• Progress in Medical Physics
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• v.16 no.2
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• pp.62-69
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• 2005

The purpose of this study has been performed to investigate the possibility of external audit program using thermoluminescence dosimetry for electron beam in korea. The TLD system consists of LiF powder, type TLD-700 read with a PCL 3 reader. In order to determine a calibration coefficient of the TLD system, the reference dosimeters are irradiated to 2 Gy in a $^{60}CO$ beam at the KFDA The irradiation is performed under reference conditions is water phantom using the IAEA standard holder for TLD of electron beam. The energy correction factor is determined for LiF powder irradiated of dose to water 2 Gy in electron beams of 6, 9, 12, 16 and 20 MeV (Varian CL 2100C). The dose is determined according to the IAEA TRS-398 and by measurement with a PTW Roos type plane-parallel chamber. The TLD for each electron energy are positioned in water at reference depth. In this study, to verify of the accuracy of dose determination by the TLD system are performed through a 'blind' TLD irradiation. The results of blind test are $2.98\%,\;3.39\%\;and\;0.01\%(1\sigma)$ at 9, 16, 20 MeV, respectively. The value generally agrees within the acceptance level of $5\%$ for electron beam. The results of this study prove the possibility of the TLD quality assurance program for electron beams. It has contributed to the improvement of clinical electron dosimetry in radiotherapy centers.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.3
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• pp.43-53
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• 2003

Unlike in analog display devices, the physical screen resolution in digital devices are fixed from the manufacturing. It is a weak point on digital devices. The screen resolution displayed in digital display devices is varied. Thus, interpolation or decimation of the resolution on the display is needed to make the input pixels equal to the screen resolution., This process is called image scaling. Many researches have been developed to reduce the hardware cost and distortion of the image of image scaling algorithm. In this paper, we proposed a Winscale algorithm. which modifies the scale up/down in continuous domain to the scale up/down in discrete domain. Thus, the algorithm is suitable to digital display devices. Hardware implementation of the image scaler is performed using Verilog XL and chip is fabricated in a 0.5${\mu}{\textrm}{m}$ Samsung SOG technology. The hardware costs as well as the scalabilities are compared with the conventional image scaling algorithms that are used in other software. This Winscale algorithm is proved more scalable than other image-scaling algorithm, which has similar H/W cost. This image-scaling algorithm can be used in various digital display devices that need image scaling process.

• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.874-882
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• 2017

As the interest on the air-pollution is gradually rising up at home and abroad, automotiv e and fuel researchers have been working on the exhaust emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward three main issues : evaporative, performance, air pollution. In addition, researcher studied the environment problems of the bio-ethanol, bio-butanol, bio-ETBE (Ethyl Tertiary Butyl Ether), MTBE (Methyl Tert iary Butyl Ether) fuel contained in the fuel as octane number improver. The researchers have many dat a about the health effects of ingestion of octane number improver. However, the data support the con clusion that octane number improver is a potential human carcinogen at high doses. Based on the bio-fuel and octane number improver types (bio-ethanol, bio-butanol, bio-ETBE, MTBE), this paper dis cussed the influence of gasoline fuel properties on the evaporative emission characteristics. Also, this p aper assessed the acceleration and power performance of gasoline vehicle for the bio-fuel property. As a result of the experiment, it was found that all the test fuels meet the domestic exhaust gas standards, and as a result of measurement of the vapor pressure of the test fuels, the bio - ethanol : 15 kPa and the biobutanol : 1.6 kPa. thus when manufacturing E3 fuel, Increasing the biobutanol content reduces evaporation gas and vapor pressure. In addition, Similar accelerating and powering performance was shown for the type of biofuel and when bio-butanol and bio-ethanol were compared accelerated perf ormance was improved by about 3.9% and vehicle power by 0.8%.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.31-44
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• 2008

The 1st phase framework data implementation of National Geographic Information Systems (NGIS) used 1:5,000 digital map with 5 years updating period which is lacking in the latest information. This is a significant factor which hinders the use of framework data. This study proposed the efficient technical method of a location based object data management and system implementation for updating framework data. First, we did an object-oriented data modeling and database design using a location based features identifier(UFID: Unique Feature IDentifier). The second, we developed the system with various functions such as a location based UFID creation, input and output, a spatial and attribute data editing, an object based data processing using UML(Unified Modeling Language). Finally, we applied the system to the study area and got high quality data of 99% accuracy and 35% benefit effect of personnel expenses compare to the previous method. We expect that this study can contribute to the maintenance of national framework data as well as the revitalization of various GIS markets by providing user the latest framework data and that we can develop the methods of a feature-change modeling and monitoring using an object based data management.

