No description available.

Liu Shubao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 90-98). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Time-Varying Quadratic Optimization --- p.1 / Chapter 1.2 --- Recurrent Neural Networks --- p.3 / Chapter 1.2.1 --- From Feedforward to Recurrent Networks --- p.3 / Chapter 1.2.2 --- Computational Power and Complexity --- p.6 / Chapter 1.2.3 --- Implementation Issues --- p.7 / Chapter 1.3 --- Thesis Organization --- p.9 / Chapter I --- Theory and Models --- p.11 / Chapter 2 --- Linearly Constrained QP --- p.13 / Chapter 2.1 --- Model Description --- p.14 / Chapter 2.2 --- Convergence Analysis --- p.17 / Chapter 3 --- Quadratically Constrained QP --- p.26 / Chapter 3.1 --- Problem Formulation --- p.26 / Chapter 3.2 --- Model Description --- p.27 / Chapter 3.2.1 --- Model 1 (Dual Model) --- p.28 / Chapter 3.2.2 --- Model 2 (Improved Dual Model) --- p.28 / Chapter II --- Engineering Applications --- p.29 / Chapter 4 --- KWTA Network Circuit Design --- p.31 / Chapter 4.1 --- Introduction --- p.31 / Chapter 4.2 --- Equivalent Reformulation --- p.32 / Chapter 4.3 --- KWTA Network Model --- p.36 / Chapter 4.4 --- Simulation Results --- p.40 / Chapter 4.5 --- Conclusions --- p.40 / Chapter 5 --- Dynamic Control of Manipulators --- p.43 / Chapter 5.1 --- Introduction --- p.43 / Chapter 5.2 --- Problem Formulation --- p.44 / Chapter 5.3 --- Simplified Dual Neural Network --- p.47 / Chapter 5.4 --- Simulation Results --- p.51 / Chapter 5.5 --- Concluding Remarks --- p.55 / Chapter 6 --- Robot Arm Obstacle Avoidance --- p.56 / Chapter 6.1 --- Introduction --- p.56 / Chapter 6.2 --- Obstacle Avoidance Scheme --- p.58 / Chapter 6.2.1 --- Equality Constrained Formulation --- p.58 / Chapter 6.2.2 --- Inequality Constrained Formulation --- p.60 / Chapter 6.3 --- Simplified Dual Neural Network Model --- p.64 / Chapter 6.3.1 --- Existing Approaches --- p.64 / Chapter 6.3.2 --- Model Derivation --- p.65 / Chapter 6.3.3 --- Convergence Analysis --- p.67 / Chapter 6.3.4 --- Model Comparision --- p.69 / Chapter 6.4 --- Simulation Results --- p.70 / Chapter 6.5 --- Concluding Remarks --- p.71 / Chapter 7 --- Multiuser Detection --- p.77 / Chapter 7.1 --- Introduction --- p.77 / Chapter 7.2 --- Problem Formulation --- p.78 / Chapter 7.3 --- Neural Network Architecture --- p.82 / Chapter 7.4 --- Simulation Results --- p.84 / Chapter 8 --- Conclusions and Future Works --- p.88 / Chapter 8.1 --- Concluding Remarks --- p.88 / Chapter 8.2 --- Future Prospects --- p.88 / Bibliography --- p.89

Embryonic stem (ES) cell maintenance and differentiation are dynamic processes controlled by various intrinsic and extrinsic factors. Identifying these factors will enhance the understanding about developmental process and improve the application of stem cells in clinic. Previous studies highlight a shift between non-oxidative and oxidative energy metabolism to play roles during differentiation. Oxidative metabolism is a major source of reactive oxygen species (ROS) which is regulated by a cytoprotective transcription factor, Nuclear factor erythroid 2-related factor 2 (Nrf2). Therefore, this study investigate relationship between metabolism, ROS, and Nrf2 during mouse ES cell differentiation. In vitro models representing early lineage differentiation were used. By measuring metabolic profiles, ROS, and Nrf2 levels from the models, Nrf2 was found related to pluripotency and ROS. However, relationship among metabolism and Nrf2 or ROS could not be detected. Gain- and loss-of-function experiments by pharmacological activator, short hairpin RNA knockdown, and CRISPR-Cas9 genome editing showed that Nrf2 could promote pluripotency and inhibit differentiation, especially during early differentiation toward neural lineage. This study suggested a new player in transcription control that governs pluripotency and differentiation.

