Araştırma Makalesi
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HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ

Yıl 2017, , 43 - 60, 08.12.2017
https://doi.org/10.17482/uumfd.364093

Öz

Bu çalışmada;
gerçek zamanda kamera görüntüsünden alınan hedef cismin; dairesel, eğik atışa
benzer ve manevralı dinamik hareket yapması durumunda, bir sonraki konumu veya
nerede olacağı Adaptive Fuzzy Time Series (AFTS) ve Exponential Smoothing (ES)
tahmin yöntemleriyle incelenmiştir. Bu hareketlerin hata değerlendirmesi,
ortalama mutlak yüzde hata (MAPE) yöntemine göre yapılmıştır. Yapılan
değerlendirmede, AFTS ile dairesel harekette %3.65, eğik atışa benzer harekette
%9.12, manevralı dinamik harekette %19.23, ES ile dairesel harekette
%4.48,  eğik atışa benzer harekette %1.13
ve manevralı dinamik harekette ise %0.61 elde edilmiştir. Dairesel harekette
AFTS ES’den, eğik atışa benzer ve manevralı dinamik harekette ise ES AFTS’den
daha iyi sonuç vermiştir. 

Kaynakça

  • Bourne, D., Corney, J., Gupta, S.K. (2011) Recent Advances and Future Challenges in Automated Manufacturing Planning, ASME J Comput Inf Sci Eng., 11(2) Doi: 10.1115/1.3593411
  • Chakraborty, B., Meher, S. (2011) 2D Trajectory-based Position Estimation and Tracking of the Ball in a Basketball Video, 2nd International Conference on Trends in Optics and Photonics, India.
  • Chakraborty, B., Meher, S. (2012) Real Time Position Estimation and Tracking of a Basketball, IEEE International Conference on Signal Processing, Computing and Control (ISPC), 1-6. Doi: 10.1109/ISPCC.2012.6224370
  • Chen, X., Schonfeld, D., Khokhar, A. (2007) Localization and Trajectory Estimation of Mobile Objects with a Single Sensor, Statistical Signal Processing, IEEE/SP, SSP’07, 363-367. Doi: 0.1109/SSP.2007.4301281
  • Eustice, R., Pizarro, O., Singh, H. (2004) Visually Augmented Navigation in an Unstructered Environment Using a Delayed State History, Proceedings of the 2004 International Conference on Robotics&Automation, IEEE. Doi: 10.1109/ROBOT.2004.1307124
  • Facco, P., Doplicher, F., Bezzo, F., Barolo, M. (2009) Moving Average PLS Soft Sensor for Online Product Quality Estimation in an Industrial Batch Polymerization Process, Journal of Process Control, 19(3), 520-529. doi.org/10.1016/j.jprocont.2008.05.002
  • Hsieh, Y-S., Su Y.C., Chen, L.G. (2012) Robust Moving Object Tracking and Trajectory Prediction for Visual Navigation in Dynamic Environments, IEEE International Conference on Consumer Electronics (ICCE), 978-1-4577-0231-0/12/$26.00. Doi: 10.1109/ICCE.2012.6162066
  • Hu, W., Xiao, X., Xie, D., Tan, T., Maybank, S. (2004) Traffic Accident Prediction Using 3-D Model-based Vehicle Tracking, IEEE Transactions on Vehicular Technology, 53(3), 677-694. Doi: 10.1109/TVT.2004.825772
  • Hu, Z., Fan, X., Song, Y., Liang, D. (2008) Joint Trajectory Tracking and Recognition Based on Bi-directional non-linear Learning, Image and Vision Computing, Elsevier, Image and Vision Computing, 10(1016). doi.org/10.1016/j.imavis.2008.11.011
  • Huang, Y-L., Horng, S-J., Kao, T-W., Kuo, I-H., Takao, T. (2012) A hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization, International Symposium on Biometrics and Security Technologies, 66-70. Doi: 10.1109/ISBAST.2012.23
  • Kosut, O., Turovsky, A., Sun, J., Ezovski, M., Tong, L., Whipps, G. (2007) Integrated Mobile and Static Sensing for Target Tracking, In Military Communications Conference, MILCOM 2007, IEEE, 1-7. doi:10.1115/1.2764515
  • Kumar, M., Garg, D.P. (2004) Sensor-Based Estimation and Control of Forces and Moments in Multiple Cooperative Robot, ASME J. Dyn. Sys. Meas., Control, 126(2), 276-283. Doi: 10.1115/1.1766029
  • Marques, P., Dias, J. (2007) Moving Target Trajectory Estimation in SAR Spatial Domain Using a Single Sensor, IEEE Trans. on Aerospace and Electronic Systems, 43(3), 864-874.
  • Nahmias, S. (1997) Production and Operations Analysis, McGraw-Hill International Editions. USA.
  • Nind, J., Torra, V. (2009) Towards the Evaluation of Time Series Prediction Methods, Information Sciences, 179(11), 1663-1677. Doi: 10.1016/j.ins.2009.01.024
