Relevant bibliographies by topics / Machine vector control

Academic literature on the topic 'Machine vector control'

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Published: 14 March 2023

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Journal articles on the topic "Machine vector control"

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Wang, De Cheng, Er Hao Liu, and Hui Lin. "One Approach for Direct Torque Control Switching Voltage Vector Selection." Applied Mechanics and Materials 246-247 (December 2012): 867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.867.

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Direct torque control selected switching voltage vector according to torque hysteresis comparator output, flux hysteresis comparator output, and sector. One switching voltage vector selection approach was proposed. It used support vector regression machine to carry out direct torque control switching voltage vector selection. The selection of eight switching voltage vectors was an eight classification problem. This classification problem was changed into regression problem by support vector regression machine. The nonlinear function used for switching voltage vector selection was gained by support vector regression machine training. Asynchronous motor direct torque control simulation result shows feasibility and effectivity of proposed method.
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NEZLI, Lazhari, and Omar Zouaid. "Sliding mode control of a Five-Phase Series- Connected Two-Motor Drive." Algerian Journal of Signals and Systems 1, no. 1 (February 2, 2021): 59–68. http://dx.doi.org/10.51485/ajss.v1i1.19.

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In this work, we study vector control and sliding mode control of series-connected five-phase two asynchronous machines supplied with a three levels inverter. After presentation of multiphase machines, we worked out the mathematical model of five phase asynchronous machine supplied with voltage inverter. Application of Park transformation reduces considerably the mathematical model of machine. After, we applied vector control and sliding mode control to the five-phase induction machine. After that, we study a multi-machine system which comport five-phase two asynchronous machines supplied with a single voltage inverter. In the last, we had the independent vector control and the sliding mode control of series-connected five-phase two asynchronous machines. We observe that an appropriate transposition of phase’s order permits an independent control of two machines.
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Vasconcelos, Carlos, Richard Stephan, and Antonio Ferreira. "Study Of The Cascaded Doubly Fed Induction Machine Dynamics Under Vector Control." Eletrônica de Potência 23, no. 1 (March 1, 2018): 39–46. http://dx.doi.org/10.18618/rep.2018.1.2719.

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Amman, Hans M. "Nonlinear control simulation on a vector machine." Parallel Computing 10, no. 1 (March 1989): 123–27. http://dx.doi.org/10.1016/0167-8191(89)90084-7.

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Yamamoto, Maeri, Epifanio Bagarinao, Itaru Kushima, Tsutomu Takahashi, Daiki Sasabayashi, Toshiya Inada, Michio Suzuki, Tetsuya Iidaka, and Norio Ozaki. "Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites." PLOS ONE 15, no. 11 (November 24, 2020): e0239615. http://dx.doi.org/10.1371/journal.pone.0239615.

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Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.
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MURATA, Toshiaki, Takeshi TSUCHIYA, and Ikuo TAKEDA. "Vector Control for Induction Machine by Primary Flux Linkage Control." Transactions of the Society of Instrument and Control Engineers 25, no. 11 (1989): 1194–201. http://dx.doi.org/10.9746/sicetr1965.25.1194.

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Alazrag, A., and L. Sbita. "Asynchronous Machine with Wind Turbine IRFO Control." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (June 30, 2022): 141–54. http://dx.doi.org/10.37394/232016.2022.17.15.

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The paper deals with a squirrel cage induction generator connected to the grid through a back-to-back converter driven by vector control. The stator-side converter controls the generator torque by means of an indirect vector control scheme. In order to reduce the system dependence from the mechanical system behavior, a torque loop is used in the current reference calculations. The battery energy storage system (BESS) plays a fundamental role in controlling and improving the efficiency of renewable energy sources. Stochasticity of wind speed and reliability of the main system components are considered. The grid-side converter controls the DC bus voltage and the reactive power in order to accomplish the grid codes. Speed control using flow directional control, indirect conventionally uses proportional integral (PI) type current regulators, which achieve satisfactory objectives on torque and flow dynamics. The objective of this article is to present an indirect vector control strategy with oriented rotor flux using current regulators of the proportional integral (PI) type, applied to an asynchronous machine supplied by a voltage inverter, capable of supplying during restrictive stresses, more satisfactory torque and flux responses. The obtained simulation results upon simulation tests of the global system are developed under the MATLAB / Simulink environment and are satisfactory.
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MURATA, Toshiaki, Takeshi TSUCHIYA, and Ikuo TAKEDA. "Vector Control for Induction Machine by State Feedback." Transactions of the Society of Instrument and Control Engineers 24, no. 6 (1988): 596–602. http://dx.doi.org/10.9746/sicetr1965.24.596.

