• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.1-10
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• 2003

This paper presents a high-precision magnetically levitated (maglev) stage to meet demanding motion specifications in the next-generation precision manufacturing and nanotechnology. Characterization of dynamic behaviors of such a motion stage is a crucial task. In this paper, we address the issues related to the stochastic modeling of the stage including transfer function identification, and noise/disturbance analysis and prediction. Provided are test results on precision dynamics, such as fine settling, effect of optical table oscillation, and position ripple. To deal with the dynamic coupling in the platen, we designed and implemented a multivariable linear quadratic regulator, and performed time-optimal control. We demonstrated how the performance of the current maglev stage can be improved with these analyses and experimental results. The maglev stage operates with positioning noise of 5 nm rms in $\chi$ and y, acceleration capabilities in excess of 2g(20 $m/s^2$), and closed-loop crossover frequency of 100 Hz.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.369-376
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• 2022

Accurately evaluating and predicting the performance of facilities is a key task in establishing a maintenance strategy for facilities. The importance of performance evaluation is becoming more pronounced, especially when the aging of facilities requires a huge budget. In this study, performance assessment models were developed for road and railway tunnels. Delphi analysis was performed to identify sub-elements necessary to evaluate the performance of a tunnel. The relative importance of the evaluation factors was derived from the AHP analysis. The correlation analysis was performed between each assessment factor and the final result to verify the significance of the model. For the correlation analysis, the survey data measured through precision safety diagnosis in tunnels in use was applied. The cost effectiveness analysis was also conducted according to the scenarios with different composition of performance factors in order to improve the practical applicability of the evaluation model developed in this study.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.67-72
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• 2005

In a park or street, we can see many people jogging or walking with their dogs that are chasing their masters. In this study, a pet robot that imitates dog's behavior is developed. The task of robot is to chase a person who is recognized as the master. The physical structure and the sensor system are designed for the task and environment. A three-wheel type locomotion system is designed as the robot's physical structure which can follow a person who is jogging in outdoor environment like a park. A sensor system, which can detect relative position of the master to the robot in highly dynamic and hazardous worlds, is developed. This sensor system consists of a signal transmitter which is held by the master and ultrasonic sensor array which are mounted on the robot. The transmitter emits RF (radio frequency) and ultrasonic signals simultaneously. The ultrasonic sensor array detects the signals and calculates direction and distance between the robot and the transmitter. The developed RF-ultrasonic sensor is evaluated through experiments. A purely reactive behavior-based control architecture is used for the robot. The behavior control performance of the robot is assessed in outdoor and indoor tests.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.40-47
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work the authors presented an iterative precision of linear decentralized learning control based on p-integrated teaming method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the loaming control field was learning in robots doing repetitive tasks such as on a]1 assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.211-217
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the teaming control field was teaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.200-207
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• 2005

This study compares two different methods of measuring brain-BOLD activation. By comparing two different methods of measurement i.e., one method calculating the neural activation area (the number of activated voxels), while the other measured the neural activation intensity (the mean intensity of selected activated yokels), this study identified the more precise method of measuring brain activation which results from the completion of a visuospatial task. 16 right-handed male college students (mean age 23.2 years) participated in this study as subjects. Functional brain images were scanned on them using a 3T MRI single-shot EPI method. No correlation was found between the levels of cognitive performance and number of activated yokels in the activated brain areas. However, a significant correlation was found between the levels of cognitive performance and the mean intensity of selected activated yokels in the parietal, frontal, and other areas. In conclusion, the method of mean intensity was considered a better index of brain activity rather than the activated yokels measurement method.

• Korean Journal of Applied Biomechanics
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• v.30 no.2
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• pp.131-144
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• 2020

Objective: The purpose of this study was to examine the effect of degrees of freedom on the multi-synergies in two hierarchies of human hand system during force production and releasing tasks. Method: In this study, the constrained movements of the aiming and releasing actions using both hands and fingers during archery-like shooting were implemented as experimental tasks. The participants produced a pulling force holding the customized frame (mimicking an archery bow, with a set of force transducers) and kept it consistently for about 5 seconds, and released fingers as quickly as possible in a self-paced manner within the next 5 seconds. An analytical method based on the uncontrolled manifold hypothesis was used to quantify the stability index (synergy index) in two hierarchies including two hands (upper hierarchy) and individual fingers (lower hierarchy). Results: The results confirmed that the positive synergy pattern showed simultaneously at the upper and lower hierarchies, and the kinetic degrees of freedom were associated with the increment of hierarchical synergy indices and the performance indices. Also, the synergy indices of both hierarchies showed significant positive correlations with the performance accuracy during the task. Conclusion: The results of this study suggest that the human control system actively uses extra degrees of freedom to stabilize task performance variables. Further increasing the degree of freedom at one level of hierarchy induces positive interactions across hierarchical control levels, which in turn positively affects the accuracy and precision of task performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.319-327
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• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.