• Journal of IKEEE
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• v.26 no.2
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• pp.265-270
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• 2022

This paper proposes the detection algorithm for switch open fault of asymmetric 6-phase PMSM based on stationary reference frame dq-axis currents. In this paper, target motor has an asymmetric structure in which two upper three windings have an electrical phase difference of 30° and a neutral point is separated. As a result, dual 3-phase PWM inverters and the detection techniques due to open failures of switch are definitely required. In this paper, the dual dq-axis current control method is used for driving the asymmetric 6-phase PMSM and the open fault switch should be detected by using variable all pass filter and low pass filter in order to detect the current amplitude. The effectiveness and usefulness of the proposed method is verified by several experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.181-186
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• 2022

The world's electric automobile sector has shifted beyond technological environmental changes to a stage that has an impact on the market environment. And automakers are shifting from the existing strategy of "technological advantage → brand enhancement → sales expansion of existing internal combustion engine vehicles" to the expansion of the electric automobile market itself, which is to enhance market competitiveness. In addition, competition in the electric automotive parts market is expected to intensify due to the expansion of the business areas of existing parts makers and the entry of new companies, and development cooperation is expected to actively proceed to improve the efficiency of major eco-friendly parts. Along with this prospect, electric vehicles are expected to change the overall structure of the automobile industry, the overall growth of the electric vehicle value chain such as batteries, power trains (motors, power management and control systems), electric vehicle production, and charging infrastructure Is expected. Therefore, in this thesis, in order to cultivate a variety of high-quality human resources that companies want to keep pace with the changing automobile industry, we study a professional manpower training program that leads the growth engine of the electric automobile industry.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.679-686
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• 2022

In this paper, we implemented an algae removal device that recognizes and removes algae existing in water using Raspberry Pi camera and DO (Dissolved Oxygen) sensor. The Raspberry Pi board recognizes the color of green algae by converting the RGB values obtained from the camera into HSV. Through this, the location of the algae is identified and when the amount of dissolved oxygen's decrease at the location is more than the reference value using the DO sensor, the algae removal device is driven to spray the algae removal solution. Raspberry Pi's camera uses OpenCV, and the motor movement is controlled according to the output value of the DO sensor and the result of the camera's green algae recognition. Algae recognition and spraying of algae removal solution were implemented through Arduino and Raspberry Pi, and the feasibility of the proposed portable algae removal device was verified through experiments.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Fire Science and Engineering
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• v.26 no.1
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• pp.61-67
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• 2012

In the three-phase power system using the three-phase motor, when any one-phase is open-phase, the unbalanced current flows and the single-phase power supplied by power supply produces overcurrent to motor coil. As a result, the enormous damage and electrical fire can be given to the power system. Recently, the thermal over-current relay (THR) or electronic motor protection relay (EMPR) is mostly used as the open-phase detection device of the three-phase motor. When the over-current or overheat of electric line is generated, it detects and operates circuit breaker, but there is the defect that the sensing speed is slow, the operation can be sometimes failed, and the precision is decreased. In order to improve these problems, this paper is proposed a new control circuit topology for openphase protection using semiconductor devices. Therefore, the proposed open-phase protection device (OPPD) enhances the sensing speed and precision, and has the advantage of simple fitting in the three-phase motor control panel in the field, as it manufactures into small size and light weight. As a result, the proposed OPPD protects the three-phase motor, minimizes the electrical fire from openphase, and contributes for the stable driving of the power system. The performance and confidence of the proposed OPPD is confirmed by a great variety of the experiments of operation characteristic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.828-834
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• 2013

In this paper, TMC (Tracker Motion Controller) as one of the many research methods for condensing efficiency improvements can be condensed into efficient solar system configuration to improve the power generation efficiency of the castle with Concentrated solar silicon and photovoltaic systems (CPV)experiments using PV systems. Microprocessor used on the solar system, tracing the development of solar altitude and latitude of each is calculated in real time. Also accept the value from the sensor, motor control and communication with the central control system by calculating the value of the current position of the sun, there is a growing burden on the applicability. Through the way the program is appropriate for solar power systems and sensors hybrid-type algorithm was implemented in the ARM core with built-in TMC, Concentrated CPV system compared to the existing PV systems, through the implementation of the TMC in the country's power generation efficiency compared and analyzed. Sensor method using existing experimental results Concentrated solar power systems to communicate the value of GPS location tracking method hybrid solar horizons in the coordinate system of the sun's azimuth and elevation angles calculated by the program in the calculations of astronomy through experimental resultslook clear day at high solar irradiation were shown to have a large difference. Stopped after a certain period of time, the sun appears in the blind spot of the sensor, the sensor error that can occur from climate change, however, do not have a cloudy and clear day solar radiation sensor does not keep track of the position of the sun, rather than the sensor of excellence could be found. It is expected that research is constantly needed for the system with ongoing research for development of solar cell efficiency increases to reduce the production cost of power generation, high efficiency condensing type according to the change of climate with the optimal development of the ability TMC.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.161-168
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• 2009

