A purely reactive navigation scheme for dynamic environments using Case-Based Reasoning

This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into account. Also, learning from different sources allows combination of their advantages into a safe and smooth path to the goal. The method has been succesfully implemented on a Pioneer robot wielding 8 Polaroid sonar sensors. Cristina Urdiales is a Lecturer at the Department of Tecnología Electrónica (DTE) of the University of Málaga (UMA). She received a MSc degree in Telecommunication Engineering at the Universidad Politécnica de Madrid (UPM) and her Ph.D. degree at University of Málaga (UMA). Her research is focused on robotics and computer vision. E.J. Pérez was born in Barcelona, Spain, in 1974. He received his title of Telecommunication Engineering from the University of Málaga, Spain, in 1999. During 1999 he worked in a research project under a grant by the Spanish CYCIT. From 2000 to the present day he has worked as Assistant Professor in the Department of Tecnología Electrónica of the University of Málaga. His research is focused on robotics and artificial vision. Javier Vázquez-Salceda is an Associate Researcher of the Artificial Intelligence Section of the Software Department (LSI), at the Technical University of Catalonia (UPC). Javier obtained an MSc degree in Computer Science at UPC. After his master studies he became research assistant in the KEMLg Group at UPC. In 2003 he presented his Ph.D. dissertation (with honours), which has been awarded with the 2003 ECCAI Artificial Intelligence Dissertation Award. The dissertation has been also recently published as a book by Birkhauser-Verlag. From 2003 to 2005 he was researcher in the Intelligent Systems Group at Utrecht University. Currently he is again member of the KEMLg Group at UPC. His research is focused on theoretical and applied issues of Normative Systems, software and physical agents' autonomy and social control, especially in distributed applications for complex domains such as eCommerce or Medicine. Miquel Sànchez-Marrè (Barcelona, 1964) received a Ph.D. in Computer Science in 1996 from the Technical University of Catalonia (UPC). He is Associate Professor in the Computer Software Department (LSI) of the UPC since 1990 (tenure 1996). He was the head of the Artificial Intelligence section of LSI (1997–2000). He is a pioneer member of International Environmental Modelling and Software Society (IEMSS) and a board member of IEMSS also, since 2000. He is a member of the Editorial Board of International Journal of Applied Intelligence, since October 2001. Since October 2004 he is Associate Editor of Environmental Modelling and Software journal. His main research topics are case-based reasoning, machine learning, knowledge acquisition and data mining, knowledge engineering, intelligent decision-support systems, and integrated AI architectures. He has an special interest on the application of AI techniques to Environmental Decision Support Systems. Francisco Sandoval was born in Spain in 1947. He received the title of Telecommunication Engineering and Ph.D. degree from the Technical University of Madrid, Spain, in 1972 and 1980, respectively. From 1972 to 1989 he was engaged in teaching and research in the fields of opto-electronics and integrated circuits in the Universidad Politécnica de Madrid (UPM) as an Assistant Professor and a Lecturer successively. In 1990 he joined the University of Málaga as Full Professor in the Department of Tecnología Electrónica. He is currently involved in autonomous systems and foveal vision, application of Artificial Neural Networks to Energy Management Systems, and in Broad Band and Multimedia Communication.     

Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state‐independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.     

Temperature-dependent microtensile testing of thin film materials for application to microelectromechanical system

A specially designed microtensile apparatus capable of carrying out a series of tests on microscale thin films for microelectromechanical system (MEMS) applications at room temperature and at temperature up to 400°C has been developed and tested, and is described here. Several MEMS-applicable thin films were measured with it, including thermally grown silicon dioxide, gold, and gold–vanadium. The silicon dioxide was tested at room temperature. Gold and gold–vanadium films were tested at room temperature and at 200 and 400°C. Examples of these results are presented.

     

Plasticity in anterior hypothalamic vasopressin correlates with aggression during anabolic-androgenic steroid withdrawal in hamsters.

In hamsters, adolescent anabolic-androgenic steroid (AAS) exposure facilitates offensive aggression, in part by altering the development and activity of anterior hypothalamic arginine vasopressin (AH-AVP). This study assessed whether these effects were lasting by examining aggression and AH-AVP during AAS withdrawal. Adolescent hamsters administered AAS were tested as adults for aggression at 1, 4, 11, 18, or 25 days of withdrawal, sacrificed the following day, and examined for AH-AVP afferent innervation using immunohistochemistry. Through Day 12 of withdrawal, aggression and AVP were significantly higher in AAS-treated hamsters than in controls. These differences were no longer observable by Day 19 of withdrawal, at which point the behavior and neurobiology of AAS-treated hamsters reverted to that observed in controls. These data indicate that adolescent AAS exposure has short-term, reversible effects on both aggression and AH-AVP, correlating AH-AVP with the aggressive/nonaggressive behavioral phenotype during AAS withdrawal. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Local dopamine production in the dorsal striatum restores goal-directed behavior in dopamine-deficient mice.

