• Computers and Concrete
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• 제8권5호
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• pp.597-616
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• 2011

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (${\rho}_s$) and the FRP volumetric ratio (${\rho}_{FRP}$). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.

• 융합신호처리학회논문지
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• 제20권2호
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• pp.78-83
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• 2019

소리 기반 기계 고장 진단은 기계의 음향 방출 신호에서 비정상적인 소리를 자동으로 감지하는 것이다. 수학적 모델을 사용하는 기존의 방법은 기계 시스템의 복잡성과 잡음과 같은 비선형 요인이 존재하기 때문에 기계 고장 진단이 어려웠다. 따라서 기계 고장 진단의 문제를 패턴 인식 문제로 해결하고자 한다. 본 논문에서 DWT와 인공신경망 기반 패턴 인식 기법을 이용한 자동화 기계 고장 진단 기법을 제안한다. 기계의 결함을 효과적으로 탐지하기 위해 DWT를 이용해 대역별 분해 후 최상위 고주파 부대역과 최하위 저주파 부대역을 제외한 나머지 부대역의 PSD를 구하여 인공신경망 기반 분류기의 입력으로 사용한다. 그 결과 본 연구에서 제안한 방법은 효과적으로 결함을 탐지할 뿐만 아니라 소리 기반의 다양한 자동 진단 시스템에도 효과적으로 활용될 수 있음을 보여준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• Computers and Concrete
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• 제11권4호
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• Journal of Construction Engineering and Project Management
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• 제5권1호
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• 멤브레인
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• 제33권1호
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• pp.34-45
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• 2023

본 연구에서는 투과 유량 모델을 개발하기 위하여, 시간, 막 전후의 압력 차, 회전 속도, 막의 기공 크기, 동점도, 농도 및 공급 유체의 밀도 등 7개의 입력 변수에 기반한 두 종류(ANN 및 SVM) 인공지능 기법을 이용하였다. 시행착오법과 실험데이터와 예측 데이터 간의 결정 계수(R²) 와 평균절대상대편차(AARD)를 포함한 두 가지 통계 변수를 통해 최적의 모델을 선정하였다. 최종적으로 얻어진 결과에서 최적화된 ANN 모델이 R² = 0.999 및 AARD% = 2.245인 투과 플럭스 예측 정확도를 보여서, R² = 0.996 및 AARD% = 4.09의 정확도를 보인 SVM 모델에 비해 더 정확함을 알 수 있었다. 또한, ANN 모델은 SVM 방식에 비해 투과 유속을 예측하는 능력도 더 높은 것으로 나타났다.

• 재무관리연구
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• 제19권2호
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• pp.211-231
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• 2002

본 연구의 목적은 VECM(Vector Error Correction Model)과 인공지능모형(Artificial Neural Networks)을 이용하여 우리나라 증권시장과 거시경제 변수들과의 장기적 관계에 대한 설명력을 비교해보고자 함에 있다. VECM이 APT(Arbitrage Pricing Theory)에 기초를 둔 선형동학모형이라고 한다면, 인공지능모형은 비모수적 비선형모형이라는 점에서, 두 방법론의 분석결과를 직접 비판하는 것은 의미있는 연구라고 할 수 있다. 인공지능모형을 주로 활용하는 선행연구들에 의하면, 증권시장은 시장의 특이패턴들로 인해 계량경제학적 접근인 선형 모형보다는 인공지능모형을 통해 증권시장의 움직임을 설명하고 예측하는 것이 더 바람직할 수도 있다는 것이다. 따라서, 본 연구에서는 VECM분석에서 자료의 안정성을 검증하고, 공적분 백터를 발견한 이후, 장기적 균형관계의 실증적 분석을 하였다. 그리고, 인공지능모형에서는 delta rule과 Sigmoid 함수를 이용한 GRNN(General Regression Neural Net)과 Back-Propagation등의 방법들을 활용하였다. 이러한 분석결과, Back-Propagation 모형이 다른 모든 모형들보다도 더 우수한 설명력을 보여주고 있었다. 이러한 결과들은 인공지능모형이 동태적인 선형 모형보다도 더 우수한 설명력을 제공할 수 있는 가능성을 보여주고 있었다.

• Neurospine
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• 제15권4호
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.940-945
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• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

• 한국게임학회 논문지
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• 제16권2호
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• pp.27-40
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• 2016

스텔스 게임에서 플레이어의 행동을 예측하는 것은 게임 디자인에 있어서 핵심적인 역할을 한다. 하지만, 플레이어와 게임 환경 간의 상호작용이 실시간으로 일어난다는 점에서 이러한 예측 프로세스를 자동화하는 것은 어려운 문제이다. 본 논문은 동적 환경에서의 스텔스 움직임을 예측하기 위한 강화학습 방법을 소개하며, 이를 위해 Q-learning과 인공신경망이 통합된 형태의 모델이 액션 시뮬레이션을 위한 분류기로 활용된다. 실험 결과들은 이러한 시뮬레이션 에이전트가 동적으로 변하는 주변 상황에 민감하게 반응함을 보여주며, 따라서 게임 레벨 디자이너가 다양한 게임 요소들을 결정하는데 유용함을 보여준다.