Current robot manipulation and control paradigms have largely been developed for static or highly structured environments such as those common in factories. For most techniques in robot trajectory generation, such as heuristic-based geometric planning, this has led to putting a high cost on contact with the world. This approach and methodology can be prohibitive to robots operating in many unmodeled and dynamic environments. This dissertation presents work on using haptic based feedback (torque and tactile sensing) to formulate a controller for robot manipulation in clutter. We define “clutter” as any environment in which we expect the robot to make both incidental and purposeful contact while maneuvering and manipulating. The controllers developed in this dissertation take the form of single or multi-time step Model Predictive Control (a form of optimal control which incorporates feedback) which attempts to regulate contact forces at multiple locations on a robot arm while reaching to a goal. The results and conclusions in this dissertation are based on extensive testing in simulation (tens of thousands of trials) and testing in realistic scenarios with real robots incorporating tactile sensing. The approach is novel in the sense that it allows contact and explicitly incorporate the contact and predictive model of the robot arm in calculating control effort at every time step. The expected broader impact of this research is progress towards a new foundation of reactive feedback controllers that will include a higher likelihood of success in many constrained and dynamic scenarios such as reaching into containers without line of sight, maneuvering in cluttered search and rescue situations or working with unpredictable human co-workers.

This thesis concerns the development of alternate bars under steady state flows. The movable bed is flat at the beginning of the experiment; the bars reach their equilibrium or developed state at the time Td. The thesis has two objectives. The first is to introduce new equations for the geometric characteristics, namely height and length, of alternate bars at the fully developed stage, and to evaluate them against the existing equations. The second objective is to present the results of two series of experiments carried out to characterize the process of development of alternate bars and obtain estimates of their time of development. The data resulting from these experiments are intended as a foundation for future work towards the establishment of a predictive equation for the development time of alternate bars. The new equations for bar height and length rest on dimensional considerations and all the available data. Bars produced under rough turbulent and transitional flows are treated separately. The proposed equations are found to consistently give more accurate estimates of alternate bar dimensions than existing equations. The experiments to quantify the time of development of alternate bars are carried out in the 21 m long, 0.76 m wide sediment transport flume of the Queen’s Coastal Engineering Laboratory. In addition to providing estimates of the time of development of alternate bars, these experiments reveal aspects of the process of development of alternate bars that had not been reported previously. In particular, they show that, all other conditions being the same (including the sediment transport capacity of the initial flow), the more pronounced alternate bars formed under shallower flows develop faster than less pronounced bars formed under deeper flows. The findings of this study highlight the fact that the previously unexplained wide variation in alternate bar dimensions is related to the plotting position of the data point in the alternate bar existence region of Ahmari and da Silva (2011). This study also sheds light on the evolution and development of alternate bars, which establishes a strong foundation for future studies on the topic. / Thesis (Ph.D, Civil Engineering) -- Queen's University, 2014-03-30 16:27:07.025

Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.

Traditional measures used in the composting industry for ammonia and odor emissions control are those involving collection and treatment such as thermal oxidation, adsorption, wet scrubbing and biofiltration. However, these methods do not address the source of the odor generation problem. The primary objective of this thesis research was to develop preventive means to minimize ammonia and odor emissions, and maximize nitrogen conservation to increase the agronomic value of compost. Laboratory-scale experiments were performed to examine the effectiveness of various technologies to minimize these emissions during the active phase of composting. These techniques included precipitating ammonium into struvite in composting matrix before it release to outside environment; the use of chemical and biological additives in the form of yeast, zeolite and alum; and the manipulation of key operational parameters during the composting process. The fact that struvite crystals were formed in manure composting media, as verified by both XRD and SEM-EDS analyses, represents novel findings from this study. This technique was able to reduce ammonia emission by 40-84%, while nitrogen content in the finished compost was increased by 37-105%. The application of yeast and zeolite with dosages of 5-10% enhanced the thermal performance of composting and the degree of degradation, and ammonia emission was reduced by up to 50%. Alum was found to be the most effective additive for both ammonia and odor emission control; ammonia emission decreased by 45-90% depending on the dosage, and odor emission assessed via an dynamic dilution olfactometer was reduced by 44% with dosages above 2.5%. This study reaffirmed that aeration is the most influential factor to odor emission. An optimal airflow rate for odor control would be 0.6 L/min.kg dry matter with an intermittent aeration system. Quantitative relationships between odor emission and key operational parameters were determined, which would enable “best management practices” to be devised and implemented for composting. An empirical odor predictive model was developed to provide a simple and direct means for simulation of composting odor emissions. The effects of operating conditions were incorporated into the model with multiplicative algorithm and linearization approximation approach. The model was validated with experimental observations.

