Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Executive functioning in cognitively normal middle-aged offspring of late-onset Alzheimer's disease patients

Episodic memory deficits are traditionally seen as the hallmark cognitive impairment during the prodromal continuum of late-onset Alzheimer's disease (LOAD). Previous studies identified early brain alterations in regions subserving executive functions in asymptomatic, middle-aged offspring of patients with LOAD (O-LOAD), suggesting that premature episodic memory deficits could be associated to executive dysfunction in this model. We hypothesized that O-LOAD would exhibit reduced executive performance evidenced by increased errors and decreased strategy use on an episodic memory task. We assessed 32 asymptomatic middle-aged O-LOAD and 28 age-equivalent control subjects (CS) with several tests that measure executive functions and the Rey Auditory Verbal Learning Test (RAVLT) to measure memory performance. All tests were scored using both traditional and process scores (quantification of errors and strategies underlying overall performance). T-tests were used to compare performance between both groups and Spearman correlations were implemented to measure associations between variables. O-LOAD participants exhibited decreased executive performance compared to CS as it relates to initiation time (Tower of London), mental switching (Trail Making Test B), and interference effects (Stroop Word-Color condition). Traditional RAVLT measures showed a poorer performance by O-LOAD and RAVLT process scores revealed increased interference effects on this group. Positive correlations (r s ) were found between the executive measures and several RAVLT measures for O-LOAD but not for CS. In conclusion, O-LOAD participants exhibited early subtle cognitive changes in executive processing. Observed memory difficulties may be associated in part to executive deficits suggesting an interplay between memory and executive functions. Process score impairments were observed earlier than clinical decline on neuropsychological scores in this at-risk cohort and might be useful cognitive markers of preclinical LOAD.Fil: Abulafia, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Fiorentini, Leticia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Loewenstein, David A.. University of Miami; Estados UnidosFil: Curiel Cid, Rosie. University of Miami; Estados UnidosFil: Sevlever, Gustavo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Nemeroff, Charles B.. University of Texas at Austin; Estados UnidosFil: Villarreal, Mirta Fabiana. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Vigo, Daniel Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Guinjoan, Salvador Martín. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentin

Detection of heat flux failures in building using a soft computing diagnostic system

The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain - heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real data sets from several Spanish cities in winter time

Learning and training techniques in fuzzy control for energy efficiency in buildings

A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain

The application of a two-step AI model to an automated pneumatic drilling process

Real-world processes may be improved through a combination of artificial intelligence and identification techniques. This work presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on a robotic drill used in the construction of industrial storage centres. The first step entails determination of the most relevant structures in the data set with the application of the connectionist architectures. The second step combines the results of the first one to identify a model for the optimal working conditions of the drilling robot that is based on low-order models such as black box that approximate the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples

Habitat fragmentation is linked to cascading effects on soil functioning and CO2 emissions in Mediterranean holm-oak-forests

We studied key mechanisms and drivers of soil functioning by analyzing soil respiration and enzymatic activity in Mediterranean holm oak forest fragments with different influence of the agricultural matrix. For this, structural equation models (SEM) were built including data on soil abiotic (moisture, temperature, organic matter, pH, nutrients), biotic (microbial biomass, bacterial and fungal richness), and tree-structure-related (basal area) as explanatory variables of soil enzymatic activity and respiration. Our results show that increased tree growth induced by forest fragmentation in scenarios of high agricultural matrix influence triggered a cascade of causal-effect relations, affecting soil functioning. On the one hand, soil enzymatic activity was strongly stimulated by the abiotic (changes in pH and microclimate) and biotic (microbial biomass) modifications of the soil environment arising from the increased tree size and subsequent soil organic matter accumulation. Soil CO2 emissions (soil respiration), which integrate releases from all the biological activity occurring in soils (autotrophic and heterotrophic components), were mainly affected by the abiotic (moisture, temperature) modifications of the soil environment caused by trees. These results, therefore, suggest that the increasing fragmentation of forests may profoundly impact the functioning of the plant-soil-microbial system, with important effects over soil CO2 emissions and nutrient cycling at the ecosystem level. Forest fragmentation is thus revealed as a key albeit neglected factor for accurate estimations of soil carbon dynamics under global change scenarios

Complex effects of habitat fragmentation on plant‐soil microbial interactions in Mediterranean Holmoak forests

Póster presentado en el 1st Global Soil Biodiversity Conference (2-5 December 2014 - Dijon, France)The adverse effects of habitat fragmentation on biodiversity have been widely explored; however, little research has been conducted to understand its effects on ecosystem functioning. Effects of forest fragmentation are tightly linked to the surrounding matrix in terms of nutrient inputs and spatial constraints, leading to complex edge effects. Soil ecosystem processes related to carbon cycling are particularly important since soils are the largest carbon pool in terrestrial ecosystems, and habitat fragmentation affects their sink capacity and their vulnerability to global change. Soil organic matter (SOM) decomposition is affected directly by the canopy cover. Thus, the effects of an agricultural matrix could be overridden by the direct effects of canopy rather than by habitat fragmentation itself. In order to evaluate which key factors could be driving SOM decomposition in fragmented landscapes, we analyzed potential enzymatic activities (β-glucosidase, chitinase and phosphatase acid) and field soil respiration in fragmented Mediterranean Holm oak forests. We evaluated if the impact of fragmentation on soil microbial functioning could be explained through its effect on microhabitat characteristics by using structural equation models. Variables measured included biotic (microbial biomass), abiotic (soil moisture, temperature, organic matter, pH, nutrients) and tree structural (stem diameter, canopy projection, leaf area index) characteristics. Tree effects on soil functioning (enzymatic activities) were potentiated by the influence of the agricultural matrix. As expected, trees created a microenvironment where the increment of SOM modified the pH, increasing soil moisture and decreasing temperature, rising the amount of microbial biomass and, therefore, improving the functioning of soil microbial community. Agricultural matrix influence on SOM decomposition was mainly indirect, through its positive effect on tree size. Mediterranean fragmented forests with high influence of agricultural matrix could increase SOM decomposition rates, decreasing soil carbon sink capacity.Peer reviewe