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Статті в журналах з теми "SWITCHING WAVELET"
Türkmenoğlu, Veli, Mustafa Aktaş, Serkan Karataş, and Halil İbrahim Okumuş. "Soft Set-Based Switching Faults Decision Making in DTC Induction Motor Drives." Journal of Circuits, Systems and Computers 24, no. 02 (November 27, 2014): 1550021. http://dx.doi.org/10.1142/s0218126615500218.
Повний текст джерелаGuillén, Daniel, Gina Idárraga-Ospina, and Camilo Cortes. "A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis." Journal of Electrical Engineering 67, no. 1 (January 1, 2016): 48–55. http://dx.doi.org/10.1515/jee-2016-0007.
Повний текст джерелаCHEN, WENJIE, XU YANG, and ZHAOAN WANG. "AN APPLICATION OF COMPLEX WAVELETS FOR HIGH FREQUENCY SWITCHING NOISE DETECTION." Journal of Circuits, Systems and Computers 18, no. 01 (February 2009): 97–102. http://dx.doi.org/10.1142/s0218126609004958.
Повний текст джерелаFUKUMA, S. "Switching Wavelet Transform for ROI Image Coding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 7 (July 1, 2005): 1995–2006. http://dx.doi.org/10.1093/ietfec/e88-a.7.1995.
Повний текст джерелаAlam, M. Shafiul, Md Shamimul Haque Chowdhury, and Muhammad Athar Uddin. "Power System Switching Transient Detection using Wavelet Transformed Based Signal Decomposition." IIUC Studies 7 (October 19, 2012): 241–48. http://dx.doi.org/10.3329/iiucs.v7i0.12270.
Повний текст джерелаGaeid, Khalaf S., Mshari Aead Asker, Nada N. Tawfeeq, and Salam Razooky Mahdi. "Computer Simulation of PMSM Motor with Five Phase Inverter Control using Signal Processing Techniques." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3697. http://dx.doi.org/10.11591/ijece.v8i5.pp3697-3710.
Повний текст джерелаLin, Chih-Min, Kun-Neng Hung, and Chun-Fei Hsu. "Adaptive Neuro-Wavelet Control for Switching Power Supplies." IEEE Transactions on Power Electronics 22, no. 1 (January 2007): 87–95. http://dx.doi.org/10.1109/tpel.2006.886630.
Повний текст джерелаLi, Hao, Meng Zhao, Hao Yan, and Xingwu Yang. "Nanoseconds Switching Time Monitoring of Insulated Gate Bipolar Transistor Module by Under-Sampling Reconstruction of High-Speed Switching Transitions Signal." Electronics 8, no. 10 (October 22, 2019): 1203. http://dx.doi.org/10.3390/electronics8101203.
Повний текст джерелаKetabi, A., M. Khoshkholgh, and R. Feuillet. "A New Approach to Nonsinusoidal Steady-State Power System Analysis." Mathematical Problems in Engineering 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/584637.
Повний текст джерелаPatcharoen, Theerasak, Suntiti Yoomak, Atthapol Ngaopitakkul, and Chaichan Pothisarn. "Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence." Open Physics 16, no. 1 (April 18, 2018): 93–104. http://dx.doi.org/10.1515/phys-2018-0016.
Повний текст джерелаДисертації з теми "SWITCHING WAVELET"
Gu, Ye. "Wavelet-based adaptive video coding for packet-switching networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37021.
Повний текст джерелаXie, Yingfu. "Maximum likelihood estimation and forecasting for GARCH, Markov switching, and locally stationary wavelet processes /." Umeå : Dept. of Forest Economics, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/2007107.pdf.
Повний текст джерелаZhao, Yi. "Studies on error control of 3-D zerotree wavelet video streaming." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1124811298.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xix, 136 p.; also includes graphics (some col.). Includes bibliographical references (p. 129-136). Available online via OhioLINK's ETD Center
Yao, Hong Mei. "Wavelet based dynamic modelling of simulated moving bed chromatographic processes." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1918.
Повний текст джерелаYao, Hong Mei. "Wavelet based dynamic modelling of simulated moving bed chromatographic processes." Curtin University of Technology, Department of Chemical Engineering, 2009. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=128426.
Повний текст джерелаThrough a systematic study on the advances of SMB modelling, design and control, a set of functionally equivalent models for SMBCP are identified and summarized for their practical applications. The limitations of the existing modelling techniques in industrial applications are also identified. Furthermore, structural analysis of the existing models is conducted for a better understanding of the functionality and suitability of each model. Suggestions are given on how to choose an appropriate model with sufficient accuracy while keeping the computational demand reasonably low for real time control.