This research work proposes two robust empirical model-based predictive control algorithms for nonlinear processes. Chemical process are generally highly nonlinear thus predictive control algorithms that explicitly account for the nonlinearity of the process are expected to provide better closed-loop performance as compared to algorithms based on linear models. Two types of models can be considered for control: first-principles and empirical. Empirical models were chosen for the proposed algorithms for the following reasons: (i) they are less complex for on-line optimization, (ii) they are easy to identify from input-output data and (iii) their structure is suitable for the formulation of robustness tests. One of the key problems of every model that is used for prediction within a control strategy is that some model parameters cannot be known accurately due to measurement noise and/or error in the structure of the assumed model. In the robust control approach it is assumed that processes can be represented by models with parameters' values that are assumed to lie between a lower and upper bound or equivalently, that these parameters can be represented by a nominal value plus uncertainty. When this uncertainty in control parameters is not considered by the controller the control actions might be insufficient to effectively control the process and in some extreme cases the closed-loop may become unstable. Accordingly, the two robust control algorithms proposed in the current work explicitly account for the effect of uncertainty on stability and closed-loop performance. The first proposed controller is a robust gain-scheduling model predictive controller (MPC). In this case the process is represented within each operating region by a state-affine model obtained from input-output data. The state-affine model matrices are used to obtain a state-space based MPC for every operating region. By combining the state-affine, disturbance and controller equations a closed-loop representation was obtained. Then, the resulting mathematical representation was tested for robustness with linear matrix inequalities (LMI's) based on a test where the vertices of the parameter box were obtained by an iterative procedure. The result of the LMI's test gives a measure of performance referred to as γ that relates the effect of the disturbances on the process outputs. Finally, for the gain-scheduling part of the algorithm a set of rules was proposed to switch between the available controllers according to the current process conditions. Since every combination of the controller tuning parameters results in a different value of γ, an optimization problem was proposed to minimize γ with respect to the tuning parameters. Accordingly, for the proposed controller it was ensured that the effect of the disturbances on the output variables was kept to its minimum. A bioreactor case study was presented to show the benefits of the proposed algorithm. For comparison purposes a non-robust linear MPC was also designed. The results show that the proposed algorithm has a clear advantage in terms of performance as compared to non-robust linear MPC techniques. The second controller proposed in this work is a robust nonlinear model predictive controller (NMPC) based on an empirical Volterra series model. The benefit of using a Volterra series model for this case is that its structure can be split in two sections that account for the nominal and uncertain parameter values. Similar to the previously proposed gain-scheduled controller the model parameters were obtained from input-output data. After identifying the Volterra model, an interconnection matrix and its corresponding uncertainty description were found. The interconnection matrix relates the process inputs and outputs and is built according to the type of cost function that the controller uses. Based on the interconnection representing the system a robustness test was proposed based on a structured singular value norm calculation (SSV). The test is based on a min-max formulation where the worst possible closed-loop error is minimized with respect to the manipulated variables. Additional factors that were considered in the cost function were: manipulated variables weighting, manipulated variables restrictions and a terminal condition. To show the benefits of this controller two case studies were considered, a single-input-single-output (SISO) and a multiple-input-multiple-output (MIMO) process. Both case studies show that the proposed controller is able to control the process. The results showed that the controller could efficiently track set-points in the presence of disturbances while complying with the saturation limits imposed on the manipulated variables. This controller was also compared against a non-robust linear MPC, non-robust NMPC and non-robust first-principles NMPC. These comparisons were performed for different levels of uncertainty and for different values of the suppression or control actions weights. It was shown through these comparisons that a tradeoff exists between nominal performance and robustness to model error. Thus, for larger weights the controller is less aggressive resulting in more sluggish performance but less sensitivity to model error thus resulting in smaller differences between the robust and non-robust schemes. On the other hand when these weights are smaller the controller is more aggressive resulting in better performance at the nominal operating conditions but also leading to larger sensitivity to model error when the system is operated away from nominal conditions. In this case, as a result of this increased sensitivity to model error, the robust controller is found to be significantly better than the non-robust one.

