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Simulation analysis of control strategies for a tank waste retrieval manipulator system

Description: A network simulation model was developed for the Tank Waste Retrieval Manipulator System, incorporating two distinct levels of control: teleoperation and supervisory control. The model included six error modes, an attentional resource model, and a battery of timing variables. A survey questionnaire administered to subject matter experts provided data for estimating timing distributions for level of control-critical tasks. Simulation studies were performed to evaluate system behavior as a function of control level and error modes. The results provide important insights for development of waste retrieval manipulators.
Date: February 1, 1995
Creator: Schryver, J.C. & Draper, J.V.
Partner: UNT Libraries Government Documents Department

Causal models of trip replanning in TravTek

Description: The TravTek operational field test was conducted to evaluate the effectiveness of route planning, route guidance and various navigational aiding modalities for Advanced Traveler Information Systems in ground vehicles. A causal network was constructed in order to achieve a better understanding of the dependencies among variables implicated in the replanning process. Causal inferences were modeled using path analysis techniques. The original Yoked Driver study reported that addition of real-time navigation planning did not increase trip efficiency during initial trip planning. Data mining of the relatively complete database revealed that the incidence of dynamic trip replanning was only 0.51% or 1 out of every 198 trips. Nevertheless, the replanning acceptance rate was 92.8%, suggesting that less conservative criteria might have been acceptable to drivers. Several points can be made based upon the path analysis techniques. Drivers who rejected better route offers were more likely to be male renters; rejected routes were apparently offered at earlier times with a lower predicted time savings and fewer maneuvers. Failure to accept a better route also apparently resulted in fewer wrong-turn deviations. Contrary to expectations, wrong-turn count and time loss appeared as semi-independent hubs in the resultant causal network. Implications of the path analysis are discussed. Proposals for in-vehicle information systems are formulated to increase driver participation as co-planner, and increase the likelihood that trip replanning will positively impact trip efficiency.
Date: July 1, 1998
Creator: Schryver, J. C.
Partner: UNT Libraries Government Documents Department

Classification of time series patterns from complex dynamic systems

Description: An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.
Date: July 1, 1998
Creator: Schryver, J.C. & Rao, N.
Partner: UNT Libraries Government Documents Department

Models of human operators: Their need and usefulness for improvement of advanced control systems and control rooms

Description: Models of human behavior and cognition (HB C) are necessary for understanding the total response of complex systems. Many such model have come available over the past thirty years for various applications. Many potential model users remain skeptical about their practically, acceptability, and usefulness. Such hesitancy stems in part from disbelief in the ability to model complex cognitive processes, and a belief that relevant human behavior can be adequately accounted for through the use of common-sense heuristics. This paper will highlight several models of HB C and identify existing and potential applications in attempt to dispel such notions. 26 refs.
Date: January 1, 1991
Creator: Knee, H.E. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

Eye-gaze determination of user intent at the computer interface

Description: Determination of user intent at the computer interface through eye-gaze monitoring can significantly aid applications for the disabled, as well as telerobotics and process control interfaces. Whereas current eye-gaze control applications are limited to object selection and x/y gazepoint tracking, a methodology was developed here to discriminate a more abstract interface operation: zooming-in or out. This methodology first collects samples of eve-gaze location looking at controlled stimuli, at 30 Hz, just prior to a user`s decision to zoom. The sample is broken into data frames, or temporal snapshots. Within a data frame, all spatial samples are connected into a minimum spanning tree, then clustered, according to user defined parameters. Each cluster is mapped to one in the prior data frame, and statistics are computed from each cluster. These characteristics include cluster size, position, and pupil size. A multiple discriminant analysis uses these statistics both within and between data frames to formulate optimal rules for assigning the observations into zooming, zoom-out, or no zoom conditions. The statistical procedure effectively generates heuristics for future assignments, based upon these variables. Future work will enhance the accuracy and precision of the modeling technique, and will empirically test users in controlled experiments.
Date: December 31, 1993
Creator: Goldberg, J. H. & Schryver, J. C.
Partner: UNT Libraries Government Documents Department