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Wang, Syed A. Nasar, Ion Boldea, Congxiao. "Vector Control of Three-Phase Flux Reversal Machine." Electric Machines & Power Systems 28, no. 2 (February 2000): 153–66. http://dx.doi.org/10.1080/073135600268432.

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Wauters, Mathieu, and Mario Vanhoucke. "Support Vector Machine Regression for project control forecasting." Automation in Construction 47 (November 2014): 92–106. http://dx.doi.org/10.1016/j.autcon.2014.07.014.

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Dissertations / Theses on the topic "Machine vector control"

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Sokola, Matija. "Vector control of induction machines using improved machine models." Thesis, Liverpool John Moores University, 1998. http://researchonline.ljmu.ac.uk/4899/.

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Ozcelik, Eray. "Speed Sensorless Vector Control Of Induction Machine." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606063/index.pdf.

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Focus of this work is closed-loop speed control of an induction machine based on direct field-oriented control (DFOC) algorithm, using estimates of speed and flux observers which utilize only stator current and voltage. Theoretical bases of the algorithms are explained in detail and their performances are investigated with simulations and experiments. Field Orientated Control is based on projections which transform a threephase time and speed dependent system into a two co-ordinate time invariant system. These projections lead to a structure similar to that of a DC machine control. Transformations are done in synchronous frame alligned to d-axis of rotor flux. So rotor flux position must be known accurately to make these transformations. Degined flux observer, in which voltage model is assisted by current model via a closed-loop to compensate voltage model&rsquo
s disadvantages, estimates the position of the rotor flux. Obtaining adequate torque control via FOC, speed loop is closed using conventional PI regulators. Speed feedback is necessary to complete control loop. Model Reference Adaptive System is studied as a speed estimator. Reactive power scheme is applied to MRAS algorithm to estimate rotor speed. In this study, the direct (rotor) flux oriented control system with flux and speed estimators is described and tested in real-time with the starter kit named TMS320F2812 eZdsp DSK and the Embedded Target for the TI C2000 DSP tool of Matlab
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Aleti, Kalyan Reddy. "E-quality control a support vector machines approach /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Sharaf-Eldin, Thanaa. "Design and control of a synchronous reluctance machine drive." Thesis, Heriot-Watt University, 1999. http://hdl.handle.net/10399/1201.

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Liu, Yu. "SLOTLESS SIX-PHASE BRUSHLESS DC MACHINE DESIGN AND STEPPING VECTOR CONTROL." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440086585.

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Mascarenhas, Manuel Maria Brás Pereira. "Speed control of induction machine based on direct torque control method." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/9957.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
Multi-level converters have been receiving attention in the recent years and have been proposed as the best choice in a wide variety of medium voltage applications. They enable a commutation at substantially reduced voltages and an improved harmonic spectrum without a series connection of devices, which is the main advantage of a multi-level structure. The use of multi-level inverters contributes to the performances amelioration of the induction machine control. In fact, the use of three level inverter (or multilevel inverter) associated with DTC control can contribute to more reducing harmonics and the ripple torque and to have a high level of output voltage. A variation of DTC-SVM with a three level neutral point clamped inverter is proposed and discussed in the literature. The goal of this project is to study, evaluate and compare the DTC and the proposed DTC-SVM technique when applied to induction machines through simulations. The simulations were carried out using MATLAB/ SIMULINK simulation package. Evaluation was made based on the drive performance, which includes dynamic torque and flux responses, feasibility and the complexity of the systems.
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Hedberg, Rikard. "Tree species classification using support vector machine on hyperspectral images." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54648.

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For several years, FORAN Remote Sensing in Linköping has been using pulseintense laser scannings together with multispectral imaging for developing analysismethods in forestry. One area these laser scannings and images are used for is toclassify the species of single trees in forests. The species have been divided intopine, spruce and deciduous trees, classified by a Maximum Likelihood classifier.This thesis presents the work done on a more spectrally high-resolution imagery,hyperspectral images. These images are divided into more, and finer gradedspectral components, but demand more signal processing. A new classifier, SupportVector Machine, is tested against the previously used Maximum LikelihoodClassifier, to see if it is possible to increase the performance. The classifiers arealso set to divide the deciduous trees into aspen, birch, black alder and gray alder.The thesis shows how the new data set is handled and processed to the differentclassifiers, and shows how a better result can be achieved using a Support VectorMachine.

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Wong, Hang Cheong. "Design and experimental evaluation of predictive engine air-ratio control using relevance vector machine." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b2182947.

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Zhang, Yuan. "Sensorless Vector Control and Field Weakening Operation of Permanent Magnet Synchronous Machines." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1291219704.

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Garg, Aditie. "Designing Reactive Power Control Rules for Smart Inverters using Machine Learning." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83558.