模型預測控制是一種基於模型的先進控制策略，它通過反復優化一個有限時域内的約束優化問題實時求解最優控制信號。作爲一種有效的多變量控制方法，模型預測控制在過程控制、機械人、經濟學等方面取得了巨大的成功。模型預測控制研究與發展的一個關鍵問題在於如何實現高性能非綫性和魯棒預測控制算法。實時優化是一項具有挑戰性的任務，尤其在優化問題時非凸優化的情況下，實時優化變得更爲艱巨。在模型預測控制取得發展的同時，以建立仿腦計算模型為目標的神經網絡研究也取得一些重要突破，尤其是在系統辨識和實時優化方面。神經網絡為解決模型預測控制面臨的瓶頸問題提供了有力的工具。 / 本篇論文重點討論基於神經動力學方法的模型預測控制的設計與分析。論文的主要目標在於設計高性能神經動力學算法進而提高模型預測控制的最優性與計算效率。論文包括兩大部分。第一部分討論如何在不需要求解非凸優化的前提下解決非綫性和魯棒模型預測控制。主要的解決方案是將非相信模型分解為帶有未知項的仿射模型，或將非綫性模型轉換為綫性變參數系統。仿射模型中的未知項通過極限學習機進行建模和數值補償。針對系統中的不塙定干擾，利用極小極大算法和擾動不變集方法獲取控制系統魯棒性。儅需要考慮多個評價指標是，採用目標規劃設計多目標優化算法。論文第一部分提出的設計方法可以將非綫性和魯邦模型預測控制設計為凸優化問題，進而採用神經動力學優化的方法進行實時求解。論文的第二部分設計了針對非凸優化的多神經網絡算法，並在此基礎上提出了模型預測控制算法。多神經網絡算法模型人類頭腦風暴的過程，同時應用多個神經網絡相互協作地進行全局搜索。神經網絡的動態方程指導其進行局部精確搜索，神經網絡之間的信息交換指導全局搜索。實驗結果表明該算法可以高效地獲得非凸優化的全局最優解。基於多神經網絡優化的的模型預測控制算法是一種創新性的高性能控制方法。論文的最後討論了應用模型預測控制解決海洋航行器的運動控制問題。 / Model predictive control (MPC) is an advanced model-based control strategy that generates control signals in real time by optimizing an objective function iteratively over a finite moving prediction horizon, subject to system constraints. As a very effective multivariable control technology, MPC has achieved enormous success in process industries, robotics, and economics. A major challenge of the MPC research and development lies in the realization of high-performance nonlinear and robust MPC algorithms. MPC requires to perform real time dynamic optimization, which is extremely demanding in terms of solution optimality and computational efficiency. The difficulty is significantly amplified when the optimization problem is nonconvex. / In parallel to the development of MPC, research on neural networks has made significant progress, aiming at building brain-like models for modeling complex systems and computing optimal solutions. It is envisioned that the advances in neural network research will play a more important role in the MPC synthesis. This thesis is concentrated on analysis and design of neurodynamic approaches to nonlinear and robust MPC. The primary objective is to improve solution optimality by developing highly efficient neurodynamic optimization methods. / The thesis is comprised of two coherent parts under a unified framework. The first part consists of several neurodynamics-based MPC approaches, aiming at solving nonlinear and robust MPC problems without confronting non-convexity. The nonlinear models are decomposed to input affine models with unknown terms, or transformed to linear parameter varying systems. The unknown terms are learned by using extreme learning machines via supervised learning. Minimax method and disturbance invariant tube method are used to achieve robustness against uncertainties. When multiobjective MPC is considered, goal programming technique is used to deal with multiple objectives. The presented techniques enable MPC to be reformulated as convex programs. Neurodynamic models with global convergence, guaranteed optimality, and low complexity are customized and applied for solving the convex programs in real time. Simulation results are presented to substantiate the effectiveness and to demonstrate the characteristics of proposed approaches. The second part consists of collective neurodynamic optimization approaches, aiming at directly solving the constrained nonconvex optimization problems in MPC. Multiple recurrent neural networks are exploited in framework of particle swarm optimization by emulating the paradigm of brainstorming. Each individual neural network carries out precise constrained local search, and the information exchange among neural networks guides the improvement of the solution quality. Implementation results on benchmark problems are included to show the superiority of the collective neurodynamic optimization approaches. The essence of the collective neurodynamic optimization lies in its global search capability and real time computational efficiency. By using collective neurodynamic optimization, high-performance nonlinear MPC methods can be realized. Finally, the thesis discusses applications of MPC on the motion control of marine vehicles. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Yan, Zheng. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 186-203). / Abstracts also in Chinese.

Han-bing Ji. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (p. 160-163). / Microfiche. Ann Arbor, Mich.: UMI, 1998. 2 microfiches ; 11 x 15 cm.

