Did you mean: Search also for broader subjects Search also for narrower subjects Search also for related subjects OPTIMIZATION FOR MACHINE LEARNING.MATHAMATICAL MODELS.MATHEMATICAL OPTIMIZATION.MACHINE LEARNING SHAKESPEARE ON MANAGEMENT.LEADERSHIP LESSONS FOR TODAY'S MANGERS MS-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing Lea, F. M Optimization for Machine Learning MS-COMPUTER ENGINEERING-2017 MSTHESIS ABSTRACT. The use and vast implementation of Discrete Fourier Transform has revolutionized the world and allowed the researchers to think of the modern world from a different perspective. The discovery of Fast Fourier Transform has laid the foundation of an entirely new dimension to the modern world. Keeping in view its utmost importance in the future industry researchers tried to design its hardware architecture as per the requirement of the application. Several architectures have been proposed time to time with new inventions in the previous designs. Some architectures consider clock rate, some take architectural area into consideration, some focuses on parallel execution of the algorithm, so on and so forth. Considering all these inputs to the industry that has been a part to modern world time to time, this research presents an empirical model based upon the optimal architectures for Fast Fourier Transform algorithm for n-bits m-points input. This empirical model is obtained by making several architectures and their respective characteristics are obtained. The data obtained is then passed through a machine learning algorithm known as Regression Algorithm. Linear, quadratic and cubic regression technique is applied to achieve the hierarchy of the designed architectural parameters and this intern will provide us with the empirical models of the architecture. This model will provide us with the specifications of the futuristic architecture that mainly depends upon the one's requirement i.e. either one considers a single parameter or a tradeoff between different hardware parameters. The parameters that are mainly considered are number of Slice LUT's, LUT FF Pairs, clock rate, number of processing elements and number of clock cycles required. This proposed methodology can be applied to any hardware architectural designs for analysis and generation of empirical models. Key Words: Discrete Fourier Transform, Fast Fourier Transform, Processing Element, Butterfly Architecture, n Radix FFT, Permutation Matrix, Kronecker Product John D. and Catherine T. MacArthur Foundation series on digital media and learning. ENCYCLOPEDIA OF GREAT MILITARY LEADERS AND SOLDIERS. FIELD MARSHAL JEFFREY AMHERST, JACOB ASTLEY, ATTILA, ALEXANDER THE GREAT, EDMUND HENRY ALLENBY, SUBATAI BA ADUR, BABAR, FRANCISCO FRANCO BAHAMONDE, WILLIAM BAILLIE, LANCE SERGEANT JOHN D. BASKEYFIELD, WILLIAM GEORGE BARKER, PIERRE G.T BEAUREGARD, JEAN BAPTISTE-JULES BERNADOTTE, WILLIAM AVERY BISHOP, NETAJI SUBHASH CHANDRA BOSE, ADMIRAL EDWARD BOSCAWEN, ALEKSEY ALEKSEYEVICH BRUSILOV, CADWALLON KING OF GWYNEDD, CAIUS JULIUS CAESAR, WILLIAM CAVENDISH, CHARLES 1, CHARLES OF LORRAINE, NOEL GODFREY CHAVASSE,CHRISTIAN OF BRUNSWICK, JOHN CHURCHILL, ROBERT CLIVE, CALBRAITH LOWERY COLE, SIR EYRIE COOTE, ROBERT CRAUFORD, OLIVER CROMWELL, WILLIAM AUGUSTUS CUMBERLAND, EDWARD THE BLACK PRICE, PRINCE OF WALES, EDWARD III KING OF ENGLAND, THOMAS FAIRFAX, ERICH VON FALKENHAYN, FERDINAND OF BRUNSWICK, NATHAN BEDFORD FORREST, FREDERICK II (THE GREAT), BERNARDO DE GALVEZ, GEORGE II, NATHANAEL GREENE, BEVIL GRENVILE, ALEXANDER HAMILTON, HANNIBAL, SIR BASIL HENRY LIDDELL HART, ADMIRAL EDWARD HAWKE, A.P HILL, ROWLAND "DADDY" HILL, GENERAL NGUYEN VAN HIEU, NELSON HARATTIO, JOHN PAUL JONES, JEAN BAPTISTE JOURDAN, STEPHEN WATTS KEARNY, MYLES WALTER KEOGH, HORATIO KITCHENER, V.V FILIP KONOWAL, PRINCE M ILARIONOVICH KUTUZOV, THE MARQUIS DELAFAYETTE, MARMADUKE LANGDALE, BENJAMIN LINCOLN, ERICH VON LUDENDORFF, DOUGLAS MACARTHUR, ADMIRAL THOMAS MATHEWS, SIR WALTER DE MAUNY, DUKE MAXIMILIAN IOF BAVARIA, GEORGE BRINTON MCCLELLAN, MAJOR GENERAL G.G MEADE, VON MOLTKE, LOUIS OSEPH MONTCALM, BRIGADIER GENERAL DANIEL MORGAN, SULTAN MURAD, NICOLAS CHARLES OUDINOT,GRAND DUKE NINHOLAS, ROBERT GEORGES NIVELLE, ORKHAN, OTHMAN, GENERAL SIR JAMES OUTRAM, HENRY PERCY HOTSPUR, JOHN JOSEPH PERSHING, WILLIAM PITT THE ELDER, PRINCE JOSEF ANTON PONIATOWSKI, COLONEL JOHN A POINDEXTER, JOHN S POINDEXTER, ELWOOD R QUESADA, PRINCE RUPERT, HERMAN MAURICE SAXE, GENERAL WINFIELD SCOTT, WILLIAM TECUMSEH SHERMAN, CHHATRAPATI SHIVAJI, PHILIP SKIPPON, TECUMSEH, JOHAN TESERCLAES TILLY, SUN TZU, FREDERICK WILLIAM I, JAMES WOLFE, SHAKA ZULU LAUNCH VEHICLES -ENCYCLOPEDIAS. ASTRONAUTICS - ENCYCLOPEDIAS.REUSABLE SPACE VEHICLE-ENCYCLOPEDIA.SPACE ,SHUTTLES-ENCYCLOPEDIAS.SPACE LAUNCH SYSTEMS, SPACE VEHICLE-ANGARA,SPACE VEHICLE- ARIANE, SPACE VEHICLE-ATHENA, SPACE VEHICLE-ATLAS,SPACE VEHICLE- CYCLONE,SPACE VEHICLE- DELTA,SPACE VEHICLE- DNEPR,SPACE VEHICLE- FALCON, SPACE VEHICLE-H-IIA,SPACE VEHICLE- K-I, SPACE VEHICLE-KAITUOZHE,SPACE VEHICLE- KOSMOS,SPACE VEHICLE- M-V, SPACE VEHICLE-MONOTAUR,SPACE VEHICLE- PEGASUS,SPACE VEHICLE- PROTON,SPACE VEHICLE- PSLV AND GSLV,SPACE VEHICLE- ROCKOT,SPACE VEHICLE- SCORPIUS,SPACE VEHICLE- SHAVIT AND LEALINK,SPACE VEHICLE- SHTIL AND VOLNA, SPACE SHUTTLE