• 한국기계가공학회지
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• 제14권3호
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.129-133
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• 2003

The work described in this paper deals with the proposed application of an Artificial Neural Network Model for the Advanced Pressurized Water Reactor APR-1400 transient identification. The approach adopted for testing the network take note of the expectation which should be fulfilled by a network for real-time application, like testing with data in on-line mode and use of actual or real-life patterns for training. The recall test performed demonstrates that use of neural network for transient identification is indeed an attractive preposition.

• 전자공학회논문지B
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• 제28B권5호
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• pp.404-410
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• 1991

In this paper,a network interface unit of token-passing bus network and its driving software are implemented based on Mini-MAP. This network interface unit performs the function of MAC layer which is responsible for transmission and reception of frames as well as the management of logical ring. The driving software performs the management of data buffer and the report of errors, if ocoured, to the higher layer. Motorola MC68824 of is used as a TBC(Token Bus Controller) and Intel80186 as a CPU for network interface unit. The operation of network interface unit is verified by self-test which checks the functioning of TBC and CPU. In addition each module of driving software is tested to check the functions regarding transmission and reception of frames.

• Asia pacific journal of information systems
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• 제10권2호
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• pp.97-123
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• 2000

This paper proposes a model for assessing enterprise network maturity. We define maturity in terms of the institutional and member support for the network(organizational maturity) and its technical sophistication(technological maturity), and examine their relationships. We build several hypotheses about the relationships between the maturity types and between the maturity of the network and various organizational factors including industry, size, and performance of the enterprise. We test the hypotheses using data collected from a questionnaire survey with Korean firms. The results show that there is a positive correlation between organizational and technological maturity. It is also found that investment in the network infrastructure has a positive influence on the organizational efficiency. However, it is found that industry type does not affect network utilization, but size does, suggesting that large firms utilize network infrastructure more than small ones do. We also identify various relationships among performance, satisfaction level and the maturity of the enterprise networks. Finally, we provide an overview on the current technological state of the Korean enterprise networks.

• 한국과학교육학회지
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• 제12권1호
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• pp.93-101
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• 1992

The purposes of this study were; 1) to apply the network analysis to evaluation of students' laboratory reports, 2) to test the applicability of the systematic network analysis. For this purposes, 7th grade 88 students from 22 middle schools in Inchon city of Korea were selected and their laboratory reports were evaluated by a evaluation criterion network developed in this study. The evaluation criterion consisted of 4 categories and 10 subcategories. The results of this study suggested that the network analysis approach was suitable one for the evaluation of students' laboratory reports. This was supported by total score distribution, means/weight, Cronbach's ${\alpha}$ coefficient(0.70), and the subcategories' reliabilities. The resluts of the factor analysis indicated that the evaluation criterion network of this study had two factors.

• 대한기계학회논문집A
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• 제31권6호
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• pp.651-658
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• 2007

A new tree-structured neural network classifier is proposed for the automatic real-time inspection of cold-rolled steel strip surface defects. The defects are classified into 3 groups such as area type, disk type, area & line type in the first stage of the tree-structured neural network. The defects are classified in more detail into 11 major defect types which are considered as serious defects in the second stage of neural network. The tree-structured neural network classifier consists of 4 different neural networks and optimum features are selected for each neural network classifier by using SFFS algorithm and correlation test. The developed classifier demonstrates very plausible result which is compatible with commercial products having high world-wide market shares.

• 산업경영시스템학회지
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• 제30권1호
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• pp.82-88
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• 2007

This paper considers a topological optimization of a computer network design with a cost constraint. The objective is to find the topological layout of links, at maximal reliability, under the constraint that the network cost is less or equal than a given level of budget. This problem is known to be NP-hard. To efficiently solve the problem, a genetic approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a simulated annealing method.

• 설비공학논문집
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• 제15권3호
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• pp.159-165
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 추계공동학술대회
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• pp.721-727
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• 2003

This study designed and implemented a system to extract of vowel from a word. The system is comprised of a voice feature extraction module and a neutral network module. The voice feature extraction module use a LPC(Linear Prediction Coefficient) model to extract a voice feature from a word. The neutral network module is comprised of a learning module and voice recognition module. The learning module sets up a learning pattern and builds up a neutral network to learn. Using the information of a learned neutral network, a voice recognition module extracts a vowel from a word. A neutral network was made to learn selected vowels(a, eo, o, e, i) to test the performance of a implemented vowel extraction recognition machine. Through this experiment, could confirm that speech recognition module extract of vowel from 4 words.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 C
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• pp.2345-2347
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• 2005

In this paper, impedance characteristics of overhead medium-voltage (MV) power lines is reported for power line communication (PLC) over an MV power line network. For analysis, a two-port equivalent network model of MV power lines is derived. By applying the transmission line theory, reflection behavior and impedance of power lines are investigated. For verification, impedance of power lines is measured at a test field for an MV PLC. The results show that impedance of MV power lines is between $200{\Omega}$ and $300{\Omega}$ and converges to a half of their characteristic impedance.