(44.192.112.123) 您好!臺灣時間:2021/03/06 06:57
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:邱愷祐
研究生(外文):Kai-Yu Chiu
論文名稱:表面聲波感測器性能預測及最佳設計
論文名稱(外文):Performance Prediction and Optimal Design of Surface Acoustic Wave Sensors
指導教授:周至宏周至宏引用關係
指導教授(外文):Jyh-Horng Chou
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:電機工程研究所碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:45
中文關鍵詞:表面聲波感測器田口基因學習演算法適應性模糊推論系統田口實驗法
外文關鍵詞:Taguchi methodANFISTGLASAW sensors
相關次數:
  • 被引用被引用:0
  • 點閱點閱:117
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出以改良的適應性模糊推論系統建立表面聲波感測器的共振頻率偏移量的預測模型,並以田口基因學習演算法決定適應函數,及修正前建部和結論部的參數後,得到最佳預測模型。再以田口基因學習演算法求得表面聲波感測器最大的共振頻率偏移量,同時得到各主要因子的最佳組合。基於田口實驗法所得到的訓練數據,作為適應性模糊推論系統的訓練資料,在有限的實驗次數下,能提供平衡且全面性分布的資料,藉此適應性模糊推論系統所建立的預測模型仍可做出準確的預測。將此預測模型作為適應函數,以田口基因學習演算法做最佳化的計算,並以表面聲波感測器模態分析的共振頻率變化量作為適應值,可以求得最大的適應值及相對的染色體,此染色體即為表面聲波感測器各主要因子的最佳化設計組合。所提出的最佳化設計,再與原設計作靈敏度的比較,以頻率變化量所增加的部分,作為改善靈敏度的依據。
In this study, prediction models of the resonant frequency shift performance of surface acoustic wave sensor constructed by improved Adaptive neuro fuzzy inference system is presented. The Taguchi-genetic learning algorithm is used in the ANFIS to find the optimal both premise and consequent parameters and to simultaneously determine the most suitable membership functions. The optimal combination of major factors is obtained by Taguchi-genetic learning algorithm in order to achieve the largest sensitivity. The data based on Taguchi method is the training data of ANFIS and is comprehensive distributed averagely, so the model can afford correct prediction in limited experiments. This model is the fitness function calculated by Taguchi-genetic learning algorithm to find the largest fitness. The chromosome represents the optimal combination the major factors. The resonant frequency shift of proposed optimal design compared with the original design is the subject of sensitivity improvement.
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第 一 章 緒論 1
1.1前言 1
1.2研究動機及文獻回顧 1
1.3論文架構 4
第 二 章 表面聲波感測器之原理與特性 5
2.1表面聲波感測器之基本原理 6
2.2表面聲波感測器之特性 7
2.2.1基材材料 7
2.2.2交指叉換能器 8
2.3田口實驗 9
2.3.1直交表 10
2.3.2訊號雜訊比(S/N) 14
2.4表面聲波感測器之靈敏度變異數分析 14
2.4.1直交表實驗 14
2.4.2變異數分析(ANOVA) 17
第 三 章 研究方法 19
3.1適應性網路模糊推論系統 19
3.1.1適應性網路模糊推論系統架構 20
3.2田口基因學習演算法 22
3.3 IANFIS 24
第 四 章 實驗與結果分析 32
4.1表面波氣體傳感器的共振頻率偏移性能預測 32
4.2最佳化參數設計 37
第 五 章 結論及未來展望 38
參考文獻 39
[1]Abdollahi, A., Jiang, Z., and Arabshahi, S. A., 2007. Evaluation on mass sensitivity of SAW sensors for different piezoelectric materials using finite-element analysis. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 54, pp. 2446-2455.
[2]Anisimkin, V. I., and E. Verona 1998. New properties of SAW gas sensing. IEEE Trans. Ultr. Ferr. and Freq. Con. 45(5): 1347-1354.
[3]Anisimkin, V. I., and Verona, E., 1998. New properties of SAW gas sensing. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 45, pp. 1347-1354.
[4]Anisimkin, V. I., and Verona, E., 2001. New capabilities for optimizing SAW gas sensors. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 48, pp. 1413-1418.
[5]Ariyakul, Y., and Nakamoto, T., 2013. Improvement of odor blender using electroosmotic pumps and SAW atomizer for low-volatile scents. IEEE Sensors Journal, Vol. 13, pp. 4918-4923.
[6]B. A. Auld, 1969. Application of Microwave Concepts to the Theory of Acoustic Fields and Waves in Solids. IEEE Trans. Microwave Theory vol. 17, Nov. pp. 800-811.
[7]B. K. Sinha and H. F. Tiersten, 1976. On the influence of uniaxial biasing stresses on the velocity of piezoelectric surface waves. in Proc. IEEE Ultrasonics Symp, pp. 475–479.
[8]B. K. Sinha and S. Locke, 1987. Acceleration and vibration sensitivity of SAW devices. IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 34, pp. 29–38.
[9]Bao X. Q., W. Burkhard, V. V. Varadan, and V. K. Varadan, 1987. SAW temperature sensor and remote reading system. Proc of IEEE Ultransonics Symp, pp. 583–586.
