Due to the development of business process reengineering¡]BPR¡^ and Internet, global logistics management and supply chain management¡]SCM¡^ have been unresistible trend for companies. Supply chain is a complex hierarchical network which links members from upstream to downstream industries. Enterprise can improve process through these linkages to cut down cost or shorten its response time to market. As the scope of inter-organizational activities enlarges, there exist many uncertainty factors in the supply chain network. The existence of such factor not only reduces the performance of supply chain as a whole but also the competitive advantage of individual companies in the supply chain. In other words, understanding and controlling SCM uncertainty factors will mitigate their deleterious impact. The concern of past research only addressed a single dimension of supply chain uncertainty, which is either focused upon production process on the manufacturing side or upon product characteristics on the demand side. This research proposed supply chain uncertainty constructs including demand, manufacturing, and supply aspects, which was based upon the perspective of leading company in a supply chain.This set of constructs was based upon literature survey and examination of focus group formed by academic and industry experts. Exploratory factor analysis was called upon to find out representative factors and correlation analysis was conducted to verify independence of the constructs. The research results indicated that channel, product characteristics, demand forcast, and demand change were included in demand uncertainty. Especially, the channel construct is the most representative one. In the supply aspect, supplier¡¦s ability, type of procurement, material characteristics and stable relationship with suppliers were included in supply uncertainty. Especially, the supplier¡¦s ability is the most representative. In the manufacturing aspect, product complexity, process complexity and engineering change were included in manufacturing uncertainty. Especially, the product complexity construct is the most representative one. This research has established supply chain uncertainty factors. It will be helpful for companies using this scale to evaluate their uncertainty situations. Comparision among heterogeneous industries is also possible because this scale can be applied to discover their differences.

The main idea of this thesis is to formulate the smooth variable structure filter (SVSF) for target tracking applications in the presence of measurement origin uncertainty. Tracking, by definition is the recursive estimation of the states of an unknown target from indirect, inaccurate and uncertain measurements. The measurement origin uncertainty introduces the data association problem to the tracking system. The SVSF estimation strategy was first presented in 2007. This filter is based on sliding mode concepts formulated in a predictor-corrector form. Essentially, the SVSF uses an existence subspace and smoothing boundary layer to bind the estimated state trajectory to within a subspace around the true trajectory. The SVSF is demonstrated to be robust to modeling uncertainties and provide extra measures of performance such as magnitude of the chattering signal. Therefore, with respect to specific nature of car tracking problems that involves modeling uncertainty, it was hypothesized that a robust estimation strategy such as the SVSF, would improve the performance of the tracking system and give more robust tracking results. Also, having the extra information provided by the SVSF strategy, i.e. the chattering magnitude signal, would lead to algorithms that could better account for measurement origin uncertainty in the context of the data association process. Further to these hypotheses, this research has focused on investigating the performance of the SVSF in the target tracking problems, advancing the development of the SVSF, and employing its characteristics to deal with data association problems. The performance of the SVSF, in its current form, can be improved when there is fewer measurements than states by using its error covariance in target tracking. As the first contribution in this research, the SVSF is formulated in the context of target tracking in clutter and combined with data association algorithms, resulting in the SVSF-based probabilistic data association (PDA) and joint probabilistic data association (JPDA) for non-maneuvering and maneuvering targets. The results are promising in the tracking scenarios with modeling uncertainties. Therefore, the thesis is then expanded by generalizing the covariance of the SVSF for the cases where the number of measurements is less than the number of states. The generalized covariance formulation is then used to derive a generalized variable boundary layer (GVBL) SVSF. This new derivation gives an estimation method that is optimal in the MMSE sense and in the meantime preserves the robustness of the SVSF. The proposed algorithm improves the performance measures and makes a more reliable tracking algorithm. This thesis explores the hypothesis that multiple target tracking performance can be substantially improved by including chattering information from SVSF-based filtering in the data association method. A Bayesian framework is used to formulate a new set of augmented association probabilities which include the chattering information. The simulation and experimental results demonstrate that the proposed augmented probabilistic data association improves the performance of the tracking system including maneuvering cars, in particular for highly cluttered environments. The derived methods are applied on simulations and also on real data from an experimental setup. This thesis is made up of a compilation of papers that include three conference papers and three journal papers. / Thesis / Doctor of Philosophy (PhD)

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves. This has led oil companies to develop smaller/marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective soluti8ons of on-line continuous multiphase flow measurement. The pattern recognition approach for clamp-on multiphase measurement employed in this research study provides one means for meeting this need. Cont/d.