Shear stiffness of pallet rack upright frames

Pallet rack uprights are braced in the cross-aisle direction forming a frame, which behaves like a built-up column. Currently two approaches prevail in the racking industry to determine the shear stiffness of these frames. The Rack Manufacturers’ Institute (RMI) code uses a theoretical formula whereas the Federation Europeenne de la Manutention (FEM) code requires testing. There is a considerable difference in the stiffness values determined by the two approaches. A literature review showed that the stiffness predicted by the RMI code could be up to 20 times higher than the experimental stiffness. The shear stiffness of the frames is influenced by the bracing pattern and the connections between the elements. Local distortion of the thin walled open section upright, joint eccentricity and slip due to bolt clearances all affect the behaviour of the frames. The present paper describes experimental and numerical studies conducted at Oxford Brookes University to evaluate shear stiffness.     

A Movie Recommendation System – An Application of Voting Theory in User Modeling

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.     

Non-linear steady-state data reconciliation: Theoretical perspective and practical scenarios

Data reconciliation (DR) is one of the primary error handling methods to reduce measurement errors in industries that may otherwise cause misleading information about the plant. In this article, the mathematical aspects of measurement errors and their treatment by DR are discussed in detail. The flaws in the existing DR methods have been identified and re-investigated. More importantly, the feasibility and health check-up of the DR problem have been discussed. The primary objective of the work is to develop a DR code based on the observations made in the present study, which involves DR solutions by both successive linearization (SL) and sequential quadratic programming (SQP) schemes. Benchmarking of the code with standard cases showed its wider suitability in solving DR problems. The algebraic SL method was found suitable for proper data health check-ups and reliable solutions, whereas SQP was robust. The developed code was tested successfully for a chemical plant as well.