• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.74-87
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• 2003

A model of STS (Science-Technology-Society) creativity education program for the gifted and talented children has been developed, based on GENEPLORE thinking process and Knowledge development theory. The GENEPLORE creative thinking process, developed by Finke et al. (1990, 1992), has two phases such as generative phase and exploratory phase. And The knowledge development theories of Piaget (1977) and Gallagher(1981) assume that knowledge-bases are developed on the basis of empirical as well as reflective abstraction, which could imply that knowledge-bases are crucial in creative thinking process. The creativity education model for the gifted and talented of the present study attempted to integrate 'the individual, creative thinking process, and social/scientific technology' by employing topics of the science-technology-society such as computer, network, biotech, robot, e-business, e-education, e-health, nanotech and entertainment and the structure and contents of the program are proposed

• Journal of Digital Convergence
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• v.8 no.2
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• pp.217-228
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• 2010

There are two streams on e-Learning. The first one is to create new value through aligning products included information systems and service. The second one is to utilize the service system and the service process for service systematization. The service system is made up human, technology, value proposition, service network, and shared information. The service process consists of design, development, operation and evaluation phases. In this paper, we design and implement the evaluation management of e-learning service based on the service science. The evaluation management service is sets of evaluation type management, general review management, award management, evaluation sheet management and evaluation result management. Feature of this paper is that we can service with different criteria to learner, guardian and evaluator. The worthy of this paper is that we construct service oriented environment possible to systematize evaluation work easily and provide evaluation results clearly.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.719-720
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• 2007

Korea, for the first time in the world, constructed 765 kV double circuits transmission lines, which has 3 phases and 6 bundles with vertical arrangement using steel pipes in 1998. Also in 2002, we developed 765kV outdoor full GIS substation with self-developed technology. KEPCO accumulated a wealth of technologies for 765kV system construction and operation, and are listed 5th in technology field in the world. With this advanced technologies, we are developing oversea business. We started with a projects, 'Development Study on the Power System Network Analysis in Myanmar' in 2001, and continued the project to transmission design, consulting for transmission technology including the education of foreign trainees in south-east and middle east asia. Currently, 12 overseas businesses including 330kV transmission system consulting in Ghana, are in progress. In 2007, beginning with 750 kV transmission consulting in China, we are operating ATT(Advanced Transmission Technology) training program, which educate engineers of government and utilities company from China, Myanmar, Cambodia, Laos and Cambodia. However, for the successful development study on the power system, design of the power system and the training service, it is essential to standardize load design criteria in consideration of temperature, wind speed, air pressure and density, etc. of the other countries. Therefore, in this paper, standardized load design criteria for Cambodia, Laos, Myanmar is explained.

• IMMUNE NETWORK
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• v.24 no.2
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• pp.14.1-14.26
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• 2024

The inflammatory response during cutaneous leishmaniasis (CL) involves immune and non-immune cell cooperation to contain and eliminate Leishmania parasites. The orchestration of these responses is coordinated primarily by CD4⁺ T cells; however, the disease outcome depends on the Th cell predominant phenotype. Although Th1 and Th2 phenotypes are the most addressed as steers for the resolution or perpetuation of the disease, Th17 cell activities, especially IL-17 release, are recognized to be vital during CL development. Th17 cells perform vital functions during both acute and chronic phases of CL. Overall, Th17 cells induce the migration of phagocytes (neutrophils, macrophages) to the infection site and CD8⁺ T cells and NK cell activation. They also provoke granzyme and perforin secretion from CD8⁺ T cells, macrophage differentiation towards an M2 phenotype, and expansion of B and Treg cells. Likewise, immune cells from the inflammatory infiltrate have modulatory activities over Th17 cells involving their differentiation from naive CD4⁺ T cells and further expansion by generating a microenvironment rich in optimal cytokines such as IL-1β, TGF-β, IL-6, and IL-21. Th17 cell activities and synergies are crucial for the resistance of the infection during the early and acute stages; however, if unchecked, Th17 cells might lead to a chronic stage. This review discusses the synergies between Th17 cells and the inflammatory infiltrate and how these interactions might destine the course of CL.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1619-1629
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• 2016

We study an efficient transmission protocol for the cellular-aided device-to-device communication model. In particular, two source-destination pairs communicate with the help of unlink and downlink cellular links. For the proposed scheme, two transmitters send their messages and the base station and two receivers receive at the first phase. Then, at the second phase, the base station sends the XOR of the messages to two receivers and they try to decode their own messages from the received signals after the first and second phases. We analyze the outage-based throughput achievable by the proposed scheme and demonstrate by simulations that the proposed scheme provides an improved outage performance compared to the conventional device-to-device communication schemes.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.263-272
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• 1998

This paper presents the new control strategy that can minimizes the torque ripple by considering the magnetic nonlinearity and phase torque averlapping intervals, and describes the whole SRM drive system using proposed control method implemented by DSP(Digital Signal Processor). To do this, inductance and torque are, at first, measured according to the variation of rotor position angle while current is kept constant at predetermined several values. From these measured values, the entire inductance and torque for any current and rotor position are inferred by using neural network. And the waveform of the reference phase torque is determined for the torque ripple to be minimized considering the torque overlap between phases. The controller is designed for the actual torque obtained by the inferred torque look-up table using measured current and rotor position angle to track the predetermined reference phase torque by delta modulation technique. To perform a real time processing and ensure the reliability of the controller, DSP is implemented.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.86-99
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• 2007

Packet filtering is to filter out potentially malicious network packets. In order to test a packet filtering function we should verify whether security policies are performed correctly as intended. However there are few existing tools to test the function. Besides, they need user participation when generating test cases or deciding test results. Many security administrators have a burden to test systematically new security policies when they establish new policies or modify the existing ones. To mitigate the burdens we suggest a new test method with minimal user articipation. Our tool automates generation steps of the test cases and the test oracles, respectively. By using the test oracles generated automatically, deciding test results is possible without user intervention. Our method realizes an automatic testing in three phases; test preparation phase, test execution, and test evaluation. As a result it may enhance confidence of test activities more highly. This paper describes the design and implementation of our test method and tool.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.