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In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported., {"references":["M.J.Swain and D.H.Ballard, \"Color indexing\", International Journal\nof Computer Vision, 1991, vol.7, no.1, pp.11-32.","H.Nezamabadi-pour, and E.Kabir,\"Image retrieval using histograms\nof unicolor and bicolor blocks and directional changes in intensity\ngradient\", 2004, Pattern Recognition Letters, vol. 25, no.14, pp. 1547-\n1557.","F.Mokhtarian and S.Abbasi, \"Shape similarity retrieval under affine\ntransforms\", Pattern Recognition, 2002, vol. 35, pp. 31-41.","A.K.Jain and A.Vailaya, \"Image retrieval using color and shape\",\nPattern Recognition, 1996, vol.29, no.8, pp.1233-1244.","B.S.Manjunath and W.Y.Ma, \"Texture feature for browsing and\nretrieval of image data\", IEEE PAMI, 1996, no. 18, vol. 8, pp. 837-\n842.","J.R.Smith and C.S.Li, \"Image classification and quering using\ncomposite region templates\", Academic Press, Computer Vision and\nUnderstanding, 1999, vol.75, pp.165-174.","C.W.Ngo, T.C.Pong and R.T.Chin, \"Exploiting image indexing\ntechniques in DCT domain\", pattern Recognition, 2001, vol. 34, pp.\n1841-1851.","J.Jiang, A.Armstrong and G.C.Feng, \"Direct content access and\nextraction from JPEG compressed images\", Pattern Recognition,\n2002, vol. 35, pp. 2511-2519.","G.Feng and J.Jiang, \"JPEG compressed image retrieval via statistical\nfeatures\", Pattern Recognition, 2003, vol. 36, pp. 977-985.\n[10] S.Climer and S.K.Bhatia, \"Image database indexing using JPEG\ncoefficients\", Pattern Recognition, 2002, vol. 35, pp. 2479-2488.\n[11] P.Ladret and A.G.Dugue, \"Categorization and retrieval of scene\nphotographs from a JPEG compressed database\", Pattern Analysis\nand Applications, 2001, no. 4, pp. 185-199.\n[12] J.Z.Wang, J.Li and G.Wiederhold, \"SIMPLIcity: semantic sensitive\nintegrated matching for picture libraries\", IEEE Trans. on Pattern\nAnalysis and Machine Intelligence, 2001, vol.23, no.9, pp.947-963.\n[13] H.W.Yoo, S.H.Jung, D.H.Jang and Y.K.Na, \"Extraction of major\nobject features using VQ clustering for content-based image\nretrieval\", Pattern Recognition, 2002, vol. 35, pp. 1115-1126.\n[14] W.Pennebaker and J.Mitchell, \"JPEG still image data compression\nstandard\" , 1993, New York: Vann strand.\n[15] A.Vailaya, A.K.Jain and H.J.Zhang, \"On image classification: city vs.\nlandscape\", Pattern Recognition, 1998, vol. 31, pp. 1921-1935.\n[16] M.Szummer and R.W.Picard, \"Indoor-outdoor image classification\",\nIEEE International Workshop on Content-Based Access of Image and\nVideo Database, in conj. With ICCV-98, Bombay, 1998.\n[17] Y.Rubner, J.Puzicha, C.Tomasi and J.M.Buhmann, \"Empirical\nevaluation of dissimilarity measures for color and texture\", Computer\nVision and Image Understanding, 2001, vol. 84, pp. 25-43."]}

This Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modelled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor.

This Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modelled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor., {"references":["","V. Tipsuwanporn, W. Piyarat and C. Tarasantisuk, ''Identification and control of brushless DC motors using on-line trained artificial neural networks,'' in Proc. Power Conversion Conf., pp. 1290-1294, Apr. 2002.","X.Li Q.Zhang and H.Xiao, ''The design of brushless DC motor servo system based on wavelet ANN, ''in Proc. Int. Conf. Machine Learning and Cybernetics, pp. 929-933, 2004.","N. Hemati, J. S. Thorp, and M. C. Leu, ''Robust nonlinear control of Brushless dc motors for direct-drive robotic applications,'' IEEE Trans. Ind. Electron., vol. 37, pp. 460-468, Dec 1990.","P. M. Pelczewski and U. H. Kunz, ''The optimal control of a constrained drive system with brushless dc motor,'' IEEE Trans. Ind. Electron., vol. 37, pp. 342-348, Oct. 1990.","F. J. Lin, K. K. Shyu, and Y. S. Lin, ''Variable structure adaptive control for PM synchronous servo motor drive,'' IEE Proc. IEE B: Elect. Power Applicat., vol. 146, pp. 173-185, Mar. 1999.","E. Cerruto, A. Consoli, A. Raciti, and A. Testa, ''A robust adaptive controller for PM motor drives in robotic applications,'' IEEE Trans. Power Electron., vol. 10, pp. 62-71, Jan. 1995.","C.-L. Lin, and H.-Y. Jan, ''Evolutionarily multiobjective PID control for linear brushless DC motor, ''in Proc. IEEE Int. Conf .Industrial Elect. Society, Nov. 2002, pp.39-45.","K. Ang, G. Chong, and Y. Li, ''PID control system analysis, design, and technology,'' IEEE Trans.Control System Technology, vol. 13, pp. 559-576, July 2005.","G. Yu, and R. Hwang, ''Optimal PID speed control of brush less DC motors using LQR approach,'' in Proc. IEEE Int. Conf. Systems, Man and Cybernetics, 2004, pp. 473-478.\n[10] C. L. Lin, and H. Y. Jan, and N. C. Shieh, ''GA-based multiobjective PID control for a linear brushless DC motor,'' IEEE/ASME Trans. Mechatronics , vol.8, No. 1, pp. 56-65, 2003.\n[11] D. B. Fogel, Evolutionary Computation toward a New Philosophy of Machine Intelligence: New York: IEEE, 1995.\n[12] R. C. Eberhart and Y. Shi, ''Comparison between genetic algorithms and particle swarm optimization,'' in Proc. IEEE Int. Conf. Evol. Comput., Anchorage, AK, May 1998, pp. 611-616.\n[13] Z.-L. Gaing, ''A particle swarm optimization approach for optimum design of PID controller in AVR system,'' IEEE Trans. Energy Conversion, vol. 19, pp. 384-391, June 2004.\n[14] J. Kennedy and R. Eberhart, ''Particle swarm optimization,'' in Proc. IEEE Int. Conf. Neural Networks, vol. IV, Perth, Australia, 1995, pp. 1942-1948. [15] M. A. Abido, ''Optimal design of power-system stabilizers using particle swarm optimization,'' IEEE Trans. Energy Conversion, vol. 17, pp.406-413, Sep. 2002.\n[16] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, ''A particle swarm optimization for reactive power and voltage control considering voltage security assessment,'' IEEE Trans. on Power Systems, Vol. 15, No. 4, Nov. 2000, pp. 1232 - 1239.\n[17] Allan R. Hambley, Electrical Engineering: Principles and Application, Prentice Hall, New Jersey 1997.\n[18] Chee-Mun Ong, Dynamic Simulation of Electric Machinery, Prentice Hall, New Jersey, 1998.\n[19] R. A. Krohling and J. P. Rey, ''Design of optimal disturbance rejection PID controllers using genetic algorithm,'' IEEE Trans. Evol. Comput., vol. 5, pp. 78-82, Feb. 2001.\n[20] Y. Mitsukura, T. Yamamoto, and M. Kaneda, ''A design of self-tuning PID controllers using a genetic algorithm,'' in Proc. Amer. Contr. Conf., San Diego, CA, June 1999, pp. 1361-1365.\n[21] R. A. Krohling, H. Jaschek, and J. P. Rey, ''Designing PI/PID controller for a motion control system based on genetic algorithm,'' in Proc. 12th IEEE Int. Symp. Intell. Contr., Istanbul, Turkey, July 1997, pp. 125-130."]}