A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

Thermodynamic and kinetic studies for the adsorption of Hg(II) by nano-TiO2 from aqueous solution

Titanium dioxide nanocrystals were employed, for the first time, for the sorption of Hg(II) ions from aqueous solutions. The effects of varying parameters such as pH, temperature, initial metal concentration, and contact time on the adsorption process were examined. Adsorption equilibrium was established in 420 min and the maximum adsorption of Hg(II) on the TiO₂ was observed to occur at pH 8.0. The adsorption data correlated with Freundlich, Langmuir, Dubinin–Radushkevich (D–R), and Temkin isotherms. The Freundlich isotherm showed the best fit to the equilibrium data. The Pseudo-first order and pseudo-second-order kinetic models were studied to analyze the kinetic data. A second-order kinetic model fit the data with the (k₂ = 2.8126 × 10^?3 g mg^?1min^?1, 303 K). The intraparticle diffusion models were applied to ascertain the rate-controlling step. The thermodynamic parameters (ΔG°, ΔH°, and ΔS°) were calculated which showed an endothermic adsorption process. The equilibrium parameter (R_L) indicated that TiO₂ nanocrystals are useful for Hg(II) removal from aqueous solutions.     

Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques

Robotic belt grinding operations are performed by mounting a workpiece to the end effector and commanding it to move along a trajectory while maintaining contact with the belt grinding wheel. A constant contact force throughout the grinding process is necessary to provide a smooth finish on the workpiece, but it is difficult to maintain this force due to a multitude of installation, manipulation, and calibration errors. The following describes a novel methodology for robotic belt grinding, which primarily focuses on system calibration and force control to improve grinding performance. The overall theory is described and experimental results of turbine blade grinding for each step of the methodology are shown.     

Bumpy Particle Adhesion and Removal in Turbulent Flows Including Electrostatic and Capillary Forces

The effect of electrostatic and capillary forces on bumpy particle adhesion and removal in turbulent flows is studied. We use the JKR theory and account for the increase of adhesion by capillary force. The effects of electrostatic forces and nonlinear hydrodynamic drag are included in the analyses. The criteria for incipient rolling and sliding detachments and electrostatic lifting removal are evaluated. A turbulence burst model is used for evaluating the peak air velocity near the substrate. The critical shear velocities for detaching particles of different sizes under different conditions are evaluated. The electric field strength needed for electrostatic removal of particles with different charges is also estimated. The results are compared with those obtained in the absence of the capillary force. Comparisons of the model predictions with the available experimental data are also presented.     

Combined Spectroscopic and Calorimetric Studies to Reveal Absorption Mechanisms and Conformational Changes of Protein on Nanoporous Biomaterials

In this study the effect of surface modification of mesoporous silica nanoparticles (MSNs) on its adsorption capacities and protein stability after immobilization of beta-lactoglobulin B (BLG-B) was investigated. For this purpose, non-functionalized (KIT-6) and aminopropyl-functionalized cubic Ia3d mesoporous silica ([n-PrNH₂-KIT-6]) nanoparticles were used as nanoporous supports. Aminopropyl-functionalized mesoporous nanoparticles exhibited more potential candidates for BLG-B adsorption and minimum BLG leaching than non-functionalized nanoparticles. It was observed that the amount of adsorbed BLG is dependent on the initial BLG concentration for both KIT-6 and [n-PrNH₂-KIT-6] mesoporous nanoparticles. Also larger amounts of BLG-B on KIT-6 was immobilized upon raising the temperature of the medium from 4 to 55 °C while such increase was undetectable in the case of immobilization of BLG-B on the [n-PrNH₂-KIT-6]. At temperatures above 55 °C the amounts of adsorbed BLG on both studied nanomaterials decreased significantly. By Differential scanning calorimetry or DSC analysis the heterogeneity of the protein solution and increase in Tm may indicate that immobilization of BLG-B onto the modified KIT-6 results in higher thermal stability compared to unmodified one. The obtained results provide several crucial factors in determining the mechanism(s) of protein adsorption and stability on the nanostructured solid supports and the development of engineered nano-biomaterials for controlled drug-delivery systems and biomimetic interfaces for the immobilization of living cells.     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

An improved Differential Evolution algorithm using learning automata and population topologies

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable strategy for the construction of a mutant vector for each individual, adaptively. The proposed automata based DE is able to maintain the diversity among the individuals and encourage them to move toward several promising areas of the search space as well as the best found position. Numerical experiments are conducted on a set of twenty four well-known benchmark functions and one real-world engineering problem. The performance comparison between ADE-Grid and other state-of-the-art DE variants indicates that ADE-Grid is a viable approach for optimization. The results also show that the proposed ADE-Grid improves the performance of DE in terms of both convergence speed and quality of final solution.     

An efficient and scalable plagiarism checking system using Bloom filters

With the easy access to the huge volume of articles available on the Internet, plagiarism is getting worse and worse. Most recent approaches proposed to address this problem usually focus on achieving better accuracy of similarity detection process. However, there are some real applications where plagiarized contents should be detected without revealing any information. Moreover, in such web-based applications, running time, memory consumption, communication and computational complexity should be also taken into account. In this paper, we propose a similar document detection system based on matrix Bloom filter, a new extension of standard Bloom filter. The experimental results on a real dataset show that the system can achieve 98% of accuracy. We also compare our approach with a method recently proposed for the same purpose. The results of the comparison show that the Bloom filter-based approach achieves much better performance than other in terms of the aforementioned factors.     

Densification improvement of spark plasma sintered TiB2-based composites with micron-, submicron- and nano-sized SiC particulates

Taguchi design of experiments methodology was used to determine the most influential spark plasma sintering (SPS) parameters on densification of TiB₂–SiC ceramic composites. In this case, four processing factors (SPS temperature, soaking time, applied external pressure and SiC particle size) at three levels were examined in order to acquire the optimum conditions. The statistical analysis identified the sintering temperature as the most effective factor influencing the relative density of TiB₂–SiC ceramics. A relative density of 99.5% was achieved at the optimal SPS conditions; i.e. temperature of 1800?°C, soaking time of 15?min and pressure of 30?MPa by adding 200-nm SiC particulates to the TiB₂ matrix. The experimental measurements and predicted values for the relative density of composite fabricated at the optimum SPS conditions and reinforced with the proper SiC particle size were almost similar. The mechanisms of sintering and densification of spark plasma sintered TiB₂–SiC composites were discussed in details.