[10]Binder, A., Bruckner, G., Schobernig, N., and Schmitt, D., 2013. Wireless surface acoustic wave pressure and temperature sensor with unique identification based on LiNbO3. IEEE Sensors Journal, Vol. 13, pp. 1801-1805.
[11]Blampain, E., Elmazria, O., Aubert, T., Assouar, B. M., and Legrani, O., 2013. AlN/sapphire: Promising structure for high temperature and high frequency saw devices. IEEE Sensors Journal, Vol. 13, pp. 4607-4612.
[12]D. A. Powell, K. Klanatar-Zadeh, and W. Wlodarski, 2004.Numericalcalculation of SAW sensitivity: Application to ZnO/LiTaO3transducers. Sens. Actuators A, vol. 115, pp. 456–461.
[13]D. Hauden, M. Planat, and J. J. Gagnepain, 1981. Nonlinear properties of surface acoustic waves: Applications to oscillators and sensors. IEEE Trans. Sonics Ultrason., vol. SU-28, pp. 342–348.
[14]Der Ho Wu, Hsin Hua Chen, 2005. Application of Taguchi robust design method to SAW mass sensing device, IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. 52, no. 12, pp. 2403–2410.
[15]G. McHale, M. L. Newton, and F. Martin, 2003 Theoretical mass,liquid, and polymer sensitivity of acoustic wave sensors withviscoelastic guiding layers. J. Appl. Phys., vol. 93, pp. 675–690.
[16]G. Schimetta, F. Dollinger, and R. Weigel, 2000. A wireless pressure measurement system using a SAW hybrid sensor, IEEE Trans. Microw. Theory Tech., vol. 48, no. 12, pp. 2730–2735.
[17]G. Xu, 2000. Direct finite-element analysis of the frequency responseof a Y-Z lithium niobate SAW filter. Smart Mater. Struct., vol.9, pp. 973–980.
[18]G. Xu, 2000. Finite element analysis of second order effects on the frequency response of a SAW device. in Proc. IEEE Ultrason.Symp, pp. 187–190.
[19]H. F. Tiersten, 1978. Perturbation theory for linear electroelastic equations for small fields superposed on a bias. J. Acoust. Soc. Am., vol. 64, pp. 832–837.
[20]H. Holland, 1975. Adaptation in Natural and ArrlJiciul Systems, Ann Arbor: The University of Michigan P ress.
[21]Hung, S. H. and Ko, C. H., 2004, Perspective Research on Surface Acoustic Wave Sensors (表面聲波感測器之前瞻研究) , Physic Bimonthly, pp.512-518, R.O.C. Taiwan.
[22]Ho, W. H, Tsai, J. T., Lin, B. T., and Chou, J. H., 2009. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Systems with Applications, Vol. 36, pp. 3216-3222.
[23]Ho, W. H., Tsai, J. T., and Wang, H. Y., 2012. Neural fuzzy network model with evolutionary learning algorithm for mycological study of foodborne fungi. International Journal of Innovative Computing, Information and Control, Vol. 8, pp. 4565-4577.
[24]Ho, W. H., Tsai, J. T., Hsu, G. M., and Chou, J. H., 2010. Process parameters optimization: a design study for TiO2 thin film of vacuum sputtering process. IEEE Trans. on Automation Science and Engineering, Vol. 7, pp. 143-146.
[25]I. Ono, S. Kobayashi, 1997. A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 246-253.
[26]I. Ono, S. Kobayashi, K. Yoshida, 2000. Optimal lens design by real-coded genetic algorithms using UNDX. Computer Methods in Applied Mechanics and Engineering, 186 (2-4), pp. 483-497.
[27]I. Ono, S. Koboyashi, and K. Yoshida, 1998. Global and multi-objective optimization for lens design by real-coded genetic algorithms. in Proceedings of International Optical Design Conference 1998, vol. 3482 of SPIE.
[28]J. C. Baumhauer and H. F. Tiersten, 1973. Nonlinear electroelastic equations for small fields superposed on a bias. J. Acoust. Soc. Am., vol. 54, pp. 1017–1034.
[29]J. H. Chou, W. H. Liao, and J. J. Li, 1998. Application of Teaguchi-Genetic Method to Design Optimal Grey-Fuzzy Controller of a Constant Turning Force Systm. Proc. of the 15th CSME Annual Conference, Taiwan, pp. 31-38.
[30]Jang, J. S. R., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man and Cybernetics, Vol. 23, pp. 665-685.
[31]Jang, J.G., 1993. Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man and Cybernetics, Vol. 23(3), pp. 665-684.
[32]L. Rayleigh, 1885. Proc. London Math. Soc.
[33]L. Reindl, I. Shrena, S. Kenshil, and R. Peter, 2003. Wireless measurement of temperature using surface acoustic waves sensors. in Proc. IEEE Int. Frequency Control Symp, Tampa, FL, pp. 935–941.
