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In the wake of 21 year old Don Sanderson’s death (January 1st, 2009), the direct result of head injuries sustained during an on-ice hockey fight, the social and political appetite for eliminating violence in amateur hockey appears to be at an all time high. Unfortunately, due to a variety of methodological and conceptual limitations previous research is currently unable to provide a unified and valid explanation for sport-specific aggression (Kirker, Tenenbaum, & Mattson, 2000). One of the primary impediments facing our understanding of sport-specific aggression is the descriptive and fragmented nature of the current body of literature. Over the years a number of independent lines of research have been undertaken, through which several psychological and social factors have been identified as potential determinants. However, in many cases these constructs have yet to be tested against athletes’ actual aggressive behaviour in sport and thus their predictive contribution to our understanding is still unknown. Consequently, the purpose of the current investigation was to assess the predictive influence of several commonly cited psychosocial constructs on amateur hockey players actual within-competition use of aggressive behaviour over a competitive season. A trait aggressive personality disposition emerged as the strongest and most stable predictor of athletes’ aggressive behaviour, accounting for 10 – 40% of the statistical variance depending upon the age and competitive level of the athletes under investigation. Differences in the overt expression of the this trait aggressive disposition between age cohorts (bantam / midget) and competitive levels (house league / rep) suggests that environmental and contextual factors also play a significant role in facilitating or repressing athletes’ aggressive behaviour. As such, the results of the current study support an interactive explanation for hockey-related aggression, whereby situational (e.g., team norms, perceived reinforcement) and personal factors (e.g., trait aggressive disposition, ego orientation) interact to either increase or decrease an athlete’s likelihood for committing aggressive penalty infractions over a competitive season. The current results are plotted and discussed within the parameters of Anderson and Bushman’s (2002) General Aggression Model (GAM), which is a frequently cited interactionist framework used in the broader study of human aggression.

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to provide the best results and was used to create the predictive model. The model was compiled to a stand-alone application with a graphical user interface using the MATLAB CompilerTM.

Multigenic diagnostic and prognostic tools can be derived for ordinal clinical outcomes using data from high-throughput genomic experiments. A challenge in this setting is that the number of predictors is much greater than the sample size, so traditional ordinal response modeling techniques must be exchanged for more specialized approaches. Existing methods perform well on some datasets, but there is room for improvement in terms of variable selection and predictive accuracy. Therefore, we extended an impressive binary response modeling technique, Feature Augmentation via Nonparametrics and Selection, to the ordinal response setting. Through simulation studies and analyses of high-throughput genomic datasets, we showed that our Ordinal FANS method is sensitive and specific when discriminating between important and unimportant features from the high-dimensional feature space and is highly competitive in terms of predictive accuracy. Discrete survival time is another example of an ordinal response. For many illnesses and chronic conditions, it is impossible to record the precise date and time of disease onset or relapse. Further, the HIPPA Privacy Rule prevents recording of protected health information which includes all elements of dates (except year), so in the absence of a “limited dataset,” date of diagnosis or date of death are not available for calculating overall survival. Thus, we developed a method that is suitable for modeling high-dimensional discrete survival time data and assessed its performance by conducting a simulation study and by predicting the discrete survival times of acute myeloid leukemia patients using a high-dimensional dataset.

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<p>Manufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.</p><p>The three major types of maintenance are reactive, preventive, and predictive. The research project focused on applying the principles of predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains the techniques and principles of applying the technology currently available to reduce downtime and maintenance cost. The predictive maintenance procedures and saving are compared with reactive and preventive methods to determine a value of return. The report will examine the benefits of using the Internet of Things technology to create autonomous self-diagnosing smart machines. The predictive maintenance plan in this research illustration will introduce health check equipment used to implement longer lasting machine components. In conclusion, the project developed out an entire predictive maintenance plan to reduce downtime and maintenance costs.<br></p><p></p><br>

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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.

