• Journal of environmental and Sanitary engineering
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• v.14 no.2
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• pp.113-119
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• 1999

In this study, the incineration of MEK and toluene was studied on a Pt supported alumina catalyst at temperature range from 200 to $350^{\circ}C$. An approach based on the Mars-van Krevelen rate model was used to explain the results. The object of this study was to study the kinetic behavior of the platinum catalyst for deep oxidation. The conversions of MEK and toluene were increased as the inlet concentration was decreased and the reaction temperature was increased. The maximum deep conversion of MEK and toluene were 91.81% and 55.69% at $350^{\circ}C$, respectively. The ${\kappa}_3$ constant increases with temperature faster than the ${\kappa}_1$ constant, that is, the surface concentration of ($VOCs{\cdots}O$) is higher than that of (O) at higher temperature according to the Mars-van Krevelen mechanism. Also the activation energy of toluene was larger than MEK for toluene is aromatic compound which have stronger bonding energy.Therefore, the catalytic incineration kinetics of MEK and toluene with Mars-van Krevelen mechanism could be used as the basic data for industrial processes.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.53-59
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• 2017

Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper- and lower-case characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

• Transactions of Materials Processing
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• v.12 no.6
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• pp.550-557
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• 2003

In this study, a web-based system was developed by utilizing finite element method and virtual system designed using Virtual Reality Modeling Language (VRML). The simulation program for axi-symetric sheet forming is developed using finite flement method. The developed system consists of two modules, client module and server module. The client module was developed by using Active-X control. The input data for FEM calculation is transferred to the server module by using communication protocol. Then sever module performs several successive processes: input data generation, forming simulation, conversion of results to VRML format. After that, the results from the simulation can be visualized on the web browser in client computer. Besides, client module offers the capability to control and navigate on virtual forming machine and calculated result. By using this system simulation result can be investigated more realistically in virtual environment including forming machine.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.3
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• pp.108-113
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• 2004

This paper presents methods to improve performance of the power supply system in a NASA deep space explorer. In the Stirling engine driven reciprocating Brushless DC (BLDC) generator, the accurate position information of the prime mover is important to diagnose the performance of the engine and prevent distortion of the output power. Since sensors to detect the position are fragile and unreliable, and conventional sensorless techniques have drawbacks in the low speed region, a novel sensorless position detection technique for the prime mover has been proposed and verified. Another major issue of the generator for the spacecraft is power density maximization. The mass of the power system is important to the mass of the satellite. Therefore, the components of the spacecraft should be lightweight. Conventional rectification methods cannot achieve the maximum power possible due to non-optimal current waveforms. The optimal current waveform for maximizing power density and minimizing machine size and weight in a nonsinusoidal power supply system has been derived, incorporated in a control system, and verified by simulation work.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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• pp.497-500
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• 2001

Due to their better photosensitivity in X-ray, the amorphous selenium based photoreceptor is widely used on the X-ray conversion materials. It was possible to control the charge carrier transport of amorphous selenium by suitably alloying a-Se with other elements(e,g. As, Cl). The charge transport properties of amorphous Selenium is decided on hole which is induced from metal to selenium in metal-selenium junction and which is transferred in a-Se bulk. This phenomenon is resulted of changing electric field owing to increasing of space charge by deep trap of a-Se bulk. In this paper, We dopped the chlorine to compensate deep hole trap and deposited blocking layer using dielectric material to prevent from increasing space charge for injection charge between metal electrode and a-Se layer. We compared space charge and the decreasing of trap density through measuring dark and photo current.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.427-432
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• 2020

딥러닝(Deep-learning) 기반의 자연어 이해(Natural Language Understanding) 기술들은 최근에 상당한 성과를 성취했다. 하지만 딥러닝 기반의 자연어 이해 기술들은 내적인 동작들과 결정에 대한 근거를 설명하기 어렵다. 본 논문에서는 벡터를 그래프로 변환함으로써 신경망의 내적인 의미 표현들을 설명할 수 있도록 한다. 먼저 인간과 기계 모두가 이해 가능한 표현방법의 하나로 그래프를 주요 표현방법으로 선택하였다. 또한 그래프의 구성요소인 노드(Node) 및 엣지(Edge)의 결정을 위한 Element-Importance Inverse-Semantic-Importance(EI-ISI) 점수와 Element-Element-Correlation(EEC) 점수를 심층신경망의 훈련방법 중 하나인 드랍아웃(Dropout)을 통해 계산하는 방법을 제안한다. 다양한 실험들을 통해, 본 연구에서 제안한 벡터-그래프(Vector2graph) 변환 프레임워크가 성공적으로 벡터의 의미정보를 유지하면서도, 설명 가능한 그래프를 생성함을 보인다. 더불어, 그래프 기반의 새로운 시각화 방법을 소개한다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.27 no.1
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• pp.121-125
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• 1998

The effects of various cooking methods (blanching, microwave heating, and deep-fat frying) on biologically active components of Angelica keiskei were determined by HPLC. Cynharoside, the biologically active component of Angelica keiskei leaves was 4.82%, which was rapidly decreased by blanching, showing 3.79%, 2.59% and 1.74% at 1 min, 2min and 3min, respectively. Microwave heating also decreased the cynaroside contents slowly by 2 min and rapidly by 3min, respectively. Microwave heating also decreased the cynaroside contents slowly by 2min and rapidly by 3 min, showing 4.25% at 1 min, 3.38% at 2 min, and 1.49% at 3 min. Among the cooking methods tested, deep-fat frying was shown to preserve the cynaroside most. Only 3.90% of cynaroside was lost by 5 min frying. The decrease in cynaroside in each cooking method was supposed to be due to the conversion of cynarside, a glycoside of flavonoid, into luteolin through lysis of glucose at C-7 position on cynaroside.

• Current Optics and Photonics
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• v.2 no.2
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• pp.153-164
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• 2018

In semiconductor manufacturing, critical dimensions indicate the features of patterns formed by the semiconductor process. The purpose of measuring critical dimensions is to confirm whether patterns are made as intended. The deposition process for an organic light emitting diode (OLED) forms a luminous organic layer on the thin-film transistor electrode. The position of this organic layer greatly affects the luminescent performance of an OLED. Thus, a system for measuring the position of the organic layer from outside of the vacuum chamber in real-time is desired for monitoring the deposition process. Typically, imaging from large stand-off distances results in low spatial resolution because of diffraction blur, and it is difficult to attain an adequate industrial-level measurement. The proposed method offers a new superresolution single-image using a conversion formula between two different optical systems obtained by a deep learning technique. This formula converts an image measured at long distance and with low-resolution optics into one image as if it were measured with high-resolution optics. The performance of this method is evaluated with various samples in terms of spatial resolution and measurement performance.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.