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WOS: 000374075100027, In this research, individual and interactional effect of curing temperature (CT), curing time (Crime), sodium concentration (SC) and silicate modulus (SM) on the compressive strength, chloride ion permeability, oxygen permeability and thermal conductivity of alkali activated slag (AAS) concretes were investigated and the best possible combination of CT, Crime, SC and SM for the maximization of compressive strength while for the minimization of chloride ion permeability, oxygen permeability and thermal conductivity of AAS concretes were determined. An experimental program that contains 21 different combinations of independent parameters (CT, Crime, SC and SM) was conducted by using the central composite design method. Alkali solution-slag ratio was kept constant at 0.53 in all production. The effects of the independent parameters were characterized and analyzed by using statistically significant quadratic regression models on the measured properties (dependent parameters). Optimum values of the independent parameters were determined by solving a multi-objective optimization problem obtained by using the proposed regression models for dependent parameters. The results of test and the analysis indicate that the independent parameters affect the dependent parameters considerably; however, the most effective parameter for the compressive strength, chloride ion permeability, oxygen permeability and thermal conductivity is the sodium concentration. The optimum combination of CT, Crime, SC and SM is 46.8 degrees C, 83 h, 5.5% and 2.0, respectively, for the minimization of chloride ion permeability, oxygen permeability and thermal conductivity and the maximization of compressive strength of AAS concretes. (C) 2016 Elsevier Ltd. All rights reserved., Turkish Scientific and Technical Research Council (TUBITAK) [MAG-214M035]; Scientific Research Division (BAP) of Mustafa Kemal University [13700]; TUBITAK; Mustafa Kemal University, Turkish Scientific and Technical Research Council (TUBITAK) (MAG-214M035) and Scientific Research Division (BAP) of Mustafa Kemal University (13700) supported this research financially. Authors are appreciating TUBITAK and Mustafa Kemal University due to their the financial assistance.

Inverse numerical model to identify unidirectional composites failure parameters.Robust and accurate scheme validated by experimental results.Flexible to use non-standardized experiments data or other optimisation techniques.Reduced computation time thanks to the XFEM. An experimental-numerical methodology for identification of mode I failure parameters, namely the critical strain energy release rate ( G Ic ) and the strength ( ? c ) of unidirectional carbon/epoxy composite is proposed. Using an inverse procedure, the experimental results of a double cantilever beam (DCB) test are used in conjunction with a counterpart extended finite element method (XFEM) cohesive zone model (CZM). In the developed numerical model, the notch and the crack path are located and enriched implicitly by a level set function and the crack propagation is controlled by a bi-linear cohesive law. The cost function accounts for the error between the numerical and the experimental results, and it is minimised in a least squares sense by updating the values of the two independent parameters. The determined critical strain energy release rate is then compared with the one obtained using the corrected beam theory (CBT), and the identified strength is compared with the one estimated experimentally. Globally, the results are very well in agreement and the proposed methodology seems to be efficient to determine accurately the composite failure parameters.

In this paper, we aim to investigate the superiority and practicability of many-parameter wavelet and Golay transforms (MPWT and MPGT) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication system (Intelligent-OFDM-TCS), based on many-parameter transforms (MPTs). New system uses inverse MPT for modulation at the transmitter and direct MPT for demodulation at the receiver. The purpose of employing the MPTs is to improve the PHY-LS of wireless transmissions against to the wide-band anti-jamming communication. Each MPT depends on finite set of independent Jacobi parameters (angles), which could be changed independently one of another. When parameters are changed, multi-parametric transform is changed too taking form of a set known (and unknown) orthogonal (or unitary) wavelet transforms. We implement the following performances as bit error rate (BER), symbol error rate (SER), peak to average power ratio (PAPR), the Shannon-Wyner secrecy capacity (SWSC) for novel Intelligent-MPWT-OFDM-TCS. Previous research has shown that the conventional OFDM TCS based on discrete Fourier transform (DFT) has unsatisfactory characteristics in BER, PARP, SWSC and in anti-eavesdropping communications. We study Intelligent-MPT-OFDM-TCS to find out optimal values of angle parameters of MPT optimized BER, PAPR, SWSC, anti-eavesdropping effects. Simulation results show that the proposed Intelligent OFDM-TCS have better performances than the conventional OFDM system based on DFT against eavesdropping.