Cooperative control of networked systems (or multi-agent systems) has attracted much attention during the past few years. But most of the existing results focus on first order and second order leaderless consensus problems with linear dynamics. The second part of this dissertation solves a higher-order synchronization problem for cooperative nonlinear systems with an active leader. The communication network considered is a weighted directed graph with fixed topology. Each agent is modeled by a higher-order nonlinear system with the nonlinear dynamics unknown. External unknown disturbances perturb each agent. The leader agent is modeled as a higher-order non-autonomous nonlinear system. It acts as a command generator and can only give commands to a small portion of the networked group. A robust adaptive neural network controller is designed for each agent. Neural network learning algorithms are given such that all nodes ultimately synchronize to the leader node with a small residual error. Moreover, these controllers are totally distributed in the sense that each controller only requires its own information and its neighbors' information. / Receding horizon control (RHC), also called model predictive control (MPC), is a suboptimal control scheme over an infinite horizon that is determined by solving a finite horizon open-loop optimal control problem repeatedly. It has widespread applications in industry. Reinforcement learning (RL) is a computational intelligence method in which an optimal control policy is learned over time by evaluating the performance of suboptimal control policies. In this dissertation it is shown that reinforcement learning techniques can significantly improve the behavior of RHC. Specifically, RL methods are used to add a learning feature to RHC. It is shown that keeping track of the value learned at the previous iteration and using it as the new terminal cost for RHC can overcome traditional strong requirements for RHC stability, such as that the terminal cost be a control Lyapunov function, or that the horizon length be greater than some bound. We propose improved RHC algorithms, called updated terminal cost receding horizon control (UTC-RHC), first in the framework of discrete-time linear systems and then in the framework of continuous-time linear systems. For both cases, we show the uniform exponential stability of the closed-loop system can be guaranteed under very mild conditions. Moreover, unlike RHC, the UTC-RHC control gain approaches the optimal policy associated with the infinite horizon optimal control problem. To show these properties, non-standard Lyapunov functions are introduced for both discrete-time case and continuous-time case. / Two topics of modern control are investigated in this dissertation, namely receding horizon control (RHC) and cooperative control of networked systems. We apply learning techniques to these two topics. Specifically, we incorporate the reinforcement learning concept into the standard receding horizon control, yielding a new RHC algorithm, and relax the stability constraints required for standard RHC. For the second topic, we apply neural adaptive control in synchronization of the networked nonlinear systems and propose distributed robust adaptive controllers such that all nodes synchronize to a leader node. / Zhang, Hongwei. / Adviser: Jie Huang. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 99-105). / 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 Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Cancer causes 13% of human deaths worldwide, 90% of which involve metastasis. The reactivation of embryonic processes in epithelial cancers—and the epithelial-mesenchymal transition (EMT) in particular—results in increased cell motility and invasiveness, and is a known mechanism for initiating metastasis. The reverse process, the mesenchymal-epithelial transition (MET), is implicated in the process of cells colonising pre-metastatic niches. Understanding the relationships between EMT, MET and metastasis is therefore highly relevant to cancer research and treatment. Key challenges include deciphering the large, uncharted space of gene function, mapping the complex signalling networks involved and understanding how the EMT and MET programmes function in vivo within specific environments and disease contexts. Inference and analysis of small-scale networks from human tumour tissue samples, scored for protein expression, provides insight into pleiotropy, complex interactions and context-specific behaviour. Small sets of proteins (10–50, representative of key biological processes) are scored using quantitative antibody-based technologies (e.g. immunofluorescence) to give static expression values. A novel inference algorithm specifically for these data, Gabi, is presented, which produces signed, directed networks. On synthetic data, inferred networks often recapitulate the information flow between proteins in ground truth connectivity. Directionality predictions are highly accurate (90% correct) if the input network structure is itself accurate. The Gabi algorithm was applied to study multiple carcinomas (renal, breast, ovarian), providing novel insights into the relationships between EMT players and fundamental processes dysregulated in cancers (e.g. apoptosis, proliferation). Survival analysis on these cohorts shows further evidence for association of EMT with poor outcome. A patent-pending method is presented for stratifying response to sunitinib in metastatic renal cancer patients. The method is based on a proportional hazards model with predictive features selected automatically using regularisation (Bayesian information criterion). The final algorithm includes N-cadherin expression, a determinant of mesenchymal properties, and shows significant predictive power (p = 7.6x10-7, log-rank test). A separate method stratifies response to tamoxifen in estrogen-receptor positive, node-negative breast cancer patients using a cross-validated support vector machine (SVM). The algorithm was predictive on blind-test data (p = 4.92 x 10-6, log-rank test). Methods developed have been made available within a web application (TMA Navigator) and an R package (rTMA). TMA Navigator produces visual data summaries, networks and survival analysis for uploaded tissue microarray (TMA) scores. rTMA expands on TMA Navigator capabilities for advanced workflows within a programming environment.