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This study evaluates the dynamic interaction between rail vehicles and the track on which they travel. The measurements were analyzed in the time and frequency domain. The effects of external parameters such as the vehicle design, subgrade stiffness, and degree of curvature were analyzed and compared for both the vehicle response and the track geometry data. This study was conducted in close cooperation with the Transportation Technology Center, Inc. (TTCI) in Pueblo, Colorado. The track and vehicle response data was collected on the High Tonnage Loop at TTCI over a span of approximately ten years. For the purpose of this study, TTCI provided this data to the Advanced Vehicle Dynamics Laboratories (AVDL) at Virginia Tech. The analysis of the data was first completed for the vehicle response and track geometry measurements separately. In this manner, the effects of the subgrade stiffness, the degree of curvature, and the vehicle design could be evaluated for each of the measurements. The analysis of the track and rail vehicle response was successfully completed, and the results from this analysis are presented in the following chapters. The analysis showed that the subgrade stiffness had a significant effect on the vertical wheel loads, the track crosslevel, and the track gauge. For an increase in the degree of curvature, the variation of the crosslevel increased. The dynamic response of the vertical and lateral wheel loads showed a significant dependence on the alignment of the rails. Improvements for future studies of the interaction between the track and rail vehicles have been recommended. / Master of Science

Wavelet intensity based algorithm developed previously at VirginiaTech has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a feature extraction algorithm. The wavelet transform is an effective tool as it allows one to narrow down upon the transient, high frequency events and is able to tell their exact location in time. According to prior work done in the field of signal processing, the local regularities of a signal can be estimated using a Lipchitz exponent at each time step of the signal. The local Lipchitz exponent can then be used to generate the wavelet intensity factor values. For each vertical acceleration value, corresponding to a specific location on the track, we now have a corresponding intensity factor. The intensity factor corresponds to break-no break information and can now be used as a feature to classify the vertical acceleration as a fault or no fault. Support Vector Machines (SVM) is used for this binary classification task. SVM is chosen as it is a well-studied topic with efficient implementations available. SVM instead of hard threshold of the data is expected to do a better job of classification without increasing the complexity of the system appreciably. / Master of Science

The ability of a Doppler LIDAR (Light Detection and Ranging) system to measure the speed of a moving rail vehicle in a non-contacting manner is extended to capture the lateral and vertical irregularities of the track itself and to evaluate the rail track quality. Using two pairs of lenses to capture speed signals from both rails individually, the track speed, curvature, and lateral and vertical geometry variations on each side are determined. LIDAR lenses are installed with a slight forward angle to generate velocity signals that contain two components: 1) the left and right track speeds, and 2) any lateral and/or vertical speed caused by track motion and/or spatial irregularities. The LIDAR system collects and outputs the track information in time domain. Separating each speed component (forward, vertical, and lateral) is possible due to the inherent separation of each phenomenon with respect to its spatial/temporal frequencies and related bandwidths. For the measurements to be beneficial in practice, the LIDAR data must be spatially located along the track. A data-mapping algorithm is then simultaneously developed to spatially match the LIDAR track geometry measurements with reference spatial data, accurately locating the measurements along the track and eliminating the need for a Global Positioning System (GPS). A laboratory-grade LIDAR system with four Doppler channels, developed at the Railway Technologies Laboratory (RTL) of Virginia Tech, is body-mounted and tested onboard a geometry measurement railcar. The test results indicate a close match between the LIDAR measurements and those made with existing sensors onboard the railcar. The field-testing conducted during this study indicates that LIDAR sensors could provide a reliable, non-contact track-monitoring instrument for field use, in various weather and track conditions, potentially in a semi-autonomous or autonomous manner. A length-based track quality index (TQI) is established to quantify the track geometry condition based on the geometry data collected by the LIDAR sensors. A phenomenological rail deterioration model is developed to predict the future degradation of geometry quality over the short track segments. The introduced LIDAR's TQI is considered as the condition-parameter, and an internal variable is assumed to govern the rail geometry degradation through a deterioration rule. The method includes the historical data, current track conditions collected by the LIDAR system, and traffic data to calculate the track deterioration condition and identify the geometry defects. In addition to rail geometry inspection, a LIDAR system can potentially be used to monitor the rail surface structure and integrity. This is possible due to the fact that the Doppler shift imposed on the laser radiation reflected from a moving surface has the Doppler bandwidth broadened in proportion to the height and width of the surface features. Two LIDAR-based rail surface measures are introduced based on LIDAR measurements to identify different rail surface conditions and materials. / Ph. D.

The accuracy of the results from a rail vehicle dynamic model is dependent on the realism of the track input to the model. An important part of the track input is the irregularities that exist on actual track. This study analyzes the irregularities inherent in railroad track geometry data, and provides an analytical method for creating track data with the irregularities for use as the input to a dynamic model. Track data, measured from various classes of track, was examined using statistical and frequency analysis techniques to identify any similarities in the characteristics of the irregularities. The results showed that each class of track had a distinctive value for the standard deviation of the alignment and profile data. It was also determined that the frequency content of all the tracks was contained within a common bandwidth. The track irregularities could then be generated with the same characteristics as an actual track. The method for creating the track irregularities was then programmed into TRAKVU. TRAKVU is a track preprocessor used in conjunction with NUCARS, a railcar dynamic modeling program¹. TRAKVU enables users to create track data and apply the appropriate irregularities so that the track will have the characteristics of the desired class of track. A validation was then performed to determine how well track created in TRAKVU simulated actual tracks. The statistical and frequency characteristics of created tracks were compared directly with actual tracks. Created track was also used as the input to a dynamic model. The predicted vehicle response was then compared to the actual vehicle response and the predicted vehicle response using measured track data as the input. The results from the validation showed that the created track performed as well as the measured track in providing the input to the model. Although the predicted response using the created track did not compare as well with the actual vehicle response, the differences could be attributed to inaccuracies in the model. ¹NUCARS and TRAKVU are copyrighted property of the Association of American Railroads. / Master of Science

