The US Congress has passed legislation dictating that all government agencies establish a plan and process for
improving energy efficiencies at their sites. In response to this legislation, Oak Ridge National Laboratory (ORNL) has
recently conducted a pilot study to explore the deployment of a wireless sensor system for a real-time measurement-based
energy efficiency optimization framework within the steam distribution system within the ORNL campus. We
make assessments on the real-time status of the distribution system by observing the state measurements of acoustic
sensors mounted on the steam pipes/traps/valves. In this paper, we describe a spectral-based energy signature scheme
that interprets acoustic vibration sensor data to estimate steam flow rates and assess steam traps health status.
Experimental results show that the energy signature scheme has the potential to identify different steam trap health status
and it has sufficient sensitivity to estimate steam flow rate. Moreover, results indicate a nearly quadratic relationship
over the test region between the overall energy signature factor and flow rate in the pipe. The analysis based on
estimated steam flow and steam trap status helps generate alerts that enable operators and maintenance personnel to take
remedial action. The goal is to achieve significant energy-saving in steam lines by monitoring and acting on leaking
steam pipes/traps/valves.
The Extreme Measurement Communications Center at Oak Ridge National Laboratory (ORNL) explores the deployment
of a wireless sensor system with a real-time measurement-based energy efficiency optimization framework in the ORNL
campus. With particular focus on the 12-mile long steam distribution network in our campus, we propose an integrated
system-level approach to optimize the energy delivery within the steam distribution system. We address the goal of
achieving significant energy-saving in steam lines by monitoring and acting on leaking steam valves/traps. Our approach
leverages an integrated wireless sensor and real-time monitoring capabilities. We make assessments on the real-time
status of the distribution system by mounting acoustic sensors on the steam pipes/traps/valves and observe the state
measurements of these sensors. Our assessments are based on analysis of the wireless sensor measurements. We describe
Fourier-spectrum based algorithms that interpret acoustic vibration sensor data to characterize flows and classify the
steam system status. We are able to present the sensor readings, steam flow, steam trap status and the assessed alerts as
an interactive overlay within a web-based Google Earth geographic platform that enables decision makers to take
remedial action. We believe our demonstration serves as an instantiation of a platform that extends implementation to
include newer modalities to manage water flow, sewage and energy consumption.
In this paper, we conduct performance evaluation study for an aviation security cargo inspection queuing system for
material flow and accountability. The queuing model employed in our study is based on discrete-event simulation and
processes various types of cargo simultaneously. Onsite measurements are collected in an airport facility to validate the
queuing model. The overall performance of the aviation security cargo inspection system is computed, analyzed, and
optimized for the different system dynamics. Various performance measures are considered such as system capacity,
residual capacity, throughput, capacity utilization, subscribed capacity utilization, resources capacity utilization,
subscribed resources capacity utilization, and number of cargo pieces (or pallets) in the different queues. These metrics
are performance indicators of the system's ability to service current needs and response capacity to additional requests.
We studied and analyzed different scenarios by changing various model parameters such as number of pieces per pallet,
number of TSA inspectors and ATS personnel, number of forklifts, number of explosives trace detection (ETD) and
explosives detection system (EDS) inspection machines, inspection modality distribution, alarm rate, and cargo closeout
time. The increased physical understanding resulting from execution of the queuing model utilizing these vetted
performance measures should reduce the overall cost and shipping delays associated with new inspection requirements.
KEYWORDS: Analytical research, Data modeling, Process engineering, Weapons, Psychology, Information security, Evolutionary algorithms, Detection and tracking algorithms, Intelligent sensors, Complex systems
Recent events highlight the need for efficient tools for anticipating the threat posed by terrorists, whether individual or
groups. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security
measures, planning for future events, halting an event in process, and ultimately mitigating and managing the
consequences of an event. To analyze such components, one must understand various aspects of threat elements like
physical assets and their economic and social impacts. To this aim, we developed a three-layer Bayesian belief network
(BBN) model that takes into consideration the relative threat of an attack against a particular asset (physical layer) as
well as the individual psychology and motivations that would induce a person to either act alone or join a terrorist group
and commit terrorist acts (social and economic layers). After researching the many possible motivations to become a
terrorist, the main factors are compiled and sorted into categories such as initial and personal indicators, exclusion
factors, and predictive behaviors. Assessing such threats requires combining information from disparate data sources
most of which involve uncertainties. BBN combines these data in a coherent, analytically defensible, and understandable
manner. The developed BBN model takes into consideration the likelihood and consequence of a threat in order to draw
inferences about the risk of a terrorist attack so that mitigation efforts can be optimally deployed. The model is
constructed using a network engineering process that treats the probability distributions of all the BBN nodes within the
broader context of the system development process.
