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RISS 인기검색어
- 검색키워드
- 저자 : Pan Ziying (검색결과 3 건)
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Measurements of pedestrian’s ioad using smartphones
Ziye Pan,Jun Chen 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.6
The applications of smartphones or other portable smart devices have dramatically changed people‟s lifestyle. Researchers have been investigating useage of smartphones for structural health monitoring, earthquake monitoring, vibration measurement and human posture recognition. Their results indicate a great potential of smartphones for measuring pedestrianinduced loads like walking, jumping and bouncing. Smartphone can catch the device‟s motion trail, which provides with a new method for pedestrain load measurement. Therefore, this study carried out a series of experiments to verify the application of the smartphone for measuring human-induced load. Shaking table tests were first conducted in order to compare the smartphones‟ measurements with the real input signals in both time and frequency domains. It is found that selected smartphones have a satisfied accuracy when measuring harmonic signals of low frequencies. Then, motion capture technology in conjunction with force plates were adopted in the second-stage experiment. The smartphone is used to record the acceleration of center-of-mass of a person. The human-induced loads are then reconstructed by a biomechanical model. Experimental results demonstrate that the loads measured by smartphone are good for bouncing and jumping, and reasonable for walking
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Experimental validation of smartphones for measuring human-induced loads
Jun Chen,Huan Tan,Ziye Pan 국제구조공학회 2016 Smart Structures and Systems, An International Jou Vol.18 No.3
The rapid technology developments in smartphones have created a significant opportunity for their use in structural live load measurements. This paper presents extensive experiments conducted in two stages to investigate this opportunity. Shaking table tests were carried out in the first stage using selected popular smartphones to measure the sinusoidal waves of various frequencies, the sinusoidal sweeping, and earthquake waves. Comparison between smartphone measurements and real inputs showed that the smartphones used in this study gave reliable measurements for harmonic waves in both time and frequency domains. For complex waves, smartphone measurements should be used with caution. In the second stage, three-dimensional motion capture technology was employed to explore the capacity of smartphones for measuring the movement of individuals in walking, bouncing and jumping activities. In these tests, reflective markers were attached to the test subject. The markers\' trajectories were recorded by the motion capture system and were taken as references. The smartphone measurements agreed well with the references when the phone was properly fixed. Encouraged by these experimental validation results, smartphones were attached to moving participants of this study. The phones measured the acceleration near the center-of-mass of his or her body. The human-induced loads were then reconstructed by the acceleration measurements in conjunction with a biomechanical model. Satisfactory agreement between the reconstructed forces and that measured by a force plate was observed in several instances, clearly demonstrating the capability of smartphones to accurately assist in obtaining human-induced load measurements.
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Recurrent neural networks for nonparametric modeling of ship maneuvering motion
Hao Lizhu,Han Yang,Shi Chao,Pan Ziying 대한조선학회 2022 International Journal of Naval Architecture and Oc Vol.14 No.1
A Recurrent Neural Network (RNN) model is presented in this paper to predict the ship maneuvering motion. Inputs to the model are the orders of rudder angle and its variation as well as the propeller speed (ship speed) and also the recursive outputs velocities of surge, sway and yaw. The past values for the velocities are retained in the inputs to indicate the influence of historical state of motion on the maneuvering prediction. The KRISO Container Ship (KCS) is taken as the study object. The data obtained from a manoeuvring mathematical model and free-running model test are respectively used to train the neural network. Tactical circles and zigzags are simulated by the RNN, the prediction for maneuvers not involved in the training set shows that the RNN in this paper has good generalization performance. The concept of uncertainty is proposed to be considered in the further work through the analysis.
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