DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Wavelets And Adaptive Grids For The Discontinuous Galerkin Method

In this paper, space adaptivity is introduced to control the error in the numerical solution of hyperbolic systems of conservation laws. The reference numerical scheme is a new version of the discontinuous Galerkin method, which uses an implicit diffusive term in the direction of the streamlines, for stability purposes. The decision whether to refine or to unrefine the grid in a certain location is taken according to the magnitude of wavelet coefficients, which are indicators of local smoothness of the numerical solution. Numerical solutions of the nonlinear Euler equations illustrate the efficiency of the method. © Springer 2005.

A biomimetic piezoelectric pump: Computational and experimental characterization

Flow pumps have been developed for classical applications in Engineering, and are important instruments in areas such as Biology and Medicine. Among applications for this kind of device we notice blood pump and chemical reagents dosage in Bioengineering. Furthermore, they have recently emerged as a viable thermal management solution for cooling applications in small-scale electronic devices. This work presents the performance study of a novel principle of a piezoelectric flow pump which is based oil the use of a bimorph piezoelectric actuator inserted in fluid (water). Piezoelectric actuators have some advantages over classical devices, such as lower noise generation and ease of miniaturization. The main objective is the characterization of this piezoelectric pump principle through computational simulations (using finite element software), and experimental tests through a manufactured prototype. Computational data, Such as flow rate and pressure curves, have also been compared with experimental results for validation purposes. (C) 2009 Elsevier B.V. All rights reserved.

Elevated plasma membrane and sarcoplasmic reticulum Ca2+ pump mRNA levels in cultured aortic smooth muscle cells from spontaneously hypertensive rats

We have observed previously that Ca2+ pump-mediated Ca2+ efflux is elevated in cultured aortic smooth muscle cells from spontaneously hypertensive rats compared to those from Wistar-Kyoto rat controls. The objective of this work was to determine if these strains differ in mRNA levels for the PMCA1 isoform of the plasma membrane Ca2+-ATPase and the SERCA2 isoform of the sarcoplasmic reticulum Ca2+-ATPase. mRNA levels were compared in cultured aortic smooth muscle cells from 10-week-old male rats. PMCA1 and SERCA2 mRNA levels were elevated in SHR compared to WKY. Angiotensin II increased the level of PMCA1 and SERCA2 mRNA in both strains. These studies provide further evidence for alterered Ca2+ homeostasis in hypertension at the level of Ca2+ transporting ATPases in the spontaneously hypertensive rat model. These data are also consistent with the hypothesis that the expression of these two Ca2+ pumps may be linked. (C) 1997 Academic Press

Modeling the optical constants of solids using acceptance-probability-controlled simulated annealing with an adaptive move generation procedure

The acceptance-probability-controlled simulated annealing with an adaptive move generation procedure, an optimization technique derived from the simulated annealing algorithm, is presented. The adaptive move generation procedure was compared against the random move generation procedure on seven multiminima test functions, as well as on the synthetic data, resembling the optical constants of a metal. In all cases the algorithm proved to have faster convergence and superior escaping from local minima. This algorithm was then applied to fit the model dielectric function to data for platinum and aluminum.

Five-Year Follow-Up of a Randomized Comparison Between Off-Pump and On-Pump Stable Multivessel Coronary Artery Bypass Grafting. The MASS III Trial

Background-Coronary artery bypass graft surgery with cardiopulmonary bypass is a safe, routine procedure. Nevertheless, significant morbidity remains, mostly because of the body`s response to the nonphysiological nature of cardiopulmonary bypass. Few data are available on the effects of off-pump coronary artery bypass graft surgery (OPCAB) on cardiac events and long-term clinical outcomes. Methods and Results-In a single-center randomized trial, 308 patients undergoing coronary artery bypass graft surgery were randomly assigned: 155 to OPCAB and 153 to on-pump CAB (ONCAB). Primary composite end points were death, myocardial infarction, further revascularization (surgery or angioplasty), or stroke. After 5-year follow-up, the primary composite end point was not different between groups (hazard ratio 0.71, 95% CI 0.41 to 1.22; P=0.21). A statistical difference was found between OPCAB and ONCAB groups in the duration of surgery (240 +/- 65 versus 300 +/- 87.5 minutes; P<0.001), in the length of ICU stay (19.5 +/- 17.8 versus 43 +/- 17.0 hours; P<0.001), time to extubation (4.6 +/- 6.8 versus 9.3 +/- 5.7 hours; P<0.001), hospital stay (6 +/- 2 versus 9 +/- 2 days; P<0.001), higher incidence of atrial fibrillation (35 versus 4% of patients; P<0.001), and blood requirements (31 versus 61% of patients; P<0.001), respectively. The number of grafts per patient was higher in the ONCAB than the OPCAB group (2.97 versus 2.49 grafts/patient; P<0.001). Conclusions-No difference was found between groups in the primary composite end point at 5-years follow-up. Although OPCAB surgery was related to a lower number of grafts and higher episodes of atrial fibrillation, it had no significant implications related to long-term outcomes.