Guo Ping. / "March 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 166-182). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

During the past two decades, numerous recurrent neural networks (RNNs) have been proposed for solving VIs and related problems. However, first, the theories of many emerging RNNs have not been well founded yet; and their capabilities have been underestimated. Second, these RNNs have limitations in handling some types of problems. Third, it is certainly not true that these RNNs are best choices for solving all problems, and new network models with more favorable characteristics could be devised for solving specific problems. / In the research, the above issues are extensively explored from dynamic system perspective, which leads to the following major contributions. On one hand, many new capabilities of some existing RNNs have been revealed for solving VIs and related problems. On the other hand, several new RNNs have been invented for solving some types of these problems. The contributions are established on the following facts. First, two existing RNNs, called TLPNN and PNN, are found to be capable of solving pseudomonotone VIs and related problems with simple bound constraints. Second, many more stability results are revealed for an existing RNN, called GPNN, for solving GVIs with simple bound constraints, and it is then extended to solve linear VIs (LVIs) and generalized linear VIs (GLVIs) with polyhedron constraints. Third, a new RNN, called IDNN, is proposed for solving a special class of quadratic programming problems which features lower structural complexity compared with existing RNNs. Fourth, some local convergence results of an existing RNN, called EPNN, for nonconvex optimization are obtained, and two variants of the network by incorporating two augmented Lagrangian function techniques are proposed for seeking Karush-Kuhn-Tucker (KKT) points, especially local optima, of the problems. / Variational inequality (VI) can be viewed as a natural framework for unifying the treatment of equilibrium problems, and hence has applications across many disciplines. In addition, many typical problems are closely related to VI, including general VI (GVI), complementarity problem (CP), generalized CP (GCP) and optimization problem (OP). / Hu, Xiaolin. / "July 2007." / Adviser: Jun Wang. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1102. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 193-207). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Li Wenjing. / "April 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 155-167). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Glioblastoma (GBM) is the most common intrinsic primary brain tumour. It is uniformly fatal, with median survival approximately 14 months. These tumours comprise a mixture of neural stem cell-like cells and more differentiated astrocytic cells. The former are thought to be responsible for tumour development and recurrence, and display self-renewal and differentiation capacity in vitro. Glioma stem cells (GSCs) are defined operationally by their capacity to initiate tumours on orthotopic transplant into immunocompromised mice. The Pollard lab has identified the neural developmental transcription factor Forkhead Box G1 (FOXG1) as the most consistently overexpressed gene in GBM-derived neural stem (GNS) cells compared to their genetically normal neural stem (NS) cell counterparts. Here we explore the function and critical downstream effectors of FOXG1 in NS and GNS cells. We find that, although FOXG1 is not essential for sustaining proliferation of NS or GNS cells (in vitro), high FOXG1 restricts astrocyte differentiation in response to BMP and can drive dedifferentiation of postmitotic astrocytes. We identify a potential cooperation with SOX2. ChIP-Seq and RNA-Seq were used to define transcriptional targets. FOXG1 directly controls critical cell cycle regulators FOXO3 and FOXO6 (two forkhead family proteins), as well as the proto-oncogene MYCN and key regulators of both DNA and chromatin methylation, including TET3 and CHD3. Pharmacological inhibitors of MYC block FOXG1-driven de-differentiation, whereas Vitamin C and 5-azacytidine – agents that disrupt DNA and chromatin methylation – can facilitate de-differentiation. CRISPR/Cas genome editing was used to genetically ablate the cell cycle inhibitor FOXO3, or remove the FOXG1-bound cis-regulatory region. These data suggest direct transcriptional repression of FOXO3 by FOXG1 may drive cells into cycle. We conclude that high levels of FOXG1 in GBM limit astrocyte differentiation commitment by direct transcriptional control of core cell cycle regulators and DNA/histone methylation.

Guo, Zhishan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 71-82). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement i --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constrained Pseudoconvex Optimization --- p.1 / Chapter 1.2 --- Recurrent Neural Networks --- p.4 / Chapter 1.3 --- Thesis Organization --- p.7 / Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Pseudo convex Optimization --- p.8 / Chapter 2.2 --- Recurrent Neural Networks --- p.10 / Chapter 3 --- Model Description and Convergence Analysis --- p.17 / Chapter 3.1 --- Model Descriptions --- p.18 / Chapter 3.2 --- Global Convergence --- p.20 / Chapter 4 --- Numerical Examples --- p.27 / Chapter 4.1 --- Gaussian Optimization --- p.28 / Chapter 4.2 --- Quadratic Fractional Programming --- p.36 / Chapter 4.3 --- Nonlinear Convex Programming --- p.39 / Chapter 5 --- Real-time Data Reconciliation --- p.42 / Chapter 5.1 --- Introduction --- p.42 / Chapter 5.2 --- Theoretical Analysis and Performance Measurement --- p.44 / Chapter 5.3 --- Examples --- p.45 / Chapter 6 --- Real-time Portfolio Optimization --- p.53 / Chapter 6.1 --- Introduction --- p.53 / Chapter 6.2 --- Model Description --- p.54 / Chapter 6.3 --- Theoretical Analysis --- p.56 / Chapter 6.4 --- Illustrative Examples --- p.58 / Chapter 7 --- Conclusions and Future Works --- p.67 / Chapter 7.1 --- Concluding Remarks --- p.67 / Chapter 7.2 --- Future Works --- p.68 / Chapter A --- Publication List --- p.69 / Bibliography --- p.71