  • Ohno, K., Takafumi, N., Satoshi, T. (2006) Real-time Robot Trajectory Estimation and 3d Map Construction using 3d Camera, Intelligent Robots and Systems, International Conference on IEEE/RSJ. Doi: 10.1109/IROS.2006.282027
  • Palit, A.K., Popovic, D. (2005) Computational intelligence in time series forecasting: Theory and engineering applications, Advances in Industrial Control, Springer Verlag, NJ USA.
  • Pantazopoulos, S.N., Pappis, C.P. (1996) A new adaptive method for extrapolative forecasting algorithms, European Journal of Operational Research, 94, 106-111. doi.org/10.1016/0377-2217(95)00195-6
  • Piepmeier, J.A., McMurray, G.V., Lipkin, H. (1998) Tracking a Moving Target with Model Independent Visual Servoing: a Predictive Estimation Approach. Doi: 10.1109/ROBOT.1998.680746
  • Piepmeier, J.A., McMurray, G.V., Lipkin H. (2004) Uncalibrated Dynamic Visual Servoing, IEEE Transactions on Robotics and Automation, 20(1). Doi: 10.1109/TRA.2003.820923
  • Prėvost, C.G., Desbiens, A., Gagnon, E. (2007) Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle, Proceedings of the 2007 American Control Conference, USA, 1805-1810. Doi: 10.1109/ACC.2007.4282823
  • Ryan, E., Pizarro, O., Singh, H. (2004) Visually Augmented Navigation in an Unstructured Environment Using a Delayed State History, Proceedings of the 2004 IEEE, International Conference on Robotics & Automation, 25-32. Doi: .1109/ROBOT.2004.1307124
  • Shen, G., Kong, X., Chen, X. (2011) A Short-term Traffic Flow Intelligent Hybrid Forecasting Model and Its Application, Control Engineering and Applied Informatics, 13(3), 65-73.
  • Tang, Y., Huang, P. (2006) Boost-Phase Ballistik Missile Trajectory Estimation with Ground Based Radar, Journal of Systems Engineering and Electronics, 17 (4), 705-708. Doi: 10.1016/S1004-4132(07)60001-2
  • Tsaurs, R.C., Kuo, T.C.(2011) The Adaptive Fuzzy Time Series Model with an Application to Taiwan’s Tourism Demand, Expert Systems with Applications, Elsevier, 38, 9164-9167. doi.org/10.1016/j.eswa.2011.01.059
  • Unrath, M., Watt, D., Alpay, M. (2007) High Performance Motion Tracking Control for Electronic Manufacturing, ASME J. Dyn. Sys. Meas., Control, 129(6), 767-776. doi:10.1115/1.2789467
  • Vallery, H., Van Asseldonk, E.H., Buss, M., Van Der Kooij, H. (2009) Reference Trajectory Generation for Rehabilitation Robots: Complementary Limb Motion Estimation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1), 23-30. Doi: 10.1109/TNSRE.2008.2008278
  • Yagimli, M., Varol, H.S. (2008) Low Cost Target Recognising and Tracking Sensory System Mobile Robot, Journal of Naval Science and Engineering, 4(1), 17-26.
  • Yagimli, M., Varol, H.S. (2009) Real Time Color Composition Based Moving Target Recognition, Journal of Naval Science and Engineering, 5(2), 89-97.
  • Yagimli, M., Varol, H.S. (2009) Adaptive Estimation Algorithm-Based Target Tracking, Journal of Naval Science and Engineering, 5(3), 16-25.
  • Yagimli, M., Varol, H.S. (2009) A GPS-based system design for the recognition and tracking of moving targets, Proceedings of 4th International Conference on Recent Advances in Space Technologies, 978-1-4244-3626-2/09 IEEE, 6-12. Doi: 10.1109/RAST.2009.5158262.
  • Yagimli, M. (2010) An Optoelectronics System Develop Which Recognizes Moving Objects and Detect Their Orbit, Marmara University, Institute of Pure and Applied Sciences, Ph.D. Thesis.
  • Yang, Y., Polycarpou, M.M., Mimai, A.A. (2007) Multi-UAV Cooperative Search Using an Opportunistic Learning Method, ASME J. Dyn. Sys. Meas., Control, 129 (5), 716-728. Doi:10.1115/1.2764515
  • Yang, J-L., Ji, H-B. (2010) High Maneuvering Target-Tracking Based on Strong Tracking Modified Input Estimation, Scientific Research and Essays, 5(13), 1683-1689.
  • Zadeh, L. (1965) Fuzzy Sets, Information and Controls.
  • Zhou, S., Yong, C., Jianjun, S. (2004) Statistical Estimation and Testing for Variation Root-cause Identification of Multistage Manufacturing Processes, IEEE Transactions on Automation Science and Engineering, 1(1), 73-83. Doi: 10.1109/TASE.2004.829427

