A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Synthesizing a uniformly spaced array pattern using integral operator for removing progressive phase shift

In this article, an analytical technique is introduced to obtain the excitation coefficients of uniformly spaced linear antenna arrays in order to achieve a desired array factor. By integration of the prescribed array factor, the array factor dependency to the progressive phase shift is eliminated. A new system of linear equations is consequently obtained whose solution represents the excitation coefficients of the array. Some examples are presented to verify the accuracy of the introduced method. The performance of this strategy is compared with those obtained by the other well‐known techniques such as Woodward‐Lawson and Fourier transform. It is shown that the presented method estimates the desired array pattern with a very good precision.     

Solution combustion synthesis of cerium oxide nanoparticles as corrosion inhibitor

In this study, combustion synthesis of cerium oxide nanoparticles was reported using cerium nitrate hexahydrate as starting material as well as urea, glycine, glucose, and citric acid as fuels. The influence of fuel type on structure, microstructure, band gap, and corrosion inhibition was investigated. X-ray diffraction (XRD) patterns and scanning electron microscopy micrographs showed that CeO₂ nanoparticles with different morphologies were obtained depending on the fuel type. Microstructural changes from unreacted gel to sponge-like morphologies were resulted by varying the fuel type from urea, glycine, and glucose to citric acid. In addition to Ce–O bonds, Fourier transform infrared analysis showed carbon bonds of carbonaceous compositions from incomplete combustion which were declined during combustion reaction. Furthermore, corrosion analyses showed that samples synthesized using urea fuel released the most Ce⁺⁴ ions and could have better protection than other samples.     

Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Making a relation between strains and stresses is an important subject in the rock engineering field. Shear behaviors of rock fractures have been extensively investigated by different researchers. Literature mostly consists of constitutive models in the form of empirical functions that represent experimental data using mathematical regression techniques. As an alternative, this study aims to present a new integrated intelligent computing paradigm to form a constitutive model applicable to rock fractures. To this end, an RBFNN-GWO model is presented, which integrates the radial basis function neural network (RBFNN) with grey wolf optimization (GWO). In the proposed model, the hyperparameters and weights of RBFNN were tuned using the GWO algorithm. The efficiency of the designed RBFNN-GWO was examined comparing it with the RBFNN-GA model (a combination of RBFNN and the Genetic Algorithm). The proposed models were trained based on the results of a systematic set of 84 direct shear tests gathered from the literature. The finding of the current study demonstrated the efficiency of both the RBFNN-GA and RBFNN-GWO models in predicting the dilation angle, peak shear displacement, and stress as the rock fracture properties. Among the two models proposed in this study, the statistical results revealed the superiority of RBFNN-GWO over RBFNN-GA in terms of prediction accuracy.

     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

Correlation of Oxygen and Aluminum Contents of Molten Titanium-Aluminum Alloys in Alumina and Calcia Crucibles

Metallurgical and Materials Transactions B - As a key part of investigating a new proposed Ti-alloy production process, Ti-Al alloys were produced by aluminothermic reduction experiments at...     

Electrosynthesis of hierarchical Cu2O–Cu(OH)2 nanodendrites supported on carbon nanofibers/poly(para-phenylenediamine) nanocomposite as high-efficiency catalysts for methanol electrooxidation

The development of highly efficient catalysts using inexpensive and earth-abundant metals is a crucial factor in a large-scale commercialization of direct methanol fuel cells (DMFCs). In this study, we explored a new catalyst based on copper nanodendrites (CuNDs) supported on carbon nanofibers/poly (para-phenylenediamine) (CNF/PpPD) nanocomposite for methanol oxidation reaction (MOR). The catalyst support was prepared on a carbon paste electrode by electropolymerization of para-phenylenediamine monomer on a drop-cast carbon nanofibers network. Afterwards, CuNDs were electrodeposited on the nanocomposite through a potentiostatic method. The morphology and the structure of the prepared nanomaterials were characterized by transmission electron microscope, scanning electron microscope, energy dispersive X-ray, X-ray diffraction, and X-ray photoelectron spectroscope. The results suggested that a three-dimensional nanodendritic structure consisting of Cu₂O and Cu(OH)₂ formed on the hybrid CNF/PpPD nanocomposite. The catalytic performance of CuNDs supported on CNF, PpPD and CNF/PpPD was evaluated for MOR under alkaline conditions. The CNF/PpPD/CuNDs exhibits a highest activity (50 mA cm^?2) and stability toward MOR over 6 h, with respect to CNF/CuNDs (40 mA cm^?2) and PpPD/CuNDs (36 mA cm^?2). This inexpensive catalyst with high catalytic activity and stability is a promising anode catalyst for alkaline DMFC applications.     

Integrated and inherently safe design and operation of a mobile power generation: Process intensification through microreactor reformer and HT-PEMFC

In the present study, due to various advantages of process miniaturization, the integrated design and operation of a mobile power generation system consisting of a microreactor reformer and a proton exchange membrane fuel cell (PEMFC) are investigated. The hydrogen fuel is supplied through autothermal steam-reforming of methanol in a microreactor leading to a safer, as well as more efficient, and economical operation. A high-temperature polymer membrane fuel cell with external accessories is applied for power generation. Then, simulation-optimization programming is applied for simultaneously optimizing the process design and operation at the same level. The result shows that the implemented procedure ensures the economic and flexible operation of the process while satisfying the safety constraints. The maximum gross power and net power generation are 109.3 and 91.9, respectively, while the cost of hydrogen production reduces from 13 to 20 $/kg to 7.7 $/kg. The fuel (methanol) consumption can be as low as 0.42 L/kWh.     

A chromium complex under water oxidation: A conversion mechanism and a comprehensive hypothesis

Water splitting toward hydrogen production is a promising method to store energy, but water oxidation is a bottleneck for water splitting. First-row transition metal complexes have been extensively used for water-oxidation reaction. However, only one chromium complex has been applied for water-oxidation reaction until now. Thus, the reinvestigation of water-oxidation reaction by this chromium complex in detail is interesting. Herein, water-oxidation reaction by the chromium complex with diphenoxy N,N′-ethylenebis(salicylimine) (SALEN) ligand is reinvestigated using scanning electron microscopy, energy dispersive spectrometry, X-ray diffraction studies, X-ray photoelectron spectroscopy, thermal gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy, and electrochemical methods. All the experiments showed that the chromium complex is neither a catalyst nor a precatalyst for water-oxidation reaction. During OER, a relationship between first-row transition metal complexes and the related metal oxides has been proposed.     

Chaotic dynamics of the vertical model in vehicles and chaos control of active suspension system via the fuzzy fast terminal sliding mode control

Journal of Mechanical Science and Technology - In this paper, the control of chaotic vibrations in vehicle suspension system is studied via a fuzzy fast terminal sliding mode method. Therefore, the...