Configurations of the Graf-Boklev (V-Style) SKI Jumper Model and Aerodynamic Parameters in a Wind Tunnel

Maryniak, Jerzy; Ładyżyńska-Kozdraś, Edyta; Tomczak, Sławomir

doi:10.2478/v10038-009-0012-4

Cited by 5 publications

(3 citation statements)

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“…The emergence of the "V-style jump" [Maryniak et al, 2009] shown in Figure 2.1 in ski jumping is a textbook example of such imitation learning by humans. Although it took decades to be recognized, soon after some athletes achieved successful results with the V-style jump in 1990s, it has become prominent in the sport and has been mastered by all the athletes performing ski jumps.…”

Section: Design Choices For Imitation Learning Algorithmsmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation.We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning.We organize our work by dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning [Bain and Sammut, 1996]) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control [Kalman, 1964] or inverse reinforcement learning [Russell, 1998]). In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples-such as robots that play table tennis [Kober and Peters, 2009] and programs that play the game of Go [Silver et al., 2016]-illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions.

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Section: Design Choices For Imitation Learning Algorithmsmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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“…A measuring device built by Seo et al in a 3 × 3 m low-speed wind tunnel measures the air force of the full-size model under different attitudes during the flight phase; establishes the polynomial models of attack angle, forward inclination angle, V angle, and air force; and analyzes the stability of radial flip [19]. Maryniak et al measured the attitude's airlift, drag, and overturning momentum under several critical angles in the flight phase [52]. Park M.J. et al measured hydrodynamics in a wind tunnel, established the Navier-Stokes equation of the airflow field, and studied the optimization of the lift-drag ratio at four critical angles of flight attitude using computational fluid dynamics [53].…”

Section: Wind Tunnel Measurementmentioning

confidence: 99%

Progress in Ski Jumping Technology Based on Biomechanical Sport Research Methods

Li,

Liu,

Xing

et al. 2024

Applied Sciences

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(1) Background: Previous studies have compared research into ski jumping in different motor processes, but there is a lack of comparative analysis of the biomechanical research methods used to investigate different ski jumping sports. (2) Content: Our study compared the advantages and disadvantages of six research methods and proposes future research directions. Motion video collection and analysis show that controlling angular momentum and achieving stable flight attitude in the take-off process are the most critical factors in ski jumping performance. Most research on force platforms focuses on dynamic performance at the time of take-off, but there are few training sites with an embedded force platform, and so, more empirical research is required. Wearable inertial measurement units, including gyroscopes and accelerometers, can be used to determine a series of forces, calculate the joint angle, and speculate the position of the centroid during motion. Surface EMG studies are primarily used to compare the activity characteristics of the lower limb muscles in the actual field of the jump, the exercise simulation, and the lack of complete training process data. Wind tunnel measurement can satisfy fluid mechanics simulation experiments and provide theoretical support for optimizing special ski jumping technology. Based on the theory of computational fluid dynamics, the optimal drag reduction posture data of ski jumpers can be derived using computer simulations. (3) Conclusions: Due to the wide range of ski jumping sports, the present research focused on the kinematics and dynamics of different movement stages, lacking the study of the complete exercise training process. The range of wearable inertial measurement and sensor equipment can cover the whole process of ski jumping, including kinematics and dynamics data, and is a feasible and reliable test method for monitoring ski jump training in natural environments. The simultaneous testing of surface electromyography, kinematics, and dynamics requires further exploration. (4) Future direction of development: Under computational fluid dynamics, wearable inertial measurement units and global navigation satellite systems (GNSSs), intelligent wind tunnel experimental training areas will become essential tools for ski jumping research.

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“…During all these changes, the skis remained parallel. The last major change was the introduction of the V-style in 1985, where the skis are not parallel but form a V [ 1 , 2 ]. Nowadays, this technique is used by almost all athletes.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Real-Time Ski Jumping Tracking System Based on Wearable Sensors

Link

Guillaume

Eskofier

2021

Sensors

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For sports scientists and coaches, its crucial to have reliable tracking systems to improve athletes. Therefore, this study aimed to examine the validity of a wearable real-time tracking system (WRRTS) for the quantification of ski jumping. The tracking system consists of wearable trackers attached to the ski bindings of the athletes and fixed antennas next to the jumping hill. To determine the accuracy and precision of the WRRTS, four athletes of the German A or B National Team performed 35 measured ski jumps. The WRRTS was used to measure the 3D positions and ski angles during the jump. The measurements are compared with camera measurements for the in-flight parameters and the official video distance for the jumping distance to assess their accuracy. We statistically evaluated the different methods using Bland–Altman plots. We thereby find a mean absolute error of 0.46 m for the jumping distance, 0.12 m for the in-flight positions, and 0.8°, and 3.4° for the camera projected pitch and V-style opening angle, respectively. We show the validity of the presented WRRTS to measure the investigated parameters. Thus, the system can be used as a tracking system during training and competitions for coaches and sports scientists. The real-time feature of the tracking system enables usage during live TV broadcasting.

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Configurations of the Graf-Boklev (V-Style) SKI Jumper Model and Aerodynamic Parameters in a Wind Tunnel

Cited by 5 publications

References 0 publications

An Algorithmic Perspective on Imitation Learning

An Algorithmic Perspective on Imitation Learning

Progress in Ski Jumping Technology Based on Biomechanical Sport Research Methods

Experimental Validation of Real-Time Ski Jumping Tracking System Based on Wearable Sensors

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