Learning user preferences of route choice behaviour for adaptive route guidance

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction     

An experiment for showing some kind of artificial understanding

In this paper we design an experiment which can be depicted as a simple scenario, a very limited 'world'. In this world, there are an actor that can pursue a project and an observer that is keeping its eyes on the actor. We try to show in the experiment that the observer can to some degree understand the actor based on its knowledge and some metaphors, i.e. understand what the actor is doing and why. As the conclusion of this experiment, we try to show some features of 'understanding'. These are (1) that 'understanding' has to be based on some preliminary knowledge; (2) that 'understanding' is a process of incremental learning; (3) that, as for symbolic systems, some metaphors are necessary for mapping real entities into concepts in mind.     

From data properties to evidence

The problem of making decisions among propositions based on both uncertain data items and arguments which are not certain is addressed. The primary knowledge discovery issue addressed is a classification problem: which classification does the available evidence support? The method investigated seeks to exploit information available from conventional database systems, namely, the integrity assertions or data dependency information contained in the database. This information allows ranking arguments in terms of their strengths. As a step in the process of discovering classification knowledge, using a database as a secondary knowledge discovery exercise, latent knowledge pertinent to arguments of relevance to the purpose at hand is explicated. This is called evidence. Information is requested via user prompts from an evidential reasoner. It is fed as evidence to the reasoner. An object-oriented structure for managing evidence is used to model the conclusion space and to reflect the evidence structure. The implementation of the evidence structure and an example of its use are outlined     

Storage and Feeding Losses of Large Round Bales

Alfalfa was harvested in large round bales or small square bales and storage and feeding losses were determined for different storage methods. Large bales were stored 1) in a barn; 2) outside in single rows and uncovered; 3) outside in two-high stacks and covered; and 4) outside in three-high stacks and covered. These bales were fed to dairy heifers with small square bales for comparison. Dry matter storage losses were 2% for large bales stored inside, 6% for large bales stored outside covered, and 15% for large bales stored outside uncovered. Feeding losses of large bales were 12% for stored inside, 25% for stored outside uncovered, and 13 to 15% for stored outside covered. Total losses for large round bales were 40% (stored outside uncovered), 20% (stored outside covered) and 15% (stored inside). Intake and gains were greatest for small bales (2.35 kg/100 kg body weight and .77 kg/d, respectively) and least for large bales (2.11 kg/100 kg body weight and .54 kg/d) stored outside uncovered.Rain penetrated 10 to 25 cm into uncovered bales stored outside. The resulting weathered hay, about 40% of the original bale dry weight, deteriorated. Heifers rummaged through this unpalatable material, and large feeding losses resulted. Storage and feeding losses of large round bales stored outside are economically important enough to warrant protection of bales.     

An Investigation of the Reduction and Reoxidation of Isolated Vanadate Sites Supported on MCM-48

The reduction and subsequent reoxidation of isolated vanadate species supported on silica was investigated using temperature-programmed reduction and oxidation, along with in-situ XANES and Raman spectroscopy. Approximately 70–80% of the vanadium was reduced to V³⁺ after reduction in H₂ at temperatures up to 923 K. Upon reduction, the vanadyl oxygen was removed and the three remaining V–O bonds are lengthened by 0.2 Å. The vanadate species are rapidly reoxidized when exposed to O₂, with the amount of oxygen uptake matching well with the amount removed during reduction. In-situ Raman spectroscopy during reoxidation in ¹⁸O₂ showed that significant scrambling occurs between gas phase oxygen and surface oxygen species during the reoxidation of the vanadate species.     

Explicit Ziv-Zakai lower bound for bearing estimation

The extended Ziv-Zakai bound for vector parameters is used to develop a lower bound on the mean square error in estimating the 2-D bearing of a narrowband planewave signal using planar arrays of arbitrary geometry. The bound has a simple closed-form expression that is a function of the signal wavelength, the signal-to-noise ratio (SNR), the number of data snapshots, the number of sensors in the array, and the array configuration. Analysis of the bound suggests that there are several regions of operation, and expressions for the thresholds separating the regions are provided. In the asymptotic region where the number of snapshots and/or SNR are large, estimation errors are small, and the bound approaches the inverse Fisher information. This is the same as the asymptotic performance predicted by the local Cramer-Rao bound for each value of bearing. In the a priori performance region where the number of snapshots or SNR is small, estimation errors are distributed throughout the a priori parameter space and the bound approaches the a priori covariance. In the transition region, both small and large errors occur, and the bound varies smoothly between the two extremes. Simulations of the maximum likelihood estimator (MLE) demonstrate that the bound closely predicts the performance of the MLE in all regions