• 한국강구조학회 논문집
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• 제16권4호통권71호
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• pp.443-452
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• 2004

인간의 뇌와 유사한 병렬 연산 모델을 활용하여 다양하고 복잡한 비선형적인 문제에 효과적으로 연관관계를 조직화 할 수 있는 인공신경망에 관한 연구가 근래에 공학의 넓은 분야에서 도입되고 그에 따른 많은 성과가 나타나고 있다. 본 연구에서는 설계자의 판단력과 경험에 의존 하던 기존의 예비구조설계 단계에 효과적인 인공신경망을 적용하여 예비 구조설계 단계에 컴퓨터를 이용한 정형화된 방법을 제시하고자 한다. 이를 위해 각 구조물의 일반적인 설계과정에 따른 다단계 신경망을 제시하고 인공신경망의 학습은 역전파알고리즘과 유전알고리즘을 적용하여 예비구조설계의 원형을 구현한다. 이와 같이 구성된 다단계 신경망을 사장교의 예비구조설계 단계에 활용하여 본 연구의 적용성과 두가지 학습기법에 따른 결과를 비교 분석 한다.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• 대한토목학회논문집
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• 제26권2B호
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• pp.145-152
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• 2006

본 연구에서는 영산강 유역의 본류를 대표하는 나주지점과 황룡강 유역을 대표하는 선암지점에 대하여 물리적인 매개변수를 이용하지 않는 인공신경망 이론을 이용하여 강우-유출 과정의 비선형 모형을 개발하였다. 본 연구결과 나주지점에서는 ANN_NJ_9 모형이 선암지점에서는 ANN_SA_9 모형이 강우-유출 특성을 가장 잘 반영하였다. 또한, 본 연구에서 개발한 GUI_FFS에 대하여 기 확보된 강우 및 유출량을 적용한 결과 실측치와 예측치 간에 0.98이상의 $R^2$값을 보임으로서 향후 수자원 및 하천계획 수립과 그에 따른 운영 및 관리에 효율성을 더할 수 있을 것이라 판단된다.

• 한국인터넷방송통신학회논문지
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• 제18권5호
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• pp.147-153
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• 2018

저전력 블루투스(BLE; Bluetooth Low Energy) 기술을 사용한 비콘은 실외에서만 위치 측위가 가능한 GPS(Global Positioning System)와 달리 실내에서도 위치 파악이 가능하다. 비콘을 사용한 실내 거리 측정에는 RSSI(Received Signal Strength Indication)값이 핵심 요소인데 그에 반해 RSSI값은 여러 환경요소로부터 영향을 받기 때문에 예측된 거리와 실제 거리의 오차가 크게 나타난다. 이러한 이슈를 다루기 위해 비콘을 사용한 ANN(Artificial Neural Network)기반 적응형 거리 측정을 제안한다. 먼저 RSSI의 잡음을 줄이기 위해 확장 칼만 필터와 신호 안정화 필터를 사용한 전처리 과정을 거친다. 그리고 각각 특정 학습 데이터 셋을 가진 다층 ANN들은 학습을 거치게 된다. 결과에서는 평균오차 0.67m를 보여주고, 0.78의 precision을 보여준다.

• Advances in Computational Design
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• 제5권2호
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권8호
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• pp.315-320
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• 2014

작곡은 작곡가의 경험을 바탕으로 표현하고자 하는 감정을 멜로디로 나타내는 창작활동이다. 따라서 작곡가의 작곡 과정을 그대로 본따서 자동작곡프로그램을 만드는 것은 매우 어렵다. 우리는 '창작은 모방을 통하여 가능하다'는 전제하에 본 논문에서 인공신경망의 학습기능을 이용하여 자동작곡시스템을 구현하는 방법을 제안한다. 이를 위하여 먼저 기존 곡을 인공신경망이 학습할 수 있는 시계열 데이터로 변환하는 방법을 제시하였다. 또한 곡의 특성상 반복되는 시계열 데이터를 제대로 학습하기 위하여 곡의 마디를 함께 학습하는 방법을 고안하였다. 학습된 인공신경망에 새로운 곡의 도입부 시계열 데이터를 만들어 넣어주면 인공신경망이 나머지 시계열 데이터를 만들어준다. 이를 음표와 박자로 변환하면 새로운 곡이 완성된다. 다만, 인공신경망의 출력은 음악이론과 다른 박자와 다른 화성의 음표를 출력할 수 있기 때문에 이를 후처리로 보정해 주어야 한다. 본 논문에서는 박자 후처리 프로그램만 구현하여 적용하였으며, 화성 후처리는 사람이 직접 하였다. 화성 후처리는 복잡하여 추후연구에서 구현할 예정이다.

• Computers and Concrete
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• 제13권6호
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• pp.765-780
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• 2014

Many building codes use the empirical equation to determine fundamental period of vibration where in effect of length, width and the stiffness of the building is not explicitly accounted for. In the present study, ANN models are developed in three categories, varying the number of input parameters in each category. Input parameters are chosen to represent mass, stiffness and geometry of the buildings indirectly. Total numbers of 206 buildings are analyzed out of which, data set of 142 buildings is used to develop these models. It is demonstrated through developed ANN models that geometry of the building and the sizes of the columns are significant parameters in the dynamic analysis of building frames. The testing dataset of these three models is used to obtain the empirical relationship between the height of the building and fundamental period of vibration and compared with the similar equations proposed by other researchers. Experiments are conducted on Mild Steel frames using uniaxial shake table. It is seen that the values obtained through the ANN models are close to the experimental values. The validity of ANN technique is verified by experimental values.

• Computers and Concrete
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• 제32권1호
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• pp.1-13
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• 2023

This paper discusses a framework for predicting the flexural strength of prestressed and non-prestressed FRP reinforced T-shaped concrete beams using soft computing techniques. An analysis of 83 tests performed on T-beams of varying widths has been conducted for this purpose with different widths of compressive face, beam depth, compressive strength of concrete, area of prestressed and non-prestressed FRP bars, elasticity modulus of prestressed and non-prestressed FRP bars, and the ultimate tensile strength of prestressed and non-prestressed FRP bars. By analyzing the data using two soft computing techniques, named artificial neural networks (ANN) and gene expression programming (GEP), the fundamental parameters affecting the flexural performance of prestressed and non-prestressed FRP reinforced T-shaped beams were identified. The results showed that although the proposed ANN model outperformed the GEP model with higher values of R and lower error values, the closed-form equation of the GEP model can provide a simple way to predict the effect of input parameters on flexural strength as the output. The sensitivity analysis results revealed the most influential input parameters in ANN and GEP models are respectively the beam depth and elasticity modulus of FRP bars.

• Computers and Concrete
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• 제23권4호
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• pp.273-279
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• 2019

Corrosion of the rebar embedded in concrete has a fundamental role in the determination of life and durability of the concrete structures. Researches have demonstrated that artificial neural networks (ANNs) can effectively predict issues such as expected damage in concrete structures in marine environment caused by chloride penetration, the potential of steel embedded in concrete under the influence of chloride, the corrosion of the steel embedded in concrete and corrosion current density in steel reinforced concrete. In this study, data from different kind of concrete under the influence of chloride ion, are analyzed using the neural network and it is concluded that this method is able to predict the bond strength between the concrete and the steel reinforcement in mentioned condition with high reliability.