaptive Filters for Dynamic Sensor Compensation,” Instrumentation and Measurement, IEEE Transactions on, Cilt 54, No 1, 245-251, 2005.

Piskorowski, J., Barcinski, T., “Dynamic Compensation of Load Cell Response: a Time-Varying Approach”, Mechanical Systems and Signal Processing, Cilt 22, No 7, 1694-1704, 2008.

Zhang, Y., Fu, H., “Dynamic Weighing Signal Processing by System Identification”, in Industrial Mechatronics and Automation (ICIMA), 2010 2nd International Conference on, Wuhan, 203-206, 2010.

Halimic, M., Balachandran, W. and Enab, Y., “Fuzzy Logic Estimator for Dynamic Weighing System”, in Fuzzy Systems, Proceedings of the Fifth IEEE International Conference on, New Orleans, LA, 2123-2129, 1996.

Bahar, H.B., Horrocks, D.H., “Dynamic Weight Estimation Using an Artificial Neural Network”, Artificial Intelligence in Engineering, Cilt 12, No 1–2, 135-139, 1998.

Almodarresi Yasin, S.M.T., White, N.M., “Application of Artificial Neural Networks to Intelligent Weighing Systems”, in Science, Measurement and Technology, IEE Proceedings, Cilt 146, No 6, 265-269, 1999.

Jian, X., Bin, M., “Investigation of Discrete Wavelet Transform for Signal De-Noising in Weight-in-Motion System”, Future Computer and Communication (ICFCC), 2010 2nd International Conference on, Wuhan, 769-772, 2010.

Xiao, J. and Lv, P., “Application of Wavelet Transform in Weigh-in-Motion”, Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, Wuhan, 1-4, 2009.

Bin, M. and Xinguo, Z., “Discrete Wavelet Transform for Signal Processing in Weight-in-Motion System”, Electrical and Control Engineering (ICECE), 2010 International Conference on, Wuhan, 4668-4671, 2010.

Jafaripanah, M., Al-Hashimi, B.M. and White, N.M., “Dynamic Sensor Compensation Using Analogue Adaptive Filter Compatible with Digital Technology”, in Circuits, Devices and Systems, IEE Proceedings , Cilt 152, No 6, 745-751, 2005.

Halimic, M., Balachandran, W., “Kalman Filter for Dynamic Weighing System”, in Industrial Electronics, Proceedings of the IEEE International Symposium on, Athens, 786-791, 1995.

Gürbüz, E., Kılıç, E., “A New Adaptive Support Vector Machine for Diagnosis of Diseases”, Expert Systems, Cilt 31, No 5, 389-397, 2014.

Saber, A., Emam, A., Amer, R., “Discrete Wavelet Transform and Support Vector Machine- Based Parallel Transmission Line Faults Classification”, IEEJ Transactions on Electrical and Electronic Engineering (IEEJ Trans), Cilt 11, 43-48, 2016.

Huang, N., Chen, H., Zhang, S., Cai, G., Li, W., Xu, D., Fang, L., “Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time_frequency Entropy and One-Class Support Vector Machine”, Entropy, Cilt 18, No 7, 1-17, 2016.

Axelberg, P.G.V., Gu, I.Y.H., Bollen, M.H.J., “Support Vector Machine for Classification of Voltage Disturbances”, IEEE TRansactions on Power Delivery, Cilt 22, No 3, 1297-1303, 2007.

Güneş, T., Polat, E., “Yüz İfade Analizinde Öznitelik Seçimi ve Çoklu SVM Sınıflandırıcılarına Etkisi”, J. Fac. Eng. Arch. Gazi Univ., Cilt 24, No 2, 7-14, 2009.

Zhang, W., Yoshida, T., Tang, X., “Text Classifciation Based on Multi-Word with Support Vetor Machine”, Knowledge- Based Systems, Cilt 21, 879-886, 2008.

Nasien, D., Haron, H., Yuhaniz, S.S., “Support Vector Machine (SVM) for English Handwritten Character Recognition”, 2010 Second International Conference on Computer Engineering and Applications, Bali Island, 249-252, 2010.