This work focuses on the robustness issues related to distributed model predictive control (DMPC) strategies in the presence of model uncertainty. The robustness of DMPC with respect to model uncertainty has been identified by researchers as a key factor in the successful application of DMPC. A first task towards the formulation of robust DMPC strategy was to propose a new systematic methodology for the selection of a control structure in the context of DMPC. The methodology is based on the trade-off between performance and simplicity of structure (e.g., a centralized versus decentralized structure) and is formulated as a multi-objective mixed-integer nonlinear program (MINLP). The multi-objective function is composed of the contribution of two indices: 1) closed-loop performance index computed as an upper bound on the variability of the closed-loop system due to the effect on the output error of either set-point or disturbance input, and 2) a connectivity index used as a measure of the simplicity of the control structure. The parametric uncertainty in the models of the process is also considered in the methodology and it is described by a polytopic representation whereby the actual process’s states are assumed to evolve within a polytope whose vertices are defined by linear models that can be obtained from either linearizing a nonlinear model or from their identification in the neighborhood of different operating conditions. The system’s closed-loop performance and stability are formulated as Linear Matrix Inequalities (LMI) problems so that efficient interior-point methods can be exploited. To solve the MINLP a multi-start approach is adopted in which many starting points are generated in an attempt to obtain global optima. The efficiency of the proposed methodology is shown through its application to benchmark simulation examples. The simulation results are consistent with the conclusions obtained from the analysis. The proposed methodology can be applied at the design stage to select the best control configuration in the presence of model errors. A second goal accomplished in this research was the development of a novel online algorithm for robust DMPC that explicitly accounts for parametric uncertainty in the model. This algorithm requires the decomposition of the entire system’s model into N subsystems and the solution of N convex corresponding optimization problems in parallel. The objective of this parallel optimizations is to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Model uncertainty is explicitly considered through the use of polytopic description of the model. The algorithm employs an LMI approach, in which the solutions are convex and obtained in polynomial time. An observer is designed and embedded within each controller to perform state estimations and the stability of the observer integrated with the controller is tested online via LMI conditions. An iterative design method is also proposed for computing the observer gain. This algorithm has many practical advantages, the first of which is the fact that it can be implemented in real-time control applications and thus has the benefit of enabling the use of a decentralized structure while maintaining overall stability and improving the performance of the system. It has been shown that the proposed algorithm can achieve the theoretical performance of centralized control. Furthermore, the proposed algorithm can be formulated using a variety of objectives, such as Nash equilibrium, involving interacting processing units with local objective functions or fully decentralized control in the case of communication failure. Such cases are commonly encountered in the process industry. Simulations examples are considered to illustrate the application of the proposed method. Finally, a third goal was the formulation of a new algorithm to improve the online computational efficiency of DMPC algorithms. The closed-loop dual-mode paradigm was employed in order to perform most of the heavy computations offline using convex optimization to enlarge invariant sets thus rendering the iterative online solution more efficient. The solution requires the satisfaction of only relatively simple constraints and the solution of problems each involving a small number of decision variables. The algorithm requires solving N convex LMI problems in parallel when cooperative scheme is implemented. The option of using Nash scheme formulation is also available for this algorithm. A relaxation method was incorporated with the algorithm to satisfy initial feasibility by introducing slack variables that converge to zero quickly after a small number of early iterations. Simulation case studies have illustrated the applicability of this approach and have demonstrated that significant improvement can be achieved with respect to computation times. Extensions of the current work in the future should address issues of communication loss, delays and actuator failure and their impact on the robustness of DMPC algorithms. In addition, integration of the proposed DMPC algorithms with other layers in automation hierarchy can be an interesting topic for future work.

Purpose: To retrospectively assess the incidence and predictive factors for self-correction of ectopic eruption of maxillary permanent first molars (EE) and the prevailing attitudes amongst surveyed specialists regarding intervention in cases of EE. Methods: Charts of patients diagnosed with EE were assessed for predictive clinical and radiographic factors. An online survey was sent to pediatric dentists and orthodontists. Results: The rate of self-correction was 71%. One third of self-corrections occurred after age 9. Increased amount of impaction (r(43)=0.59, p<.001) and degree of resorption (r(57)=0.41, p=.001) were positively correlated with irreversibility. Orthodontists estimated the spontaneous self-correction rate to be lower (t(1178)=19.2, p<.001) than pediatric dentists. Conclusions: One third of self-corrections occurred after 9 years of age and delaying treatment of EE may be a viable option when uncertain of the outcome. Reliable predictive factors of irreversibility of EE were identified. Differences exist between pediatric dentists and orthodontists regarding management of EE.

Background: Breast cancer is a complex disease, both phenotypically and etiologically. Accordingly, the responses to various treatments in the adjuvant setting among individuals vary considerably. There is a demand for tools that can distinguish patients who may benefit or may suffer from particular systemic treatments. We hypothesized that combination of data on genetic biomarkers with data from traditional clinical and pathophysiological (clinicopathologic) factors using traditional Cox model or Support Vector Machine (SVM) method, a new machine learning method, may provide a better tool for prediction of benefits to chemotherapy for the treatment of early breast cancer than using either biomarker or clinicopathologic data alone. Methods: This project included 531 patients from NCIC-CTG MA.5 trial who had data on both clinicopathologic factors, such as age, tumor size, ER status, type of surgery, tumor grade and lymph node involvement, and biomarkers assayed on tissue microarrays (TMAs), including HER2, p53, CA9, MEP21, clusterin, pAKT, COX2 and TOP2A. The Cox model and SVM methods were used to develop prognostic indices for relapse-free or overall survival with either data from TMAs and clinicopathologic assessments alone or their combination. The prognostic indices developed were then examined for their value as predictive classifiers for benefits from CEF treatment. The power of the predictive classifiers derived was evaluated and compared using the bootstrap approach. Results: None of the prognostic indices developed were found to have significant predictive value, although the prognostic index developed using SVM method based on only biomarkers yielded a marginal significant p-value (p=0.0527) for the interaction between classifier and treatment. In accordance with results published previously, the interaction between the classifier developed based on HER2 or TOP2A and treatment was significant (p=0.02 and 0.04 respectively). Comparisons based on the bootstrap approach indicate classifiers developed based on SVM performed better than those based on the Cox model method. Conclusions: Combination of data using biomarkers and clinical-pathological factors, and using either the traditional COX model method or the new machine learning method was not shown to perform better than two single previously known biomarkers in prediction of response to CEF treatment for early breast cancer. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2010-04-27 10:16:11.811

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