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Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits.
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Books on the topic "Machine vector control"

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Anghel, Cristian. Sensorless flux vector control for a permanent magnet synchronous machine with cylindrical rotor under severe starting conditions. Ottawa: National Library of Canada, 1997.

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Robyns, Benoît, Bruno Francois, Philippe Degobert, and Jean Paul Hautier. Vector Control of Induction Machines. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-901-7.

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Vector control of AC machines. Oxford [England]: Clarendon Press, 1990.

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Quang, Nguyen Phung, and Jörg-Andreas Dittrich. Vector Control of Three-Phase AC Machines. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46915-6.

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Sensorless vector and direct torque control. Oxford: Oxford University Press, 1998.

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A, Nasar S., ed. Vector control of AC drives. Boca Raton: CRC Press, 1992.

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Robyns, Benoit. Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic. London: Springer London, 2012.

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A, Lipo T., ed. Vector control and dynamics of AC drives. Oxford: Clarendon Press, 1996.

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Quang, Nguyen Phung. Vector control of three-phase AC machines: System development in the practice. [Berlin]: Springer, 2008.

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Quang, Nguyen Phung. Vector control of three-phase AC machines: System development in the practice. [Berlin]: Springer, 2008.

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Book chapters on the topic "Machine vector control"

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Boufadene, Mourad. "Vector Control of DFIM." In Modeling and Control of AC Machine using MATLAB®/SIMULINK, 12–22. Boca Raton : Taylor & Francis, CRC Press, 2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429029653-2.

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Narasimhan, Abhilash. "Support Vector Machine Based Forecasting for Renewable Energy Systems." In Studies in Infrastructure and Control, 149–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4663-8_14.

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Sun, Zonghai, Liangzhi Gan, and Youxian Sun. "Support Vector Machine Adaptive Control of Nonlinear Systems." In Lecture Notes in Computer Science, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538356_17.

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Quang, Nguyen Phung, and Jörg-Andreas Dittrich. "Machine Models as Prerequisite to Design the Controllers and Observers." In Vector Control of Three-Phase AC Machines, 61–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46915-6_3.

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Priyadharshini @ Manisha, K., and B. Sathya Bama. "Hyper-Spectral Image Classification with Support Vector Machine." In Advances in Automation, Signal Processing, Instrumentation, and Control, 587–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_51.

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Geng, Yanjuan, Dandan Tao, Liang Chen, and Guanglin Li. "Recognition of Combined Arm Motions Using Support Vector Machine." In Informatics in Control, Automation and Robotics, 807–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25992-0_108.

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Boufadene, Mourad. "Vector Control Using Nonlinear Controller for DFIM." In Modeling and Control of AC Machine using MATLAB®/SIMULINK, 23–34. Boca Raton : Taylor & Francis, CRC Press, 2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429029653-3.

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Xin-fang, Zhang, Wang Cheng-li, Su Xian-hua, and Wang Qiu-jin. "Nonlinear Predictive Functional Control Based on Support Vector Machine." In Lecture Notes in Electrical Engineering, 763–72. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1839-5_82.

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Molina Llorente, Rubén. "Model-in-the-Loop Development in a Vector Control of Induction Machine." In Practical Control of Electric Machines, 531–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34758-1_11.

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Ye, Zhi-Fei, Bao-Liang Lu, and Cong Hui. "Patent Classification Using Parallel Min-Max Modular Support Vector Machine." In Autonomous Systems – Self-Organization, Management, and Control, 157–67. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8889-6_17.

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Conference papers on the topic "Machine vector control"

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Atkinson, D. J., A. G. Jack, and H. J. Slater. "The virtual machine." In IEE Colloquium on Vector Control Revisited. IEE, 1998. http://dx.doi.org/10.1049/ic:19980062.

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Liang Chang. "The geometric relationship between Core Vector Machine and Support Vector Machine." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4593638.

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Acernese, Antonio, Carmen Del Vecchio, Luigi Glielmo, Gianfranco Fenu, and Felice Andrea Pellegrino. "A Combined Support Vector Machine and Support Vector Representation Machine Method for Production Control *." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796111.

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de Almeida, Thales E. P., Geyverson T. de Paula, Allan G. de Castro, William C. A. Pereira, and Jose R. B. De A. Monteiro. "Sensorless vector control for BLDC machine." In 2017 Brazilian Power Electronics Conference (COBEP). IEEE, 2017. http://dx.doi.org/10.1109/cobep.2017.8257380.

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Petri, Ana-Maria, and Dorin Petreus. "Vector Control of Permanent Magnet Synchronous Machine." In 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2020. http://dx.doi.org/10.1109/siitme50350.2020.9292170.

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Zhiqiang Du. "Research of doubly-fed machine vector control." In 2010 International Conference on Optics, Photonics and Energy Engineering (OPEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/opee.2010.5508170.

