Circuit simulation using distributed waveform relaxation techniques

Simulation plays an important role in the design of integrated circuits. Due to high costs and large delays involved in their fabrication, simulation is commonly used to verify functionality and to predict performance before fabrication. This thesis describes analysis, implementation and performance evaluation of a distributed memory parallel waveform relaxation technique for the electrical circuit simulation of MOS VLSI circuits. The waveform relaxation technique exhibits inherent parallelism due to the partitioning of a circuit into a number of sub-circuits. These subcircuits can be concurrently simulated on parallel processors. Different forms of parallelism in the direct method and the waveform relaxation technique are studied. An analysis of single queue and distributed queue approaches to implement parallel waveform relaxation on distributed memory machines is performed and their performance implications are studied. The distributed queue approach selected for exploiting the coarse grain parallelism across sub-circuits is described. Parallel waveform relaxation programs based on Gauss-Seidel and Gauss-Jacobi techniques are implemented using a network of eight Transputers. Static and dynamic load balancing strategies are studied. A dynamic load balancing algorithm is developed and implemented. Results of parallel implementation are analyzed to identify sources of bottlenecks. This thesis has demonstrated the applicability of a low cost distributed memory multi-computer system for simulation of MOS VLSI circuits. Speed-up measurements prove that a five times improvement in the speed of calculations can be achieved using a full window parallel Gauss-Jacobi waveform relaxation algorithm. Analysis of overheads shows that load imbalance is the major source of overhead and that the fraction of the computation which must be performed sequentially is very low. Communication overhead depends on the nature of the parallel architecture and the design of communication mechanisms. The run-time environment (parallel processing framework) developed in this research exploits features of the Transputer architecture to reduce the effect of the communication overhead by effectively overlapping computation with communications, and running communications processes at a higher priority. This research will contribute to the development of low cost, high performance workstations for computer-aided design and analysis of VLSI circuits

Dynamic Response Optimization of Vehicles through Efficient Multibody Formulations and Automatic Differentiation Techniques

