• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.2098-2100
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• 2000

We propose pulsed $CO_2$ laser below 30W by the AC(60Hz) switching control of leakage transformer primary which has some advantage of cost and size compared to a typical pulsed power supply. Pulse repetition rate is adjusted from 5Hz to 60Hz to control laser output. In this laser, a low voltage open loop control for high voltage discharge circuit is employed to avoid the HV sampling or switching and high voltage leakage transformer is used to convert rectified low voltage pulse to high voltage one. A ZCS(Zero Cross Switch) circuit and a PIC one-chip microprocessor are used to control gate signal of SCR precisely. The pulse repetition rate is limited by 60Hz due to the frequency of AC line and a high leakage inductance. The maximum laser output was obtained about 23W at pulse repetition rate of 60Hz, total gas mixture of $CO_{2}/N_{2}$/He = 1/9/15, SCR gate trigger angle 90$^{\circ}$, and total pressure of 18Torr.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.115-123
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• 2011

Distribution system loss minimization re-configuration is 0-1 planning problem, and the number of combinations requiring searches is extremely large when dealing with typical system scales. For this reason, the application of a genetic algorithm (GA) seems a tractive to solve this problem. Although Genetic algorithms are a type of random number search method, they incorporate a multi-point search feature and are therefore superior to one-point search techniques. The incorporate of GAs for solving large combinational problem has received wide attention. Further, parallel searching can be performed and the optimal solution is more easily reach ed. In this paper, for improving GA convergence characteristics in the distribution system loss minimization re-configuration problem, a chromosome "Limited Life" concept is introduced. Briefly, considering the population homogenization and genetic drift problems, natural selection is achieved by providing this new concept, in addition to natural selection by fitness. This is possible because individuals in a population have an age value. Simulation were carried out using a model system to check this method's validity.

• Journal of Sensor Science and Technology
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• v.7 no.4
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• pp.293-299
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• 1998

An objective of this study is to develop a voltage measuring device that uses a gas-filled switch (GS) on 22.9 kV-y extra-high voltage distribution lines. The voltage measuring device proposed in this paper is a kind of capacitive divider which consists of a detecting electrode attached outside of the bushing of GS, an impedance matching circuit, and a voltage buffer. It can be easily installed in an established GS without changing the structure. For the calibration and application investigations, the voltage measuring device was set up in the 25.8 kV 400 A GS, and a step pulse generator having 5 ns rise time is used. As a result, it was found that the frequency bandwidth of the voltage measuring device ranges from 1.35 Hz to about 13 MHz. The error of voltage dividing ratio which is evaluated by the commercial frequency voltage of 60 Hz was less than 0.2%. In addition, voltage dividing ratio in the commercial frequency voltage and in a non-oscillating impulse voltage were compared, and their deviation were less than 0.7%.

• Journal of the Korean Vacuum Society
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• v.7 no.2
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• pp.94-103
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• 1998

We investigated a bias effect for diamond films deposited by a HFCVD(Hot Filament Chemical Vapor Deposition) method using a methane-hydrogen gas mixture. During deposition total chamber pressure, methane concentration, filament temperature and substrate temperature was 20 torr, 1.0%, $2100^{\circ}C$ and $980^{\circ}C$ respectively. Also DC bias was applied during both the nucleation stage and the growth stage systematically. We found that negative bias enhanced the nucleation density at the nucleation stage, but it made a bad influence on the morpholohy of films at the growth stage. Positive bias enhanced the growth rate and resulted in a good morpholohy of films. Therefore we concluded that it was effective to apply the negative bias during the nucleation stage and then to switch into the positive bias during the growth stage in the fabrication of diamond films.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.914-921
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• 2022

The International Maritime Organization (IMO) has set a goal of reducing ship's carbon dioxide emissions by 70% and greenhouse gas emissions by 50% by 2050 compared to 2008. Shipowners and shipyards are promoting various R&D activities such as LNG propulsion, ammonia propulsion, electric propulsion, CO₂ capture, and shaft generators as a way to satisfy this problem. The dual shaft generator has the advantage that it can be directly applied to an existing ship through remodeling. In this paper, the total fuel reduction rate that can be obtained by applying the shaft generator to the existing ship was verified through simulation. For this purpose, the size of the medium-sized ship was defined, and the governor, diesel engine, propeller, torque switch, generator for shaft generator, propulsion motor for shaft generator, and ship model were modeled and simulated.

