International Transaction of Electrical and Computer Engineers System

ISSN (Print): 2373-1273

ISSN (Online): 2373-1281

Editor-in-Chief: Dr. Pushpendra Singh

Website: http://www.sciepub.com/journal/ITECES

   

Article

Analysis of the Operation of the Systems with Distributed Parameters with the Adjusting System Objects

1Department Computer Systems and Networks, Azerbaijan Technical University, Baku, Azerbaijan

2Department Applied Informatics, Azerbaijan Technical University, Baku, Azerbaijan


International Transaction of Electrical and Computer Engineers System. 2015, 3(1), 30-33
doi: 10.12691/iteces-3-1-4
Copyright © 2015 Science and Education Publishing

Cite this paper:
Musayev Vidadi Hasan, Huseynov Natig Etibar. Analysis of the Operation of the Systems with Distributed Parameters with the Adjusting System Objects. International Transaction of Electrical and Computer Engineers System. 2015; 3(1):30-33. doi: 10.12691/iteces-3-1-4.

Correspondence to: Musayev  Vidadi Hasan, Department Computer Systems and Networks, Azerbaijan Technical University, Baku, Azerbaijan. Email: musayev_vidadi@mail.ru

Abstract

An analysis of the trunk oil pipeline mode with multiple adjustable intermediate pumping stations is conducted on the example of the trunk oil pipeline basing on the developed computational models of transient processes in the systems with distributed parameters with the adjusting system objects.

Keywords

References

[1]  Zaitsev L.A. Regulation of operation modes of trunk pipelines. M .: Nedra, 1982, 136 pp.
 
[2]  Musayev V.H. Dynamic processes in complex systems with distributed parameters. LAP LAMBERT Academic Publishing. 2014, 127 pp.
 
[3]  Musayev V.H. Development of computational models of transient processes in the systems with distributed parameters with the adjusting system objects. Міжнародний науково-технічний журнал “Інформаційні технології та комп’ютерна інженерія” (International scientific-technical journal “Information Technology and computer engineering”), 2013, No 2, 46-63 pp.
 
[4]  Musaev V.H. Discrete method and system-structural analysis for dynamic problem solutions in the trunk pipeline systems // Machine building news M.: 2007, No10, 29-33pp.
 
[5]  Akhmadullin K.R. Methods of calculation and regulation of pumping stations of trunk pipelines // Oil industry, 2005, No 3,100-103 pp.
 
Show More References
[6]  Lisafin V.P. Study of the efficiency of stepwise control modes of OPS / Problems of the West Siberian fuel and energetic complex. Theses of the First Union Scientific-technical conference. Ufa: 1982, 103-104 pp.
 
[7]  Kadymov Y.B. Transient processes in the systems with distributed parameters. M .: Nauka, 1968, 192 pp.
 
[8]  Zhidkova M.A. Pipeline transportation of gas. Kiev, Naukova Dumka, 1973, 142 pp.
 
[9]  Charny I.A. Unsteady flow of real fluid in pipes. Moscow: Nedra, 1975, 296 pp.
 
[10]  Ditkin V.A., Prudnikov A.P. Handbook of operational calculations. M .: Higher School, 1965, 465 pp.
 
Show Less References

Article

A Graph-based Technique for the Spectral-spatial Hyperspectral Images Classification

1Department of Electrical and Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran


International Transaction of Electrical and Computer Engineers System. 2017, 4(1), 1-7
doi: 10.12691/iteces-4-1-1
Copyright © 2017 Science and Education Publishing

Cite this paper:
F. Poorahangaryan, H. Beheshti, S.A. Edalatpanah. A Graph-based Technique for the Spectral-spatial Hyperspectral Images Classification. International Transaction of Electrical and Computer Engineers System. 2017; 4(1):1-7. doi: 10.12691/iteces-4-1-1.

Correspondence to: S.A.  Edalatpanah, Department of Electrical and Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran. Email: saedalatpanah@gmail.com

Abstract

Minimum Spanning Forest (MSF) is a graph-based technique used for segmenting and classification of images. In this article, a new method based on MSF is introduced that can be used to supervised classification of hyperspectral images. For a given hyperspectral image, a pixel-based classification, such as Support Vector Machine (SVM) or Maximum Likelihood (ML) is performed. On the other hand, dimensionality reduction is carried out by Principal Components Analysis (PCA) and the first eight components are considered as the reference data. The most reliable pixels, which are obtained from the result of pixel-based classifiers, are used as markers in the construction of MSF. In the next stage, three MSF’s are created after considering three distinct criteria of similarity (dissimilarity). Ultimately, using the majority voting rule, the obtained classification maps are combined and the final classification map is formed. The simulation results presented on an AVRIS image of the vegetation area indicate that the proposed technique enhanced classification accuracy and provides an accurate classification map.

