KNOCK DETECTION ALTERNATIVES FOR PRODUCTION VEHICLES.

A method for the detection of knock using the sparking plug, and a system which allows the basic nature of the signal from the spark plug to be compared directly with that from an accelerometer are described. Results are presented for a range of engine speeds which highlight the problems and benefits of each sensing technique.

KNOCK DETECTION ALTERNATIVES FOR PRODUCTION VEHICLES.

A method for the detection of knock using the sparking plug, and a system which allows the basic nature of the signal from the spark plug to be compared directly with that from an accelerometer are described. Results are presented for a range of engine speeds which highlight the problems and benefits of each sensing technique.

The use of syntax and multiple alternatives in the VODIS voice operated database inquiry system

The paper describes the architecture of VODIS, a voice operated database inquiry system, and presents some experiments which investigate the effects on performance of varying the level of a priori syntactic constraints. The VODIS system includes a novel mechanism for incorporating context-free grammatical constraints directly into the word recognition algorithm. This allows the degree of a priori constraint to be smoothly varied and provides for the controlled generation of multiple alternatives. The results show that when the spoken input deviates from the predefined task grammar, a combination of weak a priori syntax rules in conjunction with full a posteriori parsing on a lattice of alternative word matches provides the most robust recognition performance. © 1991.

An investigation into numerical analysis alternatives for predicting reingestion in turbine disc rim cavities

Reliable means of predicting ingestion in cavities adjacent to the main gas path are increasingly being sought by engineers involved in the design of gas turbines. In this paper, analysis is to be presented that results from an extended research programme, MAGPI, sponsored by the EU and several leading gas turbine manufactures and universities. Extensive use is made of CFD modelling techniques to understand the aerodynamic behaviour of a turbine stator well cavity, focusing on the interaction of cooling air supply with the main annulus gas. The objective of the study has been to benchmark a number of CFD codes and numerical techniques covering RANS and URANS calculations with different turbulence models in order to assess the suitability of the standard settings used in the industry for calculating the mechanics of the flow travelling between cavities in a turbine through the main gas path. The modelling methods employed have been compared making use of experimental data gathered from a dedicated two-stage turbine rig, running at engine representative conditions. Extensive measurements are available for a range of flow conditions and alternative cooling arrangements. The limitations of the numerical methods in calculating the interaction of the cooling flow egress and the main stream gas, and subsequent ingestion into downstream cavities in the engine (i.e. re-ingestion), have been exposed. This has been done without losing sight of the validation of the CFD for its use for predicting heat transfer, which was the main objective of the partners of the MAGPI Work- Package 1 consortium. Copyright © 2012 by ASME.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Exploring alternatives for milk quality improvement and more efficient dairy production in a smallholder farming context – case study: Mantaro Valley (Peru)

Consumer demand is revolutionizing the way products are being produced, distributed and marketed. In relation to the dairy sector in developing countries, aspects of milk quality are receiving more attention from both society and the government. However, milk quality management needs to be better addressed in dairy production systems to guarantee the access of stakeholders, mainly small-holders, into dairy markets. The present study is focused on an analysis of the interaction of the upstream part of the dairy supply chain (farmers and dairies) in the Mantaro Valley (Peruvian central Andes), in order to understand possible constraints both stakeholders face implementing milk quality controls and practices; and evaluate “ex-ante” how different strategies suggested to improve milk quality could affect farmers and processors’ profits. The analysis is based on three complementary field studies conducted between 2012 and 2013. Our work has shown that the presence of a dual supply chain combining both formal and informal markets has a direct impact on dairy production at the technical and organizational levels, affecting small formal dairy processors’ possibilities to implement contracts, including agreements on milk quality standards. The analysis of milk quality management from farms to dairy plants highlighted the poor hygiene in the study area, even when average values of milk composition were usually high. Some husbandry practices evaluated at farm level demonstrated cost effectiveness and a big impact on hygienic quality; however, regular application of these practices was limited, since small-scale farmers do not receive a bonus for producing hygienic milk. On the basis of these two results, we co-designed with formal small-scale dairy processors a simulation tool to show prospective scenarios, in which they could select their best product portfolio but also design milk payment systems to reward farmers’ with high milk quality performances. This type of approach allowed dairy processors to realize the importance of including milk quality management in their collection and manufacturing processes, especially in a context of high competition for milk supply. We concluded that the improvement of milk quality in a smallholder farming context requires a more coordinated effort among stakeholders. Successful implementation of strategies will depend on the willingness of small-scale dairy processors to reward farmers producing high milk quality; but also on the support from the State to provide incentives to the stakeholders in the formal sector.