• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.256-258
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• 2007

A charge equalization converter with parallel-connected primary windings of transformers is proposed in digest. The proposed work effectively balance the voltage among Lithium-Ion battery cells despite each battery cell has low voltage gap compared with its SOC. The principle of the proposed work is that the equalizing energy from all battery strings moves to the lowest voltage battery through the isolated dc/dc converter controlled by the corresponding bi-directional switch. In this digest, a prototype of four Lithium-Ion battery cells is optimally designed and implemented, and experimental results show that the proposed method has excellent cell balancing performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4773-4779
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• 2012

In this paper we propose two types of reconfigurable 3-D beam steering antenna for intelligent or smart antenna system. Proposed antennas are composed of triangular(structure1.) or circuler(structure2.) loop structure and bended dipole antenna structure. This antenna can steer beam pattern of 6 direction at xy-plane state (0, 1, 2) and xz-plane state (3, 4, 5) by 4 switch motion with one antenna element. Antenna structure1. is symmetric equilibrium structures based on feeding point. There is no grounding point. As a result, designed antenna's gain is similar to dipole antenna. Also, As unbalanced structure by using CPWG in the form of a semicircular, structure2. is enhanced directivity. The operation frequency of antenna are 2.5 GHz(Structure1.) and 2.55 GHz(Structure2.), maximum gain is 1.04 ~ 2.06 dBi(Structure1. : Omni-directional beam), 1.6 ~ 4 dBi(structure2. : Directional beam). The overall HPBW is about over $160^{\circ}$ in the both of the xy-plane and xz-plane at structure1. and over $125^{\circ}$ at structure2.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.386-386
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• 2012

We studied a bilateral switching device for spin transfer torque (STT-MRAM) based on 3D device simulation. Metal/P/N+or Metal/N/P+ junction device with $30{\times}30nm2$ area which is composed of one side schottky junction at Metal/P/N+ and Metal/N/P+ provides sufficient bidirectional current flow to write data by a drain induced barrier lowering (DIBL). In this work, Junction device confirmed that write current is more than 30 uA at 2 V, It is also has high on-off ratio over 105 under read operation. Junction device has good process feasibility because metal material of junction device could have been replaced by bottom layer of MTJ. Therefore, additional process to fabricate two outer terminals is not need. so, it provides simple fabrication procedures. it is expected that Metal/P/N+ or Metal/N/P+ structure with one side schottky junction will be a promising switch device for beyond 30 nm STT-MRAM.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.75-77
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• 2018

최근 다양한 산업응용분야에서 가변커패시터의 필요성은 점점 증가하고 있다. 특히 고전력 RF플라즈마 시스템에서 임피던스 정합 회로에 사용되는 가변커패시터는 빠른 고전압 차단능력이 요구된다. 본 논문에서는 RF플라즈마 시스템의 임피던스 정합 회로에 활용되는 단방향 스위치를 사용한 전기적 가변 캐패시터 (Electrical Variable Capacitor, 이하 EVC) 회로의 전압 스트레스를 저감하는 방법에 대해서 제안한다. 제안된 방법은 13.56Mhz의 주파수와 1kW이상의 고전력 RF플라즈마 시스템에서 단방향 스위치의 전압 스트레스를 양방향스위치를 사용한 EVC 회로를 활용하여 저감한다. 본 논문에서 제안된 방법으로 전압 스트레스가 감소하여 EVC 회로를 고전력 초고속 RF플라즈마 시스템의 임피던스 정합 회로에 좀 더 효과적 으로 적용할 수 있게 된다. 시뮬레이션 및 실험을 통해 EVC 회로의 스위치에 걸리는 전압 스트레스가 40%이상 저감되는 것을 검증하였다.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.95-96
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• 2015

In this paper, a single-sourced PV PCS using the trinary asymmetric MLI with a single-ended pre-regulator is proposed. Trinary based asymmetric CHB inverters provide higher output levels for the same number of cells compared to other CHB inverters. However, there is an issue of regeneration with trinary asymmetric inverters and this complicates the system with requirement of bi-directional converters at the input. Modified commutation strategies have been used to remove the regeneration issue with compromise in THD. The single-ended pre-regulator provides the isolated dc-link voltage for the individual H-bridge cells with the advantage of having a single switch and magnetic component. This implementation increases the magnetic utilization of the inductor core and reduces the switching loss in the pre-regulator and also the reduced parts count contributes to the cost competiveness of the proposed PCS. The proposed PV PCS has been verified using simulation results in this paper.

• Science of Emotion and Sensibility
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• v.21 no.2
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• pp.137-144
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• 2018

The study figured out the operational conditions of a two-way movement actuator made of one-way shape memory alloy (OWSMA) for versatile ventilation intelligent garments. To develop a low-power actuator that consumes energy only when a garment changes its form such as opening and closing, multiple channels of OWSMA were used, and optimum diameter of the wires was examined. For the switch device, optimum voltage application unit time was determined. Optimum diameter of OWSMA wire was determined by applying 3.7V to the pre-determined candidate diameters, which demonstrated two-way operation in previous studies. In order to evaluate the optimum voltage application time, the internal diameter of the actuator was measured while increasing and decreasing by 50 ms from the unit time of voltage application. Delay time under two-way operation of the actuator was measured to minimize interference caused by heat between channels. Power of 3.7V was applied to OWSMA for assessment of optimal time, and the whole process from heating to cooling was video-recorded with a thermal image camera to determine the point of time at which the temperature of OWSMA wire dropped below the phase transformation temperature. The results showed that $0.4{\Phi}$ was the most suitable diameter, and the optimum unit time of voltage applied to open and close the actuator was 4100ms. It was also shown that the delay time should be more than 1.8 seconds between two-way operations of the actuator.

• Journal of Power Electronics
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• v.7 no.4
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• pp.343-352
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• 2007

This paper proposes a modularized charge equalization converter for hybrid electric vehicle (HEV) lithium-ion battery cells, in which the intra-module and the inter-module equalizer are Implemented. Considering the high voltage HEV battery pack, over approximately 300V, the proposed equalization circuit modularizes the entire $M^*N$ cells; in other words, M modules in the string and N cells in each module. With this modularization, low voltage stress on all the electronic devices, below roughly 64V, can be obtained. In the intra-module equalization, a current-fed DC/DC converter with cell selection switches is employed. By conducting these selection switches, concentrated charging of the specific under charged cells can be performed. On the other hand, the inter-module equalizer makes use of a voltage-fed DC/DC converter for bi-directional equalization. In the proposed circuit, these two converters can share the MOSFET switch so that low cost and small size can be achieved. In addition, the absence of any additional reset circuitry in the inter-module equalizer allows for further size reduction, concurrently conducting the multiple cell selection switches allows for shorter equalization time, and employing the optimal power rating design rule allows fur high power density to be obtained. Experimental results of an implemented prototype show that the proposed equalization scheme has the promised cell balancing performance for the 7Ah HEV lithium-ion battery string while maintaining low voltage stress, low cost, small size, and short equalization time.

• 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.