Effort is made on to the systematic investigation of different numerical methods for the solution of PDEs to circumvent the steep gradients encountered in chromatographic separation. Comprehensive studies are conducted on a single column chromatographic process represented by a transport-dispersive-equilibrium linear model. Numerical solutions from the upwind-1 finite difference, wavelet-collocation, and high resolution methods are evaluated by quantitative comparisons with the analytical solution for a range of Peclet numbers. It reveals that for a PDE system with a low Peclet number, all existing numerical methods work well, but the upwind finite difference method consumes the most time for the same degree of accuracy of the numerical solution. The high resolution method provides an accurate numerical solution for a PDE system with a medium Peclet number. The wavelet collocation method is capable of catching up steep changes in the solution, and thus can be used for solving PDE models with high singularity.
The advantages and disadvantages of the wavelet based approaches are further investigated through several case studies on real SMBCP system. A glucose-fructose separation process is firstly chosen with its relatively simple isotherm representations. Simulations are conducted using both wavelet collocation and upwind finite difference methods. For more complicated applications, an enantiomers separation process is selected. As the PDEs model exhibit a certain degree of singularity, wavelet collocation and high resolution methods are adopted for spatial discretisation. It is revealed that both the wavelet based approaches and high resolution methods are good candidates in terms of computation demand and prediction accuracy on the steep front. This is the first time that these two frontier numerical methods are used for such a complex SMB system models and our results are encouraging for the development of model-based online control scheme.
In developing a new scheme to rapidly obtain the solution at steady state for any arbitrary initial condition, the concept of Quasi-Envelope (QE) is adopted under the consideration that a SMBCP can be treated as a pseudo-oscillatory process because of a large number of continuous switching. The scheme allows larger steps to be taken to predict the slow change of starting state within each switching. Combined with previously developed wavelet-based technique, this method is successfully applied to the simulation of a SMB sugar separation process. Investigations are also carried out on the location of proper starting point for the algorithm and on the effect of changing stepsize to the convergence of iteration method. It is found that if the starting state of Quasi-Envelope is chosen to be the same as the original function, the multivalue algorithm would require similar computational effort to achieve the steady state prediction, regardless of the integration stepsize. If using constant stepsize, launching QE later is helpful when quasi-envelope displays steep change at the start-up period. A changing stepsize produces slow convergence compared to the constant stepsize strategy, thus increasing the work load where the stepsize change is occurring. Other iteration method is required to be imposed to achieve faster convergence right from the beginning. Potential applications can be seen for other chemical engineering processes with inherent cyclic behaviour.
Lin, Shih-Shan, and 林詩珊. "VIX Volatility Wavelet-CEV Model-----Using Regime Switching and Wavelet Analysis." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/98824892123640019643.
Повний текст джерела國立交通大學
財務金融研究所
93
The purpose of this research is to model the volatility index, VIX, formulated by CBOE using the concept of Regime Switching and piecewise linear structure. I adopt the Wavelet analysis to inspect the properties of CEV parameters in the stochastic volatility model. Generally speaking, volatility is relatively high in the bear market and low in the bull market. The converging rate on average is higher in the bull market than that in the bear market. Wavelet analysis which can deal with the unusual structure change in the market enables the parameter estimation to be correctly specified.
YADAV, ANKIT. "DESIGN OF SWITCHING SCHEMES FOR VOLTAGE SOURCE INVERTER." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14992.
Повний текст джерелаWang, Cheng-Wei, and 王政偉. "Identification of Power Switching Using Wavelet Transform and Neural Network." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/2683hc.
Повний текст джерела中原大學
電機工程研究所
91
Recently, power quality is becoming an increasingly important topic due to the high technology industry and precision instruments developed rapidly. The poor power quality will make the serious loss of the manufactory and sometimes will damage the equipments. Hence, improvement of power quality is an important task for utility companies and their customers. Power system transients caused by switching is one of the important problem of power quality. Hence, it is worth to understand the property of transients and the influence upon power system. This thesis focuses on the transients, which are caused by the load breaker switching and the capacitor bank switching. Several kinds of transients disturbance signals, such as load breaker switching on/off, capacitor bank switching on/off, the different phase angles of voltages while the switch actions, and the position of the switch, are simulated by the power system software EDSA. This thesis identifies these transient disturbance signals, by the other two analysis tools, which are the wavelet transform and the SOM network. This thesis studies two different power systems, which were simulated by EDSA, and finds the wavelet coefficients for identification. The study results show that the proposed method is applicable.
Chen, Po-Yuan, and 陳柏元. "Application of Wavelet Transform and Artificial Intelligencefor Identifying Locations of Switching Capacitor Transients." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/22258706215959711491.
