Successful Use of Geoelectrical Surveys in Area 3 of the Gol-e-Gohar Iron Ore Mine,Iran

Geoelectrical surveys were conducted in Area 3 of the Gol-e-Gohar iron ore mine to provide geological and hydrogeological information. Open pit mining is currently underway in the northern part of the Area, and underground mining operations are planned for the southern section. Groundwater has already been encountered in the open pit mine. Twenty five resistivity soundings were first performed in the mine area; then, induced polarization (IP) measurements were carried out to remove ambiguities between clay and water-bearing layers. To investigate fault zones as water conduits, combined resistivity profiling surveys were also carried out. These measurements provided a detailed structural map of the pit area. Resistivity and IP results have subsequently been confirmed by observations at three monitoring wells and the mine pit wall. Monitoring and piezometric wells will be drilled at locations suggested by the results of the geoelectrical surveys. Drainage galleries may be developed to dewater the open pit mine. However, another option being considered is to start the underground mining with the idea that it will initially simply serve as a dewatering mechanism.     

Energy-spectral efficient resource allocation and power control in heterogeneous networks with D2D communication

Wireless Networks - Heterogeneous networks (HetNets) provide the demand for high data rates. In this study, we analyze the coexistence of femtocells and device-to-device (D2D) communication with...     

User grouping and power allocation for energy efficiency maximization in mmWave-NOMA heterogeneous networks

Wireless Networks - This paper investigates the application of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) transmission in the fifth-generation (5G) of heterogeneous cellular...     

Digital image steganalysis based on the reciprocal singular value curve

Embedding secret messages in steganographic approaches is similar to adding some weak noises to the original media. One of the traditional ways for image steganalysis is computing a feature sets using noise residuals. From another perspective, the disturbance of natural image statistics can be explored to extract the feature vector for steganalysis. In fact, the alteration of natural scene statistics can be investigated to reveal the presence of secret messages embedded in images. Hence, the feature vectors can be constructed using such changes. In the proposed scheme, the alteration of singular value curve is used to construct the steganalysis feature vector. Two spatial and JPEG based feature vectors are extracted in the proposed statistical exploitation. The experimental results illustrate the acceptable performance of the proposed feature vectors for both universal and JPEG based steganalysis methods.

     

Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13–28 wt%), feed rate (1–5 mL/h), and tip-to-collector distance (10–23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results.     

An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in the second algorithm, for increasing DMUs’ homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs’ homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.     

The Study of Surface Properties of Tokamak First Wall Using TiN Coated on Stainless Steel

Structural properties of the synthesized TiN thin film on 316L stainless steel (S.S.316L) were studied to determine its potential application as a protective layer for first wall of Tokamaks. For this purpose we deposited TiN on stainless steel 316L (TiN/S.S.316L) via DC magnetron sputtering method and annealing at 700 °C. Before and after exposure, samples were analyzed by using scanning electron microscopy (SEM), X-ray diffraction (XRD) and a spectrophotometer. The XRD analysis for studying crystalline structure of samples shows the position and intensity of XRD peaks has changed after exposing to 500 shots of Tokamak. It was found that the S.S.316L sample was severely damaged, its reflection dropped significantly but the SEM images show that plasma exposure has not created any cracks and lines on the surface of the TiN/S.S.316L sample and mass of dust particle has been assembled in some area of the sample.     

Application of high porosity metal foams as air-cooled heat exchangers to high heat load removal systems

A numerical study has been conducted to investigate the fluid flow and heat transfer of an air-cooled metal foam heat exchanger under the high speed laminar jet confined by two parallel walls for which the range of the Reynolds number is 600–1000. Two independent numerical solvers were used and cross-validated being a FORTRAN code and the commercially available software CFD-ACE. The effects of local thermal non-equilibrium, thermal dispersion, porosity, and pore density on the heat transfer augmentation are examined for different Reynolds numbers. Application of energy flux vectors, for convection visualization, is also illustrated for a more comprehensive analysis of the problem. Finally, the performance of the metal foam heat exchanger is compared to that of conventional finned design. It is observed that the heat removal rate can be greatly improved at almost no excess cost.