Behavioral disturbances in Noonan Syndrome: Brazilian preliminary data

Different from other countries Of Europe and North American, studies about the behavioral profile of Noonan syndrome`s patients are inexistent. The objective of this study was to report the profiles of behavioral functions of 10 participants (4 females and 6 males), with mutations in the PTPN11 gene. For this assessment it was used the Inventory of Behaviors of Children and Adolescents from 6 to 18 years (CBCL/6-18) and the Inventory of Auto-Evaluation for Adults from 18 to 59 years (ASR). The main results point that in Adaptive Functioning Scale all the participants were in the normality range. In the Syndrome Scale the adult participants were in normality range and the children were in clinical range to the sub-scales anxious/depressed, somatic complaints and aggressive behavior. In the DSM-Oriented Scale, 25% of the adult patients were in the borderline clinical range and clinical range, respectively, for Avoidant Personality Problems and Antisocial Personality Problems. About the both children in this scale were in the clinical range of Affective Problems and Anxiety Problems. This relatively homogenous sample, regarding the PTPN11 gene, shows a normal adult behavioral profile, on the average. However, the individual children anti adult profiles show diverse internalizing and externalizing behavioral disturbances.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Cytokine Signatures of Innate and Adaptive Immunity in 17DD Yellow Fever Vaccinated Children and Its Association With the Level of Neutralizing Antibody

Background. The live attenuated yellow fever (YF) vaccines have been available for decades and are considered highly effective and one of the safest vaccines worldwide. Methods. The impact of YF-17DD-antigens recall on cytokine profiles of YF-17DD-vaccinated children were characterized using short-term cultures of whole blood samples and single-cell flow cytometry. This study enrolled seroconverters and nonseroconverters after primovaccination (PV-PRNT(+) and PV-PRNT(-)), seroconverters after revaccination (RV-PRNT(+)), and unvaccinated volunteers (UV-PRNT(-)). Results. The analysis demonstrated in the PV-PRNT(+) group a balanced involvement of pro-inflammatory/regulatory adaptive immunity with a prominent participation of innate immunity pro-inflammatory events (IL-12(+) and TNF-alpha(+) NEU and MON). Using the PV-PRNT(+) cytokine signature as a reference profile, PV-PRNT(+) presented a striking lack of innate immunity proinflammatory response along with an increased adaptive regulatory profile (IL-4(+) CD4(+) T cells and IL-10(+) and IL-5(+) CD8(+) T cells). Conversely, the RV-PRNT(+) shifted the overall cytokine signatures toward an innate immunity pro-inflammatory profile and restored the adaptive regulatory response. Conclusions. The data demonstrated that the overall cytokine signature was associated with the levels of PRNT antibodies with a balanced innate/adaptive immunity with proinflammatory/regulatory profile as the hallmark of PV-PRNT(MEDIUM+), whereas a polarized regulatory response was observed in PV-PRNT(-) and a prominent proinflammatory signature was the characteristic of PV-PRNT(HIGH+).

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

The development and psychometric properties of a measure of social and adaptive functioning for children and adolescents

Developed, piloted, and examined the psychometric properties of the Child and Adolescent Social and Adaptive Functioning Scale (CASAFS), a self-report measure designed to examine the social functioning of young people in the areas of school performance, peer relationships, family relationships, and home duties/self-care. The findings of confirmatory and exploratory factor analysis support a 4-factor solution consistent with the hypothesized domains. Fit indexes suggested that the 4-correlated factor model represented a satisfactory solution for the data, with the covariation between factors being satisfactorily explained by a single, higher order factor reflecting social and adaptive functioning in general. The internal consistency and 12-month test-retest reliability of the total scale was acceptable. A significant, negative correlation was found between the CASAFS and a measure of depressive symptoms, showing that high levels of social functioning are associated with low levels of depression. Significant differences in CASAFS total and subscale scores were found between clinically depressed adolescents and a matched sample of nonclinical controls. Adolescents who reported elevated but subclinical levels of depression also reported lower levels of social functioning in comparison to nonclinical controls.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

User modeling In adaptive hypermedia educational systems

This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling (UM), numerous research and developed systems already seem to promise good results, but some experimentation and implementation are still necessary to conclude about the utility of the UM. That is, the experimentation and implementation of these systems are still very scarce to determine the utility of some of the referred applications. At present, the Student Modeling research goes in the direction to make possible reuse a student model in different systems. The standards are more and more relevant for this effect, allowing systems communicate and to share data, components and structures, at syntax and semantic level, even if most of them still only allow syntax integration.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.