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Yugang Fan, Ping Li, and Zhihuan Song. "Dynamic Least Squares Support Vector Machine." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713313.

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Zainuddin, Nurulhuda, and Ali Selamat. "Sentiment analysis using Support Vector Machine." In 2014 International Conference on Computer, Communications, and Control Technology (I4CT). IEEE, 2014. http://dx.doi.org/10.1109/i4ct.2014.6914200.

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Thangaraj, Vigneswaran, and V. Natarajan. "Glaucoma diagnosis using support vector machine." In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017. http://dx.doi.org/10.1109/iccons.2017.8250750.

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Bing Lu, Wang Xi-huai, and Xiao Jian-mei. "A modified algorithm for Support Vector Machine." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597786.

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Reports on the topic "Machine vector control"

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Epel, Bernard, and Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, November 2005. http://dx.doi.org/10.32747/2005.7695874.bard.

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To cause disease, plant viruses must replicate and spread locally and systemically within the host. Cell-to-cell virus spread is mediated by virus-encoded movement proteins (MPs), which modify the structure and function of plasmodesmata (Pd), trans-wall co-axial membranous tunnels that interconnect the cytoplasm of neighboring cells. Tobacco mosaic virus (TMV) employ a single MP for cell- cell spread and for which CP is not required. The PIs, Beachy (USA) and Epel (Israel) and co-workers, developed new tools and approaches for study of the mechanism of spread of TMV that lead to a partial identification and molecular characterization of the cellular machinery involved in the trafficking process. Original research objectives: Based on our data and those of others, we proposed a working model of plant viral spread. Our model stated that MPᵀᴹⱽ, an integral ER membrane protein with its C-terminus exposed to the cytoplasm (Reichel and Beachy, 1998), alters the Pd SEL, causes the Pd cytoplasmic annulus to dilate (Wolf et al., 1989), allowing ER to glide through Pd and that this gliding is cytoskeleton mediated. The model claimed that in absence of MP, the ER in Pd (the desmotubule) is stationary, i.e. does not move through the Pd. Based on this model we designed a series of experiments to test the following questions: -Does MP potentiate ER movement through the Pd? - In the presence of MP, is there communication between adjacent cells via ER lumen? -Does MP potentiate the movement of cytoskeletal elements cell to cell? -Is MP required for cell-to-cell movement of ER membranes between cells in sink tissue? -Is the binding in situ of MP to RNA specific to vRNA sequences or is it nonspecific as measured in vitro? And if specific: -What sequences of RNA are involved in binding to MP? And finally, what host proteins are associated with MP during intracellular targeting to various subcellular targets and what if any post-translational modifications occur to MP, other than phosphorylation (Kawakami et al., 1999)? Major conclusions, solutions and achievements. A new quantitative tool was developed to measure the "coefficient of conductivity" of Pd to cytoplasmic soluble proteins. Employing this tool, we measured changes in Pd conductivity in epidermal cells of sink and source leaves of wild-type and transgenic Nicotiana benthamiana (N. benthamiana) plants expressing MPᵀᴹⱽ incubated both in dark and light and at 16 and 25 ᵒC (Liarzi and Epel, 2005 (appendix 1). To test our model we measured the effect of the presence of MP on cell-to-cell spread of a cytoplasmic fluorescent probe, of two ER intrinsic membrane protein-probes and two ER lumen protein-probes fused to GFP. The effect of a mutant virus that is incapable of cell-to-cell spread on the spread of these probes was also determined. Our data shows that MP reduces SEL for cytoplasmic molecules, dilates the desmotubule allowing cell-cell diffusion of proteins via the desmotubule lumen and reduces the rate of spread of the ER membrane probes. Replicase was shown to enhance cell-cell spread. The data are not in support of the proposed model and have led us to propose a new model for virus cell-cell spread: this model proposes that MP, an integral ER membrane protein, forms a MP:vRNAER complex and that this ER-membrane complex diffuses in the lipid milieu of the ER into the desmotubule (the ER within the Pd), and spreads cell to cell by simple diffusion in the ER/desmotubule membrane; the driving force for spread is the chemical potential gradient between an infected cell and contingent non-infected neighbors. Our data also suggests that the virus replicase has a function in altering the Pd conductivity. Transgenic plant lines that express the MP gene of the Cg tobamovirus fused to YFP under the control the ecdysone receptor and methoxyfenocide ligand were generated by the Beachy group and the expression pattern and the timing and targeting patterns were determined. A vector expressing this MPs was also developed for use by the Epel lab . The transgenic lines are being used to identify and isolate host genes that are required for cell-to-cell movement of TMV/tobamoviruses. This line is now being grown and to be employed in proteomic studies which will commence November 2005. T-DNA insertion mutagenesis is being developed to identify and isolate host genes required for cell-to-cell movement of TMV.
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