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The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically.

TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology.

The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods.

The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.

The major disadvantage of the field-oriented control (FOC) scheme of induction motors is its dependency on motor parameter variations because of the temperature rise. Among the motor parameters, rotor resistance is a parameter that can degrade the robustness of FOC scheme. An inaccurate setting of the rotor resistance in the slip frequency may result in undesirable cross coupling and performance degradation. To overcome this disadvantage, the purpose of this paper is to propose a model reference adaptive system (MRAS) rotor time constant tuning to improve the induction motor drive performance and to compensate the flux orientation error in vector control law.

First, the dynamic model and the indirect field-oriented control of induction motor are derived. Then, an inverse rotor time constant tuning is proposed based on MRAS theory where a new adaptation signal formulation is used as reference model, and the estimated stator currents obtained from induction motors (IM) state space resolution is used in the adaptive model.

The effectiveness and robustness of IM speed control with the proposed MRAS inverse rotor time constant estimator is verified through MATrix LABoratory/Simulink model simulation and laboratory experimental results. The simulation and experimental results show good transient drive performances, satisfactory for rotor resistance estimation and robustness with regard to uncertainties and load torque disturbance.

This paper presents an online tuning of the inverse rotor time constant using a new adaptation signal MRAS model. The proposed estimator is proved to guarantee the stability for different operating conditions, especially in very low/zero speed region and heavy load torque. The stability analysis of the proposed estimation procedure is also demonstrated.

The purpose of this paper is to argue that it is possible to identify a number of physical design properties of plots that are associated with coastal alteration and to develop a predictive model or metric to evaluate the potential alteration caused by that resort. Investors may have economic plot size concerns not considered by planners, tourists may prefer close proximity to deep waters, and urban planners may have failed to realize the importance of certain plot ratios or minimum dimensions etc.

A combination of geographical information system and statistical tools is used to identify and model these properties. The research uses data from three different resorts in two countries along the Red Sea coast (Hurghada and Safaga in Egypt and Jeddah in Saudi Arabia). Landsat TM7 images are obtained for each location and analysed to detect regions with the largest alteration along the coast using the post‐classification change technique. Physical properties of each village are generated using ArcGIS 9.2 both before and after alteration. Finally, a predictive decision‐support tool for urban planners and environmental auditors is formulated using stepwise discriminant analysis.

Two‐step cluster analysis and discriminate analysis show that alteration takes place based on factors that include original width and original distance to deep water in three distinct groups. The predictive model that is developed classifies any given resort into one of three clusters, by virtue of their physical design characteristics. This gives a rough prediction of the likely alteration that might take place under a given condition. Subdivision plans that ignore the power of these two factors literally “invite” owners to violate shorelines and encroach on the sea. Planners are urged to join hands with policy makers and developers to formulate guidelines to plot subdivision and environmentally acceptable design solutions to facilitate access to deep water.

Till now, there has been no metric to relate a plot's geometric properties with the encroachment it makes. If such an association could be made, a developer's resort plans may be evaluated to predict the degree by which an encroachment is likely. The model can be used to identify resorts whose properties present a risk to shorelines, dredge or fill to maximize usable land. Two benefits may be thought of for this approach. First, the model helps urban planners develop a sustainable coastal area by bearing in mind the needs of tourist‐developers as well as the properties of their adjacent coast. Second, the factors can be used to rationalize building guidelines and land use regulations to minimize such risk.