• Advances in Computational Design
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• 제2권3호
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• 한국구조물진단유지관리공학회 논문집
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• 제25권5호
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• pp.135-140
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• 2021

본 연구에서는 일반강도 범위 콘크리트의 단면에서 골재 형상의 특성을 추출하고 이를 인공신경망과 이미지 프로세싱 기술에 적용하여 콘크리트의 압축강도를 예측하였다. 이를 위하여 면적, 둘레, 길이 등과 같은 일반적인 골재 형상 특성과 함께 골재의 거리-각도 특징을 수치적으로 표현하고 물성치 예측에 활용하였다. 그 결과, 콘크리트 압축강도에 영향을 미치는 주요변수를 사용하지 않고 단면의 골재 형상 특성만을 사용하여 압축강도 예측이 가능하였으며, 인공신경망 알고리즘 구축을 통해 예측 강도와 실제 강도의 상대오차 4.43% 이내의 범위에서 콘크리트 압축강도를 예측할 수 있었다. 본 연구에서 도출된 결과를 기반으로 골재의 거리-각도 특징을 활용하여 콘크리트의 유동성, 휨·인장강도 등 다양한 특성을 예측도 가능할 것으로 판단된다.

• 소성∙가공
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• 제29권2호
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• 제어로봇시스템학회논문지
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• 제17권2호
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• pp.149-156
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• 2011

This paper reveals a distance estimation algorithm based on multi-code ultrasonic and wireless sensor network. For measuring the distances among the sensor nodes, each ultrasonic transmitter transmits multi-code ultrasonic signal simultaneously. Receivers use cross correlation method to separate the coded signals. The information of measured distances is broadcasted to each sensor node by wireless sensor network. The wireless sensor network measures the distance among the sensor nodes using the received signal strength of the broadcasting. The multi-code ultrasonic have a limitation of measurable distance. And the received signal strength is affected from an environment. This paper measures a distance using ultrasonic and a received signal strength in short range. These measured data are applied to the least square estimation algorithm. By the expansion of the fitting curve, a distance measurement in long range using the received signal strength is compensated. The coupled system reduce the error to an acceptable level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2419-2435
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• 2023

Command and control networks(C2N) exhibit evident multi-network interdependencies owing to their complex hierarchical associations, interleaved communication links, and dynamic network changes. However, the existing command and control networks do not consider the effects of dependent nodes on the load distribution. Thus, we proposed a command and control networks load allocation strategy based on interdependence strength. First, a new measure of interdependence strength was proposed based on the edge betweenness, which was followed by proposing the inter-layer load allocation strategy based on the interdependence strength. Eventually, the simulation experiments of the aforementioned strategy were designed to analyze the network invulnerability with different initial load capacity parameters, allocation model parameters, and allocation strategies. The simulation indicates that the strategy proposed in this study improved the node survival rate of the interdependent command and control networks model and successfully prevented cascade failures.

• 제어로봇시스템학회논문지
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• 제10권8호
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• pp.742-747
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• 2004

Outdoors we can easily acquire our accurate location by GPS. However, the GPS signal can't be acquired indoors because of its weak signal power level. Adequate positioning method is demanded for many indoor positioning applications. At present, wireless local area network (WLAN) is widely installed in various areas such as airport, campus, and park. This paper proposes a positioning algorithm using WLAN signal strength to provide the position of the WLAN user indoors. There are two methods for WLAN based positioning, the signal propagation method uses signal strength model over space and the empirical method uses RF power propagation database. The proposed method uses the probability distribution of the power propagation and the maximum likelihood estimation (MLE) algorithm based on power strength DB. Test results show that the proposed method can provide reasonably accurate position information.

• 벤처창업연구
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• 제11권3호
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• pp.87-96
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• 2016

본 연구의 목적은 창업자의 사회적 네트워크의 유대관계의 강도와 다양성이 창업성과에 어떠한 영향을 미치는지 알아보고, 창업자의 창업 경험이 사회적 네트워크와 성과간의 관계에 미치는 영향을 알아보고자 한다. 연구의 가설 검증을 위해 창업자 및 창업기업에 종사하는 구성원을 대상으로 설문조사를 실시하였고 PLS를 통한 경로분석을 실시하였다. 실증적 연구 분석결과 사회적 네트워크의 유대정도는 생산적 성과에 직접적인 영향을 미치지만 창의적인 성과에는 영향을 미치지 않았다. 반면 사회적 네트워크의 다양성은 생산적 성과에 영향을 미치지 않지만, 창의적인 성과에 긍정적인 영향을 미치는 것으로 나타났다. 창업경험의 조절효과는 네트워크의 유대정도와 창업 성과 간의 관계에서 유의하게 나타났으며 사회적 네트워크의 다양성과 성과간의 관계에는 영향을 미치지 않는 것으로 나타났다. 본 연구 결과를 통해 창업가의 사회적 네트워크와 창업경험의 중요성을 인지하고 창업성과를 위한 효율적인 사회적 네트워크 활용을 제시하고자 한다.

• Computers and Concrete
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• 제25권5호
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• pp.401-409
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• 2020

Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.

• 한국지반환경공학회 논문집
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• 제19권5호
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• pp.13-22
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• 2018

The number of researches on the mechanical properties of cemented sand and gravel (CSG) materials and the application of the CSG Dam has been increased. In order to explain the technical scheme of strength prediction model about the artificial neural network, we obtained the sample data by orthogonal test using the PVA (Polyvinyl alcohol) fiber, different amount of cementing materials and age, and established the efficient evaluation and prediction system. Combined with the analysis about the importance of influence factors, the prediction accuracy was above 95%. This provides the scientific theory for the further application of CSG, and will also be the foundation to apply the artificial neural network theory further in water conservancy project for the future.

• 한국포장학회지
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• 제29권3호
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.