• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.27-32
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• 2024

Recently, there has been a growing interest in integrating physical toys into video gaming within the game content business. This paper introduces a novel method that leverages low-cost camera as an alternative to using sensor attachments to meet this rising demand. We address the limitations associated with low-cost cameras and propose an optical design tailored to the specific environment of model car recognition. We overcome the inherent limitations of low-cost cameras by proposing an optical design specifically tailored for model car recognition. This approach primarily focuses on recognizing the underside of the car and addresses the challenges associated with this particular perspective. Our method employs a transfer learning model that is specifically trained for this task. We have achieved a 100% recognition rate, highlighting the importance of collecting data under various camera exposures. This paper serves as a valuable case study for incorporating low-cost cameras into vision systems.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.616-618
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• 2008

This paper propose nonlinear electromagnet suspension system model and defines electromagnet design specification of Maglev vehicle for transport system of semiconductor manufacturing line. The bandwidth of the acceleration sensor is defined and manufactured using i-mems technique acceleration sensor from this Nonlinear electromagnet suspension model. Through the simulation of non-linear model it was possible to compare the gain with the experiment to confirm the accuracy of the nonlinear suspension system. Till now mainly linear model has been used in many electromagnet suspension system, which results different gains in simulation and experiments. This more accurate non-linear model can be applied in many ways in designing electromagnet suspension systems.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Journal of Drive and Control
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• v.16 no.1
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• pp.29-35
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• 2019

The control performance of hydraulic systems is basically influenced by the performance of electrohydraulic servo valve incorporated in a hydraulic control system. In this study, a control design was proposed to improve the control performance of a servo valve with a non-contact eddy current type position sensor. A mathematical model for the valve was obtained through an experimental identification process. A PI-D control together with a feedforward (FF) control was applied to the valve. To further improve the dynamic response of the servo valve, an input shaping filter (ISF) was incorporated into the valve control system. Finally, the effectiveness of the proposed control system was verified experimentally.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.196-196
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.253-262
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• 2014

3D city model, which consisted of the 3D building models and their geospatial position and orientation, is becoming a valuable resource in virtual reality, navigation systems, civil engineering, etc. The purpose of this research is to propose the new framework to generate the 3D city model that satisfies visual and physical requirements in ground oriented simulation system. At the same time, the framework should meet the demand of the automatic creation and cost-effectiveness, which facilitates the usability of the proposed approach. To do that, I suggest the framework that leverages the mobile mapping system which automatically gathers high resolution images and supplement sensor information like position and direction of the image. And to resolve the problem from the sensor noise and a large number of the occlusions, the fusion of digital map data will be used. This paper describes the overall framework with major process and the recommended or demanded techniques for each processing step.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.661-668
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• 2012

Wireless sensor network is one of prospective methods for railway monitoring due to the long-term operation and low-maintenance performances. How to supply power to the wireless sensor nodes has drawn much attention recently. In railway monitoring, the idea of converting ambient vibration energy from vibration of railway track induced by passing trains to electric energy has made it a potential way for powering the wireless sensor nodes. In this paper, a bimorph cantilever piezoelectric energy harvester was designed based on a single degree-of-freedom model. Experimental test was also performed to validate the design. The first natural frequency of the bimorph piezoelectric energy harvester was decreased from 117.1 Hz to 65.2 Hz by adding 4 gram tip mass to the free end of the 8.6 gram energy harvester. In addition, the power generation of the piezoelectric energy harvester with 4 gram tip mass at resonant frequency was increased from 0.14 mW to 0.74 mW from $2.06m/s^2$ base excitation compared to stand-alone piezoelectric energy harvester without tip mass.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.86-90
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• 2016

A 3D ultrasonic anemometer measures the direction and velocity of wind in a 3D space. The 2D ultrasonic anemometers developed by different manufacturers do not differ significantly in terms of their form or structure. The 3D ultrasonic anemometers, on the other hand, have more diverse forms than their 2D counterparts depending on the measurement algorithms and methods. Designing and reviewing the structure at the initial stage and defining its performance objectives are time-consuming processes. The process can be made cost-effective and time-saving if the validity is tested by model design and structural interpretation, and the structure is designed to withstand high wind velocities. This study presents the results of a 3D ultrasonic anemometer on real sample data by using a 3D modeling program, CATIA, for ultrasonic anemometer modeling.