• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.3
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• pp.7-14
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• 2002

The economic dispatch is one of the major concerns in case of buying and selling generated powers between electric powers companies. This paper describes the power system security the optimal power flow, and the security-constrained economic dispatch and shows the EDSA\`s active optimal power flow software package is well suited through a simple case study.

• Journal of information and communication convergence engineering
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• v.3 no.2
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• pp.90-92
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• 2005

As the result of the study shows, Round Robin Algorithm allows servers to ensure definite traffic distribution, unless incoming data loads differ much. Although error levels are high in some cases, they were eventually alleviated by repeated tests for a long period of time. The study uses two software packages (Packet Capture and Round Robin Test Package) to check packet volume from Virtual Network Structure (data generator, virtual server, Server 1, 2, 3), and finds out traffic distribution toward Server 1, 2, and 3. The functions of implemented Round Robin Load Balancing Monitoring System include round robin testing, system monitoring, and graphical indication of data transmission and packet volume.

• The Transactions of the Korean Institute of Power Electronics
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• v.1 no.1
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• pp.47-55
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• 1996

A new generator excitation system using a boost-buck chopper as a kind of static exciters is proposed to overcome the lack of field forcing capability of the bus fed exciter under the input line fault condition. It increases or maintains the generator field current by boosting the field voltage in the case of the input AC line voltage drop during and immediately after a fault. The validity of the proposed excitation system is verified with the computer simulation. The generator stability according to the each difference exciter is tested using a commercial software package-CYME. The simulation results of the stability analysis on the generator with the proposed exciter is better than that of the bus fed exciter. This boost-buck chopper exciter can be simply implemented and controlled by the modern power electronics technology.

• Journal of Power Electronics
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• v.10 no.6
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• pp.660-666
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• 2010

This paper presents the approach followed to develop a course that introduces reliability into the design of power electronics converters. The course is part of the curriculum of a master of science in electrical engineering program, and it is aimed at providing reliability tools that can be used in a straightforward manner, while avoiding the mathematical intricacies. The reliability calculations are performed according to the Military Handbook 217, using the evaluation version of a commercial software package which greatly reduces the computational burden usually associated with this task. The course assessment shows that, after attending the course, students were able to improve the mean time between failures in a power-electronics converter, from a minimum of 5%, up to 100%.

• Journal of Power System Engineering
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• v.10 no.1
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• pp.25-33
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• 2006

거칠기가 한 벽면과 두 벽면에 설치된 사각채널에서 비압축성 유체유동과 열전달을 조사하기위해 3차원 수치모사를 행하였다. CFX (version 5) software package 를 사용하여 계산하였다. 거친 벽은 $45^{\circ}$경사진 거칠기가 설치되어 있다. 채널의 4 벽면은 일정한 열 유속으로 가열하였다. 수치계산 결과는 실험값과 잘 일치 하였다. 연구의 조건은 거칠기 피치와 높이의 비가 8이고, 거칠기 높이와 채널 수력직경의 비가 0.067이며, 레이놀즈수의 범위는 7,600에서 24,900이었다. 연구의 결과는 열전달계수와 마찰계수는 사각채널에서 거친 벽면의 수가 클수록 증가 함을 보였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1544-1552
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• 2016

If the undo/redo function is not reflected in the initial design of an application, it makes it difficult to implement the undo/redo function additionally, in this paper, we examined some examples to design the sodoku game and analyzed problems of the design to implement the undo/redo functions. For an efficient design of the undo/redo functions without using swing.undo package, we propose a class design using the Command, Memento, and Observer pattern these are used as organic. The proposed method is more efficient for distributed work than other method. We implemented a sudoku game using proposed design. In the undo/redo function testing, we could see that it works well.

• Journal of Nutrition and Health
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• v.35 no.10
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• pp.1104-1119
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• 2002

This study was to analyze the job of cooks of school foodservice. The purpose of this study were to examine importance of the present job and future-oriented job of cooks in school foodservice, and to compare the present job performance time with the future-oriented job performance time. The questionnaire was delveloped by focus group and pilot test. The work sampling was choosed to verify the objectivity of job analysis. The statistical software package was SAS 10.0. ‘Menu Management’, ‘Procurement’, ‘Receiving and Inventory control’ and ‘Cooking facilities Management’ of the present job was perceived significantly more important than the future-oriented job. ‘Procurement’, ‘Receiving and inventory control’, ‘distribution’ and ‘dishwashing’ of the future-oriented job was perceived significantly longer than the present job performance time. This study will be used to develop job specification for the cooks of school foodservice.

• Structural Engineering and Mechanics
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• v.34 no.2
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• pp.159-174
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• 2010

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.