• 대한토목학회논문집
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• 제38권5호
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• pp.671-683
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• 2018

도시유역에 대한 집중호우에 따른 침수피해가 증가하고 있으며, 기존에 수행된 많은 연구에서 입증 되어진 바와 같이 도시 침수는 하수관망의 통수능을 상회함에 따라 발생하는 내수침수에 주로 기인하고 있다. 도시화가 상당히 진행되고 인구가 밀집되어 있는 지역에 대한 침수피해는 심각한 사회 경제적 피해를 야기한다. 이에 따라 도시지역에 대한 홍수 예측을 위한 확정 및 확률론적 연구가 진행되어 왔지만, 충분한 선행시간을 확보하며 단시간에 홍수량에 대한 예측결과를 도출하기에는 부족한 실정이다. 본 연구에서는 최적의 실시간 도시 홍수 예측 기법을 제시하기 위하여 도시유출해석 기반 실시간 홍수 예측을 위한 IDNN, TDNN 그리고 NARX 동적신경망을 비교하였다. 강남 지역의 2010, 2011년 실제 호우사상에 대하여 총 홍수량 예측 결과, 입력 지연 인공신경망의 최대 Nash-Sutcliffe 효율 계수는 각각 0.86, 0.53, 시간 지연 인공신경망의 경우 0.92, 0.41, 외생변수를 이용한 비선형 자기 회귀의 경우 0.99, 0.98으로 나타났다. 연구 대상지역에 대한 각 맨홀 누적월류량을 고려한 예측 결과의 오차분석을 통하여 외생변수를 이용한 비선형 자기 회귀 기법을 사용하는 것이 추후 도시 홍수 대응체계 구축에 적합할 것으로 나타났다.

• 상하수도학회지
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• 제16권6호
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• pp.686-699
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• 2002

The purpose of this study is to estimate deterioration factors and weighting factors in pipe network which each local self-governments takes rehabilitation and replacement work present time. Deterioration factors in pipe network are able to effected of specific province or location related with water supply. Most of water supply pipes are laid under the ground, it is hard to quantify deterioration degree of water system. Moreover, the timing and economic limitation and insufficient information on the spot survey gives a difficulty to look over how old water supply system is. Accordingly, this study collects and analyses five data as the laying environment, visual analysis, analysis of soil contents, analysis of pipe material, and questionary survey data in water pipe of A city. The deterioration factor estimates 14 factors with excavation and experimental analysis and 9 factors without excavation and experimental analysis. Also, the weighting factors are estimated by using the multiple linear regressions and the linear programming. The estimated deterioration factor and weighting results are compared the analysis result of visual, pipe material, and soil contents with the Probabilistic Neural Network Model. Consequently, the model results of estimated 9 factors in this study and 14 factors show the 1-2% difference. The result show that the proposed model could be used to decide the deterioration condition of pipe line with real excavation and experimental analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• 한국지반공학회논문집
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• 제23권8호
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• pp.117-127
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• 2007

사면안정해석은 지반물성의 불확실성을 포함한 많은 불확실한 요인을 내포하는 지반공학적 문제이다. 본 연구에서는 상업용 유한차분해석 프로그램을 이용하여 확률론적 사면안정해석을 수행할 수 있는 절차를 제시하였다. 이 경우 한계상태함수가 명시적인 형태로 표현되지 않기 때문에 한계상태함수를 근사화하기 위하여 인공신경망기법을 활용한 응답면기법을 이용하였으며 파괴확률을 구하기 위해 일차 및 이차신뢰도법과 Monte Carlo simulation을 이용하였다. 제안된 절차의 적용성을 검토하기 위하여 2층 지반의 사면과 Sugar Creek제방사면에 대한 확률론적 사면안정해석을 수행하였다. 해석결과는 제안된 절차의 적정성과 다른 다양한 지반공학 문제로의 확장 적용의 가능성을 보여준다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.260-267
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• 2015

In the past, we implemented a web-based smart education platform, but this is not efficient in a smart or mobile education environment. Therefore, in this paper, we propose an Android-based e-board smart platform for a smart or mobile education system. Here, we use Anoto digital pen- and dot pattern-based technologies. This Android-based smart education platform is efficient for a smart education environment. Further, we implement the hardware and software parts of the technologies, an Anoto-based trajectory recognition algorithm, and a probabilistic neural network for handwritten digit and hand gesture recognition.

• 제어로봇시스템학회논문지
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• 제6권5호
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• Smart Structures and Systems
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• 제5권4호
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• pp.369-379
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• 2009

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1999년도 제11회 한글 및 한국어 정보처리 학술대회
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• pp.178-183
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• 1999

화자확인은 기본적으로 각 입력 음성에 대해 하나의 임계치를 기준으로 수락과 거부의 두 가지 결정을 내리나, 본 논문은 네 자리의 비밀번호를 음성으로 입력하였을 때 각 숫자음성에 대한 지역적인 결정을 두 개의 임계치를 이용하여 수락, 거부, 결정유보의 세 가지로 구분하고, 비밀번호 전체에 대한 판단 규칙을 제안하였다. 지역적 결정에 필요한 화자에 대한 신뢰척도의 측정치는 확률신경망을 통해 구하였다. 다섯 명의 화자를 대상으로 수행한 실험에서 하나의 임계치를 이용한 기존의 방식은 5.3%의 오류를 나타냈고, 본 논문에서 제안한 방식은 2.1%의 오류를 보였다.

• 천문학회지
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• 제45권1호
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• pp.1-6
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• 2012

We use a Probabilistic Neural Network (PNN) technique to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of five double-lined spectroscopic binary systems (i.e., EQ Tau, V376 And, V776 Cas, V2377 Oph and EE Cet), we find the corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by other groups via more traditional methods.