[34]M. B. Schulz, B. J. Matsinger, and M. G. Holland, 1970. Temperature dependence of surface acoustic wave velocity on α-quartz. J. Appl. Phys., vol. 41, no. 7, pp. 2755–2765.
[35]M. Z. Atashbar, B. J. Bazuin, M. Simpeh, and S. Krishnamurthy, 2005. 3D FE simulation of H2 SAW gas sensor. Sens. ActuatorsB, vol. 112, pp. 213–218.
[36]Peixoto, A. C. M., Goncalves, S. B., Silva, A. F. D., Dias, N. S., and Correia, J. H., 2013. Neural electrode array based on aluminum: fabrication and characterization. IEEE Sensors Journal, Vol. 13, pp. 3319-3342.
[37]R. Lerch, 1990. Simulation of piezoelectric devices by two-and three- dimensionalfinite elements. IEEE Trans. Ultrason., Ferroelect.,Freq. Contr., vol. 37, pp. 233–247.
[38]R. M. Taziev, E. A. Kolosovsky, and A. S. Kozlov, 1995 Pressuresensitive cuts for surface acoustic waves in α-quartz. IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. 42, no. 5, pp. 845–849.
[39]Ramakrishnan, N., Palathinkal, R. P., and Nemade H. B., 2010. Mass loading effect on high aspect ratio structures grown over surface acoustic wave resonators. Sensor Letters, Vol. 8, pp. 253-257.
[40]S. Ballandras and E. Bigler, 1992. Surface-acoustic-wave devices with low sensitivity to mechanical and thermoelastic stresses. J. Appl. Phys., vol. 72, no. 8, pp. 3272–3281.
[41]S. J. Ippolito, K. Kalantar-Zadeh, D. A. Powell, and W. Wlodarski,“A 3-dimensional finite element approach for simulatingacoustic wave propagation in layered SAW devices,” in Proc.IEEE Ultrason. Symp., 2003, pp. 303–306.
[42]S. J. Ippolito, K. Kalantar-Zadeh, W. Wlodarski, and G. L.Matthews, 2003. The study of ZnO/XY LiNbO3 layered SAW devicesfor sensing applications. in Proc. IEEE Ultrason. Sym, pp. 539–543.
[43]S. W. Wenzel and R. M. White, 1989. Analytic comparison of the sensitivities of bulk-wave, surface-wave, and flexural plate-wave ultrasonic gravimetric sensors, Appl. Phys. Lett., vol. 54, pp.1976–1978.
[44]Stoney, R., Geraghty, D., O’Donnell, G. E., 2014. Characterization of differentially measured strain using passive wireless surface acoustic wave (SAW) strain sensors. IEEE Sensors Journal, Vol. 14, pp. 722-728.
[45]Taguchi, G., Chowdhury, S., and Taguchi S., 2000. Robust Engineering. McGraw-Hill, New York.
[46]Tsai, J. T., Chou, J. H., and Liu, T. K., 2006. Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm. IEEE Trans. on Neural Networks, Vol. 17, pp. 69-80.
[47]Tsai, J. T., Liu, T. K., and Chou, J. H., 2004. Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans. on Evolutionary Computation, Vol. 8, pp. 365-377.
[48]U. Wolff, F. Dickert, G. Fischerauer, W. Greibl, and C. Ruppel, 2001. SAW sensors for harsh environments, IEEE Sens. J., vol. 1, no. 1, pp. 4–13.
[49]U. Wolff, F. Schmidt, G. Scholl, and V. Magori, 1996. Radio accessible SAW sensors for non-contact measurement of torque and temperature, in Proc. IEEE Ultransonics Symp, vol. 1, pp. 359–362.
[50]V. Kalinin, 2004. Passive wireless strain and temperature sensors based on SAW devices, in Proc. IEEE Radio and Wireless Conf., Atlanta, GA, pp. 187–190.
[51]Wu, Y., 2000. Taguchi Methods for Robust Design. The American Society of Mechanical Engineers, New York.
[52]X. Zhang, F.-Y. Wang, and L. Li, 2007. Optimal selection of piezoelectric substrates and crystal cuts for SAW-based pressure and temperature sensors. IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 54, pp. 1207–1216.
[53]Y. Fu and T. Y. Chai, 2007. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatica, vol. 43, no. 6, pp. 1101–1110.
[54]Z. Wang, J. David, N. Cheeke, and C. K. Jen, 1996. Perturbationmethod for analyzing mass sensitivity of planar multilayer acousticsensors. IEEE Trans. Ultrason., Ferroelect., Freq. Contr.,vol. 43, pp. 844–851.
[55]Zhang, H., and Kosinski, J. A., 2013. Analysis of contributions of nonlinear material constants to stress-induced velocity shifts of quartz and langasite surface acoustic wave resonators. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 60, pp. 975-985.
[56]Zheng, P., Greve, D. W., and Oppenheim, I. J., 2013. Langasite surface acoustic wave gas sensors: modeling and verification. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 60, pp. 579-586.
[57]Zheng, P., Greve, D. W., Oppenheim, I. J., Chin, T. L., and Malone, V., 2012. Langasite surface acoustic wave sensors: fabrication and testing. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 59, pp. 295-303.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