Univ., Diss.--Karlsruhe, 2005.

Halle, Wittenberg, Universiẗat, Diss., 2003.

Computer animated films require collaboration: blending artistic concept with technical skill, meeting budget constraints and adhering to deadlines. The path which production follows from initial idea to finished product is known as the pipeline. The purpose of this thesis is to collect, study and share information regarding production pipeline practices and to derive a conceptual definition. Research focused on selected companies in the United States which have produced at least one feature-length computer generated film and continue to produce them. The key finding of this thesis is a conceptual definition of digital production pipelines: A digital production pipeline must, by definition, utilize digital computing hardware and software to facilitate human work and collaboration for the overarching purpose of producing content for film. The digital production pipeline is not a structure, but rather a malleable set of components which can be arranged, configured, and adapted into new structures as needed. These malleable components are human groups with assigned task domains, and digital hardware and software systems. The human groups are normally referred to as departments or teams. The digital hardware and software systems are operating systems, software tools and applications, networks, processors, and storage. The digital production pipeline is the synergy of these two types of components into adaptable systems and structures.

Computer animated films require collaboration: blending artistic concept with technical skill, meeting budget constraints and adhering to deadlines. The path which production follows from initial idea to finished product is known as the pipeline. The purpose of this thesis is to collect, study and share information regarding production pipeline practices and to derive a conceptual definition. Research focused on selected companies in the United States which have produced at least one feature-length computer generated film and continue to produce them. The key finding of this thesis is a conceptual definition of digital production pipelines: A digital production pipeline must, by definition, utilize digital computing hardware and software to facilitate human work and collaboration for the overarching purpose of producing content for film. The digital production pipeline is not a structure, but rather a malleable set of components which can be arranged, configured, and adapted into new structures as needed. These malleable components are human groups with assigned task domains, and digital hardware and software systems. The human groups are normally referred to as departments or teams. The digital hardware and software systems are operating systems, software tools and applications, networks, processors, and storage. The digital production pipeline is the synergy of these two types of components into adaptable systems and structures.

Darmstadt, Techn. Universiẗat, Diss., 2000.

Zugl.: Aachen, Techn. Hochsch., Diss., 2009.

This dissertation defines the operational problems of, and develops solution methodologies for, a distribution of multiple products into oil pipeline subject to delivery time-windows constraints. A multiple-product oil pipeline is a pipeline system composing of pipes, pumps, valves and storage facilities used to transport different types of liquids. Typically, products delivered by pipelines are petroleum of different grades moving either from production facilities to refineries or from refineries to distributors. Time-windows, which are generally used in logistics and scheduling areas, are incorporated in this study. The distribution of multiple products into oil pipeline subject to delivery time-windows is modeled as multicommodity network flow structure and mathematically formulated. The main focus of this dissertation is the investigation of operating issues and problem complexity of single-source pipeline problems and also providing solution methodology to compute input schedule that yields minimum total time violation from due delivery time-windows. The problem is proved to be NP-complete. The heuristic approach, a reversed-flow algorithm, is developed based on pipeline flow reversibility to compute input schedule for the pipeline problem. This algorithm is implemented in no longer than O(T*E) time. This dissertation also extends the study to examine some operating attributes and problem complexity of multiple-source pipelines. The multiple-source pipeline problem is also NP-complete. A heuristic algorithm modified from the one used in single-source pipeline problems is introduced. This algorithm can also be implemented in no longer than O(T*E) time. Computational results are presented for both methodologies on randomly generated problem sets. The computational experience indicates that reversed-flow algorithms provide good solutions in comparison with the optimal solutions. Only 25% of the problems tested were more than 30% greater than optimal values and approximately 40% of the tested problems were solved optimally by the algorithms.

The problem of Third Party Interference (TPI) on Oil and Gas Pipelines is on the rise across the world. TPI is not only common in developing countries but is now occasionally experienced in developed countries including Germany and the UK. The risks posed by these third-party activities on Oil and Gas pipelines are enormous and could be measured in terms of financial costs, environmental damages as well as health and safety implications. The quest for an end to these malicious activities has triggered a lot of studies into the root causes of pipeline TPI, other causes of pipeline failure, risks associated with pipeline failure and their mitigation measures. However, despite the significance of the effects of TPI, very little has been done to proffer an enduring solution through research. This research therefore aims at developing a framework for integrated oil and gas pipeline monitoring and incident mitigation system through integration of various wireless sensors for effective monitoring of oil and gas pipelines. Having identified the existing gaps in literature as lack of reliable, accurate and standard method for oil and gas pipeline risk assessment model, the study undertook a quantitative approach to develop an effective Quantitative Risk Assessment (QRA) model for pipelines. The QRA model developed benchmarks pipeline risk assessment and gives the parameters with which standard QRA could be measured. The research findings indicate that risk associated with Nigerian Pipeline system is in the intolerable region whereas TPI is an increasing menace across the globe. Further findings show that Support Vector Machine (SVM) gave the best performance with 91.2% accuracy while Neural Networks (NN) and Decision Tree (DT) gave 63% and 57% accuracies respectively in terms of pipeline failure mode prediction accuracies. It was recommended that operators should draw out Pipeline Integrity Management (PIM) programs and store pipeline data in a format that captures number of fatalities, property damages and costs as well as volume of oil or gas spilled to ensure that accurate data is obtainable for improved PIM. In conclusion, having achieved its aim and objectives evidenced by the framework, model developed, and the recommendations presented, the research has contributed in no small measure to providing a solution to pipeline incidences.

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