One of the most obvious locomotory behaviors is gait transition (changing from walk to trot/run and changing from trot to gallop). There have been numerous attempts to explain gait transitions. These include considerations of muscle function (Taylor, 1978(Taylor, , 1985 and bone strain (Biewener and Taylor, 1986;Rubin and Lanyon, 1982), theoretical explanations based on mathematical models (Alexander, 1989;Alexander and Jayes, 1983), psychological factors (Diedrich and Warren, 1995) and engineering models (Schoner et al., 1990;Vilensky et al., 1991).The walk-trot and trot-gallop gait transitions were originally explained on the basis of metabolic economy (Hoyt and Taylor, 1981). In ponies (Equus caballus), metabolism increased curvilinearly for walking and trotting, and the gait transitions occurred at the speeds where the metabolism curves intersected. This is referred to as the 'energetically optimal transition speed' (EOTS; Hreljac, 1993) because, when the animals extended their gaits beyond the normal transition speeds, the metabolic rate was higher in the extended gait than in the normal gait. Hoyt and Taylor concluded that ponies changed gaits to minimize energetic costs. However, one limitation of this study was that gait transition speeds were not rigorously determined.Subsequently, this explanation was challenged by the 'force trigger' hypothesis. Farley and Taylor (1991) showed that the transition from trotting to galloping in ponies is correlated with musculoskeletal forces by demonstrating that the transition occurs at a slower speed when a pony carries a load. Measurements of oxygen consumption (again observed to be a curvilinear function of speed) indicated that the ponies were making the transition to a gallop at speeds where it is energetically more expensive to gallop than to trot -at speeds slower than the EOTS. In some studies, the walk-run transition in humans occurs at the EOTS (Mercier et al., 1994;Diedrich and Warren, 1995) and in others it does not (Hreljac, 1993; Minetti et al., 1994a,b). Hreljac (1993) ruled out muscle stress as the trigger for the walk-run transition in humans and suggested that the trigger is kinematic (Hreljac, 1995).In a study of horses and preferred speed , the energetics of trotting were measured on the level and up a 10% incline. In the preliminary portion of this study, we determined the speeds at which the horses would trot. We noted that, when trotting up an incline, the horses made the transition to a gallop at a slower speed than they would when on the level. Because forces are not expected to be higher when Two studies have focused on potential triggers for the trot-gallop transition in the horse. One study concluded that the transition was triggered by metabolic economy. The second study found that it was not metabolic factors but, rather, peak musculoskeletal forces that determine gait transition speeds. In theory, peak musculoskeletal forces should be the same when trotting up an incline as when trotting at the same speed on the level. Assuming this is ...

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 01 Apr 2019Machine Abstract Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; highlevel semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME'sVision and Applications (2014) 25:17-32 DOI 10.1007/s00138-013-0527-

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Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; highlevel semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME's

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractDevelopment and exploitation of oil and gas resources in increasingly difficult operating environments such as deepwater raise many technical challenges. Among these is the ability to provide assurance on the completions and production from high-cost and complex wells. Real-time, permanent wellbore and reservoir monitoring is a critical technology for providing assurance and maximizing profitability of these fields.Recent developments in fiber optic sensing technology have resulted in reliable alternatives to conventional electronic systems for permanent, downhole production and reservoir monitoring. In-well fiber optic sensors are now being developed and deployed in the field f or measuring temperature, pressure, flow rate, fluid phase fraction, and seismic response. Bragg grating-based fiber optic systems combine a high level of reliability, accuracy, resolution and stability with the ability to multiplex sensors on a single fiber, enabling complex and multilateral wells to be fully instrumented with a single wellhead penetration. These systems are being installed worldwide in a variety of operating environments for a variety of applications.This paper presents several recent deployments of in-well fiber optic monitoring systems, including descriptions of the downhole sensor assemblies, installations, and measured data. Installations of fiber optic pressure and temperature systems in a land well and in the Gulf of Mexico and an all-fiber flow and liquid fraction system in deepwater Gulf of Mexico are discussed. A general description of fiber optic sensing and Bragg grating-based sensing systems is also presented.