• Journal of Navigation and Port Research
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• v.47 no.2
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• pp.106-119
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• 2023

Due to global decarbonization movement and tightening of maritime emissions restrictions, the shipping industry is going to switch to alternative fuels. Among candidates of alternative fuel, methanol is promising for decreasing SOx and CO2 emissions, resulting in minimum climate change and meeting the goal of green shipping. In this study, a novel combined system of direct methanol solid oxide fuel cells (SOFC), proton exchange membrane fuel cells (PEMFC), gas turbine (GT), and organic Rankine cycle (ORC) targeted for marine vessels was proposed. The SOFC is the main power generator of the system, whereas the GT and PEMFC could recover waste heat from the SOFC to generate useful power and increase waste heat utilizing efficiency of the system. Thermodynamics model of the combined system and each component were established and analyzed. Energy and exergy efficiencies of subsystems and the entire system were estimated with participation of the first and second laws of thermodynamics. The energy and exergy efficiencies of the overall multigeneration system were estimated to be 76.2% and 30.3%, respectively. The combination of GT and PEMFC increased the energy efficiency by 18.91% compared to the SOFC stand-alone system. By changing the methanol distribution ratio from 0.05 to 0.4, energy and exergy efficiencies decreased by 15.49% and 5.41%, respectively. During the starting up and maneuvering period of vessels, a quick response from the power supply system and propulsion plant is necessary. Utilization of PEMFC coupled with SOFC has remarkable meaning and benefits.

• Clean Technology
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• v.16 no.3
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• pp.182-190
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• 2010

The feasibility of clean technology to minimize the $CO_2$ emission by recycling and reuse the waste materials and energy have been studied for the cement industry. A life cycle assessment (LCA) was performed for an alternative raw material-supply method to use the molted slag as the major raw material in the cement clinker manufacturing. Using this new method, a 60% of $CO_2$ could be reduced that comes out during the decarboxylation from the cement rotary kiln. The energy-efficiency improvement and the alternative energy methods that had been determined in our previous study through the environmental assessment of cement industry were applied to the study for the reduction of $CO_2$ emission. The natural gas, one of the fossil fuels, was also used as the first choice to get the result at the earliest time by the most economic and the most efficient green technology and to switch into the carbon neutral energy consumption pattern.

• Journal of the Korean Institute of Gas
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• v.21 no.5
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• pp.83-88
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• 2017

The purpose of this paper is to study for a failure examples producing in electronic control air conditioner in vehicle. The first example, it looked for the repetitive fuse cutting phenomenon that the diode using for a surge voltage prevention of inner A/C relay damaged because of no absorbing the surge voltage by short of diode when the A/C is off. The second example, it knew the icing phenomenon of evaporator that the A/C ECU didn't control the A/C because of inner cutting of fin thermo sensor. The third example, it verified the operation trouble phenomenon because of the A/C switch and for sensor burned in flames by short phenom enon when the sheath of the cable has peeled off. Therefore, the driver have to manage the optimism system of a car by thoroughgoing inspection and improvement the failure phenomenon.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.134-134
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• 2011

High-k dielectric materials such as $HfO_2$, $ZrO_2$ and $Al_2O_3$ increase gate capacitance and reduce gate leakage current in MOSFET structures. This behavior suggests that high-k materials will be promise candidates to substitute as a tunnel barrier. Furthermore, stack structure of low-k and high-k tunnel barrier named variable oxide thickness (VARIOT) is more efficient.[1] In this study, we fabricated the $WSi_2$ nanocrystals nonvolatile memory device with $SiO_2/HfO_2/Al_2O_3$ tunnel layer. The $WSi_2$ nano-floating gate capacitors were fabricated on p-type Si (100) wafers. After wafer cleaning, the phosphorus in-situ doped poly-Si layer with a thickness of 100 nm was deposited on isolated active region to confine source and drain. Then, on the gate region defined by using reactive ion etching, the barrier engineered multi-stack tunnel layers of $SiO_2/HfO_2/Al_2O_3$ (2 nm/1 nm/3 nm) were deposited the gate region on Si substrate by using atomic layer deposition. To fabricate $WSi_2$ nanocrystals, the ultrathin $WSi_2$ film with a thickness of 3-4 nm was deposited on the multi-stack tunnel layer by using direct current magnetron sputtering system [2]. Subsequently, the first post annealing process was carried out at $900^{\circ}C$ for 1 min by using rapid thermal annealing system in nitrogen gas ambient. The 15-nm-thick $SiO_2$ control layer was deposited by using ultra-high vacuum magnetron sputtering. For $SiO_2$ layer density, the second post annealing process was carried out at $900^{\circ}C$ for 30 seconds by using rapid thermal annealing system in nitrogen gas ambient. The aluminum gate electrodes of 200-nm thickness were formed by thermal evaporation. The electrical properties of devices were measured by using a HP 4156A precision semiconductor parameter analyzer with HP 41501A pulse generator, an Agillent 81104A 80MHz pulse/pattern generator and an Agillent E5250A low leakage switch mainframe. We will discuss the electrical properties for application next generation non-volatile memory device.

• 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.