以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：35

、訪客IP：3.133.143.25

姓名	陳柏豪(Bo-Hao Chen)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	以粒子群最佳化-倒傳遞類神經網路-比例積分微分控制器和影像金字塔轉換融合方法實現三維光學顯微影像系統 (Implementation of a 3DOMI System with PSO-BPNN-PID Controller and Image Pyramid Transform-Based Fusion Method)
相關論文	★ 基於適應性徑向基神經網路與非奇異快速終端滑模控制結合線上延遲估測器應用於二軸機械臂運動軌跡精確控制 ★ 新型三維光學影像量測系統之設計與控制 ★ 新型雙紐線軌跡設計與進階控制實現壓電平台快速與精確定位 ★ 基於深度座標卷積與自動編碼器給予行人實時路徑及終點位置精確預測 ★ 修正式雙紐線軌跡結合自適應積分終端滑動模態控制與逆模型遲滯補償實現壓電平台精確追蹤 ★ 以局部熵亂度分布與模板匹配方法結合自適應ORB特徵提取達成多影像精確拼接 ★ 低扭矩機械手臂機構開發與脈寬調變進階控制器設計 ★ 使用時域門控與梅森增益公式構建四埠夾具的散射參數表徵 ★ 通過強化學習與積分滑模動量觀測器實現機器手臂的強健近佳PD控制策略 ★ 基於類代理注意力特徵融合模型的聯合 實體關係抽取方法 ★ 新型修正式柵欄軌跡結合擴增狀態估測 滑模回授與多自由度Bouc-Wen遲滯前饋補償 控制器給予壓電平台快速精確追蹤 ★ 結合點雲密度熵計算方法和運動回復結構在虛幻引擎中進行影像三維點雲模型及渲染重建
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-7-1以後開放)
摘要(中)	隨著技術與科學的快速發展，科學家已開發出許多高解析度的光學顯微鏡，研究者能夠透過高解析度的光學顯微鏡來觀察尺度越來越小的樣品。然而，光學顯微鏡景深的限制，導致拍攝的二維影像只有部分區域的解析度清晰可見，造成影像資訊和觀測品質受到影響，二維影像不佳會帶來三維影像建構不精確與大面積影像縫合困難等問題；另外，傳統光學顯微鏡僅能觀測樣品的二維影像，與三維影像相比它無法讓研究員瞭解和分析樣品之真實高度、立體形狀、表面輪廓等資訊。因此，開發一個新的影像演算法來改善影像品質不佳問題，同時利用影像拼接與重建技術來產生高精確與大範圍的三維影像是當前重要研究的議題之一。 若要求光學顯微鏡拍攝出一張高解析度的二維影像，不可避免需要犧牲可觀測範圍，結果通常無法將整個量測的樣品包含在內。因此，透過控制壓電平台來移動樣品，藉此取得樣品不同位置的二維影像有助於擴展觀測範圍，同時也可以兼顧解析度之需求。不幸的是，壓電平台具有許多非線性特性影響控制追蹤表現。因此，設計一個先進的控制器來大幅減緩壓電平台的非線性特性是另一個重要的議題。簡言之，若要建立出一個高精確與大範圍的三維光學顯微影像系統，一個新的影像演算法和一個先進的控制器的設計不可或缺。 在本論文中，所提出的三維光學顯微影像系統的機構設計可以分為量測部分和掃描部分，在韌體設計包含了三維影像演算法和粒子群最佳化-倒傳遞類神經網路-比例積分微分控制器。論文所提出的三維影像演算法是由影像金字塔轉換融合方法、影像縫合技術、二維到三維轉換方法三個部分所組成。具體而言，新的影像融合規則包含最大局部區域能量法和脈衝耦合神經網路模型，來應用於影像金字塔轉換融合方法。在控制器的設計，我們結合粒子群最佳化、倒傳遞類神經網路、比例積分微分演算法三個部分。透過提出的三維光學顯微影像系統來達成高精確與大範圍的樣品三維影像之目標。所提出的控制方法與現今多種常用的控制方法比較，藉由模擬和實驗結果來證實，所提出的控制器具有更好的追蹤有效性和優越性，因此非常適合用於本論文所建構的三維光學顯微影像系統。在實驗部份，我們利用已知的標準校準樣品來驗證與量化所提出的三維光學顯微影像系統的量測能力。我們利用主觀和客觀兩種評價方式證實利用新的影像融合規則得到的融合影像明顯優於其它典型的影像融合方法。最後，我們將所提出的系統應用於量測未知的微機電系統結構樣品，對於未知結構的樣品量測表現同樣具有優異的三維影像建構能力。
摘要(英)	With the rapid evolution of technology and science, many high-resolution optical microscopes (OMs) have been developed by scientists to let researchers be capable of observing smaller and smaller samples. However, the OM’s depth of field (DOF) is limited leading to only partial areas of the captured two-dimensional (2D) images being clear, resulting in information and quality from 2D images being compromised, creating imprecise three-dimensional (3D) images, and difficulties in performing image stitching. Moreover, a high-resolution sample 2D image captured by the OM usually cannot cover the entire measured sample. Also, compared