• Journal of the Korean Society for Precision Engineering
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• v.24 no.8 s.197
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• pp.122-129
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• 2007

To evaluate the effect of artificial disc implantation and fusion on the biomechanics of adjacent motion segment, a nonlinear three-dimensional finite element model of whole lumbar spine (L1-S1) was developed. Biomechanical analysis was performed for two different types of artificial disc, ProDisc and SB $Charit{\acute{e}}$ III model, inserted at L4-L5 level and these results were also compared with fusion case. Angular motion of vertebral body, forces on the spinal ligaments and facet joint under sagittal plane loading with a compressive preload of 150 N at a nonlinear three-dimensional finite element model of Ll-S1 were compared. The implant did not significantly alter the kinematics of the motion segment adjacent to the instrumented level. However, $Charit{\acute{e}}$ III model tend to decrease its motion on the adjacent levels, especially in extension motion. Contrast to motion and ligament force changes, facet contact forces were increased in the adjacent levels as well as implanted level for constrained instantaneous center of rotation model, i.e. ProDisc model.

• Korean Journal of Applied Biomechanics
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• v.32 no.3
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• pp.69-79
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• 2022

Objective: The purpose of this study is to analyze trends related to sports and artificial intelligence (AI) to understand the trends and how they change according to time, and to establish methods to apply AI in sports. Both macro and micro perspectives related to sports utilization of AI were analyzed. Method: In this study, after analyzing and discussing various information related to the use of artificial intelligence in the sports through a search of academic journals, papers, books, and websites published recently at nationally and internationally, the application plan of artificial intelligence in the sports field was presented. Results: 1) Motion analysis technology using artificial intelligence is effective in sports where posture is important, and if it provides systematic feedback and training methods, it can help improve performance. 2) The introduction of a sports referee judgment system using artificial intelligence is expected to improve performance by restoring factual judgment and objective fairness in sports games. 3) Artificial intelligence will provide coaching staff and players with a variety of information to help improve performance through systematic coaching and improving feedback and enhanced training methods. 4) It is judged that artificial intelligence-related to sports ethics, sports ICT, sports marketing, sports prediction, etc. We think that based on the current AI research trends will have a positive impact on all sports-related areas, helping to revitalize sports. Conclusion: Motion analysis technology using artificial intelligence, sports referee judgment system, coaching using artificial intelligence, and artificial intelligence are judged to have a positive effect on all sports-related areas and help revitalize sports.

• Structural Engineering and Mechanics
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• v.37 no.6
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• pp.575-592
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• 2011

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.176-182
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• 2006

Although several artificial disc designs have been developed for the treatment of discogenic low back pain, biomechanical changes with its implantation were rarely studied. To evaluate the effect of artificial disc implantation on the biomechanics of functional spinal unit, a nonlinear three-dimensional finite element model of L4-L5 was developed with 1-mm CT scan data. Biomechanical analysis was performed for two different types of artificial disc having constrained and unconstrained instant center of rotation(ICR), ProDisc and SB Charite III model. The implanted model predictions were compared with that of intact model. Angular motion of vertebral body, forces on the spinal ligaments and facet joint, and stress distribution of vertebral endplate for flexion-extension, lateral bending, and axial rotation with a compressive preload of 400N were compared. The implanted model showed increased flexion-extension range of motion compared to that of intact model. Under 6Nm moment, the range of motion were 140%, 170% and 200% of intact in SB Charite III model and 133%, 137%, and 138% in ProDisc model. The increased stress distribution on vertebral endplate for implanted cases could be able to explain the heterotopic ossification around vertebral body in clinical observation. As a result of this study, it is obvious that implanted segment with artificial disc suffers from increased motion and stress that can result in accelerated degenerated change of surrounding structure. Unconstrained ICR model showed increased in motion but less stress in the implanted segment than constrained model.

