Heuristic simulation software

Presented at Meeting irrigation demands in a water-challenged environment: SCADA and technology: tools to improve production: a USCID water management conference held on September 28 - October 1, 2010 in Fort Collins, Colorado.Includes bibliographical references.A modern computer-based simulation tool in the form of a game for on-farm water management has been developed for application in training events for farmers, irrigators, irrigation extension specialists, and students. This training tool can be used to analyze both strategic and operational issues related to the management of on-farm water resources, and automatic analysis of the results to provide feedback to the trainees. It utilizes an interactive framework, thereby allowing the trainee (or player) to develop scenarios and test alternatives in a convenient, risk-free environment. It employs heuristic capabilities in a simulation approach for modeling all of the important aspects of on-farm water management that are essential to effective planning. The daily soil water balance, crop phenology, root development, and a seven-day weather forecast, can be monitored by the player throughout the simulated growing season. Different crop types, water delivery methods, and irrigation methods are made available to the player. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide potentially more challenging game play. Scoring and recommendations are provided at the end of the game, based on the management decisions made by the player

An intelligent modular real-time vision-based system for environment perception

A significant portion of driving hazards is caused by human error and disregard for local driving regulations; Consequently, an intelligent assistance system can be beneficial. This paper proposes a novel vision-based modular package to ensure drivers' safety by perceiving the environment. Each module is designed based on accuracy and inference time to deliver real-time performance. As a result, the proposed system can be implemented on a wide range of vehicles with minimum hardware requirements. Our modular package comprises four main sections: lane detection, object detection, segmentation, and monocular depth estimation. Each section is accompanied by novel techniques to improve the accuracy of others along with the entire system. Furthermore, a GUI is developed to display perceived information to the driver. In addition to using public datasets, like BDD100K, we have also collected and annotated a local dataset that we utilize to fine-tune and evaluate our system. We show that the accuracy of our system is above 80% in all the sections. Our code and data are available at https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-PerceptionComment: Accepted in NeurIPS 2022 Workshop on Machine Learning for Autonomous Drivin

A methodology for the design, implementation and evaluation of intelligent systems with an application to critical care medicine

This thesis illustrates the technology required to provide a new generation of clinical instrumentation systems for critical care medicine. This advance in measurement science is gained from the use of a knowledge-based component able to process information as well as data. To implement a clinical information system using knowledge-based technology requires prior knowledge of human and computer-based activity within the critical domain. A historical perspective is given to both of these topics which reflects the genesis of current practice. The application area is introduced by investigating a control system approach to managing patients who require ventilatory therapy. It was found that no current methodology is wholly appropriate when a knowledge-based component is included in the technological paradigm. Therefore, a novel methodology for system design, implementation and evaluation is proposed, and its utility tested in the aforementioned application domain. The detailed processes involved in the evolution of a prototype system which aids the clinical user in the art of ventilatory therapy are shown. Three levels of machine intelligence are shown to be required, based on: context-sensitive deterministic mechanisms; pattern cognition; and decision support elements. A wider discussion of the important points raised in the practical use of the methodology focuses upon the philosophical basis of clinical information systems and the processes of knowledge elicitation, knowledge representation and intelligent system evaluation

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

Evaluation of lntelligent Medical Systems

This thesis presents novel, robust, analytic and algorithmic methods for calculating Bayesian posterior intervals of receiver operating characteristic (ROC) curves and confusion matrices used for the evaluation of intelligent medical systems tested with small amounts of data. Intelligent medical systems are potentially important in encapsulating rare and valuable medical expertise and making it more widely available. The evaluation of intelligent medical systems must make sure that such systems are safe and cost effective. To ensure systems are safe and perform at expert level they must be tested against human experts. Human experts are rare and busy which often severely restricts the number of test cases that may be used for comparison. The performance of expert human or machine can be represented objectively by ROC curves or confusion matrices. ROC curves and confusion matrices are complex representations and it is sometimes convenient to summarise them as a single value. In the case of ROC curves, this is given as the Area Under the Curve (AUC), and for confusion matrices by kappa, or weighted kappa statistics. While there is extensive literature on the statistics of ROC curves and confusion matrices they are not applicable to the measurement of intelligent systems when tested with small data samples, particularly when the AUC or kappa statistic is high. A fundamental Bayesian study has been carried out, and new methods devised, to provide better statistical measures for ROC curves and confusion matrices at low sample sizes. They enable exact Bayesian posterior intervals to be produced for: (1) the individual points on a ROC curve; (2) comparison between matching points on two uncorrelated curves; . (3) the AUC of a ROC curve, using both parametric and nonparametric assumptions; (4) the parameters of a parametric ROC curve; and (5) the weight of a weighted confusion matrix. These new methods have been implemented in software to provide a powerful and accurate tool for developers and evaluators of intelligent medical systems in particular, and to a much wider audience using ROC curves and confusion matrices in general. This should enhance the ability to prove intelligent medical systems safe and effective and should lead to their widespread deployment. The mathematical and computational methods developed in this thesis should also provide the basis for future research into determination of posterior intervals for other statistics at small sample sizes

Health Condition Evolution for Effective Use of Electronic Records: Knowledge Representation, Acquisition, and Reasoning

Smart City initiatives aim to enhance the effective management of resources while providing quality services to citizens. Central to these initiatives is the use of large-scale datasets that enable intelligent analytics and reasoning components in support of resource optimisation and service provision. Recently, there has been a growing interest in aspects of smart living, particularly due to the increasing adoption and use of Electronic Health Records (EHR). A Smart City can introduce intelligent systems to support the usage of EHR to improve emergency response services. For instance, data derived from EHR is used in primary emergency care, as a component of emergency decision support systems and for monitoring public health. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens’ privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and the large amount of available information represents a significant barrier. This thesis investigates the use of EHR for deriving useful information about people requiring assistance during an emergency, focusing on making rich data accessible to emergency services while minimising the amount of exchanged information. To perform this task, an intelligent system needs to estimate the probability that a potentially relevant condition mentioned in a health record is still valid at the time of the emergency. During our research work, we followed a knowledge engineering approach and developed the required knowledge components to support the intelligent delivery of relevant health information about people involved in an emergency situation. These components, which include a knowledge component for representation and reasoning, and a novel knowledge base modelling the evolution of a large number of health conditions, form the basis of CONRAD, a system which is able to support effectively decision-making in an emergency scenario

USCID water management conference

Presented at Meeting irrigation demands in a water-challenged environment: SCADA and technology: tools to improve production: a USCID water management conference held on September 28 - October 1, 2010 in Fort Collins, Colorado.Includes bibliographical references.The Colorado Satellite-Linked Water Resources Monitoring System: 25 years later -- Using state water law for efficient water use in the West -- On-farm strategies for deficit or limited irrigation to maximize operational profit potential in Colorado's South Platte Basin -- Economics of groundwater management alternatives in the Republican Basin -- Effects of policies governing water reuse on agricultural crops -- Flow calibration of the Bryan Canal radial gate at the United Irrigation District -- Considering canal pool resonance in controller design -- Synthetic canal lining evaluation project -- South Platte Ditch Company: demonstration flow monitoring and data collection project -- The case for ditch-wide water rights analysis in Colorado -- Bore wells: a boon for tail end users -- Irrigation efficiency and water users' performance in water management: a case study on the Heran distributary, Sanghar, Sindh, Pakistan -- Initiating SCADA projects in irrigation districts -- Use of GIS as a real time decision support system for irrigation districts -- Interaction of Advanced Scientific Irrigation Management (ASIM) with I-SCADA system for efficient and sustainable production of fiber on 10,360 hectares -- Improving irrigation system performance in the Middle Rio Grande through scheduled water delivery -- Cost-effective SCADA development for irrigation districts: a Nebraska case study -- Accomplishments from a decade of SCADA implementation in Idaho's Payette Valley -- Critical success factors for large scale automation experiences from 10,000 gates -- Mapping ET in southeastern Colorado using a surface aerodynamic temperature model -- Alfalfa crop coefficients developed using a weighing lysimeter in southeast Colorado -- Turfgrass ET from small lysimeters in northeast Colorado -- Monitoring turf water status with infrared thermometry -- Training tool for on-farm water management using heuristic simulation software -- Water production functions for high plains crops -- Assessment of economic and hydrologic impacts of reduced surface water supply for irrigation via remote sensing -- Developing corn regional crop coefficients using a satellite-based energy balance model (ReSET) in the South Platte River area of Colorado