• Spatial Information Research
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• v.11 no.1
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• pp.41-60
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• 2003

It is noted that the paper mapping system for University Building Facility has many limitations in terms of data maintenance, real-time GIS data acquisition, and economic efficiency. The aim of this research was to evaluate an operational potential of an on site real-time mobile GIS technique to resolve the problem faced in the university. The idea is based upon the recent trends in the field of 'Telecommunication and Information Technology' that uses a PDA (personal Digital Assistants), wireless network computing, mobile computing, etc. A real time mobile GIS approach has been adopted, in which a PDA is linked to a wireless internet and field workers record data on the computer at the site and analyse data on site. While there should be a considerable number and variety of factors associated with real-time mobile GIS quality, this research focuses on three criteria that are identified as fundamental to customer requirements; (1) data quarry (2) spatial analysis (3) real-time GIS database building. 'Art--empirical study for a case study facility has been conducted to confirm the validity for the system. The system has been checked experimentally, enabled the field users to quarry the data required simply and execute spatial analysis (buffer, overlay etc.,) accommodating versatile alternatives on the site. Detailed visual maps can be generated over large areas quickly and easily. The PDA interface, in particular, were ideally suited for field users to interactively displaying positional information with attribute data. This system has shown to be quite convenient to maintaining a highly reliable database since it could playa crucial role in documenting at real-time basis temporal and spatial changes occurred in the facilities. It is anticipated that this research output will greatly serve to introduce the reliable and cost-effective facility mapping system in the university by overcoming serious constraints suffered from the past non-real time mobile GIS approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.662-671
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• 2020

Recently, for the purposes of reducing carbon dioxide(CO₂) emissions in the island area, countermeasures to decrease the operation rate of diesel generator(DG) and to increase one of renewable energy sources(RES) is being studied. In particular, the demonstration and installation of stand-alone micro-grid(MG) system which is composed of DG, RES and energy storage system(ESS) has been implemented in some island areas such as Gapa-do, Gasa-do and Ulleung-do island. However, many power quality(PQ) problems may be occurred due to an intermittent output of RES including photovoltaic(PV) system and wind power(WP) system in a normal operating of constant voltage & constant frequency(CVCF) inverter-based MG system. Therefore, this paper presents a modeling of the 30kW scale MG system using PSCAD/EMTDC, and also implements a 30kW scale CVCF inverter-based MG system as test devices to analyze normal operating characteristics of MG system. From the simulation and test results, it is confirmed that the proposed methods are useful and practical tools to improve PQ problems such as under-voltage, over-voltage and unbalanced load in CVCF inverter-based MG system.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.51-58
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• 2002

The conventional code division multiple access(CDMA) detector shows severe degradation in communication quality as the number of users increases due to multiple access interferences(MAI). This problem thus restricts the user capacity. Various multiuser detection algorithms have been proposed to overcome the MAI problem. The existing detectors can be generally classified into one of the two categories : linear multiuser detection and subtractive interference cancellation detectors. In the linear multiuser detection, a linear transform is applied to the soft outputs of the conventional detector. In the subtractive interference cancellation detection, estimates of the interference are generated and subtracted out from the received signal. There has been great interest in the family of the subtractive interference cancellation detection because the linear multiuser detection exhibits the disadvantage of taking matrix inversion operations. The successive interference cancellation (SIC) and the parallel interference cancellation (PIC) are the two most popular structures in the subtractive interference cancellation detector. The SIC structure is very simple in hardware complexity, but has the disadvantage of increased processing delay time, while the PIC structure is good in performance, but shows the disadvantage of increased hardware complexity. In this paper we propose a mixed structure form of SIC and PIC in order to achieve good performance as well as simple hardware complexity. A performance analysis of the proposed scheme has been made, and the superior characteristics of the mixed structure are demonstrated by extensive computer simulations. 

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.5 s.347
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• pp.35-44
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• 2006

Reflective semiconductor optical amplifiers(R-SOAs) were designed with high gain, wide optical bandwidth, high thermal reliability and wide modulation bandwidth in TO-can package for the transmitter of wavelength division multiplexed-passive optical network(WDM-PON) application. Double trench structure and current block layer were introduced in designing the active layer of R-SOA to enable high speed modulation. The injection power requirement and the viable temperature range of WDM-PON system are experimentally analysed in based on Amplified Spontaneous Emission(ASE)-injected R-SOAs. The effect of the different injection spectrum in the gain-saturated R-SOA was experimentally characterized based on the measurements of excessive intensity noise, Q factor, and BER. The proposed spectral pre-composition method reduces the bandwidth of injection source below the AWG bandwidth and thereby avoids spectrum distortion impeding the intensity noise reduction originated from the amplitude squeezing.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.268-273
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• 2017

Recently, deep learning encoder-based approach has been actively applied in the field of sentiment classification. However, Long Short-Term Memory network deep learning encoder, the commonly used architecture, lacks the quality of vector representation when the length of the documents is prolonged. In this study, for effective classification of the sentiment documents, we suggest the use of attention method-based deep learning encoder that generates document vector representation by weighted sum of the outputs of Long Short-Term Memory network based on importance. In addition, we propose methods to modify the attention method-based deep learning encoder to suit the sentiment classification field, which consist of a part that is to applied to window attention method and an attention weight adjustment part. In the window attention method part, the weights are obtained in the window units to effectively recognize feeling features that consist of more than one word. In the attention weight adjustment part, the learned weights are smoothened. Experimental results revealed that the performance of the proposed method outperformed Long Short-Term Memory network encoder, showing 89.67% in accuracy criteria.