El diseño y desarrollo de sistemas de suspensión para vehículos se basa cada día más en el diseño por ordenador y en herramientas de análisis por ordenador, las cuales permiten anticipar problemas y resolverlos por adelantado. El comportamiento y las características dinámicas se calculan con precisión, bajo coste, y recursos y tiempos de cálculo reducidos. Sin embargo, existe una componente iterativa en el proceso, que requiere la definición manual de diseños a través de técnicas “prueba y error”. Esta Tesis da un paso hacia el desarrollo de un entorno de simulación eficiente capaz de simular, analizar y evaluar diseños de suspensiones vehiculares, y de mejorarlos hacia la solución optima mediante la modificación de los parámetros de diseño. La modelización mediante sistemas multicuerpo se utiliza aquí para desarrollar un modelo de autocar con 18 grados de libertad, de manera detallada y eficiente. La geometría y demás características de la suspensión se ajustan a las del vehículo real, así como los demás parámetros del modelo. Para simular la dinámica vehicular, se utiliza una formulación multicuerpo moderna y eficiente basada en las ecuaciones de Maggi, a la que se ha incorporado un visor 3D. Así, se consigue simular maniobras vehiculares en tiempos inferiores al tiempo real. Una vez que la dinámica está disponible, los análisis de sensibilidad son cruciales para una optimización robusta y eficiente. Para ello, se presenta una técnica matemática que permite derivar las variables dinámicas dentro de la formulación, de forma algorítmica, general, con la precisión de la maquina, y razonablemente eficiente: la diferenciación automática. Este método propaga las derivadas con respecto a las variables de diseño a través del código informático y con poca intervención del usuario. En contraste con otros enfoques en la bibliografía, generalmente particulares y limitados, se realiza una comparación de librerías, se desarrolla una formulación híbrida directa-automática para el cálculo de sensibilidades, y se presentan varios ejemplos reales. Finalmente, se lleva a cabo la optimización de la respuesta dinámica del vehículo citado. Se analizan cuatro tipos distintos de optimización: identificación de parámetros, optimización de la maniobrabilidad, optimización del confort y optimización multi-objetivo, todos ellos aplicados al diseño del autocar. Además de resultados analíticos y gráficos, se incluyen algunas consideraciones acerca de la eficiencia. En resumen, se mejora el comportamiento dinámico de vehículos por medio de modelos multicuerpo y de técnicas de diferenciación automática y optimización avanzadas, posibilitando un ajuste automático, preciso y eficiente de los parámetros de diseño. ABSTRACT Each day, the design and development of vehicle suspension systems relies more on computer-aided design and computer-aided engineering tools, which allow anticipating the problems and solving them ahead of time. Dynamic behavior and characteristics are thus simulated accurately and inexpensively with moderate computational times and resources. There is, however, an iterative component in the process, which involves the manual definition of designs in a trialand-error manner. This Thesis takes a step towards the development of an efficient simulation framework capable of simulating, analyzing and evaluating vehicle suspension designs, and automatically improving them by varying the design parameters towards the optimal solution. The multibody systems approach is hereby used to model a three-dimensional 18-degrees-of-freedom coach in a comprehensive yet efficient way. The suspension geometry and characteristics resemble the ones from the real vehicle, as do the rest of vehicle parameters. In order to simulate vehicle dynamics, an efficient, state-of-the-art multibody formulation based on Maggi’s equations is employed, and a three-dimensional graphics viewer is developed. As a result, vehicle maneuvers can be simulated faster than real-time. Once the dynamics are ready, a sensitivity analysis is crucial for a robust optimization. To that end, a mathematical technique is introduced, which allows differentiating the dynamic variables within the multibody formulation in a general, algorithmic, accurate to machine precision, and reasonably efficient way: automatic differentiation. This method propagates the derivatives with respect to the design parameters throughout the computer code, with little user interaction. In contrast with other attempts in the literature, mostly not generalpurpose, a benchmarking of libraries is carried out, a hybrid direct-automatic differentiation approach for the computation of sensitivities is developed, and several real-life examples are analyzed. Finally, a design optimization process of the aforementioned vehicle is carried out. Four different types of dynamic response optimization are presented: parameter identification, handling optimization, ride comfort optimization and multi-objective optimization; all of which are applied to the design of the coach example. Together with analytical and visual proof of the results, efficiency considerations are made. In summary, the dynamic behavior of vehicles is improved by using the multibody systems approach, along with advanced differentiation and optimization techniques, enabling an automatic, accurate and efficient tuning of design parameters

Resource optimization and dynamic state management in a collaborative virtual environment.

