A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

A Monte Carlo simulation study of the mechanical and conformational properties of networks of helical polymers. I. General concepts

We study the mechanical and conformational properties of networks of helical polymers with a combination of Monte Carlo simulations based on the Wang-Landau algorithm and the three-chain model. We find that the stress-strain behavior of these networks has novel features not observed in typical networks made of synthetic polymers. In particular, we find that as these networks are stretched they first strengthen, then soften and finally strengthen again. This non-monotonic behavior of the stress correlates with the one of the helical content and is rationalized by the elongation-induced formation and melting of the helical structure of the polymer. We complement these results with a study of other conformational properties of the polymer strands that clarify the molecular mechanisms behind the mechanical behavior of these networks. Finally, we present a qualitative comparison of some of our results with the theoretical ones recently reported by Kutter and Terentjev.     

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.     

Sustaining Rural Landscapes: the role of integrated tourism

The aim of a European Union-funded research project entitled 'Supporting and Promoting Integrated Tourism in Europe's Lagging Rural Regions' (SPRITE) is to analyse and develop the potential for better-integrated tourism in the rural landscapes of Europe. Integrated tourism can be defined as that which is explicitly linked to the localities in which it takes place and, in practical terms, has clear connections with local resources, activities, products, production and service industries, and a participatory local community. Enhancing the sustainable economic potential of rural landscapes requires partnerships among rural people and the sustainable yet productive use of rural resources. A 'culture economy' approach to integrated tourism is proposed, which emphasizes the importance of local identity, the strategic commodification of resources and of place, and the importance of extra-local forces in enabling local activities. It is suggested that this approach may represent a shift beyond more traditional approaches to rural development.     

Integrating neural network technology and noise analysis

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check valves and bearings illustrate the usefulness of the methodology developed.     

换热网络数值模拟稳定性分析

换热网络的数值模拟在换热网络的定量分析中起着重要作用。但在模拟计算中,有时表现出数值稳定性差。提高数值稳定性的方法,目前多通过改善算法来实现。本文通过对换热网络数值模拟的误差分析,提出了误差放大因子的概念,并依此选择迭代变量。通过对迭代变量的选择,达到提高换热网络数值模拟稳定性的目的。     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

NEURAL NETS SPEED FLOW CALCULATION

Neural Nets Successfully Solve Complex Fluid Flow Problem. Much research is being done in the area of neural networks, and industry is actively seeking successful application to real world problems. We describe here a successful application. We have used neural networks to model complex coolant flow patterns, such as those encountered in design of hypersonic aircraft. Previous calculation methods, while reasonably accurate, are iterative and extremely time consuming. Our new approach uses a hierarchical neural network architecture to model coolant flow distribution in multiple heat exchanger panels. This method is direct, fast, and accurate.     

Seismic safety of lifelines—an optimum design method

Lifeline systems are vulnerable to two types of hazards arising from potential earthquake sources. These are the hazard of a fault-rupture strike on elements of a lifeline system and the hazard of overstress induced in different elements of the system because of the ground vibration. An optimum design method is presented in this paper for the design of a lifeline system for a maximum accepted probability of failure because of any of the two modes of failure. The method may be used to determine an optimum path between a number of fixed points which represent supply or demand stations in the system.     

Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.