KEYWORDS: Telecommunications, Performance modeling, Systems modeling, Visualization, Molybdenum, Visual analytics, Visual process modeling, Sensors, Computational complexity theory, Communication theory
Combat resiliency is the ability of a commander to prosecute, control, and consolidate his/her's sphere of influence
in adverse and changing conditions. To support this, an infrastructure must exist that allows the commander to view the
world in varying degrees of granularity with sufficient levels of detail to permit confidence estimates to be levied against
decisions and course of actions. An infrastructure such as this will include the ability to effectively communicate
context and relevance within and across the battle space. To achieve this will require careful thought, planning, and
understanding of a network and its capacity limitations in post-event command and control. Relevance and impact on
any existing infrastructure must be fully understood prior to deployment to exploit the system's full capacity and
capabilities. In this view, the combat communication network is considered an integral part of or National
communication network and infrastructure. This paper will describe an analytical tool set developed at ORNL and RNI
incorporating complexity theory, advanced communications modeling, simulation, and visualization technologies that
could be used as a pre-planning tool or post event reasoning application to support response and containment.
Beginning in 2010, the U.S. will require that all cargo loaded in passenger aircraft be inspected. This will require more
efficient processing of cargo and will have a significant impact on the inspection protocols and business practices of
government agencies and the airlines. In this paper, we develop an aviation security cargo inspection queuing simulation
model for material flow and accountability that will allow cargo managers to conduct impact studies of current and
proposed business practices as they relate to inspection procedures, material flow, and accountability.
This paper describes the development of a comprehensive human modeling environment, the Virtual Human, which will be used initially to model the human respiratory system for purposes of predicting pulmonary disease or injury using lung sounds. The details of the computational environment, including the development of a Virtual Human Thorax, a database for storing models, model parameters, and experimental data, and a Virtual Human web interface are outlined. Preliminary progress in developing this environment will be presented. A separate paper at the conference describes the modeling of sound generation using computational fluid dynamics and the modeling of sound propagation in the human respiratory system.
Kara Kruse, Paul Williams, Glenn Allgood, Richard Ward, Shaun Gleason, Michael Paulus, Nancy Munro, Gnanamanika Mahinthakumar, Chandrasegaran Narasimhan, Jeffrey Hammersley, Dan Olson
Fundamental to the understanding of the various transport processes within the respiratory system, airway fluid dynamics plays an important role in biomedical research. When air flows through the respiratory tract, it is constantly changing direction through a complex system of curved and bifurcating tubes. As a result, numerical simulations of the airflow through this tracheobronchial system must be capable of resolving such fluid dynamic phenomena as flow separation, recirculation, secondary flows due to centrifugal instabilities, and shear stress variation along the airway surface. Anatomic complexities within the tracheobronchial tree, such as sharp carinal regions at asymmetric bifurcations, have motivated the application of the incompressible Computational Fluid Dynamics code PHI3D to the modeling of airflow. Developed at ORNL, PHI3D implements the new Continuity Constraint Method. Using a finite-element methodology, complex geometries can be easily simulated with PHI3D using unstructured grids. A time- accurate integration scheme allows the simulation of both transient and steady-state flows. A realistic geometry model of the central airways for the fluid flow studies was obtained from pig lungs using a new high resolution x-ray computed tomography system developed at ORNL for generating 3D images of the internal structure of laboratory animals.
Walter Reed Army Institute of Research and Oak Ridge National Laboratory have developed a prototype pulmonary diagnostic system capable of extracting signatures from adventitious lung sounds that characterize obstructive and/or restrictive flow. Examples of disorders that have been detailed include emphysema, asthma, pulmonary fibrosis, and pneumothorax. The system is based on the premise that acoustic signals associated with pulmonary disorders can be characterized by a set of embedded signatures unique to the disease. The concept is being extended to include cardio signals correlated with pulmonary data to provide an accurate and timely diagnoses of pulmonary function and distress in critically injured soldiers that will allow medical personnel to anticipate the need for accurate therapeutic intervention as well as monitor soldiers whose injuries may lead to pulmonary compromise later. The basic operation of the diagnostic system is as follows: (1) create an image from the acoustic signature based on higher order statistics, (2) deconstruct the image based on a predefined map, (3) compare the deconstructed image with stored images of pulmonary symptoms, and (4) classify the disorder based on a clustering of known symptoms and provide a statistical measure of confidence. The system has produced conformity between adults and infants and provided effective measures of physiology in the presence of noise.
Arcing in high-energy systems can have a detrimental effect on the operational performance, energy efficiency, life cycle and operating and support costs of a facility. In can occur in motors, switching networks, and transformers and can pose a serious threat to humans who operate or work around the systems. To reduce this risk and increase operational efficiency, it is necessary to develop a capability to diagnose single and multiple arcing events in order to provide an effective measure of system performance. This calculated parameter can then be used to provide an effective measure of system health as it relates to arcing and its deleterious effects. This paper details the development of a model-based matched filter for an antenna that recognizes single and/or multiple arcing events in a direct current motor and calculates a functional measure of activity and a confidence factor based on an estimate of how well the data fit the matched filter model parameters. A principal component analysis is then performed on the descriptive statistics calculated from the model's input data stream to develop cluster centers for classifying non- arcing and arching events that are invariant to system operation set point. This approach also has a deployment benefit in that the PCA decreases the computational load on the classifier system by reducing the order of the system.