Yim-Pan Chui.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 126-132).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction to Collaborative Virtual Environments --- p.1Chapter 1.2 --- Barriers to Resource Management and Optimization --- p.3Chapter 1.3 --- Thesis Contributions --- p.5Chapter 1.4 --- Application of this Research Work --- p.6Chapter 1.5 --- Thesis Organization --- p.6Chapter 2 --- Resource Optimization - Intelligent Server Partitioning --- p.9Chapter 2.1 --- Introduction --- p.9Chapter 2.2 --- Server Partitioning --- p.13Chapter 2.2.1 --- Related Works --- p.15Chapter 2.2.2 --- Global Optimization Approaches --- p.17Chapter 2.3 --- Hybrid Genetic Algorithm Paradigm --- p.17Chapter 2.3.1 --- Drawbacks of traditional GA --- p.18Chapter 2.3.2 --- Problem Modeling --- p.19Chapter 2.3.3 --- Discussion --- p.24Chapter 2.4 --- Results --- p.25Chapter 2.5 --- Concluding Remarks --- p.28Chapter 3 --- Dynamic State Management - Dead Reckoning of Attitude --- p.32Chapter 3.1 --- Introduction to Dynamic State Management --- p.32Chapter 3.2 --- The Dead Reckoning Approach --- p.35Chapter 3.3 --- Attitude Dead Reckoning by Quaternion --- p.37Chapter 3.3.1 --- Modeling of the Paradigm --- p.38Chapter 3.3.2 --- Prediction Step --- p.39Chapter 3.3.3 --- Convergence Step --- p.40Chapter 3.3.4 --- Overall Algorithm --- p.46Chapter 3.4 --- Results --- p.47Chapter 3.5 --- Conclusion --- p.51Chapter 4 --- Polynomial Attitude Extrapolation --- p.52Chapter 4.1 --- Introduction --- p.52Chapter 4.2 --- Related Works on Kalman Filtering --- p.53Chapter 4.3 --- Historical Propagation of Quaternion --- p.54Chapter 4.3.1 --- Cumulative Extrapolation --- p.54Chapter 4.3.2 --- Method I. Vandemonde Approach --- p.55Chapter 4.3.3 --- Method II. Lagrangian Approach --- p.58Chapter 4.4 --- History-Based Attitude Management --- p.60Chapter 4.4.1 --- Multi-order Prediction --- p.60Chapter 4.4.2 --- Adaptive Attitude Convergence --- p.63Chapter 4.4.3 --- Overall Algorithm --- p.67Chapter 4.5 --- Results --- p.69Chapter 4.6 --- Conclusion --- p.77Chapter 5 --- Forward Difference Approach on State Estimation --- p.78Chapter 5.1 --- Introduction --- p.78Chapter 5.2 --- Positional Forward Differencing --- p.79Chapter 5.3 --- Forward Difference on Quaternion Space --- p.80Chapter 5.3.1 --- Attitude Forward Differencing --- p.83Chapter 5.3.2 --- Trajectory Blending --- p.84Chapter 5.4 --- State Estimation --- p.86Chapter 5.5 --- Computational Efficiency --- p.87Chapter 5.6 --- Results --- p.88Chapter 5.7 --- Conclusion --- p.96Chapter 6 --- Predictive Multibody Kinematics --- p.98Chapter 6.1 --- Introduction --- p.98Chapter 6.2 --- Dynamic Management of Multibody System --- p.100Chapter 6.2.1 --- Multibody Representation --- p.100Chapter 6.2.2 --- Paradigm Overview --- p.101Chapter 6.3 --- Motion Estimation by Joint Extrapolation --- p.102Chapter 6.3.1 --- Individual Joint Extrapolation --- p.102Chapter 6.3.2 --- Forward Propagation of Joint State --- p.104Chapter 6.3.3 --- Pose Correction --- p.107Chapter 6.4 --- Limitations on Predictive Articulated State Management --- p.108Chapter 6.5 --- Implementation and Results --- p.109Chapter 6.6 --- Conclusion --- p.112Chapter 7 --- Complete System Architecture --- p.113Chapter 7.1 --- Server Cluster Model --- p.113Chapter 7.1.1 --- Peer-Server Systems --- p.114Chapter 7.1.2 --- Server Hierarchies --- p.114Chapter 7.2 --- Multi-Level Resource Management --- p.115Chapter 7.3 --- Aggregation of State Updates --- p.116Chapter 7.4 --- Implementation Issues --- p.117Chapter 7.4.1 --- Medical Visualization --- p.117Chapter 7.4.2 --- Virtual Walkthrough Application --- p.118Chapter 7.5 --- Conclusion --- p.119Chapter 8 --- Conclusions and Future directions --- p.121Chapter 8.1 --- Conclusion --- p.121Chapter 8.2 --- Future Research Directions --- p.122Chapter A --- Quaternion Basis --- p.124Chapter A.1 --- Basic Quaternion Mathematics --- p.124Chapter A.2 --- The Exponential and Logarithmic Maps --- p.125Bibliography --- p.12

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria

Higher level techniques for the artistic rendering of images and video

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