In this paper, we aim to investigate the superiority and practicability of many-parameter transforms (MPTs) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication systems based on complex and quaternion MPTs. The new systems use inverse MPT (IMPT) for modulation at the transmitter and MPT for demodulation at the receiver. The purpose of employing the MPT is to improve: 1) the PHY-LS of wireless transmissions against to the wide-band anti-jamming and anti-eavesdropping communication; 2) the bit error rate (BER) performance with respect to the conventional OFDM-TCS; 3) the peak to average power ratio (PAPR). Each MPT depends on finite set of independent parameters (angles). When parameters are changed, many-parametric transform is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. For this reason, the concrete values of parameters are specific “key” for entry into OFDM-TCS. Vector of parameters belong to multi-dimension torus space. Scanning of this space for find out the “key” (the concrete values of parameters) is hard problem. MPT has the form of the product of the Jacobi rotation matrixes and it describes a fast algorithm for MPT. The main advantage of using MPT in OFDM TCS is that it is a very flexible anti-eavesdropping and anti-jamming Intelligent OFDM TCS. To the best of our knowledge, this is the first work that utilizes the MPT theory to facilitate the PHY-LS through parameterization of unitary transforms.

WOS: 000393002700019, In this paper, bond strength and abrasion resistance of alkali activated concretes (AAC) were examined experimentally by using the central composite design (CCD) method. AAC were designed and produced considering the sodium concentration (SC), silicate modules (SM), curing temperature (CT) and exposed curing time (ECT) as the CCD's independent parameters. Twenty-one AAC mixtures were established depend on the various combinations of independent parameters in CCD at 95% confidence level. Effects of each independent parameter on the dependent parameters were statistically analyzed using experimental measurements and best possible combination of the independent parameters were defined for the maximization of the compressive strength, split tensile strength, UPV and bond behavior of AAC and for the minimization of abrasion value of AAC by solving the multi-objective optimization problems which is generated using the proposed regression models for the dependent parameters. Test results demonstrate that all studied independent parameters have the noteworthy effect on the properties of AAC statistically; however, the most effective independent parameter is SC. The optimum values of the parameters studied were defined as CT of 66 degrees C, ECT of 14.76 h, SC of 5.72% and SM of 1.0 for the defined multi-objective optimization problem. (C) 2016 Elsevier Ltd. All rights reserved., Turkish Scientific and Technical Research Council [MAG-214M035]; Scientific Research Division of Mustafa Kemal University [13700], This research was supported financially by Turkish Scientific and Technical Research Council (MAG-214M035) and Scientific Research Division of Mustafa Kemal University (13700). Authors thank to TUBITAK and Mustafa Kemal University.

Specialist bibliographies reveal that there are many parameters for fully describing the acoustics of a hall. Are all of these parameters absolutely necessary? Although hall acoustics is a complex discipline, we can nevertheless see that many parameters measure aspects that are very similar to musical perception. Our aim in this paper is to use factor analysis to find a small number of statistically independent parameters that will enable musical performance halls to be characterised using just a few parameters. At the same time, we aim to identify other parameters that will be useful to researchers as part of a lineal combination. To achieve this, we have measured impulse responses in 17 halls and determined the most common 18 parameters. By using factor analysis, we have identified the three key factors that satisfy the required characteristics. This result confirms those obtained in an earlier study [1] of nine halls. The factors obtained strongly correlate with other classic parameters. Factor 1 correlates with reverberation time; Factor 2 correlates with the objective parameters for the impression of space; and Factor 3 correlates with parameters of strength. This last factor also correlates strongly with objective parameters for listener envelopment (LEV) [2], The translation of this paper was funded by the Universidad Politecnica de Valencia, Spain.

