Artificial Intelligence in Engineering最新文献

The artificial neural network (NN) methodology presented in this paper has been developed for selection of powder and process parameters for Powder Metallurgy (PM) part manufacture. This methodology differs from the statistical modelling of mechanical properties in that it is not necessary to make assumptions regarding the form of the functions relating input and output variables. Employment of a NN approach allows specification of multiple input criterion, and generation of multiple output recommendations. The inputs comprise the required mechanical properties for the PM material. The system employs this data within the NN in order to recommend suitable metal powder compositions and process settings. Comparison of predicted and experimental PM materials data has confirmed the accuracy of the NN approach, for predicting the materials and process settings needed for attainment of required process outcomes.

This work presents an approach for the application of artificial intelligence planning techniques to the automatic generation of control sequences for manufacturing systems. These systems have some special features that must be considered in the planning process, but there are difficulties when the usual models of action are used to deal with these features. In this work, a specialized interval-based model of action is defined by extending the classic model of strips giving it more expressiveness so that it is able to deal with these features. In consequence, a specialized planning algorithm for this model of action, called machine, is defined based on a general partial order planning scheme, and it is able to obtain control sequences for manufacturing systems. These control sequences are actually the control program skeleton and may be easily translated into real control programs expressed as GRAFCET charts.

Natural-language-based tools can be valuable for automating software engineering, in particular for automating software testing. However, efforts to automate software engineering rarely include natural-language texts, instead of focusing on source code or on encoding knowledge in specialized formats. The specification information from text (SIFT) tool demonstrates the potential for incorporating existing texts directly into an automated testing system; it generates tests directly from information extracted from specification documents or user manuals. SIFT provides a general framework for using domain-specific parsing techniques, and has shown its utility in constructing tests for the OpenVMS operating system interface routines.

In this paper a modified discrete adaptive control system with neural estimator and neural controller is presented. The structure of the adaptive controller is based on the model presented by Etxebarria (Etxebarria V. Adaptive control of discrete systems using neural networks. IEE Proc. Control Theory Application, Vol. 141, No. 4, July, 1995) where the stability of the control procedure is proved. The Widrow–Hoff procedure of learning and the DARMA model is used for identifying and adjustment of neural network parameters, applied to adaptive control of discrete systems. In this paper the procedure of Etxebarria is modified. The learning rate of the neural network is improved and accelerated using the PD, PI and PID input controllers for input neurons. The effect of adding a momentum term (the past record of the learning) to the learning rule of the neural network is studied. The results are compared and discussed using the examples of Etxebarria and two other case studies. The procedure is extended to multi-input multi-output systems and cases studied are simulated.

In this work, the LV-control problem in binary distillation columns is addressed. With least prior knowledge, a linear reference model with unknown terms is obtained. The time variations of the unknown terms are estimated using two on-line trained perceptrons. These estimates are subsequently used to design a feedback linearizing-like controller. The closed-loop behavior is analyzed through numerical examples. The resulting controller shows robustness against external disturbances and set-point changes.

In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control (such as the computed torque technique) require high computational time and can result in a poor control performance, if the specific model-structure selected does not properly reflect all the dynamics. The control technique proposed here has provided satisfactory results. A decentralised model has been assumed here where a controller is associated with each joint and a separate neural network is used to adjust the parameters of each controller. Neural networks have been used to adjust the parameters of the controllers, being the outputs of the neural networks, the control parameters.

This article presents the results of using a novel Negative Feedback Artificial Neural Network for extraction of models of the thermal structure of oceanographic water masses and to forecast time series in real time. The results obtained using this model are compared with those obtained using a Linear Regression and an ARIMA model. The article presents the Negative Feedback Artificial Neural Network, shows how it extracts the model behind the data set and discuses the Artificial Neural Network’s forecasting abilities.

The offset lithographic printing process requires the operator to make appropriate and timely on-line adjustments to compensate for color deviations from the desired print. An operator acquires proficiency by working on the same machine over a period of several years; thus he is able to apply adjustments according to its specific characteristics. It was found that this machine-specific knowledge consists of articulated and unarticulated knowledge. A connectionist representation was designed to map the observable variables to the operator's adjustments; while a forward chaining expert system was developed to represent the operator's articulated knowledge. A weight-based conflict resolution technique was constructed to dynamically update the knowledge base. This paper begins by presenting the press characterization problem. Then the development of the system is described. Finally, an analysis of results that cover all possible categories is documented.

A fuzzy temporal profile (FTP) is a model through which we describe the evolution of a certain physical parameter V over time. Thus we define a set of significant points (X₀,X₁,…,X_N), and we approximate the evolution curve by way of linear sections between them. Each section is defined by way of an imprecise constraint on duration, on increase in value and on slope between the points connected by the section.

In this article we show a possible method of matching an FTP with a signal, which will enable the detection of profiles of interest on the trace of a physical parameter over time.

Based on principal component analysis (PCA), eigenspace transformation (EST) was demonstrated to be a potent metric in automatic face recognition and gait analysis by template matching, but without using data analysis to increase classification capability. Gait is a new biometric aimed to recognise subjects by the way they walk. In this article, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with EST for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously. Each image template is projected from the high-dimensional image space to a low-dimensional canonical space. Using template matching, recognition of human gait becomes much more accurate and robust in this new space. Experimental results on a small database show how subjects can be recognised with 100% accuracy by their gait, using this method.