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Biswal, B. N.
- An Approach to Study Image Denoising using Doubly Sparse Transform Technique
Abstract Views :268 |
PDF Views:0
Authors
Affiliations
1 Electronics & Tele-Communication Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
1 Electronics & Tele-Communication Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
Source
Technology Spectrum Review, Vol 1, No 2 (2016), Pagination: 13-16Abstract
In this Paper the sparse domain of signals in a certain area or dictionary has been widely used in many applications in image, audio, biological and other signal analysis. Analytical Sparse transforms such as discrete cosine transform (DCT) and its counterpart i.e. wavelet transform (WT) have been extensively used in the areas of image compression standards where as synthesis sparsifying dictionaries have become extensively used especially in applications such as image de-noising and medical image reconstruction. In this work, we discuss about the square sparsifying transforms which is the product from a fixed, fast transform so as to consider the DCT and an adaptive constrained matrix to be sparse. Such transforms can be studied and implemented efficiently.Keywords
Dictionary Learning, Sparse Representation, Image De-Noising, Wavelet Transform, Discrete Cosine Transform.
References
- S. Ravishankar, and Y. Bresler, “Learning Doubly Sparse Transforms for Images,” IEEE Transactions on Signal Processing, vol. 22, no. 12, pp. 4598-4612, Dec. 2013.
- R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: Learning sparse dictionaries for sparse signal approximation,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1553–1564, Mar. 2010.
- M. Yaghoobi, S. Nam, R. Gribonval, and M. E. Davies, “Constrained overcomplete analysis operator learning for cosparse signal modelling,” IEEE Transactions on Signal Processing, vol. 61, no. 9, pp. 2341–2355, May 2013.
- S. Ravishankar and Y. Bresler, “Learning sparsifying transforms,” IEEE Transactions on Signal Processing, vol. 61, no. 5, pp. 1072–1086, Mar. 2013.
- M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Signal Processing, vol. 15, no. 12, pp. 3736–3745, Dec. 2006.
- M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, Nov. 2006
- Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based rain streaks removal via image decomposition,” IEEE Transactions on Signal Processing, vol. 21, no. 4, pp. 1742-1755, APRIL. 2012.
- S. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
- J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” Journal of Machine Learning Research, vol. 11, pp. 19-60, Jan. 2010.
- An Intelligent Method for Automatic Generation Control of Two Area Interconnected Power System Using GSA Based PID Controller
Abstract Views :279 |
PDF Views:4
Authors
P. M. Dash
1,
B. N. Biswal
2
Affiliations
1 Department of Electrical Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
2 Department of Computer Science & Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
1 Department of Electrical Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
2 Department of Computer Science & Engineering, Bhubaneswar Engineering College, Bhubaneswar, Odisha, IN
Source
Technology Spectrum Review, Vol 2, No 1 (2017), Pagination: 11-17Abstract
Numbers of optimization techniques has been developed by several researchers to control the load frequency of two area system. In this paper a population based Gravitational Search Algorithm (GSA) is implemented for the same purpose. Here the two area non reheat thermal system is equipped with Proportional-Integral-Derivative (PID) controller whose gains are tuned by using GSA optimization technique and the behavior of the proposed system is investigated by taking Integral Time multiple Absolute Error (ITAE) objective function. The dynamic responses with PID controller are compared with PI controller under different loading condition of the same system. Different system parameters (±25% to ±50%) are considered for the purpose of analysis to obtain the effectiveness and robustness of the system.Keywords
Automatic Generation Control (AGC), Gravitational Search Algorithm (GSA), Load Frequency Control, PID Controller.References
- O. I. Elgerd, “Electric energy systems theory: An introduction,” New Delhi: Tata McGraw-Hill, 1983.
- O. I. Elgerd, “Electric energy systems theory: An introduction,” McGraw Hill Co., 2001.
- O. I. Elgerd, and C. E. Fosha, “Optimum megawatt-frequency control of multi-area electric energy systems,” IEEE Trans. on Power Apparatus, vol. PAS-89, no. 4, pp.556-563, April 1970.