Real Time Trajectory Tracking of Moving Objects Using Adaptive Fuzzy Time Series and Exponential Smoothing Forecasting Techniques

Yıl 2017, , 43 - 60, 08.12.2017
https://doi.org/10.17482/uumfd.364093

Öz

In his study; in cases where the
targeted object which is taken from a real time camera shot is in a circular
motion, quasi projectile motion and maneuvering dynamic motion, its later
location where it will be is examined using the Adaptive Fuzzy Time Series
(AFTS) and Exponential Smoothing (ES) estimation methods. Error evaluation of
these motions was performed according to the Mean Absolute Percentage Error
(MAPE) method. In the conducted evaluation, with AFTS, the circular motion was
found to be 3.65%, quasi projectile motion 9.12%, and maneuvering
dynamic motion 19.23%, and with ES, circular motion 4.48%, quasi projectile
motion 1.13% and maneuvering dynamic motion was found to be
0.61%. AFTS gives better results than ES for the circular motion but ES gives
better results than AFTS for quasi projectile and maneuvering
dynamic motions.

Kaynakça

  • Bourne, D., Corney, J., Gupta, S.K. (2011) Recent Advances and Future Challenges in Automated Manufacturing Planning, ASME J Comput Inf Sci Eng., 11(2) Doi: 10.1115/1.3593411
  • Chakraborty, B., Meher, S. (2011) 2D Trajectory-based Position Estimation and Tracking of the Ball in a Basketball Video, 2nd International Conference on Trends in Optics and Photonics, India.
  • Chakraborty, B., Meher, S. (2012) Real Time Position Estimation and Tracking of a Basketball, IEEE International Conference on Signal Processing, Computing and Control (ISPC), 1-6. Doi: 10.1109/ISPCC.2012.6224370
  • Chen, X., Schonfeld, D., Khokhar, A. (2007) Localization and Trajectory Estimation of Mobile Objects with a Single Sensor, Statistical Signal Processing, IEEE/SP, SSP’07, 363-367. Doi: 0.1109/SSP.2007.4301281
  • Eustice, R., Pizarro, O., Singh, H. (2004) Visually Augmented Navigation in an Unstructered Environment Using a Delayed State History, Proceedings of the 2004 International Conference on Robotics&Automation, IEEE. Doi: 10.1109/ROBOT.2004.1307124
  • Facco, P., Doplicher, F., Bezzo, F., Barolo, M. (2009) Moving Average PLS Soft Sensor for Online Product Quality Estimation in an Industrial Batch Polymerization Process, Journal of Process Control, 19(3), 520-529. doi.org/10.1016/j.jprocont.2008.05.002
  • Hsieh, Y-S., Su Y.C., Chen, L.G. (2012) Robust Moving Object Tracking and Trajectory Prediction for Visual Navigation in Dynamic Environments, IEEE International Conference on Consumer Electronics (ICCE), 978-1-4577-0231-0/12/$26.00. Doi: 10.1109/ICCE.2012.6162066
  • Hu, W., Xiao, X., Xie, D., Tan, T., Maybank, S. (2004) Traffic Accident Prediction Using 3-D Model-based Vehicle Tracking, IEEE Transactions on Vehicular Technology, 53(3), 677-694. Doi: 10.1109/TVT.2004.825772
  • Hu, Z., Fan, X., Song, Y., Liang, D. (2008) Joint Trajectory Tracking and Recognition Based on Bi-directional non-linear Learning, Image and Vision Computing, Elsevier, Image and Vision Computing, 10(1016). doi.org/10.1016/j.imavis.2008.11.011
  • Huang, Y-L., Horng, S-J., Kao, T-W., Kuo, I-H., Takao, T. (2012) A hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization, International Symposium on Biometrics and Security Technologies, 66-70. Doi: 10.1109/ISBAST.2012.23
  • Kosut, O., Turovsky, A., Sun, J., Ezovski, M., Tong, L., Whipps, G. (2007) Integrated Mobile and Static Sensing for Target Tracking, In Military Communications Conference, MILCOM 2007, IEEE, 1-7. doi:10.1115/1.2764515
  • Kumar, M., Garg, D.P. (2004) Sensor-Based Estimation and Control of Forces and Moments in Multiple Cooperative Robot, ASME J. Dyn. Sys. Meas., Control, 126(2), 276-283. Doi: 10.1115/1.1766029
  • Marques, P., Dias, J. (2007) Moving Target Trajectory Estimation in SAR Spatial Domain Using a Single Sensor, IEEE Trans. on Aerospace and Electronic Systems, 43(3), 864-874.
  • Nahmias, S. (1997) Production and Operations Analysis, McGraw-Hill International Editions. USA.
  • Nind, J., Torra, V. (2009) Towards the Evaluation of Time Series Prediction Methods, Information Sciences, 179(11), 1663-1677. Doi: 10.1016/j.ins.2009.01.024