Cataloged from PDF version of article. We study the bicriteria load balancing problem on two independent parameters under the allowance of object reallocation. The scenario is a system of M distributed file servers located in a cluster, and we propose three online approximate algorithms for balancing their loads and required storage spaces during document placement. The first algorithm is for heterogeneous servers. Each server has its individual trade-off of load and storage space under the same rule of selection. The other two algorithms are for homogeneous servers. The second algorithm combines the idea of the first one and the best existing solution for homogeneous servers. Using document reallocation, we obtain a smooth trade-off curve of the upper bounds of load and storage space. The last one bounds the load and storage space of each server by less than three times of their trivial lower bounds, respectively; and more importantly, for each server, the value of at least one parameter is far from its worst case. The time complexities of these three algorithms are O(log M) plus the cost of document reallocation.

WOS: 000378152800012, Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (P-min) and maximum (P-max) power generation values as well as the total power generation (P-sum) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, P-min, P-max, and P-sum, were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models. Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phaseof study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.

2nd International Conference on Computational and Experimental Science and Engineering (ICCESEN), WOS: 000384810700099, Science Citation Index Expanded, Conference Proceedings Citation Index- Science, A new model was developed to predict the mechanical properties of St22 grade cold rolled deep drawing steel by gene expression programming. To obtain a dataset to find out the effect of reduction rate on the mechanical properties of cold rolled and galvanized steel sheet, an experimental program was constructed in the real production plant by keeping all other process parameters constant. The training and testing data sets of gene expression programming model were obtained from the test results. For gene expression programming model, mechanical properties (yield strength, ultimate tensile strength and elongation) before cold rolling, chemical composition, initial sheet thickness and reduction rate were used as independent input variables, while mechanical properties after cold rolling (yield strength, ultimate tensile strength and elongation) were used as dependent output variables. Before constructing the gene expression programming models for dependent variables, dataset was analyzed using the analysis of variance and statistically significant (P

The goal of this paper is to find the global minimum for three impulses orbital transfer between coplanar non co-axial orbits. Since there is no analytical solution for the problem, it is necessary to resort to a numerical approach. As first step, we extensively cover the space of possible solutions with the aim to get approximations for independent parameters to use as initial values for a gradient-based optimization method to seek for exact values. A comparison between the optimal solutions in the two impulse and three impulse cases shows that optimality depends on the ratio of semi-latus recti, argument of periapsis and on the eccentricity of initial and final orbits. We see also that the three impulses transfer case leads to different local minima, which could be useful when there are imposed contsraints on the problem.

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In this work, factorial ball-milling experiments have been applied to Bi2Te3 material, for the first time, aiming to investigate the effect of the main process parameters on the structural features and thermoelectric properties of the ball-milled materials. The selected main parameters were the duration of milling, the speed and the ball-to-material ratio. Analysis suggests a strong effect of the speed and duration of processing, whereas the ball-to-material ratio is of minor importance. This approach is advantageous for better understanding of the milling mechanism and the importance of the role of each independent parameter as well as their interaction. All experiments led to nanocrystalline Bi2Te 3, whose structural features were studied. The nanocrystalline size was estimated based on x-ray diffraction analysis, while transmission electron microscopy (TEM) studies were also performed to confirm the presence of nanoscale crystals. A mathematical model was developed based on statistical analysis for prediction of the crystalline size and the Seebeck coefficient of the nanopowders. The thermoelectric properties were also investigated on selected, highly dense pellets fabricated via hot-pressing of the nanopowders. © 2012 TMS. 42 1652 1660 1652-1660

International audience; The accuracy and predictability of predictive methods to determine the flammability characteristics of chemical compounds are of drastic significance in the chemical industry. This work aims at continuing application of the gene expression programming (GEP) mathematical strategy to modify the existing thermophysical properties correlations available in the literature to pursue the following objectives: optimization of the number of independent parameters, amplification of the generality, and improvement of the accuracy and predictability. This work deals with presenting a simple corresponding states model to predict the upper flammability limit temperature of 1462 organic compounds from 76 chemical families. The parameters of the correlation include the critical temperature and the acentric factor of the compounds. The obtained statistical parameters including average absolute relative deviation of the results from DIPPR 801 database values (1.7, 1.8, 1.7% for training, optimization, and prediction sets, respectively) demonstrate improved accuracy of the presented correlations.