The American textile industry has lost an estimated 400,000 jobs to offshore competitors since 1980. It is predicted they will lose an additional 600,000 jobs by the year 2002. These losses and their resulting economic threat to the U.S. textile industry can be attributed to the low operating costs of their offshore competition. In order to stem these rising losses, the American textile industry entered into an agreement with the U.S. Department of Energy (DOE) in a program called the American Textile Partnership (AMTEXTM). Since the minimum U.S. labor rate is well above that of its offshore competitors, one of the competitive factors the U.S. industry hopes to gain is a higher quality fabric. To facilitate this, a Computer-Aided Fabric Evaluation (CAFE) System has been developed at Oak Ridge National Laboratory (ORNL) and Lockheed Martin Energy Systems, Inc. (LMES). The system is based on a class 3-a laser and a set of cylindrical lenses allowing for 1-D imaging of single yarns thrown in the fill direction. It has been designed to be located close to the point of fabric formation providing data and information on structure, patterns, and material defects of the fabric as it is being formed.
The Anticipatory System (AS) formalism developed by Robert Rosen provides some insight into the problem of embedding intelligent behavior in machines. AS emulates the anticipatory behavior of biological systems. AS bases its behavior on its expectations about the near future and those expectations are modified as the system gains experience. The expectation is based on an internal model that is drawn from an appeal to physical reality. To be adaptive, the model must be able to update itself. To be practical, the model must run faster than real-time. The need for a physical model and the requirement that the model execute at extreme speeds, has held back the application of AS to practical problems. Two recent advances make it possible to consider the use of AS for practical intelligent sensors. First, advances in transducer technology make it possible to obtain previously unavailable data from which a model can be derived. For example, acoustic emissions (AE) can be fed into a Bayesian system identifier that enables the separation of a weak characterizing signal, such as the signature of pump cavitation precursors, from a strong masking signal, such as a pump vibration feature. The second advance is the development of extremely fast, but inexpensive, digital signal processing hardware on which it is possible to run an adaptive Bayesian-derived model faster than real-time. This paper reports the investigation of an AS using a model of cavitation based on hydrodynamic principles and Bayesian analysis of data from high-performance AE sensors.
Clay Easterly, Glenn Allgood, Keith Eckerman, Helmut Knee, Mike Maston, Greg McNeilly, John Munro, Nancy Munro, Ross Toedte, Blake Van Hoy, Richard Ward
KEYWORDS: Systems modeling, Data modeling, Virtual reality, Diagnostics, Computing systems, Process modeling, Visualization, Medicine, Visual process modeling, Space operations
The virtual human will be a research/simulation environment having an integrated system of biophysical models, data, and advanced computational algorithms. It will have a Web-based interface for easy, rapid access from several points of entry. The virtual human will serve as a platform for national and international users from governments, academia and industry to investigate the widest range of human biological and physical response to stimuli, be they biological, chemical, or physical. This effort will go far beyond the modeling of anatomy to incorporate refined computational models of whole-body processes, using mechanical and electrical tissue properties, and biology from physiology to biochemical information. The platform will respond mechanistically to varied and potentially iterative stimuli that can be visualized multi- dimensionally. This effort is in the formative stage of a several-year process that will lead to a program that is of similar proportion to the human genome, but will be much more computationally intensive. The main purpose of this paper is to communicate our early ideas about the philosophic basis of the program, to identify some of the applications for which the virtual human would be used, to elicit comments, and to provide a basis to identify prospective collaborators.
Traffic management can be thought of as a stochastic queuing process where the serving time at one of its control points is dynamically linked to the global traffic pattern, which is, in turn, dynamically linked to the control point. For this closed-loop system to be effective, the traffic management system must sense and interpret a large spatial projection of data originating from multiple sensor suites. This concept is the basis for the development of a traffic flow wide-area surveillance (TFWAS) system. This paper presents the results of a study by Oak Ridge National Laboratory to define the operational specifications and characteristics, to determine the constraints, and to examine the state of technology of a TFWAS system in terms of traffic management and control. In doing so, the functions and attributes of a TFWAS system are mapped into an operational structure consistent with the Intelligent Vehicle Highway System (IVHS) concept and the existing highway infrastructure. This mapping includes identifying candidate sensor suites and establishing criteria, requirements, and performance measures by which these systems can be graded in their ability and practicality to meet the operational requirements of a TFWAS system. In light of this, issues such as system integration, applicable technologies, impact on traffic management and control, and public acceptance are addressed.
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