A mixed hypergraph is a triple H = (X; C;D), where X is the vertex set, and each of C;D is a list of subsets of X. A strict t-coloring is a onto mapping from X to {1, 2,…,t} such that each c belongs to C contains two vertices have a common value and each d belongs to D has two vertices have distinct values. If H has a strict t-coloring, then t belongs to S(H), such S(H) is called the feasible set of H, and k is a gap if there are a value larger than k and a value less than k in the feasible set but k is not. We find the minimum and maximum gap of a mixed hypergraph with more than 5 vertices. Then we consider two special cases of the gap of mixed hypergraphs. First, if the mixed hypergraphs is spanned by a complete bipartite graph, then the gap is decided by the size of bipartition. Second, the (l,m)-uniform mixed hypergraphs has gaps if l > m/2 >2, and we prove that the minimum number of vertices of a (l,m)-uniform mixed hypergraph which has gaps is (m/2)( l -1) + m.

The control of nonholonomic wheeled mobile robots (WMRs) has gained a lot of attention in the field of robotics over the past few decades as WMRs provide an increased range of motion resulting in a larger workspace. This research focuses on the application of Model Predictive Control (MPC) for real-time trajectory tracking of a nonholonomic WMR. MPC is a control strategy in which the control law is designed based on optimizing a cost function. The input and output constraints that may arise in practical situations can be directly incorporated into the control system using MPC. Computation time is the biggest hurdle in adapting MPC strategies for trajectory tracking. This research applies a non-feasible active set MPC algorithm developed in [1] which is faster than the traditional active set methods (ASMs). A discrete-time linear model of a general WMR is used for the simulation. MATLAB simulations are performed for tracking circular as well as square trajectories using the discretized WMR model and the non-feasible ASM (NF-ASM). The performance of NF-ASM is compared to two other well-known traditional algorithms, i.e. Fletcher’s ASM and MATLAB’s Quadratic Programming algorithm. It is shown that, although all these algorithms are capable of providing satisfactory trajectory tracking performance, NF-ASM is a better choice in terms of the simulation time and required number of iterations for realtime trajectory tracking of any type as long as the constraints on the inputs stay active for a long period during the simulation. / UOIT

Cable robots are a type of robotic manipulator that has recently attracted interest for large workspace manipulation tasks. Cable robots are relatively simple in form, with multiple cables attached to a mobile platform or end-effector. The end-effector is manipulated by motors that can extend or retract the cables. Cable robots have many desirable characteristics, including low inertial properties, high payload-to-weight ratios, potentially vast workspaces, transportability, ease of disassembly/reassembly, reconfigurability and economical construction and maintenance. However, relatively few analytical tools are available for analyzing and designing these manipulators. This thesis focuses on expanding the existing theoretical framework for the design and analysis of cable robots in two areas: disturbance robustness and workspace generation. Underconstrained cable robots cannot resist arbitrary external disturbances acting on the end-effector. Thus a disturbance robustness measure for general underconstrained single-body and multi-body cable robots is presented. This measure captures the robustness of the manipulator to both static and impulsive disturbances. Additionally, a wrench-based method of analyzing cable robots has been developed and is used to formulate a method of generating the Wrench-Feasible Workspace of cable robots. This workspace consists of the set of all poses of the manipulator where a specified set of wrenches (force/moment combinations) can be exerted. For many applications the Wrench-Feasible Workspace constitutes the set of all usable poses. The concepts of robustness and workspace generation are then combined to introduce a new workspace: the Specified Robustness Workspace. This workspace consists of the set of all poses of the manipulator that meet or exceed a specified robustness value.

Plant efficiency and profitability are becoming increasingly important and operating at the most optimal point is a necessity. The definition of proper operational bounds on output variables such as product quality, production rates etc., is critical for plant optimisation. The use of operational bounds that do not lie within the region of the output operational space of the plant can result in the control system attempting to operate the plant in a non attainable region. The use of operational bounds that lie within the bounds of the output operational space of the plant and if the output operational space is non convex can also result in the control system attempting to operate the plant in a non attainable region. This results in non feasible optimisation. A numerical intersection algorithm has been developed that identifies the feasible region of operation known as the desired operational space. This is accomplished by finding the intersection of the required operational space and the achievable output operational space. The algorithm was simulated and evaluated on a case study under various scenarios. These scenarios included specifying operational bounds that lie partially within the bounds of the achievable operational space and also specifying operational bounds that lie within the bounds of the operational space which was non convex. The results yielded a desired operational space with bounds that were guaranteed to lie within an attainable region on the output operational space. The desired operational space bounds were also simplified into a rectangle with high and low limits that can be readily used in control systems. / Dissertation (MEng)--University of Pretoria, 2012. / Chemical Engineering / unrestricted

