Showing papers in "Engineering Applications of Artificial Intelligence in 2003"

TL;DR: The way in which both conventional control methods and so-called distributed, intelligent control methods can be enhanced by the availability of accurate, timely information about an item is examined.

TL;DR: A study to compare the performance of bearing fault detection using two different classifiers, namely, artificial neural networks and support vector machines (SMVs), using time-domain vibration signals of a rotating machine with normal and defective bearings.

TL;DR: An integration of multi-criteria decision analysis (MCDA) and inexact mixed integer linear programming (IMILP) methods to support selection of an optimal landfill site and a waste-flow-allocation pattern such that the total system cost can be minimized.

TL;DR: A neural network model is presented for forecasting the freeway link travel time using the counter propagation neural (CPN) network and it is shown that the new model based on the CPN network, and the learning coefficients proposed by Adeli and Park, is nearly two orders of magnitude faster than the BP network.

TL;DR: The most important steps of this process of application of pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and nowadays hybrid artificial intelligence techniques in manufacturing are outlined and some new results are introduced with special emphasis on hybrid AI and multistrategy machine learning approaches.

TL;DR: The importance of simulation metamodeling through artificial neural networks (ANNs) is discussed, and general guidelines for the development of ANN-based simulation meetamodels are provided.

TL;DR: The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations.

TL;DR: This paper presents an agent-based approach for supporting logistics and production planning, taking into account not only production schedules but also availability and cost of logistic service providers.

TL;DR: Using the numerical model linked to a search method and the knowledge base, results can be produced for determining optimum settings of casting conditions, which are conducive to the best strand surface temperature profile and metallurgical length.

TL;DR: The approach is validated by numerical simulation which shows that the designed static output feedback H ∞ controller can achieve good active suspension performance in spite of its simplicity and the non-fragility characteristics over controller gain variations.

TL;DR: Rough Set Theory as a new fault-diagnosing tool is used to identify the valve fault for a multi-cylinder diesel engine and it is shown that this new method is effective for valve fault diagnosis and is a new powerful tool that can be applied in contingency management.

TL;DR: The advantage of the proposed RRBF network is to combine the learning flexibility of the RBF network with the dynamic performances of the local recurrence given by the looped-neurons.

TL;DR: A Problem-Oriented Multi-Agent-Based E-Service System (POMAESS) where the maintenance decision-making results from collaboration of maintenance processes and experts to form a Distributed Artificial Intelligence environment is introduced.

TL;DR: The high performance of Elman's RNN was shown by means of two different applications: detecting anomalies introduced from the simulated power operation of a high-temperature gas cooled nuclear reactor and detecting motor bearing damage using a coherence function approach for induction motors.

TL;DR: This paper describes the application of fuzzy set theory to the GSI System by incorporating judgement and experience of practising engineers and Mamdani fuzzy algorithm was constructed using 22 “if–then” rules for evaluating discontinuity parameters and their ratings considered in the G SI System.

TL;DR: This research investigates the applicability of Bayesian decision networks to study the impact of design decisions on the life cycle performance, including environmental friendliness, of a product.

TL;DR: A novel approach on the basis of a neural network model designed to provide better predictions of nitrogen contents in treated effluents to be used for groundwater recharge is presented, indicating that a well-trained neural networkmodel is helpful in the assessment of wastewater reclamation practice to achieve significant cost savings.

TL;DR: A modified hyperbolic tangent function is used as the activation function of an auto-tuning neuron, which provides two adjustable parameters to flexibly determine the magnitude and the shape of function.

TL;DR: A multi-layer back-propagation neural network is employed to model the nonlinear relationships between the removal rates of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability.

TL;DR: The mechanism behind this phenomenon is formally elaborated, principles for the design of components for emergent systems for multi-agent manufacturing control are proposed, and the applicability of the above insights and principles has significant limitations.

TL;DR: It was found that a robust GA operator set obtained from the literature was capable of achieving a near-optimum model parameter set for the sensitive model but not for the insensitive model.

TL;DR: In this study, the recent advancements of AI-based technologies for management and control of pollution minimization and mitigation processes are examined and technologies of expert systems, fuzzy logic, and neural networks emerge as the most frequently employed approaches for realizing process control.

TL;DR: A neural network based facial recognition program (FADER—FAce DEtection and Recognition) was developed and tested, using single images of each subject to train the system.

TL;DR: An agent-based architecture developed as a part of the KSNet-approach to the knowledge logistics targeted to timely provide an appropriate personalized knowledge for an intelligent support of decision-makers is described.

TL;DR: Modelling and control of 300 MW steam-boiler combustion system using neurofuzzy methodology using Associate memory network (AMN) to represent the nonlinear model of steam pressure system based on local mechanism model and dynamic experiments.

TL;DR: This paper uses two negotiation algorithms to compare the effect of ‘‘preference estimation’’—one that is based on negotiation through relaxation and the other that extends this with preference estimations.

TL;DR: A new fuzzy data mining technique consisting of two phases to find fuzzy if–then rules for classification problems: one to find frequent fuzzy grids by using a pre-specified simple fuzzy partition method to divide each quantitative attribute, and the other to generate fuzzy classification rules fromrequent fuzzy grids.

TL;DR: A genetic algorithm is proposed to solve the problem in which m facilities are to be positioned to n available sites such that the total cost of construction and interactive cost due to facility layout constraints are minimised.

TL;DR: A proactive QC approach is introduced by resorting to artificial intelligence and engineering fundamentals for solving a class of engineering problems for which conventional reactive QC approaches are feeble due to system complexity and uncertainties, such as that in paint applications.

TL;DR: A fuzzy multi-criteria decision support procedure for enhancing information delivery within extended enterprise networks complemented with a case study in mold and die industry is introduced.