A framework for flexible integration in robotics and its applications for calibration and error compensation

Robotics has been considered as a viable automation solution for the aerospace industry to address manufacturing cost. Many of the existing robot systems augmented with guidance from a large volume metrology system have proved to meet the high dimensional accuracy requirements in aero-structure assembly. However, they have been mainly deployed as costly and dedicated systems, which might not be ideal for aerospace manufacturing having low production rate and long cycle time. The work described in this thesis is to provide technical solutions to improve the flexibility and cost-efficiency of such metrology-integrated robot systems. To address the flexibility, a software framework that supports reconfigurable system integration is developed. The framework provides a design methodology to compose distributed software components which can be integrated dynamically at runtime. This provides the potential for the automation devices (robots, metrology, actuators etc.) controlled by these software components to be assembled on demand for various assembly applications. To reduce the cost of deployment, this thesis proposes a two-stage error compensation scheme for industrial robots that requires only intermittent metrology input, thus allowing for one expensive metrology system to be used by a number of robots. Robot calibration is employed in the first stage to reduce the majority of robot inaccuracy then the metrology will correct the residual errors. In this work, a new calibration model for serial robots having a parallelogram linkage is developed that takes into account both geometric errors and joint deflections induced by link masses and weight of the end-effectors. Experiments are conducted to evaluate the two pieces of work presented above. The proposed framework is adopted to create a distributed control system that implements calibration and error compensation for a large industrial robot having a parallelogram linkage. The control system is formed by hot-plugging the control applications of the robot and metrology used together. Experimental results show that the developed error model was able to improve the 3 positional accuracy of the loaded robot from several millimetres to less than one millimetre and reduce half of the time previously required to correct the errors by using only the metrology. The experiments also demonstrate the capability of sharing one metrology system to more than one robot

Design and implementation of a modular controller for robotic machines

This research focused on the design and implementation of an Intelligent Modular Controller (IMC) architecture designed to be reconfigurable over a robust network. The design incorporates novel communication, hardware, and software architectures. This was motivated by current industrial needs for distributed control systems due to growing demand for less complexity, more processing power, flexibility, and greater fault tolerance. To this end, three main contributions were made. Most distributed control architectures depend on multi-tier heterogeneous communication networks requiring linking devices and/or complex middleware. In this study, first, a communication architecture was proposed and implemented with a homogenous network employing the ubiquitous Ethernet for both real-time and non real-time communication. This was achieved by a producer-consumer coordination model for real-time data communication over a segmented network, and a client-server model for point-to-point transactions. The protocols deployed use a Time-Triggered (TT) approach to schedule real-time tasks on the network. Unlike other TT approaches, the scheduling mechanism does not need to be configured explicitly when controller nodes are added or removed. An implicit clock synchronization technique was also developed to complement the architecture. Second, a reconfigurable mechanism based on an auto-configuration protocol was developed. Modules on the network use this protocol to automatically detect themselves, establish communication, and negotiate for a desired configuration. Third, the research demonstrated hardware/software co-design as a contribution to the growing discipline of mechatronics. The IMC consists of a motion controller board designed and prototyped in-house, and a Java microcontroller. An IMC is mapped to each machine/robot axis, and an additional IMC can be configured to serve as a real-time coordinator. The entire architecture was implemented in Java, thus reinforcing uniformity, simplicity, modularity, and openness. Evaluation results showed the potential of the flexible controller to meet medium to high performance machining requirements

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

CONVR 2011 : Proceedings of the 11th International Conference on Construction Applications of Virtual Reality

Proceedings of the 11th International Conference on Construction Applications of Virtual Realit

Banking theory based distributed resource management and scheduling for hybrid cloud computing

