• 한국멀티미디어학회논문지
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• 제8권1호
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• pp.62-70
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• 2005

본 논문에서는 모바일 ad-hoc 네트워크에 서 QoS 멀티캐스트 라우팅 을 지원하기 위한 구조들을 제안한다. 제안된 구조는 다음처럼 구성되어져 있다. 첫째, 안정된 멀티캐스트 서비스를 지원하기 위한 기반 구조로 이동성 기반 클러스터링을 제시한다. 둘째, 라우팅 경로와 네트워크의 안정성을 지원하고 평가할 수 있는 엔트로피 기반 모델을 제시하고 QoS 라우팅 지원 방안에 대해서 논의한다. 셋째, 앞에서 제시된 구조들을 사용하여 QoS 멀티캐스트 라우팅을 지원하기 위한 방법을 제시한다. 제안된 구조에 대한 성능 평가는 OPNET(Optimized Network Engineering Tool)을 사용한 모델링과 시뮬레이션을 통하여 이루어진다.

• 전기학회논문지
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• 제64권9호
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• Nuclear Engineering and Technology
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• 제47권2호
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• pp.139-147
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• 2015

Recently, severe accidents in nuclear power plants (NPPs) have become a global concern. The aim of this paper is to predict the hydrogen buildup within containment resulting from severe accidents. The prediction was based on NPPs of an optimized power reactor 1,000. The increase in the hydrogen concentration in severe accidents is one of the major factors that threaten the integrity of the containment. A method using a fuzzy neural network (FNN) was applied to predict the hydrogen concentration in the containment. The FNN model was developed and verified based on simulation data acquired by simulating MAAP4 code for optimized power reactor 1,000. The FNN model is expected to assist operators to prevent a hydrogen explosion in severe accident situations and manage the accident properly because they are able to predict the changes in the trend of hydrogen concentration at the beginning of real accidents by using the developed FNN model.

• Structural Engineering and Mechanics
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• 제63권2호
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• 한국전기전자재료학회논문지
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• 제18권1호
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• pp.1-5
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• 2005

A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.

• 융합보안논문지
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• 제13권1호
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• pp.45-50
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• 2013

미래전은 정보통신, 센서, 유도 항법 기술 등 군사과학기술의 비약적인 발전으로 인하여 이를 적용한 복합무기체계 운용이 보편화되고 전투요소간 고도의 상호운용성과 시간의 작전속도가 요구되는 네트워크 중심의 작전 환경이 조성될 것이다. 이러한 작전환경 속에서 미래전의 양상은 작전요소를 수직 수평적으로 연동시켜 실시간 센서-to-슈터를 구현하는 네트워크 중심전이 될 것이다. 이에 본 연구는 아프가니스탄 및 이라크 등 현재 미군이 수행중인 네트워크 중심전 전장상황에서 발생하는 전장 정보 대량 획득 및 처리 시 제반 문제점과 그 해결방향을 조사, 분석함으로써 향후 미래 전장환경 변화에 대한 우리의 대처방안을 도출하고자 한다.

• Journal of Communications and Networks
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• 제15권1호
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• pp.31-37
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• 2013

Mobile ad hoc networks (MANET) refers to a network designed for special applications for which it is difficult to use a backbone network. In MANETs, applications are mostly involved with sensitive and secret information. Since MANET assumes a trusted environment for routing, security is a major issue. In this paper we analyze the vulnerabilities of a pro-active routing protocol called optimized link state routing (OLSR) against a specific type of denial-of-service (DOS) attack called node isolation attack. Analyzing the attack, we propose a mechanism called enhanced OLSR (EOLSR) protocol which is a trust based technique to secure the OLSR nodes against the attack. Our technique is capable of finding whether a node is advertising correct topology information or not by verifying its Hello packets, thus detecting node isolation attacks. The experiment results show that our protocol is able to achieve routing security with 45% increase in packet delivery ratio and 44% reduction in packet loss rate when compared to standard OLSR under node isolation attack. Our technique is light weight because it doesn't involve high computational complexity for securing the network.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권5호
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• pp.290-294
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• 2003

Using a backpropagation neural network (BPNN), a high power semiconductor device was empirically modeled. The device modeled is a n-LDMOSFET and its electrical characteristics were measured with a HP4156A and a Tektronix curve tracer 370A. The drain-source current $(I_{DS})$ was measured over the drain-source voltage $(V_{DS})$ ranging between 1 V to 200 V at each gate-source voltage $(V_{GS}).$ For each $V_{GS},$ the BPNN was trained with 100 training data, and the trained model was tested with another 100 test data not pertaining to the training data. The prediction accuracy of each $V_{GS}$ model was optimized as a function of training factors, including training tolerance, number of hidden neurons, initial weight distribution, and two gradients of activation functions. Predictions from optimized models were highly consistent with actual measurements.

• 전력전자학회논문지
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• 제25권1호
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• pp.21-28
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• 2020

Integrated bidirectional OBC/LDC was developed to reduce the volume for elements, avoid space restriction, and increase efficiency in EV vehicles. In this study, a DC-DC converter in integrated OBC/LDC circuits was composed of an SRC circuit with a stable output voltage relative to an LLC circuit using a theoretical method and simulation. The resonant network of the selected circuit was optimized to minimize the power loss and element volume under constraints for the buck converter and the battery charging range. Moreover, the validity of the optimal model was verified through an analysis using a theoretical method and a numerical analysis based on power loss at the optimized resonant frequency.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.363-363
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• 2023

Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.