The sport of weightlifting has been competed since the first modern Olympiad. Competition in weightlifting consists of 3 attempts the snatch and clean and jerk declared by the athlete and their coach prior to the starting of the lift. While waiting for the athlete’s lift, waiting periods can change and warm up attempts may need to be adjusted. Often, coaches prescribe either a complete “light power” or partial “heavy pull” repetition of the competed movement during a long wait. Previous literature indicates that a heavier stimulus may cause a “post-activation potentiation”, or “post-activation performance enhancement” effect on the subsequent lift. However, some evidence indicates that a heavy pull closely preceding a subsequent clean may disrupt technique. Despite the common practice to perform a heavy pull or lighter power clean or snatch in the warmup area, little information is known about whether this movement will potentiate the following repetition or disrupt technique. The purpose of this investigation was to investigate the potentiation and technique effects of the heavy pull and light power on a subsequent clean. Methods: The subjects (males n = 9; females n= 2) were eleven well-trained athletes (weightlifting, track and field, crossfit) in the clean. After warmup, a series of cleans were performed leading to a 90 % 1 RM clean followed by a 75% power clean or 112% clean pull (order randomly assigned), this was followed by a 90% clean. Kinematics were measured using Qualisys M3 motion capture. Subjective effort was measured after each 90% clean using rating of perceived exertion (RPE). Results: Men were stronger than women how ever there was no difference in the outcome. Peak bar velocity was not statistically different pre-post (p≥ 0.5). Vertical displacement was not statistically different pre-post (p≥ 0.5). Horizontal displacement was not statistically different (p≥ 0.5). Catch phase duration was not statistically different (p≤ 0.5), however effect size indicates small to moderate decreases in duration in both conditions. Stronger athletes appeared to have less technical disruption compared to lesser lifters. Following the power clean there was a statistically significant reduction in RPE (p ≤0.5, cohen’s d=0.595 95%CI=0.171 to 1.02).

Seit Mitte der 1990er Jahre haben sich Fast-Track-Behandlungskonzepte in der Kardioanästhesie etabliert. Diese zielen darauf ab, unter Verwendung kurzwirksamer Anästhetika eine frühzeitige postoperative, tracheale Extubation der Patienten zu gewährleisten, und folgend die Dauer der postoperativen Behandlung auf der Intensivstation und im Krankenhaus sowie die Inzidenz an Komplikationen zu senken. Kernstück eines multimodalen, kardioanästhesiologischen Fast-Track-Konzeptes am Herzzentrum in Leipzig (HZL) ist eine postanästhesiologische Aufwacheinheit (PACU) mit 3 Patientenplätzen, in der Patienten postoperativ betreut werden, ohne dass eine Aufnahme auf die Intensivstation (ICU) erfolgen muss. Ziel dieser Arbeit ist, den Einfluss der PACU im Rahmen des Fast-Track-Konzeptes im Vergleich zur Behandlung auf der Intensivstation zu untersuchen. Hierzu führten wir eine prospektiv-randomisierte kontrollierte Studie mit insgesamt 200 elektiven, kardiochirurgischen Patienten durch. Resultat der Studie war eine signifikant kürzere Dauer bis zur Extubation in der PACU im Vergleich zur Kontrollgruppe, zudem war die Verweildauer in der PACU im Median signifikant kürzer als auf der ICU. Hinsichtlich der postoperativen Mortalität und Morbidität zeigten sich keine wesentlichen Unterschiede. Hieraus lässt sich schlussfolgern, dass eine postoperative Fast-Track-Behandlung in einer dedizierten PACU im Vergleich zur ICU zur früheren Extubation und Verlegung auf die weiterversorgenden Stationen führt, ohne dass die Sicherheit der Patienten beeinträchtigt wird.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The Transportable Range Augmentation and Control System (TRACS), Mobile Telemetry System (MTS), is a versatile system capable of supporting anywhere when called upon. The MTS is designed to operate anywhere on land. It is unknown how the system will perform on a floating platform without a stabilizing gimbal. The operation of a tracking system at sea generally require the use of a three-axis pedestal. The MTS is a two-axis pedestal. This paper is a report on how the MTS responds to simulated ocean-motion. Testing the system on a body of water is very expensive, especially out in the desert. The MTS was tested in the desert area of Las Cruces, New Mexico in the parking lot of EMI Technologies, prime contractor, using two forklifts to simulate ship motion in the pitch and yaw planes. The location is perfect for crossover dynamics tests. The tests conducted were for the purpose of determining if the MTS could auto-track a moving signal in space while it also moves due to “simulated ocean swells” that increase the generated tracking error signal levels in an opposite or in addition to the ones generated from the space vehicle. There is no gyroscopic correction. Successful results of the tests could preclude the use of a gyroscopically stabilized gimbaled platform necessary to keep the tracking system steady for auto-tracking a target during “6 degrees of freedom” disturbances. Several thousand dollars can be saved if the concept can be proven.