Under the mandator consolidation act of 1915, the county-unit school district in Utah had its beginning (10). Although, through this consolidation movement, many small school districts were combined into larger ones, the elimination of the small districts did not, necessarily, provide for the elimination of the small schools. Due to several factors including distance, poor communication and transportation facilities, and a strong desire on the part of the people who lived in small communities for a voice in school matters, the consolidation of school attendance areas did not keep pace with school district reorganization. With the improvement of highways and communication facilities and the rapid rise of the motor vehicle as a means of fast, dependable travel, school boards have considered consolidation of attendance areas as one of the possibilities open to them to improve the quality of education in their districts.

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Online consolidation fiber placement is emerging as an automated manufacturing process for the fabrication of large composite material complex structures. While traditional composite manufacturing techniques limited the products' size, geometrical shapes and laminate patterns, robotic automation of the fiber placement process allows the manufacture of complex bodies with any desired surface pattern or towpreg's direction. Therefore, a complete understanding of the robot kinematic capabilities should be made to accurately position the structure's substrate in the workcell and to compute the feasible product dimensions and sizes. A Matlab algorithm is developed to verify the feasibility of straight-line trajectory paths and to locate all valid towpreg segments in the workspace, with no focus on optimization. The algorithm is applied preliminary to a three-link planar arm; and a 6-dof Merlin robot is subsequently considered to verify the towpreg layouts in the three-dimensional space. The workspace is represented by the longest feasible segments and plotted on parallel two-dimensional planes. The analysis is extended to locate valid square areas with predetermined dimensions. The fabrication of isotropic circular coupons is then tested with two different compaction heads. The results allow the formulation of a geometric correlation between the end-effector dimensional measures and the orientation of the end-effector with respect to the towpreg segments. / Master of Science

When designing a ship, a designer often begins with "an idea" of what the ship might look like and what specifications the ship should meet. The multidisciplinary design optimization model is a tool that combines an analysis and an optimization process and uses a measure of merit to obtain what it infers to be the best design. All that the designer has to know is the range of values of certain design variables that confine the design within a lower and an upper bound. The designer then feeds the MDO model with any arbitrary design within the bounds and the model searches for the best design that minimizes or maximizes a measure of merit and also meets a set of structural and stability requirements. The model is multidisciplinary because the analysis process, which calculates the measure of merit and other performance parameters, can be a combination of sub-processes used in various fields of engineering. The optimization process can also be a variety of mathematical programming techniques depending on the type of the design problem. The container ship design problem is a combination of discreet and continuous sub-problems. But to avail the advantages of gradient-based optimization algorithms, the design problem is molded into a fully continuous problem. The efficiency and effectiveness with which an optimization process achieves the best design depends on how well the design problem is posed for the optimizer and how well that particular optimization algorithm tackles the type of design problems posed before it. This led the author to investigate the details of the analysis and the optimization process within the MDO model and make modifications to each of the processes, so that the two become more compatible towards achieving a better final design. Modifications made within the optimization algorithm were then used to develop a generalized modification method that can be used to improve any gradient-based optimization algorithm. / Master of Science

It is estimated that Periodontal Diseases effects up to 90% of the adult population. Given the complexity of the host environment, many factors contribute to expression of the disease. Age, Gender, Socioeconomic Status, Smoking Status, and Race/Ethnicity are all known risk factors, as well as a handful of known comorbidities. Certain vitamins and minerals have been shown to be protective for the disease, while some toxins and chemicals have been associated with an increased prevalence. The role of toxins, chemicals, vitamins, and minerals in relation to disease is believed to be complex and potentially modified by known risk factors. A large comprehensive dataset from 1999-2003 from the National Health and Nutrition Examination Survey (NHANES) contains full and partial mouth examinations on subjects for measurement of periodontal diseases as well as patient demographic information and approximately 150 environmental variables. In this dissertation, a Feasible Solution Algorithm (FSA) will be used to investigate statistical interactions of these various chemical and environmental variables related to periodontal disease. This sequential algorithm can be used on traditional statistical modeling methods to explore two and three way interactions related to the outcome of interest. FSA can also be used to identify unique subgroups of patients where periodontitis is most (or least) prevalent. In this dissertation, FSA is used to explore the NHANES data and suggest interesting relationships between the toxins, chemicals, vitamins, minerals and known risk factors that have not been previously identified.

In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.