Cloud computing is a computing model in which the network offers a dynamically scalable service based on virtualized resources. The resources in the cloud environment are heterogeneous and geographically distributed. The user does not need to know how to manage those who support the cloud computing infrastructure. From the view of cloud computing, all hardware, software and networks are resources. All of the resources are dynamically scalable on demand. It can offer a complete service for the user even when these service resources are geographically distributed. The user pays for only what they use (pay-per-use). Meanwhile, the transaction environment will decide how to manage resource usage and cost, because all of the transactions have to follow the rule of the market. How to manage and schedule resources effectively becomes a very important part of cloud computing, and how to setup a new framework to offer a reliable, safe and executable service are very important issues. The approach herein is a new contribution to cloud computing. It not only proposes a hybrid cloud computing model based on banking theory to manage transactions among all participants in the hybrid cloud computing environment, but also proposes a "Cloud Bank" framework to support all the related issues. There are some of technology and theory been used to offer contributions as below: 1. This thesis presents an Optimal Deposit-loan Ratio Theory to adjust the pricing between the resource provider and resource consumer to realize both benefit maximization and cloud service optimization for all participants. 2. It also offers a new pricing schema using Centralized Synchronous Algorithm and Distributed Price Adjustment Algorithm to control all lifecycles and dynamically price all resources. 3. Normally, commercial banks apply four factors mitigation and to predict the risk: Probability of Default, Loss Given Default, Exposure at Default and Maturity. This thesis applies Probability of Default model of credit risk to forecast the safety supply of the resource. The Logistic Regression Model been used to control some factors in resource allocation. At the same time, the thesis uses Multivariate Statistical analysis to predict risk. 4. The Cloud Bank model applies an improved Pareto Optimality Algorithm to build its own scheduling system. 5. In order to archive the above purpose, this thesis proposes a new QoS-based SLA-CBSAL to describe all the physical resource and the processing of thread. In order to support all the related algorithms and theories, the thesis uses the CloudSim simulation tools give a test result to support some of the Cloud Bank management strategies and algorithms. The experiment shows us that the Cloud Bank Model is a new possible solution for hybrid cloud computing. For future research direction, the author will focus on building real hybrid cloud computing and simulate actual user behaviour in a real environment, and continue to modify and improve the feasibility and effectiveness of the project. For the risk mitigation and prediction, the risks can be divided into the four categories: credit risk, liquidity risk, operational risk, and other risks. Although this thesis uses credit risk and liquidity risk research, in a real trading environment operational risks and other risks exist. Only through improvements to the designation of all risk types of analysis and strategy can our Cloud Bank be considered relatively complete

1995 BRAC Commission

Indian Wells Valley - Tactical Warfare Systems; Panama City, FL - Community Growth Capacity Study; Puget Sound, WA - Delegation. Box 110, L-031

Decoding Legalese Without Borders: Multilingual Evaluation of Language Models on Long Legal Texts

Pretrained transformers have sparked an explosion of research in the field of Natural Language Processing (NLP). Scaling up language models based on the transformer architecture in terms of size, compute, and data led to impressive emergent capabilities that were considered unattainable in such a brief span, a mere three years ago, prior to the launch of GPT-3. These advances catapulted the previously niche field of legal NLP into the mainstream, at the latest, with GPT-4 passing the bar. Many products based on GPT-4 and other large language models are entering the market at an increasing pace, many of those targeting the legal field. This dissertation makes contributions in two key areas within Natural Language Processing (NLP) focused on legal text: resource curation and detailed model analysis. First, we curate an extensive set of multilingual legal datasets, train a variety of language models on these, and establish comprehensive benchmarks for evaluating Large Language Models (LLMs) in the legal domain. Second, we conduct a multidimensional analysis of model performance, focusing on metrics like explainability and calibration in the context of Legal Judgment Prediction. We introduce novel evaluation frameworks and find that while our trained models exhibit high performance and better calibration than human experts, they do not necessarily offer improved explainability. Furthermore, we investigate the feasibility of re-identification in anonymized legal texts, concluding that large-scale re-identification using LLMs is currently unfeasible. For future work, we propose exploring domain adaptation and instruction tuning to enhance language model performance on legal benchmarks, while also advocating for a detailed examination of dataset overlaps and model interpretability. Additionally, we emphasize the need for dataset extension to unexplored legal tasks and underrepresented jurisdictions, aiming for a more comprehensive coverage of the global legal landscape in NLP resources