Establishing Availability Requirements Using Characteristics Factors and Expert Opinion

Description: System design engineers must translate permitted overall facility downtime into detailed design and operating specifications for numerous systems and subsystems that make up the facility. The process of assigning reliability and maintainability requirements to individual equipment systems to attain a desired overall availability is known as availability apportionment. Apportionment is normally required early in conceptual design when little or no hardware information is available. Apportionment, when coupled with availability prediction, enables the selection of viable alternative configurations, identifies problem areas, and provides redirection of the program into more productive areas as necessary. A method for apportioning, or budgeting, overall facility availability requirements among systems and subsystems is presented. An example of applying this methodology to the Spallation Neutron Source (SNS) facility is given.
Date: June 18, 2000
Creator: Haire, M.J. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

RAM simulation model for SPH/RSV systems

Description: The US Army`s Project Manager, Crusader is sponsoring the development of technologies that apply to the Self-Propelled Howitzer (SPH), formerly the Advanced Field Artillery System (AFAS), and Resupply Vehicle (RSV), formerly the Future Armored Resupply Vehicle (FARV), weapon system. Oak Ridge National Laboratory (ORNL) is currently performing developmental work in support of the SPH/PSV Crusader system. Supportive analyses of reliability, availability, and maintainability (RAM) aspects were also performed for the SPH/RSV effort. During FY 1994 and FY 1995 OPNL conducted a feasibility study to demonstrate the application of simulation modeling for RAM analysis of the Crusader system. Following completion of the feasibility study, a full-scale RAM simulation model of the Crusader system was developed for both the SPH and PSV. This report provides documentation for the simulation model as well as instructions in the proper execution and utilization of the model for the conduct of RAM analyses.
Date: December 31, 1995
Creator: Schryver, J.C.; Primm, A.H. & Nelson, S.C.
Partner: UNT Libraries Government Documents Department

Spallation Neutron Source Availability Top-Down Apportionment Using Characteristic Factors and Expert Opinion

Description: Apportionment is the assignment of top-level requirements to lower tier elements of the overall facility. A method for apportioning overall facility availability requirements among systems and subsystems is presented. Characteristics that influence equipment reliability and maintainability are discussed. Experts, using engineering judgment, scored each characteristic for each system whose availability design goal is to be established. The Analytic Hierarchy Process (AHP) method is used to produce a set of weighted rankings for each characteristic for each alternative system. A mathematical model is derived which incorporates these weighting factors. The method imposes higher availability requirements on those systems in which an incremental increase in availability is easier to achieve, and lower availability requirements where greater availability is more difficult and costly. An example is given of applying this top-down apportionment methodology to the Spallation Neutron Source (SNS) facility.
Date: October 1, 1999
Creator: Haire, M.J. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

Incorporation of RAM techniques into simulation modeling

Description: This work concludes that reliability, availability, and maintainability (RAM) analytical techniques can be incorporated into computer network simulation modeling to yield an important new analytical tool. This paper describes the incorporation of failure and repair information into network simulation to build a stochastic computer model represents the RAM Performance of two vehicles being developed for the US Army: The Advanced Field Artillery System (AFAS) and the Future Armored Resupply Vehicle (FARV). The AFAS is the US Army`s next generation self-propelled cannon artillery system. The FARV is a resupply vehicle for the AFAS. Both vehicles utilize automation technologies to improve the operational performance of the vehicles and reduce manpower. The network simulation model used in this work is task based. The model programmed in this application requirements a typical battle mission and the failures and repairs that occur during that battle. Each task that the FARV performs--upload, travel to the AFAS, refuel, perform tactical/survivability moves, return to logistic resupply, etc.--is modeled. Such a model reproduces a model reproduces operational phenomena (e.g., failures and repairs) that are likely to occur in actual performance. Simulation tasks are modeled as discrete chronological steps; after the completion of each task decisions are programmed that determine the next path to be followed. The result is a complex logic diagram or network. The network simulation model is developed within a hierarchy of vehicle systems, subsystems, and equipment and includes failure management subnetworks. RAM information and other performance measures are collected which have impact on design requirements. Design changes are evaluated through ``what if`` questions, sensitivity studies, and battle scenario changes.
Date: July 1, 1995
Creator: Nelson, S.C. Jr.; Haire, M.J. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