to 3D image microscopic systems, 2D OM image systems cannot allow researchers to understand and analyze the height, shape, and surface profile of the samples. Therefore, developing a novel 3D image algorithm to address the OM’s limited DOF problem, execute image stitching, and produce 3D images is urgently required. When a high-resolution 2D image is taken by an OM, the observable area inevitably needs to be sacrificed; hence, the entire measured sample is usually not included. Thus, the piezoelectric stage is designed to move the measured sample, which helps increase OM measurement performance. Unfortunately, the piezoelectric stage has unavoidable nonlinear characteristics that will impact the controller’s tracking performance. Consequently, designing an advanced controller to address the piezoelectric stage’s nonlinear characteristics is essential. In a nutshell, it is imperative to establish a 3D optical microscopic imaging (3DOMI) system with a novel 3D image algorithm and an advanced controller. In this thesis, the proposed 3DOMI system is classified into measurement and scanning parts, including the 3D image algorithm and PSO-BPNN-PID controller. The 3D image algorithm is composed of an image pyramid transform (IPT)-based fusion method, image stitching, and a 2D to 3D conversion approach. Specifically, the novel image fusion rule contains the maximum local area energy method (MLAEM) and pulse-coupled neural network (PCNN) model applied to the IPT-based fusion method. The PSO-BPNN-PID controller combines particle swarm optimization (PSO), back propagation neural network (BPNN), and proportional-integral-derivative (PID) algorithms. The target is to generate extensive and highly precise sample 3D images through the proposed 3DOMI system. Comparing the proposed control method with various traditional control schemes, we used simulations and experimental results to demonstrate that the proposed controller is effective, superior, and more suitable for the proposed 3DOMI system. Furthermore, a known standard sample will be used to calibrate and quantify the measurement capability of the proposed 3DOMI system. In particular, subjective and objective evaluation methods confirm that the fused images obtained by the novel image fusion rules outperform other classical image fusion methods. Ultimately, the proposed 3DOMI system is applied to measure an unknown microelectromechanical systems (MEMS) structure sample, which also performs excellently.
