User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

Multiplicative multi-attribute auction based optimal network selection for heterogeneous wireless networks

One of the remarkable features of the next generation network is the integration of heterogeneous wireless networks, which enables mobile users with multi-mode terminals access to the best available network seamlessly. However, most of previous work only takes account of either maximizing single user＇s utility or the whole network＇s payoff, rarely considers the negotiation between them. In this paper, we propose a novel network selection approach using improved multiplicative multi-attribute auction （MMA）. At first, an improved MMA method is put forward to define the user＇s utility. Additionally, user cost is defined by considering allocated bandwidth, network load intensity and cost factor parameter. And last the best suitable network is selected according to the user＇s performance-cost-ration. Simulation results confirm that the proposed scheme outperforms the existing scheme in terms of network selection＇s fairness, user＇s performance-cost-ration, load balancing and the number of accommodated users.

Implementation of a new network equilibrium model of travel choices

This paper develops a new combined network equilibrium model by using more behaviorally sound mathematical forms to represent the four travel choices（i.e., trip frequency,destination, mode, and route） in a conventional travel demand forecasting process. Trip frequency choice relates to the traveler decision on “making a trip” or “not making a trip”so it is given by a binary logit model. Destination choice is formulated as a parameterized dogit model of which the captivity parameters（expressed as functions of independent variables） allow individual travelers to be captive to specific destinations. Mode choice is given by a two-level nested logit model to avoid IIA restriction. Trip assignment is based on Wardrop＇s “user-optimized” principle. All model forms describing travel choices are in response to the level of services incurred by the transportation system. Through the introduction of inclusive values, the traveler decisions concerning trip frequency, destination, mode, and route choices are inherently interrelated and jointly determined.To obtain solutions of the new combined model, it was reformulated as an equivalent convex programming problem with linear constraints, a great advantage from the computational aspects. The model was applied empirically to a transportation network in New Jersey. The application results show that the new model is consistently better than the commonly used logit combined model in reproducing the observed trip flows from origin zones, origin to destination（O-D） trip flows, O-D trip flows by mode, and trip flows on the network links.