Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214

Decision support system for multi-objective forest management: a study in the Queen Elizabeth National Forest Park in Scotland

SIGLEAvailable from British Library Document Supply Centre- DSC:D91694 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

An architectural framework for developing advanced integrated environmental monitoring systems

The environment has become a topic of great public and academic concern. Monitoring studies of environmental variables are needed to allow control strategies and policy-/decision-making to be applied effectively. This thesis presents an overview of the main issues and requirements for the capabilities of integration, flexibility and scalability for complex environmental monitoring applications. The scope and depth of the topics are considerable and are developing rapidly in line with new technology and computational techniques. The author proposes and designs an architectural framework to develop advanced integrated environmental monitoring systems (A-ITEMS), which feeds the requirements of complex environmental monitoring systems. Afterwards, in terms of the theoretical and technical investigation on the A-ITEMS, this thesis demonstrates the key ideas by implementing an Integrated Watershed Telemetry (IWT) system. The practical design and simulation implementation of the IWT system does show that the A-ITEMS has the significant flexibility and capability to adapt to complex environmental monitoring applications

Polyhedral+Dataflow Graphs

This research presents an intermediate compiler representation that is designed for optimization, and emphasizes the temporary storage requirements and execution schedule of a given computation to guide optimization decisions. The representation is expressed as a dataflow graph that describes computational statements and data mappings within the polyhedral compilation model. The targeted applications include both the regular and irregular scientific domains. The intermediate representation can be integrated into existing compiler infrastructures. A specification language implemented as a domain specific language in C++ describes the graph components and the transformations that can be applied. The visual representation allows users to reason about optimizations. Graph variants can be translated into source code or other representation. The language, intermediate representation, and associated transformations have been applied to improve the performance of differential equation solvers, or sparse matrix operations, tensor decomposition, and structured multigrid methods

An ontology-based approach towards coupling task and path planning for the simulation of manipulation tasks

This work deals with the simulation and the validation of complex manipulation tasks under strong geometric constraints in virtual environments. The targeted applications relate to the industry 4.0 framework; as up-to-date products are more and more integrated and the economic competition increases, industrial companies express the need to validate, from design stage on, not only the static CAD models of their products but also the tasks (e.g., assembly or maintenance) related to their Product Lifecycle Management (PLM). The scientific community looked at this issue from two points of view: - Task planning decomposes a manipulation task to be realized into a sequence of primitive actions (i.e., a task plan) - Path planning computes collision-free trajectories, notably for the manipulated objects. It traditionally uses purely geometric data, which leads to classical limitations (possible high computational processing times, low relevance of the proposed trajectory concerning the task to be performed, or failure); recent works have shown the interest of using higher abstraction level data. Joint task and path planning approaches found in the literature usually perform a classical task planning step, and then check out the feasibility of path planning requests associated with the primitive actions of this task plan. The link between task and path planning has to be improved, notably because of the lack of loopback between the path planning level and the task planning level: - The path planning information used to question the task plan is usually limited to the motion feasibility where richer information such as the relevance or the complexity of the proposed path would be needed; - path planning queries traditionally use purely geometric data and/or “blind” path planning methods (e.g., RRT), and no task-related information is used at the path planning level Our work focuses on using task level information at the path planning level. The path planning algorithm considered is RRT; we chose such a probabilistic algorithm because we consider path planning for the simulation and the validation of complex tasks under strong geometric constraints. We propose an ontology-based approach to use task level information to specify path planning queries for the primitive actions of a task plan. First, we propose an ontology to conceptualize the knowledge about the 3D environment in which the simulated task takes place. The environment where the simulated task takes place is considered as a closed part of 3D Cartesian space cluttered with mobile/fixed obstacles (considered as rigid bodies). It is represented by a digital model relying on a multilayer architecture involving semantic, topologic and geometric data. The originality of the proposed ontology lies in the fact that it conceptualizes heterogeneous knowledge about both the obstacles and the free space models. Second, we exploit this ontology to automatically generate a path planning query associated to each given primitive action of a task plan. Through a reasoning process involving the primitive actions instantiated in the ontology, we are able to infer the start and the goal configurations, as well as task-related geometric constraints. Finally, a multi-level path planner is called to generate the corresponding trajectory. The contributions of this work have been validated by full simulation of several manipulation tasks under strong geometric constraints. The results obtained demonstrate that using task-related information allows better control on the RRT path planning algorithm involved to check the motion feasibility for the primitive actions of a task plan, leading to lower computational time and more relevant trajectories for primitive actions

Predicting length of stay (LOS) in a hospital post-sugery

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe amount of time a patient stays in the hospital after a surgery has been an issue that hospital management faces, a longer stay in the recovery room involves a high cost to the hospital and consumes a lot of hospital resources, manpower and equipment. The amount of time is difficult to predict precisely since there are many external and internal factors that account for a longer or shorter stay and it is difficult for a team to consider all these factors and make this estimation manually. With the advancement of machine learning methods and models this prediction can be made automatically. The aim of this study was to create a predicting model that look at the patient data and the procedure data and predicts the amount of time the patient will stay after the surgery to make the current prediction of the length of stay by the hospital more accurate and compliment the current surgery scheduling and discharge system. To achieve the objective, a data mining approach was implemented. Python Language was used, with particular emphasis on Scikit-Learn, pandas and Seaborn packages. Tables from a relational database were processed and extracted to build a dataset. Exploratory data analysis was performed, and several model configurations were tested. The main differences that separate the models are outlier treatment, sampling techniques, feature scalers, feature engineering and type of algorithm – Linear Regression, Decision Trees Regressor, Multilayer Perceptron Regressor, Random Forest Regressor, Light Gradient Boosting Machine Regressor and Gradient Boosting Regressor. A total of 32993 hospital episodes were observed on this study. Out of these, 2006 were eliminated due to some data anomalies, namely, values that were wrong or impossible. The data was split in training and test data. Several model configurations were tested. The main differences that separate the models are outlier treatment, feature scalers, feature engineering and the type of algorithm. The best performing model had a score of 0.73 R2 which was obtained by using the Light Gradient Boosting Machine Regressor Algorithm using outlier removal, Robust Scaling and using all the features in the dataset