Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.     

A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.     

Preparation of polypropylene‐based nanocomposites using nanosized MCM‐41 as support and in situ polymerization

MCM‐41 nanoparticles were used for preparing nanocomposites through the in situ polymerization of propylene. The performance of the catalytic system and the final properties of the materials obtained are highly dependent on the methodology used for impregnation of the catalyst onto the support particles, and therefore an optimization study for the impregnation methodology of the catalyst (Me₂Si(Ind)₂ZrCl₂) was carried out. Two different methodologies were used; the results in terms of catalytic activity and polymer molecular masses indicated that the most promising one involved the pre‐activation of the catalyst with the cocatalyst, methylaluminoxane, followed by impregnation onto the MCM‐41 nanoparticles. Thus, an optimized route for the preparation of polypropylene nanocomposites achieving significant improvements in catalyst activity was developed. The nanocomposite materials were characterized by GPC, TGA and DSC. The dispersion state and the size of the nanoparticles incorporated in the polypropylene matrix were investigated by transmission electron microcopy. Additionally, this methodology allows simultaneous control of the desired amount of support and the concentration of catalyst to be used in the in situ polymerization. © 2015 Society of Chemical Industry     

冷加工和时效处理顺序对6061铝合金力学性能的影响(英文)

研究不同的析出硬化和冷加工组合对6061铝合金拉伸性能的影响。结果表明,在不同的热处理过程中,在180℃单时效4h能提高合金的强度和伸长率。然而,双时效处理不能改善其力学性能。另外,预时效对随后的析出硬化有负面影响。合金力学性能的变化归因于析出硬化、应变硬化和加工软化的竞争而引起的显微组织演变。     

A Puzzle of Quantum Phase Transition on Gd-IMC by Tuning of Conduction Electron Concentration

Based on the critical unstable of both crystal and magnetic structure of Gd-intermetallic compound near the competition of two strongly independent subsystem （＂local 4f7＂ and ＂conduction electron concentration＂）, a new QPT （quantum point transition） is predicted by calculation of： （1） The band structure and density of state by density functional theory where a strong narrowing fluidity of fermions around EF with shifted to negative value ＂-0.8 eV ＂is observable in the Gd-intermetalliccompound system while in the Y-case, it is not dominated. An antiferromagnetic state on the fluidity of conduction band can be investigated; （2） The internal magnetic field due to short range exchange interaction Jij between the nearest neighbor of local magnetic moment of stable s-state of Gd （L = 0） through the mean field approximation and of Eigenvalue-Eigenfunction ~.（k） are calculated. While a strong negative value of Jy is predicted, the eigenvalue L（k） of the system shows a strong antiferromagnetic phase in the reciprocal lattice direction 〈010〉, 〈001〉 in the correlation length 3.38 ~A. Although the antiferromagnetic state at Rc 〈_ 3.38 °A is a puzzle but it is completely dominated at Rc = 9 °A after passing through the competition between ）.λmin（O） and λmin（π） in the ranger of 3.2 °A 〈 Rc 〈 9 °A. Since both of the antiferromagnetic subsystems are sensitive to the predicted KF, the effect of decreasing of conduction electron is proposed to investigate, the change of the antiferromagnetic ordering state to the competition of ferromagnetic state （in direction 〈010〉） and antiferromagnetic state （in direction 〈001 〉 and 〈 100〉） resulted to paramagnetic state in the long range Rc = 9 °A.     

Applying a normalized ratio scale technique to assess influences of urban expansion on land surface temperature of the semi-arid city of Erbil

The difference between surface and air temperature within a city and its surrounding area is a result of variations in surface cover, thermal capacity, and 3-dimensional geometry. This research has examined and quantified the decreasing daytime land surface temperature (LST) in Erbil, Kurdistan region of Iraq, and the influence of rapid urban expansion on urban heat/cool island effect over a 20 year period. Land-use/land-cover change across this time period is also established using pixel samples. The current study proposes the application of the normalized ratio scale (NRS) to adjust the temperature of images acquired at different dates to the same range. Eleven satellite images acquired by Landsat 4, 5, 7, and 8 during the period 1992–2013 are used to retrieve LST. The results indicate that 55.3 km² of city land cover changed from bare soil to urban; consequently, the mean LST of the new urbanized area decreased by 2.28°C. The normalized difference vegetation index (NDVI) of Sami Abdul-Rahman (S.A.) Park increased from 0.09 ± 0.01 to 0.32 ± 0.11, resulting in a decrease of the mean LST by 7.29°C. This study shows that the NRS method is appropriate for detecting temperature trends from urbanization using remote-sensing data. It also highlights that urban expansion may lead to a decrease in daytime LST in drylands.     

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R²) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.

     

Effects of layup sequences on stresses of thick composite cantilever tubes

In this study, a new simple-input displacement-based method is used to study effects of layup sequences on stresses, strains, and deformations of thick laminated orthotropic cantilever straight tubes under transverse loading. Three-dimensional stress distributions are obtained based on the most general displacement field of elasticity. A layer-wise theory which includes the full three-dimensional constitutive relations is used. A non-dimensional simple coefficient is introduced to compare interlaminar radial stresses of different layup sequences. Finally, some design guidelines are proposed to consider effects of layup sequences of laminated thick composite tubes subjected to shearing load.