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Nam C.T.,National Kaohsiung University of Applied Sciences | Yang W.-D.,National Kaohsiung University of Applied Sciences | Duc L.M.,Danang University of Technology
Bulletin of Materials Science | Year: 2013

TiO2 nanotubes were synthesized by the solvothermal process at low temperature in a highly alkaline water-methanol mixed solution. Their characteristics were identified by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), specific surface area (BET), Fourier transform infrared spectroscopy (FTIR) and UV-Vis absorption spectroscopy. The as-prepared samples were tested by the photodegradation reaction of methylene blue (MB) dye under visible-light irradiation. The ratios of methanol and water, as well as calcination temperature, affected the morphology, nanostructure and photocatalytic performance. The methanol solvent plays an important role in improving crystallization of the anatase phase, which affects the photocatalytic reaction. Titanate nanotubes were synthesized in methanol-water volume ratios of 10:90, 20:80 and 30:70 which still had high absorbability. Titania nanotubes formed at a calcination temperature of 300 °C using methanol-water volume ratio of 30:70 showed highest photocatalytic performance, much higher than that using water solvent and TiO2-P25 powder. © Indian Academy of Sciences. Source

Nguyen-Le H.,Danang University of Technology | Le-Ngoc T.,McGill University | Tran N.H.,University of Akron
IEEE Transactions on Vehicular Technology | Year: 2011

This paper is concerned with the problem of turbo (iterative) processing for joint channel and carrier frequency offset (CFO) estimation and soft decoding in coded multiple-input-multiple-output (MIMO) orthogonal frequency-division-multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. In doubly selective channel modeling, a basis expansion model (BEM) is deployed as a fitting parametric model to reduce the number of channel parameters to be estimated. Under pilot-aided Bayesian estimation, CFO and BEM coefficients are treated as random variables to be estimated by the maximum a posteriori technique. To attain better estimation performance without sacrificing spectral efficiency, soft bit information from a soft-input-soft-output (SISO) decoder is exploited in computing soft estimates of data symbols to function as pilots. These additional pilot signals, together with the original signals, can help to enhance the accuracy of channel and CFO estimates for the next iteration of SISO decoding. The resulting turbo estimation and decoding performance is enhanced in a progressive manner by benefiting from the iterative extrinsic information exchange in the receiver. Both extrinsic information transfer chart analysis and numerical results show that the iterative receiver performance is able to converge fast and close to the ideal performance using perfect CFO and channel estimates. © 2011 IEEE. Source

Nguyen D.S.,Danang University of Technology
IEEE International Conference on Industrial Engineering and Engineering Management | Year: 2016

Nowadays, requirements of clients and customers for the quality of product are more and more tightened and complicated. The quality assurance of manufactured product is a key to success in the context of global and competitive economy. Many different parts of final product are made from raw material by multistage manufacturing processes in different places. The risk is that the final manufactured product does not fully meet the requirements. Thus, the paper proposes a method based on Bayesian networks that allows to model impact factors in a multistage machining process on product quality. The root cause analysis can be implemented by using the Bayesian network model. As a result, product quality predicted earlier at design stage can help product designer adjust the product designed and manufacturing processes in order to obtain a robust design with promised quality. © 2015 IEEE. Source

Nguyen A.-T.,University of Liege | Nguyen A.-T.,Danang University of Technology | Reiter S.,University of Liege | Rigo P.,University of Liege
Applied Energy | Year: 2014

Recent progress in computer science and stringent requirements of the design of "greener" buildings put forwards the research and applications of simulation-based optimization methods in the building sector. This paper provides an overview on this subject, aiming at clarifying recent advances and outlining potential challenges and obstacles in building design optimization. Key discussions are focused on handling discontinuous multi-modal building optimization problems, the performance and selection of optimization algorithms, multi-objective optimization, the application of surrogate models, optimization under uncertainty and the propagation of optimization techniques into real-world design challenges. This paper also gives bibliographic information on the issues of simulation programs, optimization tools, efficiency of optimization methods, and trends in optimization studies. The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods (surrogate models) for large-scale building optimization problems; and reducing time and effort for such activities. Further effort is also required to quantify the robustness in optimal solutions so as to improve building performance stability. © 2013 Elsevier Ltd. Source

Duy T.H.,Danang University of Technology
2014 IEEE 5th International Conference on Communications and Electronics, IEEE ICCE 2014 | Year: 2014

Adaptive Neuro Fuzzy Inference System (ANFIS) is a kind of neuro-fuzzy model, combining fuzzy system and neural network. It incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. Based on Takagi-Sugeno Fuzzy Inference System, ANFIS focus on the accuracy and it is widely used in control and identification systems. However, when the fuzzy rule base is large, it proved to be slow because of the computation time. This paper introduces an enhanced algorithm, implemented on FPGA platform, to speed up the standard ANFIS algorithm in digital image processing. © 2014 IEEE. Source

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