Showing papers in "Engineering Applications of Artificial Intelligence in 2009"

TL;DR: This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles and points out the challenges and research opportunities for the future.

TL;DR: The obtained results show the proposed model can predict both short- and long-term precipitation events because of using multi-scale time series as the ANN input layer.

TL;DR: Several simulation examples as well as comparisons of DE with two other state-of-the-art optimization techniques over the same problems demonstrate the superiority of the proposed approach especially for actuating fractional-order plants.

TL;DR: A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed and has been extensively tested with success.

TL;DR: This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations for tunnel boring machine performance as a function of rock properties.

TL;DR: This editorial introduces the special issue of the Elsevier journal, Engineering Application of Artificial Intelligence, on Distributed control of production systems, which focuses on the possible applications of distributed approaches for the design, evaluation and implementation of new control architectures for production systems.

TL;DR: Two memetic algorithms (genetic algorithms hybridized with a local search) able to solve both the VFMP and the HVRP are presented, based on chromosomes encoded as giant tours, without trip delimiters and on an optimal evaluation procedure.

TL;DR: A study is presented on the application of particle swarm optimization (PSO) combined with other computational intelligence (CI) techniques for bearing fault detection in machines and shows the effectiveness of the selected features and the classifiers in the detection of the machine condition.

TL;DR: A suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application.

TL;DR: An interactive group decision-making methodology is proposed to select/rank IS providers under multiple criteria to satisfy an acceptable level of group agreement and reliability in the largest office furniture manufacturer in Konya-Turkey.

TL;DR: An improved multiobjective particle swarm optimization (MOPSO) algorithm is developed to derive a set of Pareto-optimal solutions and local search is used to increase its search efficiency in the proposed version of MOPSO.

TL;DR: A multi-agent approach for the dynamic maintenance task scheduling for a petroleum industry production system using the SARSA algorithm, which simultaneously insure effective maintenance scheduling and the continuous improvement of the solution quality by means of reinforcement learning.

TL;DR: A robust new scheme is presented in this paper for optimally selecting values of the parameters especially that of the scale parameter of the Gaussian kernel function involved in the training of the SVDD model.

TL;DR: This paper proposes a new authentication and encryption method that conforms to the EPC Class 1 Generation 2 standards to ensure RFID security between tags and readers and proves its feasibility for use in several applications.

TL;DR: A new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances and linguistic fuzzy control rules can be directly incorporated into the controller and combine the H∞ attenuation technique.

TL;DR: A new FCRM clustering algorithm (NFCRMA) is presented, which is deduced from the fuzzy clustering objective function of F CRM with Lagrange multiplier rule, possessing integrative and concise structure.

TL;DR: Multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity-preserving mechanism are used for Pareto optimization of GMDH-type neural networks used for modelling an explosive cutting process using some input-output experimental data.

TL;DR: The current research challenges associated to the application of SoA into reconfigurable supply chains are enumerated and detailed with the aim of providing a roadmap into a major adoption of SOA to support agile reconfigured supply chains.

TL;DR: This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach that has been implemented with simulated experiments highlighting the effectiveness of the approach.

TL;DR: A modified genetic algorithm approach to deal with those DS models with maintenance consideration, aiming to minimize the makespan of the jobs is proposed, compared with other existing approaches to demonstrate its reliability.

TL;DR: A handwriting recognition system based on visual coding and genetic algorithm ''GA'' applied on Arabic script and the results obtained prove that the new method based on hybridization between visual codes and GA is a powerful method.

TL;DR: A hybrid genetic algorithm-adaptive network-based FIS (GA-ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs).

TL;DR: The model predictive control strategy is applied to engine air/fuel ratio control using neural network model, which uses information from multivariables and considers engine dynamics to do multi-step ahead prediction and is more accurate and robust compared with non-adaptive model based methods.

TL;DR: The Gaussian process (GP) prior approach for the modelling of nonlinear dynamic systems is introduced and the relationship between the GP model and the radial basis function neural network is explained.

TL;DR: In this paper, heuristic algorithms such as simulated annealing (SA), genetic algorithm (GA) and hybrid algorithm (hybrid-GASA) were applied to tool-path optimization problem for minimizing airtime during machining to propose a hybrid approach in which GA provides a good initial solution for SA runs.

TL;DR: The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.

TL;DR: This study addresses the design and the training of a Multi-Layer Perceptron classifier for identification of wood veneer defects from statistical features of wood sub-images using the evolutionary ANNGaT algorithm.

TL;DR: This paper presents a holonic and isoarchic approach to the Flexible Manufacturing System (FMS) control, based on a flat holonic form, where each holon is a model for each entity of the FMS, with a unifying level of communication between holons.

TL;DR: The results suggest an encouraging performance by the support vector machines learning technique in comparison to both empirical relation as well as neural network approach in scaling up the results from laboratory to field conditions for the purpose of scour prediction.

TL;DR: A holonic control architecture and implementing issues for agile job shop assembly with networked intelligent robots, based on the dynamic simulation of material processing and transportation, is described and two solutions for production planning are proposed.