To determine whether dopamine signaling in the dorsal striatum is sufficient for performance of goal-directed behaviors, local dopamine production was restored in the dorsal striatum of dopamine-deficient (DD) mice through viral-mediated gene therapy. Virally rescued DD (vrDD) mice were tested for learning of an appetitive T-maze task designed to measure goal-directed behavior. The results indicate that in contrast with the performance of DD mice that have dysregulated dopamine signaling, vrDD mice were able to perform the T-maze task and reverse their behavior as well as sham-operated control mice. The authors conclude that finely tuned dopaminergic signaling within the dorsal striatum is sufficient for performance of goal-directed behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Relation of emotion-related regulation to children's social competence: A longitudinal study.

The differential relations of children's emotion-related regulation (i.e., effortful control and impulsivity) to their personality resiliency, adult-rated popularity, and social competence were examined in children who were 4.5-7.9 years old and who were remeasured 2 years later. Parents and teachers reported on all constructs, and children's attentional persistence was observed. Structural equation modeling was used to test the mediating role of resiliency on the relations between regulation/control and popularity using two-wave longitudinal data. The results provide some evidence of the mediating role of resiliency in the relations between effortful control and popularity, provide some evidence of bidirectional effects, and also buttress the view that emotional regulation should be differentiated into effortful and reactive forms of control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Intoxicated behavioral disinhibition and the heart rate response to alcohol.

This study examined the association between the heart rate (HR) response to alcohol intoxication, which is thought to reflect sensitivity to alcohol-induced reward and alcohol-induced behavioral disinhibition. High- and low-HR responders to alcohol participated in a go/no-go task, under sober and intoxicated conditions. Errors of commission on this task have previously been related to behavioral disinhibition. High-HR responders made more intoxicated commission errors as compared with low-HR responders. High-HR responders also reported increased alcohol consumption, and controlling for the latter did not alter the significant association between high-HR responders and increased intoxicated errors of commission. These results are consistent with previous findings of an increased risk for addictive and disinhibited behavioral propensities in individuals with a high-HR response to alcohol intoxication. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

CEA and AREVA R&D on HTR fuel fabrication and presentation of the CAPRI experimental manufacturing line

In the framework of the French V/HTR fuel development and qualification program, the Commissariat à l’Energie Atomique (CEA) and AREVA are conducting R&D projects covering the mastering of UO₂ coated particle and fuel compact fabrication technology. To fulfill this task, a review of past knowledge, of existing technologies and a preliminary laboratory-scale work program have been conducted with the aim of retrieving the know-how on HTR coated particle and compact manufacture:

• The different stages of UO₂ kernel fabrication GSP process have been reviewed, reproduced and improved.

• The experimental conditions for the chemical vapor deposition of coatings have been defined on dummy kernels and development of innovative characterization methods has been carried out.

• Former CERCA compacting process has been reviewed and updated.

In parallel, an experimental manufacturing line for coated particles, named GAIA, and a compacting line based on former CERCA compacting experience have been designed, constructed and are in operation since early 2005 at CEA Cadarache and CERCA Romans, respectively. These two facilities constitute the CAPRI line (CEA and AREVA PRoduction Integrated line).The major objectives of the CAPRI line are:

• to recover and validate past knowledge,

• to produce representative HTR TRISO fuel meeting industrial standards,

• to permit the optimization of reference fabrication processes for kernels and coatings defined previously at a laboratory-scale and the investigation of alternative and innovative fuel design (UCO kernel, ZrC coating),

• to test alternative compact process options and

• to fabricate and characterize fuel required for irradiation and qualification purpose.

This paper presents the status of progress of R&D conducted on HTR fuel particles and compact manufacture by early 2005 and the potential of the laboratory-scale HTR fuel CAPRI line.     

Linear versus nonlinear neural modeling for 2-D pattern recognition

This paper compares the classification performance of linear-system- and neural-network-based models in handwritten-digit classification and face recognition. In inputs to a linear classifier, nonlinear inputs are generated based on linear inputs, using different forms of generating products. Using a genetic algorithm, linear and nonlinear inputs to the linear classifier are selected to improve classification performance. Results show that an appropriate set of linear and nonlinear inputs to the linear classifier were selected, improving significantly its classification performance in both problems. It is also shown that the linear classifier reached a classification performance similar to or better than those obtained by nonlinear neural-network classifiers with linear inputs.