In this paper, we develop an Intelligent Wheelchair(IW) control system for the people with various disabilities. The aim of the proposed system is to increase the mobility of severely handicapped people by providing an adaptable and effective interface for a power wheelchair. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an Intelligent Wheelchair(IW) is determined by the inclination of the user's face, while proceeding and stopping are determined by the shape of the user's mouth. To analyze these gestures, our system consists of facial feature detector, facial feature recognizer, and converter. In the stage of facial feature detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region detected based on edge information. The extracted features are sent to the facial feature recognizer, which recognize the face inclination and mouth shape using statistical analysis and K-means clustering, respectively. These recognition results are then delivered to a converter to control the wheelchair. When assessing the effectiveness of the proposed system with 34 users unable to utilize a standard joystick, the results showed that the proposed system provided a friendly and convenient interface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.1-8
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• 2020

Air ducts installed for ventilation inside buildings accumulate contaminants during their service life. Robots are installed to clean the air duct at low cost, but they are still not fully automated and depend on manpower. In this study, an intersection detection algorithm for autonomous driving was applied to an air duct cleaning robot. Autonomous driving of the robot was achieved by calculating the distance and angle between the extracted point and the center point through the intersection detection algorithm from the camera image mounted on the robot. The training data consisted of CAD images of the duct interior as well as the cross-point coordinates and angles between the two boundary lines. The deep learning-based CNN model was applied as a detection algorithm. For training, the cross-point coordinates were obtained from CAD images. The accuracy was determined based on the differences in the actual and predicted areas and distances. A cleaning robot prototype was designed, consisting of a frame, a Raspberry Pi computer, a control unit and a drive unit. The algorithm was validated by video imagery of the robot in operation. The algorithm can be applied to vehicles operating in similar environments.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1585-1592
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• 2018

Due to un-balancing weight allocation on the wheelchair the existing wheelchair system are faced with the risk of flipping or falling when a wheelchair goes up to a hill. In to order to be safer during riding the wheelchair, in this paper, we proposed a real-time new solution using the integrated Gyro Sensor and Tilt Sensor for controlling the balance. Because the typical property of wheelchair is for the special user who meets the difficulty in moving on foot the maintain the balance of wheel-chair systems have become important and helpful. In our method, we calculate the seat angle using information from Tilt Sensor. However, due to the law of inertia when a wheelchair is moving there is a deviation in the output value of Tilt Sensor. Therefore, we have to optimize the value of the angle by utilizing the acceleration that is the output of the Gyro Sensor. We took the advantages by using the combination of Gyro and Tilt sensors. Moreover, we also solved the consumption issue of the whole system. Through various experimentations with usage of ZigBee sensor module, the power consumption for the balancing system is reduced significantly.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.269-274
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• 2013

To proportionally control the electronic valve position of the actuator, we designed and fabricated PCU, CPT, and rotary absolute optical encoder for the detection of absolute angular position in the actuator. We also designed and constructed the test system by using DAQ hardware and Labview. We designed PCU to convert 1-5 V, 0-5 V, 0-10 V, 2-10 V voltage signals and 4-20 mA current signals to the voltage signals in the common 0.5-2.5 V range. We designed CPT to output 4-20 mA current signals corresponding to the valve positions based on the PWM signal input from the MCU. We also designed 20 bit optical encoder by using infrared LED and infrared transistor and made the serial communication with the main board possible. When we tested PCU and CPT with DAQ hardware and Labview software, they operated correctly with the small errors within ${\pm}0.003$ V and ${\pm}0.01$ mA, respectively, showing that our actuator has the excellent performance to employ as the industrial proportional-valve-actuator. The resolution of the encoder was $11.25^{\circ}$ and the maximum revolution to detect was 32,768.