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In this paper we investigate document ranking methods in thesaurus-based boolean retrieval systems, and propose a new thesaurus-based ranking algorithm called the Extended Relevance (E-Relevance) algorithm. The E-Relevance algorithm integrates the extended boolean model and the thesaurus-based relevance algorithm. Since the E-Relevance algorithm has all the desirable properties of the extended boolean model, it avoids the various problems of previous thesaurus-based ranking algorithms. The E-Relevance algorithm also ranks documents effectively by using term dependence information from the thesaurus. We have shown through performance comparison that the proposed algorithm achieves higher retrieval effectiveness than the others proposed earlier.

SUMMARYAn embedded two-axis solar tracking system using Laboratory Virtual Instrumentation Engineering Workbench to write the operation and control algorithms was developed for enhancing solar energy utilization. The system consists of a real-time processor, two motion-control modules, two step drives, two step motors, feedback devices, and other accessories needed for functional stability. The real-time processor allows the solar tracker to be used as a stand-alone, real-time system that can operate automatically without any external control. The system combines two different solar tracking methods: the optical method and the astronomical method. Cadmium Sulfide (CdS) sensors are employed to continuously generate feedback signals to the controller, ensuring high-precision solar tracking even under adverse conditions. The CdS sensor is a resistor whose electric resistance decreases with increasing incident light intensity. A database of solar altitude, azimuth, and sunrise and sunset times is provided by this solar tracking system. Other solar trackers operating in an astronomical method may access and use this database over the Internet. Solar position and sunrise and sunset times in the database were compared with those of the Astronomical Applications Department of the U.S. Naval Observatory. The differences were found to be negligible.

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There have been several document ranking methods to calculate the conceptual distance or closeness between a Boolean query and a document. Though they provide good retrieval effectiveness in many cases, they do not support effective weighting schemes for queries and documents and also have several problems resulting from inappropriate evaluation of Boolean operators. We propose a new method called Knowledge‐Based Extended Boolean Model (kb‐ebm) in which Salton's extended Boolean model is incorporated. kb‐ebm evaluates weighted queries and documents effectively, and avoids the problems of the previous methods. kb‐ebm provides high quality document rankings by using term dependence information from is‐a hierarchies The performance experiments show that the proposed method closely simulates human behaviour.