關鍵字(中)	★ 壓電平台 ★ 粒子群最佳化 ★ 倒傳遞類神經網路 ★ 比例積分微分 ★ 影像融合方法 ★ 影像縫合 ★ 二維到三維轉換方法 ★ 三維光學顯微影像系統	關鍵字(英)	★ Piezoelectric stage ★ particle swarm optimization ★ back propagation neural network ★ proportional-integral-derivative ★ image fusion method ★ image stitching ★ 2D to 3D conversion approach ★ 3D optical microscopic imaging system
論文目次	摘要 i ABSTRACT iii 誌謝 vi Table of Content vii List of Figures ix List of Tables xii Explanation of Symbols xiii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Survey 3 1.2.1 3D Optical Microscopy 4 1.2.2 Piezoelectric Stage 9 1.3 Contribution 13 1.4 Thesis Organization 15 Chapter 2 Preliminaries 16 2.1 Fundamentals of Piezoelectric Actuation 16 2.1.1 Piezoelectric Effect 16 2.1.2 Hysteresis Phenomenon 18 2.1.3 Creep Effect 20 2.2 The Theory of Operation for the Optical Microscope System 21 2.3 Proportional-Integral-Derivative (PID) 26 2.4 Back Propagation Neural Network (BPNN) 28 2.5 Particle Swarm Optimization (PSO) 33 Chapter 3 System Design 38 3.1 3DOMI System Measurement Part 39 3.2 3DOMI System Scanning Part 41 3.3 Hardware Equipment 44 Chapter 4 Controller Design 46 4.1 Scan Trajectory of XY-axis Piezoelectric Stage 47 4.2 PSO-BPNN-PID Control 49 4.2.1 Control Algorithm 50 4.2.2 Stability Analysis 61 4.3 Simulation Results 64 Chapter 5 3D Image Algorithm 72 5.1 Depth of Field (DOF) 75 5.2 Image Pyramid Transform (IPT)-Based Fusion Method 76 5.2.1 Gaussian Pyramid (GP) 79 5.2.2 Laplacian Pyramid (LP) 81 5.2.3 Image Fusion Rule 83 5.3 Image Stitching 87 5.4 2D to 3D Conversion Approach 89 Chapter 6 Experimental Results 91 6.1 Experimental Setup 91 6.2 Trajectory Tracking Performance 94 6.3 3DOMI System Results 100 6.3.1 Height Calibration Standard (HS-500MG) 101 6.3.2 MEMS Structure Sample 114 Chapter 7 Conclusions 123 Reference 124
參考文獻	[1] W. J. Croft, “Under the microscope: a brief history of microscopy,” World Scientific, vol. 5, 2006. [2] K. D. Vernon-Parry, “Scanning electron microscopy: an introduction,” III-Vs Review, vol. 13, no. 4, pp. 40-44, 2000. [3] D. B. Williams and C. B. Carter, “The transmission electron microscope,” Springer Us, pp. 3-17, 1996. [4] J. Tersoff and D. R. Hamann, “Theory of the scanning tunneling microscope,” Physical Review B, vol. 31, no. 2, pp. 805, 1985. [5] G. Binnig, C. F. Quate, and C. Gerber, “Atomic force microscope,” Physical review letters, vol. 56, no. 9, pp. 930, 1986. [6] F. Zenhausern, M. P. O’boyle, and H. K. Wickramasinghe, “Apertureless near‐field optical microscope,” Applied Physics Letters, vol. 65, no. 13, pp. 1623-1625, 1994. [7] M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nature methods, vol. 3, no. 10, pp. 793-796, 2006. [8] M. G. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” Journal of microscopy, vol. 198, no. 2, pp. 82-87, 2000. [9] H. Shroff, C. G. Galbraith, J. A. Galbraith, and E. Betzig, “Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics,” Nature methods, vol. 5, no. 5, pp. 417-423, 2008. [10] M. Muhammad and T. S. Choi, “Sampling for shape from focus in optical microscopy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 564-573, 2012. [11] Y. Wei, C. Wu, Y. Wang, and Z. Dong, “Efficient shape reconstruction of microlens using optical microscopy,” IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7655-7664, 2015. [12] S. Wang, T. Wu, K. Wang, Z. Peng, N. Kwok, and T. Sarkodie-Gyan, “3-D particle surface reconstruction from multiview 2-d images with structure from motion and shape from shading (january 2020),” IEEE Transactions on Industrial Electronics, vol. 68, no. 2, pp. 1626-1635, 2021. [13] P. Satzer, D. Burgstaller, W. Krepper, and A. Jungbauer, “Fractal dimension of antibody‐peg precipitate: light microscopy for the reconstruction of 3d precipitate structures,” Engineering in Life Sciences, vol. 20, no. 3-4, pp. 67-78, 2020. [14] J. W. Judy, D. L. Polla, and W. P. Robbins, “A linear piezoelectric