Keywords

References

[1]  L. J. P. Maaten, E. O. Postma, and H. J. Herik, “Dimensionality reduction:A comparative review,” Univ. Maastricht, Amsterdam, The Netherlands, Tech. Rep, 2009.
 
[2]  S. Moussaoui, H. Hauksdottir, F. Schmidt, C. Jutten, J. Chanussot,D. Brie, S. Douté, and J. A. Benediktsson, “On the decomposition of Mars hyperspectral data by ICA and Bayesian positive sourceseparation,” Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, 2008.
 
[3]  R. Dianat and Sh. Kasaei, “Dimension Reduction of Optical RemoteSensing Images via Minimum Change Rate Deviation Method,” IEEE Trans.Geosci. Remote Sens., Vol. 48, no. 1, pp. 198-206, 2010.
 
[4]  H. Huang, J. Liu and Y. Pan, “Semi-Supervised Marginal Fisher Analysisfor Hyperspectral Image Classification,” XXII ISPRS Congress, pp.377-382, 2012.
 
[5]  D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. NewYork: Wiley, 2003.
 
Show More References
[6]  H. Zhou, Z. Mao, and D. Wang, “Classification of coastal areas by airborne hyperspectral image,” in Proc. SPIE Opt. Technol. Atmos., Ocean,Environ. Stud., vol. 5832, pp. 471-476, 2005.
 
[7]  C.H. Li, B.C. Kuo, C.T.Lin and C. S. Huang, “A Spatial–Contextual Support Vector Machinefor Remotely Sensed Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 784-799, 2012.
 
[8]  M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Evaluation of kernels for multiclass classification of hyperspectral remote sensing data,” in Proc. ICASSP, pp. II-813-II-816, 2006.
 
[9]  Mountrakis, G., Im, J., & Ogole, C., “Support vector machines in remote sensing: A review.” ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, no. 3, pp. 247-259, 2011.
 
[10]  F. Mirzapour and H. Ghassemian, “Improving hyperspectral image classification by combining spectral, texture, and shape featuresImproving hyperspectral image classification by combining spectral, texture, and shape features”, International Journal of Remote Sensing, vol 36, no. 4, pp. 1070-1096, Feb. 2015.
 
[11]  M. Dalla Mura, J.A. Benediktsson B. Waske and L. Bruzzone, “Extended profiles with morphological attribute filters for the analysis classification of hyperspectral data ,” Int. J. Remote Sens., vol. 31, no. 22, pp. 5975-5991,2010.
 
[12]  J.. Li, J. M. Bioucas-Dias, and A. Plaza, “Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geos. Remote Sens., vol. 50, no. 3, pp. 809-823, 2012.
 
[13]  M. Borhani and H. Ghassemian, “Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields ”, Iranian Conference on Intelligent Systems (ICIS 2014), pp. 1-6, 2014.
 
[14]  Y. Tarabalka, J, Chanussot, and A. Benediktsson, “ Segmentation and classification of hyperspectral images using watershed transformation,” J. Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, 2010.
 
[15]  Y. Tarabalka, J. A. Benediktsson, and J, Chanussot, “Spcetral-spatial classification of hyperspectral imagery based on partitional clustering techniques,” IEEE Trans. Geo. and Remote Seng, vol. 47, no. 8, pp, 2373-2987, 2009.
 
[16]  Y. Tarabalka, J. Chanussot, and J.A. Benediktsson, “Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers,” IEEE Trans. Systems, Man, and Cybernetics: Part B, vol. 40, no. 5, pp. 1267-1279, Oct. 2010.
 
[17]  Y. Tarabalka, J.A. Benediktsson, J. Chanussot, and J.C. Tilton, “Multiple spectral-spatial classification approach for hyperspectral data,” IEEE Trans. Geosci. Remote Sens, vol. 48, no. 11, pp. 4122-4132, Nov. 2010.
 