Повний текст джерела中原大學
電機工程研究所
93
In recent years, due to the rapid developments of the hi-tech industry as well as much more usages of the precise production equipments and test instruments, the far high power quality (PQ) is demanded nowadays. Hence, improvement of power quality is an important task for utility companies and their customers. Generally, power quality problems include voltage swell, voltage sag, power harmonic, three-phase imbalance, frequency variation and voltage flicker. Besides, the electromagnetic transient phenomenon of the power system, such as capacitor switching, can cause incident of the voltage and current transients that would result in over voltage transient due to the resonance phenomenon. The high voltage and current transient may result in damage of devices in the power systems and malfunction of protection equipment of sensitive loads. Therefore, capacitor switching transient is a serious threat to power electronic equipments in the viewpoint of PQ. Actually, the accurate location and time of PQ problem are useful for responsibility authority and accident correction Therefore, identifying and locating the locations of transient sources have attracted more attention of utility engineers and scholars. This thesis presents a new method for efficiently locating the sources associated for utility capacitor switching transients. The proposed method first combines wavelet transform and Parserval theorem to extract the features of the transients. Then proper location number for metering measurements by fuzzy clustering is determined. Finally, the features and transient source location is trained by neural networks. Diverse patterns of PQ events are simulated by Matlab6.5/NeuroSolutions software Finally, an 18-bus power system is used for testing, Simulation results obtained by using Matlab6.5/NeuroSolutions show that the proposed approach is effective and relatively accurate in comparison with existing approaches.
Yun, Yang Ting, and 楊婷勻. "Application of Wavelet Transform to the Intermittent Switching Characteristics of Gap Flow between Two Bluff Bodies Arranged Side by Side." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/17365600826166661334.
Повний текст джерела國立中興大學
土木工程學系
92
The flow characteristics of gap-flow downstream of two side-by-side bluff bodies are provided with temporal variation of unstable phenomenon in the uniform fluid field. The distinct identities of vortex shedding obtained by the single point measurement method and the analysis of Fast Fourier Transform (FFT) are insufficient. The primary objective of the present study is to develop a unique measurement technique that combines the use of two one-component fiber laser Doppler velocimetries (FLDV) and particle image velocimetry (PIV), aiming to perform simultaneous velocity measurement to integrate both time and flow domains of the switching gap flow and the wide and narrow wakes corresponding to these two stationary cylinders including square, circular, and flat forms. Moreover, by using the Wavelet transform (WT), the energy-history of each component shedding frequency can be described as time-frequency domain. First, this study aims to validate the feasibility of Wavelet transform which is then applied to the analysis of velocity time signal obtained by FLDV. Meanwhile, the temporal variation of the full-field velocity maps in the flow domain of interest can be obtained by employing PIV. To synchronize the PIV and two FLDV data during measurement, the two FLDV velocity signals and PIV camera triggering signal were transferred into the data acquisition board in the same time, so the corresponding time (or phase) of each PIV velocity map can be precisely determined. Therefore, the instantaneous frequency of the wide and narrow wakes (analyzed by wavelet transform) as well as the corresponding instantaneous full-field velocity map (including the deflected gap flow) can be identified. Based upon the experimental results, it is found that the switching characteristics of gap-flow patterns can be classified into three categories. These categories are defined as : (1) gap-flow switching from one side to the other side instantaneously; (2) gap-flow switching spontaneously from one side to the center direction, in which the gap flow was oriented parallel to the free-stream flow for short duration, and then biasing to original side; (3) gap-flow switching spontaneously from one side to the other, and in the intermediate time where existing another short duration stable flow pattern (which can be termed an intermediate flow pattern). Furthermore, the correlation between two temporal frequency of wide and narrow wakes were studied completely. As we known, Fast Fourier transform (FFT) and wavelet transform which provided with the time-mean and instantaneous frequency contents respectively, and the spectrum analysis results of them were compared in detail.
Книги з теми "SWITCHING WAVELET"
Glabisz, Wojciech. Pakietowa analiza falkowa w zagadnieniach mechaniki. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2004.
Знайти повний текст джерелаЧастини книг з теми "SWITCHING WAVELET"
Liò, Pietro, Anna T. Lawniczak, Shengkun Xie, and Jiaying Xu. "Wavelet-Domain Statistics of Packet Switching Networks Near Traffic Congestion." In Bio-Inspired Computing and Communication, 268–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92191-2_24.
Повний текст джерелаPenm, Jack, and R. D. Terrell. "Sparse-Patterned Wavelet Neural Networks and Their Applications to Stock Market Forecasting." In Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration, 161–70. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295216_8.
Повний текст джерелаJammazi, Rania. "Oil Shock Transmission to Stock Market Returns: Wavelet-Multivariate Markov Switching GARCH Approach." In The Interrelationship Between Financial and Energy Markets, 71–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55382-0_4.
