Time filter

Source Type

Si Sa Ket, Thailand

Saejueng K.,Sisaket Rajabhat University | Panthama N.,University of Science and Arts of Iran
Natural Product Research

Chemical constituents of crude ethyl acetate extract of roots of Akschindlium godefroyanum (Kuntze) H. Ohashi were investigated and seven flavonoids were isolated. Their structures were identified based on spectroscopic methods as well as by comparison with spectral data reported in the literature as six flavanonols and a flavonol including 7,4'-dihydroxy-5,3'-dimethoxyflavanonol (1), neophellamuretin (2), taxifolin (3), erycibenin D (4), geraldol (5), fustin (6) and garbanzol (7). Compounds 2, 4 and 7 were found in the genus Akschindlium for the first time. Compounds 3, 5 and 6 appeared to have free radical scavenging activities using DPPH assay with IC50 of 21, 40 and 15 μg/mL, respectively. © 2014 © 2014 Taylor & Francis. Source

Chuentawat R.,Suranaree University of Technology | Chuentawat R.,Nakhon Ratchasima Rajabhat University | Bunrit S.,Suranaree University of Technology | Ruangudomsakul C.,Suranaree University of Technology | And 3 more authors.
Lecture Notes in Engineering and Computer Science

We applied traditional time series analysis and artificial neural network (ANN) techniques to model and forecast the power consumption of Bangkok's metropolitan area. Time series data in terms of units of household electricity usage were obtained from the Metropolitan Electricity Authority of Thailand. The data had been collected monthly from January 2010 to May 2015. Forecasting models with different parameters are generated from both techniques using the training data, which are the series from January 2010 to December 2014. The remaining data from January 2015 to May 2015 are employed as the testing data. Forecasting performance of each model is measured by the rooted mean square error (RMSE) and the mean absolute percentage error (MAPE) metrics. The traditional time series forecasting models studied in this research are GLM, HoltWinters, and ARIMA. For ANN, we examine four models using 3 layers with different number of neurons ranging from 4 to 7: 3L-4N, 3L-5N, 3L-6N, and 3L-7N. The experimental results reveal that ARIMA is superior among the traditional time series models. For the intelligent based models, 3L-6N is the best of ANN models. Moreover, the MAPE metric of the 3L-6N model is less than the ARIMA model. As a result, we can conclude that ANN model is more powerful in forecasting power distribution units than the traditional time series models. Source

Wongthanavasu S.,Khon Kaen University | Ponkaew J.,Sisaket Rajabhat University
Expert Systems with Applications

Over the last few decades, classification applied to numerous applications in science, engineering, business and industries have rapidly been increased, especially for big data. However, classifiers dealing with complicated high dimension problems with non-conforming patterns with high accuracy are rare, especially for bit-level features. It is a challenging research problem. This paper proposed a novel efficient classifier based on cellular automata model, called Cellular Automata-based Classifier (CAC). CAC possesses the promising capability to deal with non-conforming patterns in the bit-level features. It was developed on a new kind of the proposed elementary cellular automata, called Decision Support Elementary Cellular Automata (DS-ECA). The classification capability of DS-ECA is promising since it can describe very complicated decision rule in high dimension problems with less complexity. CAC comprises double rule vectors and a decision function, the structure of which has two layers; the first layer is employed to evolve an input pattern into feature space and the other interprets the patterns in feature space as binary answer through the decision function. It has a time complexity of learning at O(n2), while the classification for one instance is O(1), where n is a number of bit patterns. For classification performance, 12 datasets consisting of binary and non-binary features are empirically implemented in comparison with Support Vector Machines (SVM) using k-fold cross validation. In this respect, CAC outperforms SVM with the best kernel for binary features, and provides the promising results equivalent to SVM on average for non-binary features. © 2015 Elsevier Ltd. All rights reserved. Source

Chindaprasirt P.,Khon Kaen University | Sinsiri T.,Suranaree University of Technology | Napia C.,Sisaket Rajabhat University | Jaturapitakkul C.,King Mongkuts University of Technology Thonburi
Indian Journal of Engineering and Materials Sciences

In this paper, the properties of solidified waste using ordinary Portland cement (OPC) containing silica fume (SF) and fly ash (FA) as a binder are reported. Silica fume and fly ash are used to partially replace ordinary Portland cement by 10% and 30% by weight, respectively. Plating sludge is used of 40, 50 and 60% by weight of the binder. A water to binder (w/b) ratio of 0.40 is used for all of the mixtures. The compressive strength of the solidified wastes is investigated. The leachability of heavy metals is determined by TCLP and XRD, and XRF is used to study the chemical properties, while the fractured surfaces are studied by SEM, and the pore size distribution is studied by MIP. The test results show that the setting time of the blended cement increased as the amount of plating sludge in the mix increased. In addition, the compressive strength of the blended cement increased with increasing curing duration time but at a decreasing rate. The compressive strengths at 28 days of the SF solidified waste mixes are 12.4, 2.7, 1.34 MPa and those of FA solidified waste mixes are 1.1, 1.0, 0.5 MPa at the plating sludge of 40, 50 and 60% by weight of the binder, respectively. The quality of the solidified waste containing SF and FA is better than that with OPC alone in terms of the effectiveness in reducing the leachability. The concentrations of heavy metals in the leachates are within the limits specified by the US EPA. Source

Sisaket Rajabhat University | Entity website

, 31 2557 16:15 | | : 6989

Discover hidden collaborations