[18]  K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot and J. A. Benediktsson, “Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach,” IEEE Trans. Geosci. Remote Sens, vol. 21, NO. 4, pp. 2008-2021, APRIL 2012.
 
[19]  R. Pike, S. K. Patton, G. Lu, L. V. Halig, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection”, Conference: Proc. SPIE 9034, Medical Imaging 2014.
 
[20]  Cortes, C.; Vapnik, V. “Support-vector networks”. Machine Learning, vol 20, no. 3. pp 273-297, 1995.
 
[21]  R.O. Duda, P.e. Hart, and D.G. Stock, “Pattern classification”, Second Edition, wiely, 2001.
 
[22]  T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.
 
Show Less References

Article

Methods for Evaluating Some Integrations Involving Inverse Trigonometric Functions

1Department of Information Technology, Nan Jeon University of Science and Technology, Tainan City, Taiwan


International Transaction of Electrical and Computer Engineers System. 2017, 4(1), 8-13
doi: 10.12691/iteces-4-1-2
Copyright © 2017 Science and Education Publishing

Cite this paper:
Chii-Huei Yu. Methods for Evaluating Some Integrations Involving Inverse Trigonometric Functions. International Transaction of Electrical and Computer Engineers System. 2017; 4(1):8-13. doi: 10.12691/iteces-4-1-2.

Correspondence to: Chii-Huei  Yu, Department of Information Technology, Nan Jeon University of Science and Technology, Tainan City, Taiwan. Email: chiihuei@mail.nju.edu.tw

Abstract

This paper uses the mathematical software Maple as the auxiliary tool to study the integral problems. The infinite series expressions of some types of indefinite integrals involving the powers of inverse trigonometric functions can be obtained by using power series expansions, binomial series and integration term by term theorem. Moreover, some examples are proposed to demonstrate the calculations. The research approach adopted in this study is to get the answers through manual calculations and then use Maple to verify the answers. This kind of research method not only allows us to find the calculation errors, but also helps us to modify the direction of original thinking from manual calculation and Maple calculation. Therefore, Maple provides us insights into the problems.

Keywords

References

[1]  A. A. Adams, H. Gottliebsen, S. A. Linton, and U. Martin, “Automated theorem proving in support of computer algebra: symbolic definite integration as a case study,” Proceedings of the 1999 International Symposium on Symbolic and Algebraic Computation, Canada, pp. 253-260, July 28- 31, 1999.
 
[2]  M. A. Nyblom, “On the evaluation of a definite integral involving nested square root functions,” Rocky Mountain Journal of Mathematics, Vol. 37, No. 4, pp. 1301-1304, 2007.
 
[3]  C. Oster, “Limit of a definite integral,” SIAM Review, Vol. 33, No. 1, pp. 115-116, 1991.
 
[4]  C. -H. Yu, “Application of complex integral on solving some integral problems of trigonometric functions,” International Journal of Research, Vol. 3, Issue. 14, pp. 4663-4668, 2016.
 
[5]  C. -H. Yu, “Integral problems of trigonometric functions,” International Journal of Scientific Research in Science and Technology, Vol. 2, Issue. 1, pp. 63-67, 2016.
 
Show More References
[6]  C. -H. Yu, “Solving some definite integrals using Parseval’s theorem,” American Journal of Numerical Analysis, Vol. 2, No. 2, pp. 60-64, 2014.
 
[7]  C. -H. Yu,“Some types of integral problems,” American Journal of Systems and Software, Vol. 2, No. 1, pp. 22-26, 2014.
 
[8]  C. -H. Yu, “Using Maple to study the double integral problems,” Applied and Computational Mathematics, Vol. 2, No. 2, pp. 28-31, 2013.
 
[9]  C. -H. Yu, “Using Maple to study the integrals of trigonometric functions,” Proceedings of the 6th IEEE/International Conference on Advanced Infocomm Technology, Taiwan, No. 00294, July 6-9, 2013.
 
[10]  C. -H. Yu, “A study on double Integrals,” International Journal of Research in Information Technology, Vol. 1, Issue. 8, pp. 24-31, 2013.
 
[11]  C. -H. Yu, “Application of Parseval’s theorem on evaluating some definite integrals,” Turkish Journal of Analysis and Number Theory, Vol. 2, No. 1, pp. 1-5, 2014.
 