stepper motor with submicrometer step size and centimeter travel range,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 37, no. 5, pp. 428-437, 1990. [15] R. Curtis, T. Mitsui, and E. Ganz, “An ultrahigh vacuum high speed scanning tunneling microscope,” Review of Scientific Instruments, vol. 68, no. 7, pp. 2790-2796, 1997. [16] G. Schitter and A. Stemmer, “Identification and open-loop tracking control of a piezoelectric tube scanner for high-speed scanning-probe microscopy,” IEEE Transactions on Control Systems Technology, vol. 12, no. 3, pp. 449-454, 2004. [17] D. Huang, D. Min, Y. Jian, and Y. Li, “Current-cycle iterative learning control for high-precision position tracking of piezoelectric actuator system via active disturbance rejection control for hysteresis compensation,” IEEE Transactions on Industrial Electronics, vol. 67, no. 10, pp. 8680-8690, 2019. [18] Q. Xu, “Adaptive integral terminal third-order finite-time sliding-mode strategy for robust nanopositioning control,” IEEE Transactions on Industrial Electronics, vol. 68, no. 7, pp. 6161-6170, 2020. [19] S. Xie and J. Ren, “Tracking control using recurrent-neural-network-based inversion model: a case study on a piezo actuator,” IEEE Transactions on Industrial Electronics, vol. 68, no. 11, pp. 11409-11419, 2020. [20] L. Li, W. W. Huang, X. Wang, and L. Zhu, “Dual-notch-based repetitive control for tracking lissajous scan trajectories with piezo-actuated nanoscanners,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022. [21] L. Liu, H. Yun, Q. Li, X. Ma, S. L. Chen, and J. Shen, “Fractional order based modeling and identification of coupled creep and hysteresis effects in piezoelectric actuators,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 2, pp. 1036-1044, 2020. [22] J. Curie and P. Curie, “Développement, par pression, de l’électricité polaire dans les cristaux hémièdres à faces inclinées,” Comptes rendus, vol. 91, pp. 294-295, 1880. [23] G. Lippmann, “Principle of the conservation of electricity,” Ann. de Chimie et de Physique, vol. 24, pp. 145, 1881. [24] J. Curie and P. Curie, “Contractions et dilatations produites par des tensions électriques dans les cristaux hémièdres à faces inclines,” Compt. Rend, vol. 93, pp. 1137-1140, 1881. [25] A. Von Hippel, “Ferroelectricity, domain structure, and phase transitions of barium titanate,” Reviews of Modern Physics, vol. 22, no. 3, pp. 221, 1950. [26] G. Shirane, E. Sawaguchi, and Y. Takagi, “Dielectric properties of lead zirconate,” Physical Review, vol. 84, no. 3, pp. 476, 1951. [27] H. Jin, X. Gao, K. Ren, J. Liu, L. Qiao, M. Liu, W. Chen, Y. He, S. Dong, Z. Xu, and F. Li, “Review on piezoelectric actuators based on high-performance piezoelectric materials,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 11, pp. 3057-3069, 2022. [28] P. Ge and M. Jouaneh, “Tracking control of a piezoceramic actuator,” IEEE Transactions on Control Systems Technology, vol. 4, no. 3, pp. 209-216, 1996. [29] P. J. Chen and S. T. Montgomery, “A macroscopic theory for the existence of the hysteresis and butterfly loops in ferroelectricity,” Ferroelectrics, vol. 23, pp. 199-207, 1980. [30] F. Zhang, C. Zhang, L. Zhang, R. Cheng, R. Li, Q. Pan, and Q. Huang, “Hysteresis segmentation modeling and experiment of piezoelectric ceramic actuator,” IEEE Sensors Journal, vol. 22, no. 21, pp. 21153-21162, 2022. [31] S. O. R. Moheimani and B. J. G. Vautier, “Resonant control of structural vibration using charge-driven piezoelectric actuators,” IEEE Transactions on Control Systems Technology, vol. 13, no. 6, pp. 1021-1035, 2005. [32] Y. Feng and Y. Li, “System identification of micro piezoelectric actuators via rate-dependent prandtl-ishlinskii hysteresis model based on a modified pso algorithm,” IEEE Transactions on Nanotechnology, vol. 20, pp. 205-214, 2021. [33] J. Liu, J. Wang, and Q. Zou, “Decomposition-learning-based output tracking