Uyar, M., Yıldırım, S., Gençoğlu, M.T., “Güç Kalitesindeki Bozulma Türlerinin Sınıflandırılması için Bir Örüntü Tanıma Yaklaşımı”, J. Fac. Eng. Arch. Gazi Univ., Cilt 26, No 1, 41-56, 2011.

Thukaram, D., Khincha, H. P., Vijaynarasimha, H. P., “Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems”, IEEE Transactions on Power Delivery, Cilt 20, No 2, 710-721, 2005.

Aydın, İ., Karaköse, M., Akın, E., “Zaman Serisi Veri Madenciliği ve Destek Vektör Makinalar Kullanan Yeni Bir Akıllı Arıza Sınıflandırma Yöntemi”, J. Fac. Eng. Arch. Gazi Univ., Cilt 23, No 2, 431-440, 2008

Hwang, D.H., Youn, Y.W., Sun, J.H., Choi, K.H., Lee, J.H., Kim, Y.H., “Support Vector Machine Based Bearing Fault Diagnasis for Induction Motors Using Vibration Signals”, J Electr Eng Technol., Cilt 10, No 4, 1558-1565, 2015.

Magnin, B., Mesrob, L., Kinkingnehun, S., Pelegrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehericy, S., Benali, H., “Support Vector Machine-Based Classification of Alzheimer’s Disease From Whole-Brain Anatomical MRI”, Neuroradiology, Cilt 61, 73-83, 2009.

Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D., “Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data”, Bioinformatics, Cilt 16, No 10, 906-914, 2000.

Widodo, A., Yang, B.S., “Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis”, Mechanical Systems and Signal Processing, Cilt 21, 2560-2574, 2007.

Qi, Z., Tian, Y., Shi, Y., “Robust Twin Support Vector Machine for Pattern Classification”, Pattern Recognition, Cilt 46, 305-316, 2013.

Adaminejad, H., Shayegani, I., Ohammadi, M., Farjah, E., “An Algorithm for Power Quality Events Core Vector Machine Based Classification”, Modares journal of electrical engineering, Cilt 12, No 4, 2013.

Liu, M., Wang, M., Wang, J., Li, D., “Comparison of Random Forest, Support Vector Machine and Back Propagation Neural Network for Electronic Tongue Data Classification: Application to the Recognition of Orange Beverage and Chinese Vinegar”, Sensors and Actuators B, Cilt 177, 970-980, 2013.

Cheng, L., Bao, W., “Remote Sensing Image Classification Based on Optimized Support Vector Machine”, TELKOMNIKA Indonesian Journal of Eletrical Engineeering, Cilt 12, No 2, 1037-1045, 2014.

Shen, L., Chen, H., Yu, Z., Kang, W., Zheng, B., Li, H., Yang, B., Liu, D., “Evolving Support Vetor Mahines Using Fruit Fly Optimization for Medical Data Classification”, Knowldege-Based Systems, Cilt 96, 61-75, 2016.

Kang, S., Cho, S., “Approximating Support Vector Machine with Artificial Neural Network for Fast Prediction”, Expert Systems with Applications, Cilt 41, 4989-4995, 2014.

Yin, S., Zhu, X., Jing, C., “Fault Detection Based on a Robust one Class Support Vector Machine”, Neurocomputing, Cilt 145, 263-268, 2014.

Wang, Z.T., Zhao, N.B., Wang, W.Y., Tang, R., Li, S.Y., “A Fault Diagnasis Approach for Gasturbine Exhaust Gas Temperature Based on Fuzzy C-Means Clusterşng and Support Vector Machine”, Hindawi Publishing Corporation Mathematical Problems in Engineering, Cilt 2015, 1-11, 2015.

Arıkan, Ç., Özdemir, M., “Classification of Power Quality Disturbances at Power System Frequency and out of Power System Frequency Using Support Vector Machines”, Przeglad Elektrotechniczny, Cilt 89, No 1a, 284-291, 2013.

Türk Gıda Kodeksi Yumurta Tebliği, Gıda Tarım Hayvancılık Bakanlığı, Resmi Gazete, Sayı: 29211, 20 Aralık 2014.

Franc V, Hlavac, V. Statistical Pattern Recognition Toolbox for MATLAB. User’s Guide. Prague, Czech Republic: Czech Technical University, 2010.




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