  • Ohno, K., Takafumi, N., Satoshi, T. (2006) Real-time Robot Trajectory Estimation and 3d Map Construction using 3d Camera, Intelligent Robots and Systems, International Conference on IEEE/RSJ. Doi: 10.1109/IROS.2006.282027
  • Palit, A.K., Popovic, D. (2005) Computational intelligence in time series forecasting: Theory and engineering applications, Advances in Industrial Control, Springer Verlag, NJ USA.
  • Pantazopoulos, S.N., Pappis, C.P. (1996) A new adaptive method for extrapolative forecasting algorithms, European Journal of Operational Research, 94, 106-111. doi.org/10.1016/0377-2217(95)00195-6
  • Piepmeier, J.A., McMurray, G.V., Lipkin, H. (1998) Tracking a Moving Target with Model Independent Visual Servoing: a Predictive Estimation Approach. Doi: 10.1109/ROBOT.1998.680746
  • Piepmeier, J.A., McMurray, G.V., Lipkin H. (2004) Uncalibrated Dynamic Visual Servoing, IEEE Transactions on Robotics and Automation, 20(1). Doi: 10.1109/TRA.2003.820923
  • Prėvost, C.G., Desbiens, A., Gagnon, E. (2007) Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle, Proceedings of the 2007 American Control Conference, USA, 1805-1810. Doi: 10.1109/ACC.2007.4282823
  • Ryan, E., Pizarro, O., Singh, H. (2004) Visually Augmented Navigation in an Unstructured Environment Using a Delayed State History, Proceedings of the 2004 IEEE, International Conference on Robotics & Automation, 25-32. Doi: .1109/ROBOT.2004.1307124
  • Shen, G., Kong, X., Chen, X. (2011) A Short-term Traffic Flow Intelligent Hybrid Forecasting Model and Its Application, Control Engineering and Applied Informatics, 13(3), 65-73.
  • Tang, Y., Huang, P. (2006) Boost-Phase Ballistik Missile Trajectory Estimation with Ground Based Radar, Journal of Systems Engineering and Electronics, 17 (4), 705-708. Doi: 10.1016/S1004-4132(07)60001-2
  • Tsaurs, R.C., Kuo, T.C.(2011) The Adaptive Fuzzy Time Series Model with an Application to Taiwan’s Tourism Demand, Expert Systems with Applications, Elsevier, 38, 9164-9167. doi.org/10.1016/j.eswa.2011.01.059
  • Unrath, M., Watt, D., Alpay, M. (2007) High Performance Motion Tracking Control for Electronic Manufacturing, ASME J. Dyn. Sys. Meas., Control, 129(6), 767-776. doi:10.1115/1.2789467
  • Vallery, H., Van Asseldonk, E.H., Buss, M., Van Der Kooij, H. (2009) Reference Trajectory Generation for Rehabilitation Robots: Complementary Limb Motion Estimation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1), 23-30. Doi: 10.1109/TNSRE.2008.2008278
  • Yagimli, M., Varol, H.S. (2008) Low Cost Target Recognising and Tracking Sensory System Mobile Robot, Journal of Naval Science and Engineering, 4(1), 17-26.
  • Yagimli, M., Varol, H.S. (2009) Real Time Color Composition Based Moving Target Recognition, Journal of Naval Science and Engineering, 5(2), 89-97.
  • Yagimli, M., Varol, H.S. (2009) Adaptive Estimation Algorithm-Based Target Tracking, Journal of Naval Science and Engineering, 5(3), 16-25.
  • Yagimli, M., Varol, H.S. (2009) A GPS-based system design for the recognition and tracking of moving targets, Proceedings of 4th International Conference on Recent Advances in Space Technologies, 978-1-4244-3626-2/09 IEEE, 6-12. Doi: 10.1109/RAST.2009.5158262.
  • Yagimli, M. (2010) An Optoelectronics System Develop Which Recognizes Moving Objects and Detect Their Orbit, Marmara University, Institute of Pure and Applied Sciences, Ph.D. Thesis.
  • Yang, Y., Polycarpou, M.M., Mimai, A.A. (2007) Multi-UAV Cooperative Search Using an Opportunistic Learning Method, ASME J. Dyn. Sys. Meas., Control, 129 (5), 716-728. Doi:10.1115/1.2764515
  • Yang, J-L., Ji, H-B. (2010) High Maneuvering Target-Tracking Based on Strong Tracking Modified Input Estimation, Scientific Research and Essays, 5(13), 1683-1689.
  • Zadeh, L. (1965) Fuzzy Sets, Information and Controls.
  • Zhou, S., Yong, C., Jianjun, S. (2004) Statistical Estimation and Testing for Variation Root-cause Identification of Multistage Manufacturing Processes, IEEE Transactions on Automation Science and Engineering, 1(1), 73-83. Doi: 10.1109/TASE.2004.829427
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Mustafa Yağımlı

