• Advances in concrete construction
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• 제13권6호
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• pp.411-422
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• 2022

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

• Journal of Information Technology Applications and Management
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• 제22권3호
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• pp.129-141
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• 2015

The purpose of this study is to provide an explanation for the association between the characteristics of a network and the type of innovation by considering the effect of absorptive capacity. To do so, this study examined the moderating effects of absorptive capacity on the characteristics of network-innovation relationship in a technical-driven venture company. In order to obtain a better understanding about consequences caused by interfirm network, information was obtained from 169 Korean IT venture companies. Results confirmed that the network diversity is positively associated with exploration. Our results also suggested that the network strength is positively associated with exploitation. Finally, we found a positive two-way interaction between absorptive capacity and the network diversity-exploration relationship. Then, we discussed implications and directions for future research.

• E2M - 전기 전자와 첨단 소재
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• 제9권4호
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• pp.364-371
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• 1996

In this study, in order to investigate the applicability of IPN structure to epoxy resin which has been widely used as electrical and electronic insulating materials, DC dielectric breakdown properties and morphology were compared and analyzed according to variation of network structure, using the single network structure specimen formed of epoxy resin alone, interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin, and interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin/polyurethane resin. As results of the measunnent of DC dielectric breakdown strength at 50[.deg. C] and 130[>$^{\circ}C$], IPN specimen formed of epoxn, resin 100[phr] and methacrylic acid resin 35[phr] was the most excellent, and which corresponded to the SEM phenomena. The effect of IPN was more remarkable at high temperature region than at low temperature region. It is supposed that the defect of epoxy resin, dielectric breakdown strength is lowered remarkably at high temperature region, be complemented according to introducing IPN method.

• 한국멀티미디어학회논문지
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• 제11권8호
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• pp.1072-1081
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• 2008

홈 네트워크에 참여하는 노드들에게 네트워크 주소를 자동으로 할당하는 방식은 주소 할당 서버에 의해 유일한 주소를 제공하거나, 혹은 노드 자체적으로 랜덤한 주소를 생성하여 주소 중복 검사를 통해 유일한 주소인 경우에 사용하는 방식으로 나뉜다. 본 논문에서는 주소할당 서버를 이용하는 방식과 노드 자체적으로 생성하는 방식의 단점을 보완할 수 있는 새로운 주소 할당 방식으로서 네트워크에 기 참여하고 있는 노드들 중의 하나로부터 간단한 수식에 의해 유일한 주소를 제공받을 수 있도록 하는 방식을 제안한다. 이 때 신규 참여 노드의 주소 요청 패킷을 가장 강한 신호 세기로 수신하는 노드가 우선적으로 주소를 할당하도록 하여 전력선 기반 홈 네트워크 환경에서 필요로 하는 자동 중계에 활용 가능토록 하였으며, 제안하는 방식은 C# 프로그래밍을 이용한 시뮬레이터 개발을 통해 중복 검사가 불필요한 유일한 주소가 할당됨을 입증하였다.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.676-682
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• 2011

Sybil attack can disrupt proper operations of wireless sensor network by forging its sensor node to multiple identities. To protect the sensor network from such an attack, a number of countermeasure methods based on RSSI (Received Signal Strength Indicator) and LQI (Link Quality Indicator) have been proposed. However, previous works on the Sybil attack detection do not consider the fact that Sybil nodes can change their RSSI and LQI strength for their malicious purposes. In this paper, we present a Sybil attack detection method based on a transmission power range. Our proposed method initially measures range of RSSI and LQI from sensor nodes, and then set the minimum, maximum and average RSSI and LQI strength value. After initialization, monitoring nodes request that each sensor node transmits data with different transmission power strengths. If the value measured by monitoring node is out of the range in transmission power strengths, the node is considered as a malicious node.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
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• pp.145-152
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• 2004

The flow stress value was calculated by comparing predicted and measured roll force. Using basic on-line roll force model and logged mill data the flow stress equation of high strength steel for automobile was derived. The flow stress equation consists of the flow stress equation of carbon steel and flow stress factor calculated by neural network with input parameters not only carbon contents, strip temperature, strain, and strain rate, but also compositions such as Mn, p, Ti, Nb, and Mo. Using the flow stress equation and basic roll force model, precision roll force model of high strength steel for automobile was derived. Using test set of logged mill data the flow stress equation was verified.

• Advances in Computational Design
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• 제8권1호
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Computers and Concrete
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• 제15권5호
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.