• Journal of Navigation and Port Research
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• v.48 no.2
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• pp.116-124
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• 2024

Automatic docking of small planing ship is a critical aspect of maritime operations, requiring accurate prediction of motion states to ensure safe and efficient maneuvers. This study investigates the use of Artificial Neural Network (ANN) to predict motion state of a small planing ship to enhance navigation automation in port environments. To achieve this, simulation tests were conducted to control a small planing ship while docking at various heading angles in calm water and in waves. Comprehensive analysis of the ANN-based predictive model was conducted by training and validation using data from various docking situations to improve its ability to accurately capture motion characteristics of a small planing ship. The trained ANN model was used to predict the motion state of the small planning ship based on any initial motion state. Results showed that the small planing ship could dock smoothly in both calm water and waves conditions, confirming the accuracy and reliability of the proposed method for prediction. Moreover, the ANN-based prediction model can adjust the dynamic model of the small planing ship to adapt in real-time and enhance the robustness of an automatic positioning system. This study contributes to the ongoing development of automated navigation systems and facilitates safer and more efficient maritime transport operations.

• Progress in Medical Physics
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• v.24 no.1
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• pp.76-83
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• 2013

Previous studies about effect of respiratory motion on diagnostic imaging and radiation therapy have been performed by monitoring external motions but these can not reflect internal organ motion well. The aim of this study was to develope the artificial pulmonary nodule able to perform non-invasive implantation to dogs in the thorax and to evaluate applicability of the model to respiratory motion studies on PET image acquisition and radiation delivery by phantom studies. Artificial pulmonary nodule was developed on the basis of 8 Fr disposable gastric feeding tube. Four anesthetized dogs underwent implantation of the models via trachea and implanted locations of the models were confirmed by fluoroscopic images. Artificial pulmonary nodule models for PET injected $^{18}F$-FDG and mounted on the respiratory motion phantom. PET images of those acquired under static, 10-rpm- and 15-rpm-longitudinal round motion status. Artificial pulmonary nodule models for radiation delivery inserted glass dosemeter and mounted on the respiratory motion phantom. Radiation delivery was performed at 1 Gy under static, 10-rpm- and 15-rpm-longitudinal round motion status. Fluoroscpic images showed that all models implanted in the proximal caudal bronchiole and location of models changed as respiratory cycle. Artificial pulmonary nodule model showed motion artifact as respiratory motion on PET images. SNR of respiratory gated images was 7.21. which was decreased when compared with that of reference images 10.15. However, counts of respiratory images on profiles showed similar pattern with those of reference images when compared with those of static images, and it is assured that reconstruction of images using by respiratory gating improved image quality. Delivery dose to glass dosemeter inserted in the models were same under static and 10-rpm-longitudinal motion status with 0.91 Gy, but dose delivered under 15-rpm-longitudinal motion status was decreased with 0.90 Gy. Mild decrease of delivered radiation dose confirmed by electrometer. The model implanted in the proximal caudal bronchiole with high feasibility and reflected pulmonary internal motion on fluoroscopic images. Motion artifact could show on PET images and respiratory motion resulted in mild blurring during radiation delivery. So, the artificial pulmonary nodule model will be useful tools for study about evaluation of motion on diagnostic imaging and radiation therapy using laboratory animals.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.229-237
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• 2022

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.907-910
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• 2005

Although several artificial disc designs have been developed for the treatment of discogenic low back pain, biomechanical change with its implantation was rarely studied. To evaluate the effect of artificial disc implantation on the biomechanics of functional spinal unit, nonlinear three-dimensional finite element model of L4-L5 was developed with 1-mm CT scan data. Two models implanted with artificial discs, SB $Charit\acute{e}$ or Prodisc, via anterior approach were also developed. The implanted model predictions were compared with that of intact model. Angular motion of vertebral body, force on spinal ligaments and facet joint, and the stress distribution of vertebral endplate for flexion-extension, lateral bending, and axial rotation with a compressive preload of 400 N were compared. The implanted model showed increased flexion-extension range of motion and increased force in the vertically oriented ligaments, such as ligamentum flavum, supraspinous ligament and interspinous ligament. The increase of facet contact force on extension were greater in implanted models. The incresed stress distribution on vertebral endplate for implanted cases indicated that additinal bone growth around vertebral body and this is matched well with clinical observation. With axial rotation moment, relatively less axial rotation were observed in SB $Charit\acute{e}$ model than in ProDisc model.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

• ETRI Journal
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• v.36 no.6
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.