• Journal of Korean Orthopaedic Sports Medicine
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• v.2 no.2
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• pp.77-81
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• 2003

Purpose: The biomechanics and kinematics of knee joint were reviewed in this article. And then the common sports injuries were presented. Anatomy and Kinetics: None of the pairs of bearing surfaces in the knee joint is exactly congruent This allows the knee six degrees of freedom of motion. Tibiofemoral Kinematics: In flexion and extension, the axis of motion is not perpendicular to the medial-lateral plane of the joint, nor is it perpendicular to the axis of longitudinal rotation. This results in coupled varus angulation and internal rotation with flexion and in valgus angulation and external rotation with extension. Patellofemoral Articulation: Loads across the patellofemoral joint are indirectly related to the angle of knee flexion and directly related to the force generated within the quadriceps mechanism. Fractures of the Patella: Nonoperative treatment is indicated if the extensor mechanism is intact and if displacement of fragment is minimal. The specific type of internal fixation depends on the fracture pattern. It is important to repair retinaculum. Acute and Recurrent Patellar Instability: The degree of dysplasia and the extent of the instability play a large part in determining the success of nonoperative treatment. Patients who experience recurrent dislocations and patients with major anatomic variations require surgery to minimize their instability. Sports Injuries in School-age Atheletes: Patellar pain in young athletes groups a number of conditions, including Idiopathic Adolescent Anterior Knee Pain, Osgood- Schlatter Disease, and Sinding-Larsen-Johansson Disease.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.6
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• pp.516-526
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• 2016

This paper introduces a hovering-type autonomous underwater vehicle (AUV) developed for research and its fundamental motion performance results obtained by simulation and field test. The AUV can control its motion in four degrees of freedom (DOF) by means of its horizontal and vertical thrusters, and it is designed to provide a test-bed that facilitates ease of operation and experimentation. Prior to the field tests, six DOF equations of motion are developed, and a simulation program is constructed using MATLAB and Simulink to verify the essential motion performance of the designed vehicle. Furthermore, a proportional-integral-derivative (PID) controller and fuzzy PID controller are designed, and their performances are verified through a simulation. Field tests are performed to verify the motion performance of the AUV; way-point tracking is executed by the PID and fuzzy PID controllers. The results confirmed appropriate control performance under current disturbances.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.4
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• pp.259-265
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• 2016

The present study aims to design and utilize a model test system of a Caisson in wet towing condition, to assess towing stability of a 9,300 ton class caisson. The suggested towing system was designed to provide regular tension on the towline, whereas the previous model test system towed the model in constant speed. The new model test system was expected to reproduce the towing condition more realistically than the test system with constant speed condition, as the tugboat in actual towing condition tows the towline with constant power. Model tests were conducted in a towing tank with 1/30 scaled model. In the model tests, six-degrees-of-freedom motion of the caisson model and tension on the towline were measured and analyzed. By using the new system, fluctuation of the motion of model and tension on the towline decreased. The variation in the draft and initial trim was applied in the model tests. In the initial trim condition, the motion and towing force decreased.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.