Yayımlanma Tarihi 8 Aralık 2017
Gönderilme Tarihi 10 Mart 2017
Kabul Tarihi 21 Eylül 2017
Yayımlandığı Sayı Yıl 2017

Kaynak Göster

APA Yağımlı, M. (2017). HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 22(3), 43-60. https://doi.org/10.17482/uumfd.364093
AMA Yağımlı M. HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ. UUJFE. Aralık 2017;22(3):43-60. doi:10.17482/uumfd.364093
Chicago Yağımlı, Mustafa. “HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 22, sy. 3 (Aralık 2017): 43-60. https://doi.org/10.17482/uumfd.364093.
EndNote Yağımlı M (01 Aralık 2017) HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 22 3 43–60.
IEEE M. Yağımlı, “HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ”, UUJFE, c. 22, sy. 3, ss. 43–60, 2017, doi: 10.17482/uumfd.364093.
ISNAD Yağımlı, Mustafa. “HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 22/3 (Aralık 2017), 43-60. https://doi.org/10.17482/uumfd.364093.
JAMA Yağımlı M. HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ. UUJFE. 2017;22:43–60.
MLA Yağımlı, Mustafa. “HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 22, sy. 3, 2017, ss. 43-60, doi:10.17482/uumfd.364093.
Vancouver Yağımlı M. HAREKETLİ NESNELERİN UYARLANABİLİR BULANIK ZAMAN SERİLERİ VE ÜSSEL DÜZELTME TAHMİN TEKNİKLERİNİ KULLANARAK GERÇEK ZAMANLI YÖRÜNGELERİNİN İZLENMESİ. UUJFE. 2017;22(3):43-60.

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir).  Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

Bursa Uludağ Üniversitesi, Mühendislik Fakültesi Dekanlığı, Görükle Kampüsü, Nilüfer, 16059 Bursa. Tel: (224) 294 1907, Faks: (224) 294 1903, e-posta: mmfd@uludag.edu.tr