[12]  C. -H. Yu, “Evaluation of two types of integrals using Maple,” Universal Journal of Applied Science, Vol. 2, No. 2, pp. 39-46, 2014.
 
[13]  C. -H. Yu, “Studying three types of integrals with Maple,” American Journal of Computing Research Repository, Vol. 2, No. 1, pp. 19-21, 2014.
 
[14]  C. -H. Yu, “The application of Parseval’s theorem to integral problems,” Applied Mathematics and Physics, Vol. 2, No. 1, pp. 4-9, 2014.
 
[15]  C. -H. Yu, “A study of some integral problems using Maple,” Mathematics and Statistics, Vol. 2, No. 1, pp. 1-5, 2014.
 
[16]  C. -H. Yu, “Solving some definite integrals by using Maple,” World Journal of Computer Application and Technology, Vol. 2, No. 3, pp. 61-65, 2014.
 
[17]  C. -H. Yu, “Using Maple to study two types of integrals,” International Journal of Research in Computer Applications and Robotics, Vol. 1, Issue. 4, pp. 14-22, 2013.
 
[18]  C. -H. Yu, “Application of Maple on some type of integral problem,” (in Chinese) Proceedings of the Ubiquitous-Home Conference 2012, Taiwan, pp.206-210, October 25, 2012.
 
[19]  C. -H. Yu, “Solving some integrals with Maple,” International Journal of Research in Aeronautical and Mechanical Engineering, Vol. 1, Issue. 3, pp. 29-35, 2013.
 
[20]  C. -H. Yu, “Application of Maple on the integral problem of some type of rational functions,” (in Chinese) Proceedings of the Annual Meeting and Academic Conference for Association of IE, Taiwan, D357-D362, December 15, 2012.
 
[21]  C. -H. Yu, “A study on integral problems by using Maple,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue. 7, pp. 41-46, 2013.
 
[22]  C. -H. Yu, “Application of Maple on some integral problems,” (in Chinese) Proceedings of the International Conference on Safety & Security Management and Engineering Technology 2012, Taiwan, pp. 290-294, May 31, 2012.
 
[23]  C. -H. Yu, “Evaluating some integrals with Maple,” International Journal of Computer Science and Mobile Computing, Vol. 2, Issue. 7, pp. 66-71, 2013.
 
[24]  C. -H. Yu, “Application of Maple on evaluation of definite integrals,” Applied Mechanics and Materials, Vols. 479-480 (2014), pp. 823-827, 2013.
 
[25]  C. -H. Yu, “A study of the integrals of trigonometric functions with Maple,” Proceedings of the Institute of Industrial Engineers Asian Conference 2013, Taiwan, Springer, Vol. 1, pp. 603-610, 2013.
 
[26]  C. -H. Yu, “Application of Maple on the integral problems,” Applied Mechanics and Materials, Vols. 479-480 (2014), pp. 849-854, 2013.
 
[27]  C. -H. Yu, “Evaluating some types of definite integrals,” American Journal of Software Engineering, Vol. 2, Issue. 1, pp. 13-15, 2014.
 
[28]  C. -H. Yu and S. -Y. Huang, “Using Maple to evaluate two types of special integrals,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 1, Issue. 3, pp. 1-5, 2016.
 
[29]  C. -H. Yu and B. -H. Chen, “Solving some types of integrals using Maple,” Universal Journal of Computational Mathematics, Vol. 2, No. 3, pp. 39-47, 2014.
 
[30]  C. -H. Yu and S. -D. Sheu, “Using area mean value theorem to solve some double integrals,” Turkish Journal of Analysis and Number Theory, Vol. 2, No. 3, pp. 75-79, 2014.
 
[31]  C. -H. Yu and S. -D. Sheu, “Infinite series forms of double integrals,” International Journal of Data Envelopment Analysis and *Operations Research*, Vol. 1, No. 2, pp. 16-20, 2014.
 
[32]  C. -H. Yu and S. -D. Sheu, “Evaluation of triple integrals,” American Journal of Systems and Software, Vol. 2, No. 4, pp. 85-88, 2014.
 
[33]  D. Zwillinger, CRC Standard Mathematical Tables and Formulae, 31st ed., Chapman & Hall/CRC, New York, 2003.
 
[34]  T. M. Apostol, Mathematical Analysis, 2nd ed., Addison-Wesley, Boston, 1975.
 
Show Less References