• International Journal of Precision Engineering and Manufacturing
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• v.6 no.2
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• pp.19-25
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• 2005

In this paper, the feedforward control with least mean square (LMS) adaptive algorithm is proposed and examined to reduce rotating error by runout of an active magnetic bearing system. Using eddy-current type gap sensors for control, the electrical runout caused by non-uniform material properties of sensor target produces rotational error amplified in feedback control loop, so this runout should be eliminated to increase rotating accuracy. The adaptive feedforward controller is designed and examined its tracking performances and stability numerically with established frequency response function. The designed feedforward controller was applied to a grinding spindle system which is manufactured with a 5.5 kW internal motor and 5-axis active magnetic bearing system including 5 eddy current gap sensors which have approximately 15∼30㎛ of electrical runout. According to the experimental results, the error signal in radial bearings is reduced to less than 5 ,Urn when it is rotating up to 50,000 rpm due to applying the feedforward control for first order harmonic frequency, and corresponding vibration of the spindle is also removed.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.222-230
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• 2003

Artificial intelligence has been used in the configuration design stage of high-speed train. The traction system of a high-speed train is composed of transformers, motor blocks, and traction motors of which locations and number in the trainset should be determined in the early stage of the train conceptual design. Components of the traction system are heavy parts in the train, so it gives strong influence to the top speeds and overall train configuration of high-speed trains. Top speeds have been predicted using the neural network with the associated data of the traction system. The neural networks have been learned with data sets of many commercially operated high-speed trains, and the predicted results have been compared with the actual values. The configuration design of the train set of a high-speed train determines the basic specification of the train and layout of the traction system. The neural networks is a useful design tool when there is not sufficient data for the configuration design and we need to use the existing data of other train for the prediction of trainset in development.

• Korean Journal of Applied Biomechanics
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• v.34 no.2
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• pp.71-80
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• 2024

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (f_CoP, f_AJtorque and f_EEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using f_EEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

• Clinical and Experimental Pediatrics
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• v.63 no.11
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• pp.438-446
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• 2020

Background: Most developmental screening tools in Korea are adopted from foreign tests. To ensure efficient screening of infants and children in Korea, a nationwide screening tool with high reliability and validity is needed. Purpose: This study aimed to independently develop, standardize, and validate the Korean Developmental Screening Test for Infants and Children (K-DST) for screening infants and children for neurodevelopmental disorders in Korea. Methods: The standardization and validation conducted in 2012-2014 of 3,284 subjects (4-71 months of age) resulted in the first edition of the K-DST. The restandardization and revalidation performed in 2015-2016 of 3.06 million attendees of the National Health Screening Program for Infants and Children resulted in the revised K-DST. We analyzed inter-item consistency and test-retest reliability for the reliability analysis. Regarding the validation of K-DST, we examined the construct validity, sensitivity and specificity, receiver operating characteristic curve analysis, and a criterion-related validity analysis. Results: We ultimately selected 8 questions in 6 developmental domains. For most age groups and each domain, internal consistency was 0.73-0.93 and test-retest reliability was 0.77-0.88. The revised K-DST had high discriminatory ability with a sensitivity of 0.833 and specificity of 0.979. The test supported construct validity by distinguishing between normal and neurodevelopmentally delayed groups. The language and cognition domain of the revised K-DST was highly correlated with the K-Bayley Scales of Infant Development-II's Mental Age Quotient (r=0.766, 0.739), while the gross and fine motor domains were highly correlated with Motor Age Quotient (r=0.695, 0.668), respectively. The Verbal Intelligence Quotient of Korean Wechsler Preschool and Primary Scales of Intelligence was highly correlated with the K-DST cognition and language domains (r=0.701, 0.770), as was the performance intelligence quotient with the fine motor domain (r=0.700). Conclusion: The K-DST is reliable and valid, suggesting its good potential as an effective screening tool for infants and children with neurodevelopmental disorders in Korea.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.283-296
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• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.411-416
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• 2006

Fault detection and diagnosis is the most important technology in condition-based maintenance(CBM) system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence techniques. Finally, conclusions are given based on the experiment results.

• Journal of Power Electronics
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• v.8 no.3
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• pp.280-290
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• 2008

This paper deals with an investigation and evaluation of the performance of a state observer based Permanent Magnet Synchronous Motor (PMSM) drive controlled by PI (Proportional Integral), PID (Proportional Integral and Derivative), SMC (sliding mode control), ANN (Artificial neural network) and FLC (Fuzzy logic) speed controllers. A detailed study of the steady state and dynamic performance of estimated speed and angle is given to demonstrate the capability of the controllers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.35-43
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• 2000

This paper was studied on developmental direction of telecommunications for disabilities welfare. Method of this study is delphi method. Persons with disabilities is classed as motor disability, visual handicap, hearing impairment, and language and speech disorders. Persons with motor disability is needs as follow, speed recognition technology, video recognition technology, breath capacity recognition technology. Persons with visual handicap is needs as follow, display recognition technology, speed recognition technology, text recognition technology, intelligence conversion handling technology, video recognition - speed synthetic technology. Persons with hearing impairment and language - speech disorders is needs as follow, speed signal handling technology, speed recognition technology, intelligence conversion handling technology, video recognition technology, speed synthetic technology the results of this study is as follow: first, disabilities telecommunications organization must be constructed. Second, persons with disabilities in need of universal service. Third, Persons with disabilities in need of information education, Fourth, studying for telecommunications in need of support. Fifth, small telecommunications company in need of support. Sixth, software industry in need of new development. Seventh, Persons with disabilities in need of standard guideline for telecommunications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.556-558
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• 2022

This paper proposes a CanSat system with a vehicle tracking function based on Jetson Nano, a high-performance small computer capable of operating artificial intelligence algorithms. The CanSat system consists of a CanSat and a ground station. The CanSat falls in the atmosphere and transmits the data obtained through the installed sensors to the ground station using wireless communication. The existing CanSat is limited to the mission of simply transmitting the collected information to the ground station, and there is a limit to efficiently performing the mission due to the limited fall time and bandwidth limitation of wireless communication. The Jetson Nano based CanSat proposed in this paper uses a pre-trained neural network model to detect the location of a vehicle in each image taken from the air in real time, and then uses a 2-axis motor to move the camera to track the vehicle.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.81-91
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• 2006

Recently, G.A studies have studied and demonstrated that artificial intelligence like G.A networks, G.A PI controller. The design techniques of PI controller using G.A with the newly proposed teaming algorithm was presented, and the designed controller with AC servo motor system. The goal of this paper is to design the AC servo motor using genetic algorithm and to control drive robot. And in this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for genetic algorithm PI controller. Our experimental results show that this approach increases overall classification accuracy rate significantly. Finally, we executed for the implementation of high performance speed control system. It is used a 16-bit DSP, IMS320LF2407, which is capable of the high speed and floating point calculation.