Повний текст джерелаDghais, Amel Abdoullah, and Mohd Tahir Ismail. "Modeling Relationship Between Stock Market of UK and MENA Countries: A Wavelet Transform and Markov Switching Vector Error Correction Model Approach." In Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015), 165–73. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2772-7_17.
Повний текст джерела"Dynamic Chaos Spectrum of Chaotic Switching Converters I: Wavelet Method." In Chaos Analysis and Chaotic EMI Suppression of DC-DC Converters, 141–71. Singapore: John Wiley & Sons Singapore Pte. Ltd, 2014. http://dx.doi.org/10.1002/9781118451106.ch8.
Повний текст джерелаK., Uvanesh, Suraj Kumar Nayak, Biswajeet Champaty, Goutam Thakur, Biswajit Mohapatra, D. N. Tibarewala, and Kunal Pal. "Development of a Surface EMG-Based Control System for Controlling Assistive Devices." In Robotic Systems, 765–85. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch040.
Повний текст джерелаKhan, Laiq, and Rabiah Badar. "Hybrid Adaptive NeuroFuzzy Bspline Based SSSC Damping Control Paradigm." In Handbook of Research on Novel Soft Computing Intelligent Algorithms, 787–828. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4450-2.ch026.
Повний текст джерелаBotsford, Louis W., J. Wilson White, and Alan Hastings. "Age-structured models: Short-term transient dynamics." In Population Dynamics for Conservation, 87–121. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198758365.003.0004.
Повний текст джерелаТези доповідей конференцій з теми "SWITCHING WAVELET"
Ibrahim, Atubga David Atia, Bonny Ernestina Linda та Wang Zinan. "SNR Enhancement by Discrete Wavelet Transform filtering on Φ-OTDR sensing for vibration detection". У Photonics in Switching and Computing. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/psc.2020.jtu3f.5.
Повний текст джерелаTan, Rodney H. G., and V. K. Ramachandaramurthy. "Capacitor bank switching classification using scale selection continuous wavelet transform." In 2009 International Conference on Power Electronics and Drive Systems (PEDS 2009). IEEE, 2009. http://dx.doi.org/10.1109/peds.2009.5385785.
Повний текст джерелаBerman, Kevin, Aharon Z. Karon, Alessio Medda, and Krishan K. Ahuja. "Detection of Screech Mode-Switching Using the Discrete Wavelet Transform." In 2018 AIAA/CEAS Aeroacoustics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-3464.
Повний текст джерелаDong, Huajun, Hongyan Wang, and Jijun Shen. "Study of wavelet denoising in images of vacuum switching arc." In 2012 International Conference on Computer Science and Information Processing (CSIP). IEEE, 2012. http://dx.doi.org/10.1109/csip.2012.6308785.
Повний текст джерелаXiaoming Liu, Xiangning Liu, Chunguang Hou, Xue Leng, and Zenghui Lai. "Arc fault diagnosis and analysis based on wavelet neural network." In 2011 1st International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST). IEEE, 2011. http://dx.doi.org/10.1109/icepe-st.2011.6122965.
Повний текст джерелаZhou, Dengdeng, Zhigang Liu, Weili Bai, Jian Xie, and Hua Yang. "A Line Selection Method Based on Capacitor Switching by Complex Wavelet." In 2009 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2009. http://dx.doi.org/10.1109/appeec.2009.4918051.
Повний текст джерелаKun-Neng Hung, Chih-Min Lin, and Fu-Shan Ding. "Adaptive Neural Control for Switching Power Supplies Using Gaussian Wavelet Networks." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247182.
Повний текст джерелаGuojiang, Zhang, Shi Mingming, Zhang Chenyu, Sha Haoyuan, and She Changjia. "Harmonic Impedance Detection Based on Capacitor Switching and Wavelet Packet Analysis." In 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2019. http://dx.doi.org/10.1109/cyber46603.2019.9066771.
Повний текст джерелаLi Ling-ling, Jun-Hao Li, Peng-Ju He, and Cheng-Shang Wang. "The use of wavelet theory and ARMA model in wind speed prediction." In 2011 1st International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST). IEEE, 2011. http://dx.doi.org/10.1109/icepe-st.2011.6123016.
Повний текст джерелаNovakov, Stevan, Chung-Horng Lung, Ioannis Lambadaris, and Nabil Seddigh. "Studies in applying PCA and wavelet algorithms for network traffic anomaly detection." In 2013 IEEE 14th International Conference on High Performance Switching and Routing (HPSR). IEEE, 2013. http://dx.doi.org/10.1109/hpsr.2013.6602310.
Повний текст джерела