• Molecules and Cells
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• v.45 no.10
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• pp.695-701
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• 2022

Homeostatic regulation of meristematic stem cells accomplished by maintaining a balance between stem cell self-renewal and differentiation is critical for proper plant growth and development. The quiescent center (QC) regulates root apical meristem homeostasis by maintaining stem cell fate during plant root development. Cell cycle checkpoints, such as anaphase promoting complex/cyclosome/cell cycle switch 52 A2 (APC/C^CCS52A2), strictly control the low proliferation rate of QC cells. Although APC/C^CCS52A2 plays a critical role in maintaining QC cell division, the molecular mechanism that regulates its activity remains largely unknown. Here, we identified SCF^FBS1, a ubiquitin E3 ligase, as a key regulator of QC cell division through the direct proteolysis of CCS52A2. FBS1 activity is positively associated with QC cell division and CCS52A2 proteolysis. FBS1 overexpression or ccs52a2-1 knockout consistently resulted in abnormal root development, characterized by root growth inhibition and low mitotic activity in the meristematic zone. Loss-of-function mutation of FBS1, on the other hand, resulted in low QC cell division, extremely low WOX5 expression, and rapid root growth. The 26S proteasome-mediated degradation of CCS52A2 was facilitated by its direct interaction with FBS1. The FBS1 genetically interacted with APC/C^CCS52A2-ERF115-PSKR1 signaling module for QC division. Thus, our findings establish SCF^FBS1-mediated CCS52A2 proteolysis as the molecular mechanism for controlling QC cell division in plants.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.6
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• pp.573-582
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• 2010

In this paper, X-band transceiver for high resolution wideband SAR systems is designed and fabricated. Also as a technique for enhancing the performance, error compensation algorithm is presented. The transceiver for SAR system is composed of transmitter, receiver, switch matrix and frequency generator. The receiver especially has 2 channel mono-pulse structure for ground moving target indication. The transceiver is able to provide the deramping signal for high resolution mode and select the receive bandwidth for receiving according to the operation mode. The transceiver had over 300 MHz bandwidth in X-band and 13.3 dBm output power which is appropriate to drive the T/R module. The receiver gain and noise figure was 39 dB and 3.96 dB respectively. The receive dynamic range was 30 dB and amplitude imbalance and phase imbalance of I/Q channel was ${\pm}$0.38 dBm and ${\pm}$3.47 degree respectively. The transceiver meets the required electrical performances through the individual tests. This paper shows the pulse error term depending on SAR performance was analyzed and range IRF was enhanced by applying the compensation technique.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.1 s.343
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• pp.111-120
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• 2006

In this paper, a node architecture of WDM metro network for connecting broadband access networks to converge wire/wireless networks. In consideration of the proposed node architecture and network requirements we proposed and evaluated medium access control protocols. We review WDM related technologies of sub-carrier multiplexing and optical components in order to resolve the bottleneck between optical backbone networks md access networks, and a access node architecture sharing common wavelength is introduced. Source-stripping (SS) MAC protocol Is evaluated under the proposed functional node architecture. DS+IS (Destination-Stripping and Source-Stripping) and DS+IS (Destination-Stripping and Intermediate-Stripping) MAC protocols are described to increase the slot-reuse factor which is low on SS MAC protocol. The key function of new MAC protocols regards the optical switch module of proposed node architecture and helps intermediate or source access nodes for dropping slots to destinations of different wavelength group. Thus, slot-reuse factor increases as the MAC protocols reduce the unnecessary ring-rotation of transferred slots. We use a numerical analysis to expect bandwidth efficiency and maximum throughput by slot-reuse factor Throughput network simulation, the verification of throughput, queuing delay, and transmission fairness are compared among MAC protocols.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.745-752
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• 2016

In the era of the Internet of Things, where all physical objects are connected to the Internet, we suggest a remote control system using a Raspberry Pi single-board computer with ZigBee, which can turn an indoor light-emitting diode (LED) and a multiple-tap on and off, and with a smart phone can control the brightness of the LED as well as an electronic door lock. By connecting an infrared (IR) transmitter module to the Raspberry Pi, we can control home appliances, such as an air conditioner, and we can also monitor indoor images, indoor temperatures, and illumination by using a smart phone app. We developed a method of finding out IR transmission codes required for remote-controllable appliances with an AVR micro-controller. We suggest a method to remotely open and shut an office door by novating the door lock. The brightness level of an LED (between 0 and 10) can be controlled through a PWM signal generated by an ATmega88 microcontroller. A mutiple-tap is controlled using an ATmega32, a photo-coupler, and a TRIAC. The signals for measured temperature and illumination are converted from analog to digital by using the ATtiny44A microcontroller transmitting to a Raspberry Pi through SPI communication. Then, we connect a camera to the CSI head of the Raspberry Pi. We can turn on the smart multiple-tap for a certain period of time, or we can schedule the multi-tap to turn on at a specific time. To reduce standby power, people usually pull out a power code from multiple-taps or turn off a switch. Our method helps people do the same thing with a smart phone, if they are away from home.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.