to simultaneous hysteresis and dynamics control: high-speed large-range nanopositioning example,” IEEE Transactions on Control Systems Technology, vol. 29, no. 4, pp. 1775-1782, 2021. [34] S. Kang, H. Wu, Y. Li, X. Yang, and J. Yao, “A fractional-order normalized bouc–wen model for piezoelectric hysteresis nonlinearity,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 126-136, 2022. [35] Y. Liu, J. She, H. Duan, and N. Qi, “Hybrid model based on maxwell-slip model and relevance vector machine,” IEEE Transactions on Industrial Electronics, vol. 68, no. 10, pp. 10050-10057, 2021. [36] S. Kang, H. Wu, X. Yang, Y. Li, J. Yao, B. Chen, and H. Lu, “Discrete-time predictive sliding mode control for a constrained parallel micropositioning piezostage,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 3025-3036, 2022. [37] H. Jung and D. G. Gweon, “Creep characteristics of piezoelectric actuators,” Review of scientific Instruments, vol. 71, no. 4, pp. 1896-1900, 2000. [38] D. B. Murphy and M. W. Davidson, “Fundamentals of light microscopy and electronic imaging,” John Wiley & Sons, 2012. [39] F. L. Pedrotti, L. M. Pedrotti, and L. S. Pedrotti, “Introduction to optics,” Cambridge University Press, 2017. [40] B. R. Masters, “Ernst abbe and the foundation of scientific microscopes,” Optics and photonics news, vol. 18, no. 2, pp. 18-23, 2007. [41] S. Weisenburger and V. Sandoghdar, “Light microscopy: an ongoing contemporary revolution,” Contemporary Physics, vol. 56, no. 2, pp. 123-143, 2015. [42] M. Mansuripur, “Distribution of light at and near the focus of high-numerical-aperture objectives,” JOSA A, vol. 3, no. 12, pp. 2086-2093, 1986. [43] E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Archiv für mikroskopische Anatomie, vol. 9, no. 1, pp. 413-468, 1873. [44] A. Rahman and A. K. Rahman, “Nanoscale metrology of line patterns on semiconductor by continuous wave terahertz multispectral reconstructive 3-d imaging overcoming the abbe diffraction limit,” IEEE Transactions on Semiconductor Manufacturing, vol. 32, no. 1, pp. 7-13, 2019. [45] L. Rayleigh, “XXXI. Investigations in optics, with special reference to the spectroscope,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 8, no. 49, pp. 261-274, 1879. [46] P. Urone and R. Hinrichs, “College physics,” 1998. [47] S. Bennett, “A brief history of automatic control,” IEEE Control Systems Magazine, vol. 16, no. 3, pp. 17-25, 1996. [48] J. G. Ziegler and N. B. Nichols, “Optimum settings for automatic controllers,” Transactions of the American society of mechanical engineers, vol. 64, no. 8, pp. 759-765, 1942. [49] K. H. Ang, G. Chong, and Y. Li, “PID control system analysis, design, and technology,” IEEE transactions on control systems technology, vol. 13, no. 4, pp. 559-576, 2005. [50] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, vol. 5, pp. 115-133, 1943. [51] R. Rojas, “Neural networks: a systematic introduction,” Springer Science & Business Media, 2013. [52] M. Cilimkovic, “Neural networks and back propagation algorithm,” Institute of Technology Blanchardstown, Blanchardstown Road North Dublin, vol. 15, no. 1, 2015. [53] D. T. Larose and C. D. Larose, “Discovering knowledge in data: an introduction to data mining,” John Wiley & Sons, vol. 4, 2014. [54] M. Carrasco-Robles and L. Serrano, “A novel minimum-size activation function and its derivative,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 56, no. 4, pp. 280-284, 2009. [55] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” MHS’95. Proceedings of the sixth international symposium on micro machine and human science, pp. 39-43, 1995. [56] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN′95-international conference on neural networks, vol. 4, pp. 1942-1948, 1995. [57] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), pp. 69-73, 1998. [58] C. D. Mackay, “Charge-coupled devices in astronomy,” Annual