- D. Rerkpreedapong, A. Hasanovic, and A. Feliachi, “Robust load frequency control using genetic algorithms and linear matrix inequalities,” IEEE Transactions Power Systems, vol. 18, no. 2, pp. 855-861, 2003.
- T. C. Yang, H. Cimen, and Q. M. Zhu, “Decentralized load frequency controller design based on structured singular values,” IEE Proc-Generat Trans Distrib.,vol. 145, no. 1, pp. 7-14, 1998.
- P. Kundur, Power System Stability and Control, New York: Mc-Grall Hill, 1994.
- S. P. Ghosal, “Multi-area frequency and tie-line power flow control with fuzzy logic based integral gain scheduling,” Jour. Inst. Elec. Engg., vol. 84, pp. 135-141, 2003.
- S. P. Ghosal, “Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system,” Electric Power Systems Research, vol. 70, no. 2, pp. 115-127, Jul. 2004.
- M. L. Kothari, J. Nanda, D. P. Kothari, and D. Das,“Discrete-mode automatic generation control of a twoarea reheat thermal system with new area control error,” IEEE Transactions on Power Systems, vol. 4, no. 2, pp.730-738, May 1989.
- K. Venkateswarlu, and A. K. Mahalanabis, “Load frequency control using output feedback,” Journal of the Institution of Engineers, India, pt. El-4, vol. 58, pp. 200203, Feb. 1978.
- S. Pothiya, I. Ngamroo, S. Runggeratigul, and P.Tantaswadi, “Design of optimal fuzzy logic based PI controller using multiple tabu Search algorithms for load frequency control,” International Journal of Control, Automation and Systems, vol. 4, no. 2, pp. 155-164, 2006.
- M. L. Kothari, J. Nanda, D. P. Kothari, and D. Das, “Discrete mode AGC of a two area reheat thermal system with new ACE,” IEEE Trans. Power Systems, vol. 4, pp.730-738, May 1989.
- D. K. Chaturvedi, P. S. Satsangi, and P. K. Kalra, “Load frequency control: A generalized neural network approach,” International Journal of Electrical Power & Energy Systems, vol. 21, pp. 405-415, 1999.
- E. Çam, and I. Kocaarslan, “Fuzzy logic controller in interconnected electrical power systems for load-frequency control,” International Journal of Electrical Power & Energy Systems, vol. 27, no. 8, pp. 542-549, 2005.
- G. Panda, S. Panda, and C. Ardil, “Automatic generation control of interconnected power system with generation rate constraints by hybrid neuro fuzzy approach,” International Journal of Electrical, Computer, Electronics and Comm. Engineering, vol, 6, no, 4, 2012.
- S. R. Khunita, and S. Panda, “A novel approach for automatic generatio control of a multi area power system,” IEEE Conference Electrical and Computer Engineering, Niagarea fall, Canada, 2011.
- E. Rashedi, H. Nezamabadi-pour, and J. S. Saryazdi, “GSA: A gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
- E. Rashedi, H. Nezamabadi-pour, and J. S. Saryazdi, “Filter modeling using gravitational search algorithm,” Engineering Applications of Artificial Intelligence, vol. 24, pp. 117-122, 2011.
- S. Ramesh, and A. Krishnan, “Modified genetic algorithm based load frequency controller for interconnected power system,” International Journal of Electrical and Power Engineering, vol. 3, no. 1, pp. 26-30, 2009.
- S. K. Mohapatra, B. S. Dash, and P. M. Dash, “Application of GSA optimized PI controller parameters in automatic generation control for interconnected power system,” International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES),2016.
- S. K. Mohaptra and S. Panda, “Stability enhancement with SSSC based controller design in presence of nonlinear voltage dependent load,” International Journal of Intelligent Systems Technologies and Applications, vol.15, no. 2, pp. 163-186, 2016.