Experimental validation of navigation workload metrics

Description: Advanced digital computer display interfaces in the control room may increase operator workload. Workstation monitors provide limited display area, and information is represented in large-scale display networks. Display navigation may generate disorienting effects, require additional resources for window management, and increase memory and data integration requirements. Six ORNL employees participated in an experiment to validate proposed metrics of navigation workload in the advanced control room. The task environment was a display network consisting of 25 windows resembling a simplified Safety Parameter Display System for Pressurized Water Reactors. A repeated measures design with 3 within subjects factors was employed. The factors were task difficulty, navigation distance level, and a blocking factor. Participants were asked to monitor a single parameter or two parameters. Fourteen candidate metrics were tested. Analysis of variance of the modified task load index (MTLX) and rating subscales demonstrated substantial support for the claim that navigation of large-scale display networks can impose additional mental load. Primary and secondary task performance measures exhibited ceiling effects. Memory probes for these tasks were inadequate because they were recognition-based and coarse. Eye gaze measures were not validated, indicating a need for more refined data reduction algorithms. Strong positive correlations were found between MTLX and both navigation duration and standard deviation of pupil diameter. Further study and increased statistical power are required to validate objective navigation workload metrics.
Date: April 1, 1994
Creator: Schryver, J. C. & Wachtel, J. A.
Partner: UNT Libraries Government Documents Department

Using Artificial Neural Networks to Assess Changes in Microbial Communities

Description: We evaluated artificial neural networks (ANNs) as a technique for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker (SLB) data collected from two soil microcosm experiments using traditional statistical techniques and ANN. Two phases of data analysis were done; pattern recognition and prediction. In general, the ANNs were better able to detect patterns and relationships in the SLB data than were the traditional statistical techniques.
Date: April 19, 1999
Creator: Brandt, C.C.; Macnaughton, S.; Palumbo, A.V.; Pfiffner, S.M. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

Using Artificial Neural Networks to Assess Microbial Communities

Description: We are evaluating artificial neural networks (ANNs) as tools for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker data collected from two soil microcosm experiments using an autoassociative ANN. In one experiment, the microcosms were exposed to O, 100, or 250 ppm of metals, and in the other experiment the microcosms were exposed to O or 500 ppm of metals. The ANNs were able to distinguish between microcosms exposed and not exposed to metals in both experiments.
Date: September 8, 1998
Creator: Almeida, J.S.; Brand, C.C.; Palumbo, A.V.; Pfiffner, S.M. & Schryver, J.C.
Partner: UNT Libraries Government Documents Department

Mining multi-dimensional data for decision support

Description: While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and Human-Computer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each data mining task requires a custom solution that depends upon the character and quantity of the data. This paper presents a two-stage approach for handling the prediction of personal bankruptcy using credit card account data, combining decision tree and artificial neural network technologies. Topics to be discussed include the pre-processing of data, including data cleansing, the filtering of data for pertinent records, and the reduction of data for attributes contributing to the prediction of bankruptcy, and the two steps in the mining process itself.
Date: June 1, 1998
Creator: Donato, J.M.; Schryver, J.C.; Hinkel, G.C.; Schmoyer, R.L. Jr.; Grady, N.W. & Leuze, M.R.
Partner: UNT Libraries Government Documents Department