Kyung Kwon Jung

Publications (4)1.74 Total impact

• Chapter: The Group Management System Based on Wireless Sensor Network and Application on Android Platform

  Seung Joon Lee, Min Chul Kim, Yeo Sun Kyung, Kyung Kwon Jung, Joo Woong Kim, Yong Gu Lee, Ki Hwan Eom
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  ABSTRACT: This paper presents a group management system with WSN(Wireless Sensor Network) and smart phone devices. The proposed system was comprised of personal device based on WSN, smart phone device which is used by group manager and web server. The sensor node called as personal device which is used by group members sends a data packet every 2 seconds to the manager device. The manager can check their group member’s distance from him within a 30-meter radius and battery residual quantity with the manager device. Manager device sends its latitude and longitude data from GPS(Global Positioning System) and information of personal devices to web server. Therefore, other person such as group member’s parents can assure their children’s safety and security through the web page. The RSSI value from sensor node was converted into distance data by computed log-normal path loss model. KeywordsGroup Management System–Wireless Sensor Network–Android Platform–Web Server–Log-Normal Path Loss Model
  09/2011: pages 152-159;
• Chapter: The Study of Low Power Consumption for Single Hop Wireless Sensor Network

  Min Chul Kim, Seung Joon Lee, Yeo Sun Kyung, Hyun Kwan Lee, Sung Boo Jung, Kyung Kwon Jung, Ki Hwan Eom
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  ABSTRACT: This paper proposed the Low power configuration of Single hop WSN(Wireless Sensor Network) system. When the RF communication is done each tag node during the WSN systems operating, power consumption is greatest. There for, if you configure the Network with the RF communication module turn on/off periodically, power consumption less then operating the module all the time without it toggles. However, some data omissions may occur in which transmission and receipt is done. So this paper proposed the algorithm for low power system without data omissions. KeywordsWSN–Low power algorithm–RF module Toggle–Data omission–Power consumption
  09/2011: pages 143-151;
• Article: Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.

  Ki Hwan Eom, Seung Joon Lee, Yeo Sun Kyung, Chang Won Lee, Min Chul Kim, Kyung Kwon Jung
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  ABSTRACT: Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.
  Sensors 01/2011; 11(11):10266-82. · 1.74 Impact Factor
• Chapter: Noise Reducing of Multi-sensor RFID System by Improved Kalman Filter

  Yeosun Kyung, Seung Joon Lee, Minchul Kim, Chang Won Lee, Kyung Kwon Jung, Ki-Hwan Eom
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  ABSTRACT: For reducing noise in multi-sensor RFID (Radio Frequency Identification) system, we proposed the GA-Kalman Filter method in this paper. The proposed method is that membership functions of the fuzzy logic system are optimized by genetic algorithm (GA) under off-line, and then fuzzy logic system is constructed by the optimization parameters under on-line. Multi-sensors, humidity, oxygen and temperature, are used to our experiments, and are impacted by correlated noises. One of the most important factors of RFID sensor network system is accuracy in sensor data measurement. However, correlated noises are occurred in multi-sensor system. Kalman Filter has been widely applied to solve the noise problem which is occurred sensor data measurement. In this paper, the proposed GA-Fuzzy Kalman Filter method has the noise reducing compared to the general Kalman Filter method. KeywordsMulti-Sensor System–Kalman Filter–RFID–GA-Fuzzy Kalman Filter–Noise Reducing
  01/1970: pages 170-180;