review of astronomy and astrophysics, vol. 24, no. 1, pp. 255-283, 1986. [59] https://www.piezosystem.com/ [60] https://www.ni.com/zh-tw.html [61] https://www.fpic.com.tw/index.php [62] https://www.advanceinstrument.com/index_en.php [63] W. Li, “Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller,” IEEE transactions on fuzzy systems, vol. 6, no. 4, pp. 449-463, 1998. [64] C. L. Hwang and C. Jan, “State-estimator-based feedback control for a class of piezoelectric systems with hysteretic nonlinearity,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 35, no. 5, pp. 654-664, 2005. [65] Z. Meng, L. Zhang, H. Wang, X. Ma, H. Li, and F. Zhu, “Research and design of precision fertilizer application control system based on pso-bp-pid algorithm,” Agriculture, vol. 12, no. 9, pp. 1395, 2022. [66] J. Wang, M. Li, W. Jiang, Y. Huang, and R. Lin, “A design of fpga-based neural network pid controller for motion control system,” Sensors, vol. 22, no. 3, pp. 889, 2022. [67] D. Zhao, C. Sun, Q. Wang, and W. Yang, “Neural network based pid control for quadrotor aircraft,” Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques: 5th International Conference, IScIDE 2015, Suzhou, China, June 14-16, 2015, Revised Selected Papers, Part II, vol. 5, pp. 287-297, 2015. [68] C. Hou, X. Yu, Y. Cao, C. Lai, and Y. Cao, “Prediction of synchronous closing time of permanent magnetic actuator for vacuum circuit breaker based on pso-bp,” IEEE Transactions on dielectrics and Electrical Insulation, vol. 24, no. 6, pp. 3321-3326, 2017. [69] X. Ji, B. Yang, and Q. Tang, “Acoustic seabed classification based on multibeam echosounder backscatter data using the pso-bp-adaboost algorithm: a case study from jiaozhou bay, china,” IEEE Journal of Oceanic Engineering, vol. 46, no. 2, pp. 509-519, 2020. [70] J. Zuo, Y. Wu, Z. Wang, and S. Dong, “A novel hybrid method for indirect measurement dynamometer card using measured motor power in sucker rod pumping system,” IEEE Sensors Journal, vol. 22, no. 14, pp. 13971-13980, 2022. [71] Z. L. Gaing, “A particle swarm optimization approach for optimum design of pid controller in avr system,” IEEE transactions on energy conversion, vol. 19, no. 2, pp. 384-391, 2004. [72] J. Robinson and Y. Rahmat-Samii, “Particle swarm optimization in electromagnetics,” IEEE transactions on antennas and propagation, vol. 52, no. 2, pp. 397-407, 2004. [73] W. M. Lin and C. M. Hong, “A new elman neural network-based control algorithm for adjustable-pitch variable-speed wind-energy conversion systems,” IEEE transactions on power electronics, vol. 26, no. 2, pp. 473-481, 2010. [74] R. Peesapati, A. Yadav, V. K. Yadav, and N. Kumar, “Gsa–fapso-based generators active power rescheduling for transmission congestion management,” IEEE Systems Journal, vol. 13, no. 3, pp. 3266-3273, 2019. [75] R. Xu, J. Xu, and D. C. Wunsch, “A comparison study of validity indices on swarm-intelligence-based clustering,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 4, pp. 1243-1256, 2012. [76] G. F. Franklin, J. D. Powell, and M. L. Workman, “Digital control of dynamic systems (vol. 3),” Reading, MA: Addison-wesley, 1998. [77] S. Tzafestas and N. P. Papanikolopoulos, “Incremental fuzzy expert pid control,” IEEE Transactions on Industrial Electronics, vol. 37, no. 5, pp. 365-371, 1990. [78] W. B. Zhao and L. T. Zhenfan, “Neural network based online self-learning adaptive pid control,” Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No. 00EX393), vol. 2, pp. 908-910, 2000. [79] P. A. N. Lei, F. E. N. G. Hao, W. A. N. G. Hai-hua, and Z.H.A.N.G. Tian-wei, “Variable pitch control strategy of wind power generation based on bpnn-pid algorithm,” IEEE Sustainable Power and Energy Conference (iSPEC), pp. 105-112, 2019. [80] D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft computing, vol. 22, pp. 387-408, 2018. [81] R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization: an overview,” Swarm intelligence, vol. 1, pp. 33-57, 2007. [82] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol. 3, pp. 1945-1950, 1999. [83] Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” Evolutionary Programming VII: 7th International Conference, EP98 San Diego, California, USA, March 25–27, 1998 Proceedings, vol. 7, pp. 591-600, 1998. [84] A. M. Lyapunov, “The general problem of the stability of motion,” International journal of control, vol. 55, no. 3, pp. 531-534, 1992. [85] T. Yabuta and T. Yamada, “Learning control using neural networks,” Proceedings. IEEE International Conference on Robotics and Automation, pp. 740-745, 1991. [86] Z. Ren, T. Zhang, X. Liu, and J. Lin, “A novel neuro pid controller of remotely operated robotic manipulators,” IEEE Transactions on Circuits and Systems II: Express Briefs, 2022. [87] W. Qian and M. Li, “Convergence analysis of standard particle swarm optimization algorithm and its improvement,” Soft Computing, vol. 22, pp. 4047-4070, 2018. [88] G. Xu and G. Yu, “Reprint of: on convergence analysis of particle swarm optimization algorithm,” Journal of Computational and Applied Mathematics, vol. 340, pp. 709-717, 2018. [89] M. Liu, H. Zhang, H. Yao, and C. Yuan, “Research on structure of shallow bpnn-pid,” IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), pp. 757-760, 2022. [90] N. L. Salvaggio and J. Shagam, “Basic photographic materials and processes (4th ed.),” Routledge, 2019. [91] M. Bass, “Handbook of optics: volume i-geometrical and physical optics, polarized light, components and instruments,” McGraw-Hill Education, 2010. [92] S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, “Pixel-level image fusion: a survey of the state of the art,” information Fusion, vol. 33, pp. 100-112, 2017. [93] D. K. Sahu and M. P. Parsai, “Different image fusion techniques–a critical review,” International Journal of Modern Engineering Research (IJMER), vol. 2, no. 5, pp. 4298-4301, 2012. [94] V. P. S. Naidu and J. R. Raol, “Pixel-level image fusion using wavelets and principal component analysis,” Defence Science Journal, vol. 58, no. 3, pp. 338, 2008. [95] K. A. Kalpoma and J. I. Kudoh, “Image fusion processing for ikonos 1-m color imagery,” IEEE Transactions on Geoscience and remote sensing, vol. 45, no. 10, pp. 3075-3086, 2007. [96] M. Yin, X. Liu, Y. Liu, and X. Chen, “Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 49-64, 2018. [97] J. Du, W. Li, and B. Xiao, “Anatomical-functional image fusion by information of interest in local laplacian filtering domain,” IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 5855-5866, 2017. [98] Y. Yang, S. Tong, S. Huang, and P. Lin, “Multifocus image fusion based on nsct and focused area detection,” IEEE Sensors Journal, vol. 15, no. 5, pp. 2824-2838, 2015. [99] E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer, vol. 29, no. 6, pp. 33-41, 1984. [100] B. Javidi, C. M. Do, S. H. Hong, and T. Nomura, “Multi-spectral holographic three-dimensional image fusion using discrete wavelet transform,” Journal of Display Technology, vol. 2, no. 4, pp. 411-417, 2006. [101] P. Hill, A. Achim, M. E. Al-Mualla, and D. Bull, “Contrast sensitivity of the wavelet, dual tree complex wavelet, curvelet, and steerable pyramid transforms,” IEEE Transactions on Image Processing, vol. 25, no. 6, pp. 2739-2751, 2016. [102] W. Li, X. Xiao, P. Xiao, H. Wang, and F. Xu, “Change detection in multitemporal sar images based on slow feature analysis combined with improving image fusion strategy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 3008-3023, 2022. [103] H. Kaur, D. Koundal, and V. Kadyan, “Image fusion techniques: a survey,” Archives of computational methods in Engineering, vol. 28, pp. 4425-4447, 2021. [104] P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” Readings in computer vision, pp. 671-679, 