Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways

The scour below spillways can endanger the stability of the dams. Hence, determining the scour depth downstream of spillways is of vital importance. Recently, soft computing models and, in particular, artificial neural networks (ANNs) have been used for scour depth prediction. However, ANNs are not as comprehensible and easy to use as empirical formulas for the estimation of scour depth. Therefore, in this study, two decision-tree methods based on model trees and classification and regression trees were employed for the prediction of scour depth downstream of free overfall spillways. The advantage of model trees and classification and regression trees compared to ANNs is that these models are able to provide practical prediction equations. A comparison between the results obtained in the present study and those obtained using empirical formulas is made. The statistical measures indicate that the proposed soft computing approaches outperform empirical formulas. Results of the present study indicated that model trees were more accurate than classification and regression trees for the estimation of scour depth.     

Constrained Portfolio Selection using Particle Swarm Optimization

This paper presents a novel heuristic method for solving an extended Markowitz mean–variance portfolio selection model. The extended model includes four sets of constraints: bounds on holdings, cardinality, minimum transaction lots and sector (or market/class) capitalization constraints. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio is equal to a predefined number. The sector capitalization constraints reflect the investors’ tendency to invest in sectors with higher market capitalization value to reduce their risk of investment.The extended model is classified as a quadratic mixed-integer programming model necessitating the use of efficient heuristics to find the solution. In this paper, we propose a heuristic based on Particle Swarm Optimization (PSO) method. The proposed approach is compared with the Genetic Algorithm (GA). The computational results show that the proposed PSO effectively outperforms GA especially in large-scale problems.     

Maximal covering location problem (MCLP) with fuzzy travel times

This paper presents a fuzzy maximal covering location problem (FMCLP) in which travel time between any pair of nodes is considered to be a fuzzy variable. A fuzzy expected value maximization model is designed for such a problem. Moreover, a hybrid algorithm of fuzzy simulation and simulated annealing (SA) is used to solve FMCLP. Some numerical examples are presented, solved and analyzed to show the performance of the proposed algorithm. The results show that the proposed SA finds solutions with objective values no worse than 1.35% below the optimal solution. Furthermore, the simulation-embedded simulated annealing is robust in finding solutions.     

Extended active observer for force estimation and disturbance rejection of robotic manipulators

The current control methods applied to robotic manipulators either require full state and force measurements, or use the state and force estimation in the absence of any kind of disturbance. As an alternative approach, a new adaptive motion control approach for robotic manipulators extending the existing active observer for simultaneous inertial parameters and force estimation is proposed. The scheme provides accurate force and full state estimation in the presence of robot inertial parameter variations and measurement noise, both subsequently used in the design of a controller. Since the proposed method relies mainly on the position of the plant, it significantly reduces the difficulty and cost of implementation. The velocity, parameter and force signals are estimated from the position. The approach is applied to a typical two-degree-of-freedom (2DOF) robotic manipulator through computer simulation. The results are encouraging and demonstrate the noise rejection ability of the scheme.     

Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks

Artificial Intelligence Review - Community detection aims to partition a set of nodes with more similarities in the set than out of it based on different criteria like neighborhood similarity or...     

Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains

The “Bullwhip Effect” is a well-known example of supply chain inefficiencies and refers to demand amplification as moving up toward upstream echelons in a supply chain. This paper concentrates on representing a robust token-based ordering policy to facilitate information sharing in supply chains in order to manage the bullwhip effect. Takagi–Sugeno–Kang and hybrid multiple-input single-output fuzzy models are proposed to model the mechanism of token ordering in the token-based ordering policy. The main advantage of proposed fuzzy models is that they eliminate the exogenous and constant variables from the procedure of obtaining the optimal amount of tokens which should be ordered in every period. These fuzzy approaches model the mentioned mechanism through a push–pull policy. A four-echelon SC with fuzzy lead time and unlimited production capacity and inventory is considered to survey the outcomes. Numerical experiments confirm the effectiveness of proposed policies in alleviating BWE, inventory costs and variations.     

One-dimensional nonlinear vibration analysis and coupled thermoelasticity based on Green-Naghdi model

Journal of Mechanical Science and Technology - The present article reports on a study on nonlinear coupled thermoelasticity based on Green-Naghdi type III model in one-dimensional form. Unlike...     

In situ thermal reduction of graphene oxide for high electrical conductivity and low percolation threshold in polyamide 6 nanocomposites

Electrically conductive and thermally stable polyamide 6 (PA 6) nanocomposites were prepared through one-step in situ polymerization of ε-caprolactam monomer in the presence of electrically insulating and thermally unstable graphene oxide (GO) nanosheets. These nanocomposites show a low percolation threshold of ∼0.41 vol.% and high electrical conductivity of ∼0.028 S/m with only ∼1.64 vol.% of GO. Thermogravimetric analysis and X-ray photoelectron spectroscopy results of GO before and after thermal treatment at the polymerization temperature indicate that GO was reduced in situ during the polymerization process. X-ray diffraction patterns and scanning electron microscopy observation confirm the exfoliation of the reduced graphene oxide (RGO) in the PA 6 matrix. The low percolation threshold and high electrical conductivity are attributed to the large aspect ratio, high specific surface area and uniform dispersion of the RGO nanosheets in the matrix. In addition, although GO has a poor thermal stability, its PA 6 nanocomposite is thermally stable with a satisfactory thermal stability similar to those of neat PA 6 and PA 6/graphene nanocomposite. Such a one-step in situ polymerization and thermal reduction method shows significant potential for the mass production of electrically conductive polymer/RGO nanocomposites.     

Fuzzy agent-based expert system for steel making process

In this paper, a fuzzy multi-agent system is proposed for electric arc furnace steel making processes. Each process of electric arc furnace steel making is assigned to be an agent, which works independently whilst coordinates and cooperates with other acquaintance agents. Adaptive neuro-fuzzy inference system is used to generate agents’ knowledge bases. Contract net protocol is used as negotiation protocol in the proposed multi-agent system.