Multifidelity surrogate modeling based on radial basis functions

Multiple models of a physical phenomenon are sometimes available with different levels of approximation. The high fidelity model is more computationally demanding than the coarse approximation. In this context, including information from the lower fidelity model to build a surrogate model is desirable. Here, the study focuses on the design of a miniaturized photoacoustic gas sensor which involves two numerical models. First, a multifidelity metamodeling method based on Radial Basis Function, the co-RBF, is proposed. This surrogate model is compared with the classical co-kriging method on two analytical benchmarks and on the photoacoustic gas sensor. Then an extension to the multifidelity framework of an already existing RBF-based optimization algorithm is applied to optimize the sensor efficiency. The co-RBF method does not bring better results than co-kriging but can be considered as an alternative for multifidelity metamodeling.     

A participative end-user method for multi-perspective business process elicitation and improvement

A business process can be characterized by multiple perspectives (intentional, organizational, operational, functional, interactional, informational, etc). Business process modeling must allow different stakeholders to analyze and represent process models according to these different perspectives. This representation is traditionally built using classical data acquisition methods together with a process representation language such as BPMN or UML. These techniques and specialized languages can easily become hard, complex and time consuming. In this paper, we propose ISEA, a participative end-user modeling approach that allows the stakeholders in a business process to collaborate together in a simple way to communicate and improve the business process elicitation in an accurate and understandable manner. Our approach covers the organizational perspective of business processes, exploits the information compiled during the elicitation of the organizational perspective and touches lightly an interactional perspective allowing users to create customized interface sketches to test the user interface navigability and the coherence within the processes. Thus, ISEA can be seen as a participative end-user modeling approach for business process elicitation and improvement.     

Tool path smoothing of a redundant machine: Application to Automated Fiber Placement

Automated Tape Laying and Fiber Placement of composite materials are the two principal automated processes used for fabrication of large composite structures in aeronautical industry. The aluminum parts produced by High Speed Machining tend to be replaced by carbon fiber composite parts realized with these processes. However, structural parts present reinforcement zones which disturb the tool path follow-up and generate an increase of the manufacturing time. Thus, this paper deals with the optimization of tool paths of a 7-axis machine tool of Fiber Placement with the objective of reducing the manufacturing time while ensuring the requested quality of the final part. In this paper, two complementary methods are detailed. The first method takes advantage of the degree of redundancy of the machine tool to decrease the kinematic loads of the control joints. The second method aims to smooth the orientation of the machine head along the tool path while ensuring quality constraints. These two methods are then applied on a test tool path and bring to a significant decrease of the manufacturing time (32.9%).     

Adaptive records for volume irradiance caching

In this paper, we present a new irradiance caching scheme using Monte Carlo ray tracing for efficiently rendering participating media. The irradiance cache algorithm is extended to participating media. Our method allows to adjust the density of cached records depending on illumination changes. Direct and indirect contributions can be stored in the records but also multiple scattering. An adaptive shape of the influence zone of records, depending on geometrical features and irradiance variations, is introduced. To avoid a high density of cached records in low interest areas, a new method controls the density of the cache when adding new records. This record density control depends on the interpolation quality and on the photometric characteristics of the medium. Reducing the number of records accelerates both the computation pass and the rendering pass by decreasing the number of queries to the cache data structure (Kd-tree). Finally, instead of using an expensive ray marching to find records that cover the ray, we gather all the contributive records along the ray. With our method, pre-computing and rendering passes are significantly speeded-up.     

Artificial Intelligence and Environmental Decision Support Systems

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.     

On the accurate determination of thermal diffusivity of liquids by using the photopyroelectric thickness scanning method

An enhanced accurate method of measuring the thermal diffusivity of liquids by the sample's thickness scan of the phase of the photopyroelectric signal is presented. The method, making use of the absolute values of the phase and sample thickness, leads to very accurate results for the room temperature values of thermal diffusivity (about +/-0.3%). The high accuracy of the method is due to a very precise control of the sample's thickness variation (0.1 microm step), to a proper localization of the thickness scan range, and to a new procedure of data analysis. The high accuracy of the method recommends it for the study of processes associated with small changes of the thermal parameters.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Numerical Schemes for Variational Inequalities Arising in International Asset Pricing

This paper introduces a valuation model of international pricing in the presence of political risk. Shipments between countries are charged with shipping costs and the country specific production processes are modelled as diffusion processes. The political risk is modelled as a continous time jump process that affects the drift of the returns in the politically unstable countries. The valuation model gives rise to a singular stochastic control problem that is analyzed numerically. The fundamental tools come from the theory of viscosity solutions of the associated Hamilton–Jacobi–Bellman equation which turns out to be a system of integral-differential Variational Inequalities with gradient constraints.     

Efficient and robust shot change detection

In this article, we deal with the problem of shot change detection which is of primary importance when trying to segment and abstract video sequences. Contrary to recent experiments, our aim is to elaborate a robust but very efficient (real-time even with uncompressed data) method to deal with the remaining problems related to shot change detection: illumination changes, context and data independency, and parameter settings. To do so, we have considered some adaptive threshold and derivative measures in a hue-saturation colour space. We illustrate our robust and efficient method by some experiments on news and football broadcast video sequences.

Experimental and theoretical determination of low electron energy loss spectra of Ag and Ru

We show the experimental and calculated q-dependent low energy loss electron energy loss spectrum of Ru and Ag. The spectra were calculated within the time-dependent density-functional theory including local-field effects. For Ag, the momentum transfer was parallel to the (110) direction. For Ru the three main directions (010), (110) and (001) were investigated. The agreement between theory and experiment is very good for Ag and for momentum transfers parallel to the (001) direction of Ru. For momentum transfers parallel to the in-plane directions (110) and (010) the agreement for Ru is not satisfactory, which could be attributed to relativistic effects or to strong localization of the 4d states of Ru.