• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• Computers and Concrete
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• v.18 no.1
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• pp.1-16
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• 2016

In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of $R^2$, RMS and MAPE. For the testing of dataset, the $R^2$, RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the $R^2$, RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.1
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• pp.38-40
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• 2014

In this paper, proposed method achieves delay reduction and throughput improvement by utilizing token-ring method in infrastructure network. Access Point gives token passing effect by successively transmitting ACK frame including a node's Association ID. Not only can this method considerably reduce time for medium access, but also improve throughput. Furthermore, AP offers more frequent medium access opportunity to node having highest data queue among nodes associated by AP. these method can evenly offer medium access opportunity according to Queue's volumes.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.897-908
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• 2018

In this paper, we propose a security framework for a cluster drones network using the MAVLink (Micro Air Vehicle Link) application protocol based on FANET (Flying Ad-hoc Network), which is composed of ad-hoc networks with multiple drones for IoT services such as remote sensing or disaster monitoring. Here, the drones belonging to the cluster construct a FANET network acting as WTRP (Wireless Token Ring Protocol) MAC protocol. Under this network environment, we propose an efficient algorithm applying the Lightweight Encryption Algorithm (LEA) to the CTR (Counter) operation mode of WPA2 (WiFi Protected Access 2) to encrypt the transmitted data through the MAVLink application. And we study how to apply LEA based on CBC (Cipher Block Chaining) operation mode used in WPA2 for message security tag generation. In addition, a modified Diffie-Hellman key exchange method is approached to generate a new key used for encryption and security tag generation. The proposed method and similar methods are compared and analyzed in terms of efficiency.