• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.545-552
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• 2010

In this paper, we study on a radio multiple sensing interface module to accurately decide serious error sources that are happened by unexpectable problems in industrial fields, in addition, we study a reliability improvement scheme to guarantee the integrity of multiple sensing data. For multiple sensing interface module, communication drivers such as USART, TWI, ADC and GPIO-I2C are implemented. to improve the transmission reliability, reed-Solomon code is used. From the simulation result of this system in indoor environment, we confirm that the reliability of RS coded data is improved about 5 times than uncoded data. Moreover, we prove that multiple sensing interface module is suitable to diagnose error sources of industrial automaton equipment.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.910-916
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• 2015

A sensorless motor control scheme with conventional back-Electro Motive Force (EMF) sensing based on zero crossing point (ZCP) detection has been widely used in various applications. However, there are several problems with the conventional method for effectively driving sensorless brushless motors. For example, a phase mismatch of 30 degrees occurs between the ZCP and commutation time. Additionally, most of the motor speed/current controls are achieved based on a pulse width modulation (PWM) method, which generates significant noise that distracts the back-EMF sensing. Due to the PWM switching, the ZCP is not deterministic, and thus the efficiency of the motor is reduced because the phase transition points become uncertain. Moreover, the motor driving performance is degraded at a low speed range due to the effect of PWM noise. To solve these problems, an improved back-EMF detection method based on a differential line method is proposed in this paper. In addition, the proposed sensorless BLDC driver addresses the problems by using a variable voltage driver generated from a buck converter. The variable voltage driver does not generate the PWM switching noise. Consequently, the proposed sensorless motor driver improves 1) the signal-to-noise ratio of back-EMF, 2) the operation range of a BLDC motor, and 3) the torque characteristics. The proposed sensorless motor driver is verified through simulations and experiments.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.21-28
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• 2020

In this paper, a new type of electric wheelbarrow is proposed and developed. The developed electric wheelbarrow is equipped with an attitude reference system(ARS) sensor, which consists of 3-axis acceleration sensor and 2-axis Gyro sensor so that it can estimate pitch angle and roll angle. When an operator tilts the wheelbarrow up and down, the pitch angle is detected. The sign of the pitch angle is interpreted as the operator's intention for moving the wheelbarrow forward or backward and the controller drives the wheel of the wheelbarrow with the velocity according to the magnitude of the detected pitch angle. A cargo box of the wheelbarrow is designed to rotate and is controlled to maintain level always, so an operator can handle the electric wheelbarrow easily and safely. The wheelbarrow consists of an in-wheel motor, a DC motor, motor drives, an ARS sensor considering economical use in industrial field. Three experiments are performed to verify the feasibility and stability of the electric wheelbarrow.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.61-76
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• 2018

Everything is coming into the world of network where modern people are living. The Internet of Things that attach sensors to objects allows real-time data transfer to and from the network. Mobile devices, essential for modern humans, play an important role in keeping all traces of everyday life in real time. Through the social network services, information acquisition activities and communication activities are left in a huge network in real time. From the business point of view, customer needs analysis begins with SNS data. In this research, we want to build an automatic collection system of SNS contents of web environment in real time using Python. We want to help customers' needs analysis through the typical data collection system of Instagram, Twitter, and YouTube, which has a large number of users worldwide. It is stored in database through the exploitation process and NLP process by using the virtual web browser in the Python web server environment. According to the results of this study, we want to conduct service through the site, the desired data is automatically collected by the search function and the netizen's response can be confirmed in real time. Through time series data analysis. Also, since the search was performed within 5 seconds of the execution result, the advantage of the proposed algorithm is confirmed.