1987. [105] R. Eckhorn, H. J. Reitboeck, M. T. Arndt, and P. Dicke, “Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex,” Neural computation, vol. 2, no. 3, pp. 293-307, 1990. [106] T. Lindblad, J. M. Kinser, and J. G. Taylor, “Image processing using pulse-coupled neural networks,” Heidelberg: Springer, 2005. [107] C. Mingrui, Y. Junyi, and C. Guanghui, “Multi-focus image fusion algorithm using lp transformation and pcnn,” IEEE International conference on software engineering and service science (ICSESS), pp. 237-241, 2015. [108] C. Panigrahy, A. Seal, and N. K. Mahato, “MRI and spect image fusion using a weighted parameter adaptive dual channel pcnn,” IEEE Signal Processing Letters, vol. 27, pp. 690-694, 2020. [109] R. Nie, J. Cao, D. Zhou, and W. Qian, “Multi-source information exchange encoding with pcnn for medical image fusion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 3, pp. 986-1000, 2021. [110] L. Zhang, G. Zeng, J. Wei, and Z. Xuan, “Multi-modality image fusion in adaptive-parameters spcnn based on inherent characteristics of image,” IEEE Sensors Journal, vol. 20, no. 20, pp. 11820-11827, 2020. [111] R. Szeliski, “Image alignment and stitching: a tutorial,” Foundations and Trends® in Computer Graphics and Vision, vol. 2, no. 1, pp. 1-104, 2007. [112] P. F. Alcantarilla, A. Bartoli, and A. J. Davison, “KAZE features,” Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12, pp. 214-227, 2012. [113] M. Muja and D. G. Lowe, “Fast approximate nearest neighbors with automatic algorithm configuration,” VISAPP (1), vol. 2, no. 331-340, pp. 2, 2009. [114] R. Hartley and A. Zisserman, “Multiple view geometry in computer vision,” Cambridge university press, 2003. [115] https://www.budgetsensors.com/ [116] H. Ullah, Y. Zhao, F. Y. Abdalla, and L. Wu, “Fast local laplacian filtering based enhanced medical image fusion using parameter-adaptive pcnn and local features-based fuzzy weighted matrices,” Applied Intelligence, pp. 1-20, 2022. [117] B. Meher, S. Agrawal, R. Panda, and A. Abraham, “A survey on region based image fusion methods,” Information Fusion, vol. 48, pp. 119-132, 2019. [118] W. Wang and F. Chang, “A multi-focus image fusion method based on laplacian pyramid,” J. Comput., vol. 6, no. 12, pp. 2559-2566, 2011. [119] P. Jagalingam and A. V. Hegde, “A review of quality metrics for fused image,” Aquatic Procedia, vol. 4, pp. 133-142, 2015. [120] L. Jian, R. Rayhana, L. Ma, S. Wu, Z. Liu, and H. Jiang, “Infrared and visible image fusion based on deep decomposition network and saliency analysis,” IEEE Transactions on Multimedia, vol. 24, pp. 3314-3326, 2022. [121] O. S. Faragallah, H. El-Hoseny, W. El-Shafai, W. Abd El-Rahman, H. S. El-Sayed, E. S. M. El-Rabaie, F. E. Abd El-Samie, and G. G. Geweid, “A comprehensive survey analysis for present solutions of medical image fusion and future directions,” IEEE Access, vol. 9, pp. 11358-11371, 2020. [122] L. Qiu, D. Liu, W. Zhao, H. Cui, and Z. Sheng, “Real-time laser differential confocal microscopy without sample reflectivity effects,” Optics Express, vol. 22, no. 18, pp. 21626-21640, 2014. [123] J. W. Wu, J. J. Chen, M. L. Chiang, and L. C. Fu, “Design and control of phase-detection mode atomic force microscopy for reconstruction of cell contours in three dimensions,” IEEE Transactions on Nanotechnology, vol. 13, no. 4, pp. 639-649, 2014. [124] R. J. Balla, D. T. Jantz, N. Kurapati, R. Chen, K. C. Leonard, and S. Amemiya, “Nanoscale intelligent imaging based on real-time analysis of approach curve by scanning electrochemical microscopy,” Analytical chemistry, vol. 91, no. 15, pp. 10227-10235, 2019. [125] J. W. Wu, S. A. Chao, and T. K. Hsu, “Design and control of a novel 3-d piezoelectric scanning coaxial optical microscope system,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-11, 2023.
指導教授	吳俊緯(Jim-Wei Wu)	審核日期	2023-8-14
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare