Current Search:  Fuzzy Logic (x)

View All Items

  • CSV Spreadsheet
(1 - 3 of 3)
Development of an Automated Method for Identification of Wet and Dry Channel Segments Using LiDAR Data and Fuzzy Logic Cluster Analysis
Title
Development of an Automated Method for Identification of Wet and Dry Channel Segments Using LiDAR Data and Fuzzy Logic Cluster Analysis.
Creator
Rowney, Chris, Wang, Dingbao, Medeiros, Stephen, Kibler, Kelly, University of Central Florida
Abstract / Description
Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In...
Show moreResearch into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area.Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.
Show less
Date Issued
2015
Identifier
CFE0006053, ucf:50975
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006053
A Framework for Quantifying and Managing Overcrowding in Healthcare Facilities
Title
A Framework for Quantifying and Managing Overcrowding in Healthcare Facilities.
Creator
Albar, Abdulrahman, Elshennawy, Ahmad, Rabelo, Luis, Lee, Gene, Rahal, Ahmad, University of Central Florida
Abstract / Description
Emergency Departments (EDs) represent a crucial component of any healthcare infrastructure. In today's world, healthcare systems face growing challenges in delivering efficient and time-sensitive emergency care services to communities. Overcrowding within EDs represents one of the most significant challenges for healthcare quality that adversely impacts clinical outcomes, patient safety, and overall satisfaction. Research in this area has resulted in creating several ED crowding indices, such...
Show moreEmergency Departments (EDs) represent a crucial component of any healthcare infrastructure. In today's world, healthcare systems face growing challenges in delivering efficient and time-sensitive emergency care services to communities. Overcrowding within EDs represents one of the most significant challenges for healthcare quality that adversely impacts clinical outcomes, patient safety, and overall satisfaction. Research in this area has resulted in creating several ED crowding indices, such as National Emergency Department Overcrowding Scale (NEDOCS) and Emergency Department Work Index (EDWIN) that have been developed to provide measures aimed at mitigating overcrowding. Recently, efforts made by researchers to examine the validity and reproducibility of these indices have shown that they are not reliable in accurately assessing overcrowding in regions beyond their original design settings. The shortcomings of such indices stem from their reliance upon the perspective and feedback of only clinical staff and the exclusion of other stakeholders. These limited perspectives introduce bias in the results of ED overcrowding indices. This study starts with confirming the inaccuracy of such crowding indices through examining their validity within a new healthcare system. To overcome the shortcomings of previous indices, the study presents a novel framework for quantifying and managing overcrowding based on emulating human reasoning in overcrowding perception. The framework of the proposed study takes into consideration emergency operational and clinical factors such as patient demand, patient complexity, staffing level, clinician workload, and boarding status when defining the crowding level. The hierarchical fuzzy logic approach is utilized to accomplish the goals of this framework by combining a diverse pool of healthcare expert perspectives while addressing the complexity of the overcrowding issue. The innovative design of the developed framework reduces bias in the assessment of ED crowding by developing a knowledge-base from the perspectives of multiple experts, and allows for its implementation in a variety of healthcare settings. Statistical analysis of results indicate that the developed index outperform previous indices in reflecting expert subjective assessments of overcrowding.
Show less
Date Issued
2016
Identifier
CFE0006521, ucf:51378
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006521
TOWARD BUILDING A SOCIAL ROBOT WITH AN EMOTION-BASED INTERNAL CONTROL AND EXTERNAL COMMUNICATION TO ENHANCE HUMAN-ROBOT INTERACTION
Title
TOWARD BUILDING A SOCIAL ROBOT WITH AN EMOTION-BASED INTERNAL CONTROL AND EXTERNAL COMMUNICATION TO ENHANCE HUMAN-ROBOT INTERACTION.
Creator
Marpaung, Andreas, Lisetti, Christine, University of Central Florida
Abstract / Description
In this thesis, we aim at modeling some aspects of the functional role of emotions on an autonomous embodied agent. We begin by describing our robotic prototype, Cherry--a robot with the task of being a tour guide and an office assistant for the Computer Science Department at the University of Central Florida. Cherry did not have a formal emotion representation of internal states, but did have the ability to express emotions through her multimodal interface. The thesis presents the results of...
Show moreIn this thesis, we aim at modeling some aspects of the functional role of emotions on an autonomous embodied agent. We begin by describing our robotic prototype, Cherry--a robot with the task of being a tour guide and an office assistant for the Computer Science Department at the University of Central Florida. Cherry did not have a formal emotion representation of internal states, but did have the ability to express emotions through her multimodal interface. The thesis presents the results of a survey we performed via our social informatics approach where we found that: (1) the idea of having emotions in a robot was warmly accepted by Cherry's users, and (2) the intended users were pleased with our initial interface design and functionalities. Guided by these results, we transferred our previous code to a human-height and more robust robot--Petra, the PeopleBot™--where we began to build a formal emotion mechanism and representation for internal states to correspond to the external expressions of Cherry's interface. We describe our overall three-layered architecture, and propose the design of the sensory motor level (the first layer of the three-layered architecture) inspired by the Multilevel Process Theory of Emotion on one hand, and hybrid robotic architecture on the other hand. The sensory-motor level receives and processes incoming stimuli with fuzzy logic and produces emotion-like states without any further willful planning or learning. We will discuss how Petra has been equipped with sonar and vision for obstacle avoidance as well as vision for face recognition, which are used when she roams around the hallway to engage in social interactions with humans. We hope that the sensory motor level in Petra could serve as a foundation for further works in modeling the three-layered architecture of the Emotion State Generator.
Show less
Date Issued
2004
Identifier
CFE0000286, ucf:46228
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000286