Artificial neural networks applied to bandsawing process control

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

Ground-level ozone prediction using a neural network model based on meteorological variables and applied to the metropolitan area of Sao Paulo

A neural network model to predict ozone concentration in the Sao Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00-12:00 hr) and afternoon (13:00-17: 00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00-17:00 hr). The correlation coefficient between computed and measured values was 0.82 and 0.88 for the maximum and average ozone concentration, respectively. The model presented good performance as a prediction tool for the maximum ozone concentration. For prediction periods from 1 to 5 days 0 to 23% failures (95% confidence) were obtained.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network

[EN] Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a latitude by longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFSMODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6?atm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

Tactile perception - Perception of tactile distance changes with body site: a neural network modelling study.

La distorsione della percezione della distanza tra due stimoli puntuali applicati sulla superfice della pelle di diverse regioni corporee è conosciuta come Illusione di Weber. Questa illusione è stata osservata, e verificata, in molti esperimenti in cui ai soggetti era chiesto di giudicare la distanza tra due stimoli applicati sulla superficie della pelle di differenti parti corporee. Da tali esperimenti si è dedotto che una stessa distanza tra gli stimoli è giudicata differentemente per diverse regioni corporee. Il concetto secondo cui la distanza sulla pelle è spesso percepita in maniera alterata è ampiamente condiviso, ma i meccanismi neurali che manovrano questa illusione sono, allo stesso tempo, ancora ampiamente sconosciuti. In particolare, non è ancora chiaro come sia interpretata la distanza tra due stimoli puntuali simultanei, e quali aree celebrali siano coinvolte in questa elaborazione. L’illusione di Weber può essere spiegata, in parte, considerando la differenza in termini di densità meccano-recettoriale delle differenti regioni corporee, e l’immagine distorta del nostro corpo che risiede nella Corteccia Primaria Somato-Sensoriale (homunculus). Tuttavia, questi meccanismi sembrano non sufficienti a spiegare il fenomeno osservato: infatti, secondo i risultati derivanti da 100 anni di sperimentazioni, le distorsioni effettive nel giudizio delle distanze sono molto più piccole rispetto alle distorsioni che la Corteccia Primaria suggerisce. In altre parole, l’illusione osservata negli esperimenti tattili è molto più piccola rispetto all’effetto prodotto dalla differente densità recettoriale che affligge le diverse parti del corpo, o dall’estensione corticale. Ciò, ha portato a ipotizzare che la percezione della distanza tattile richieda la presenza di un’ulteriore area celebrale, e di ulteriori meccanismi che operino allo scopo di ridimensionare – almeno parzialmente – le informazioni derivanti dalla corteccia primaria, in modo da mantenere una certa costanza nella percezione della distanza tattile lungo la superfice corporea. E’ stata così proposta la presenza di una sorta di “processo di ridimensionamento”, chiamato “Rescaling Process” che opera per ridurre questa illusione verso una percezione più verosimile. Il verificarsi di questo processo è sostenuto da molti ricercatori in ambito neuro scientifico; in particolare, dal Dr. Matthew Longo, neuro scienziato del Department of Psychological Sciences (Birkbeck University of London), le cui ricerche sulla percezione della distanza tattile e sulla rappresentazione corporea sembrano confermare questa ipotesi. Tuttavia, i meccanismi neurali, e i circuiti che stanno alla base di questo potenziale “Rescaling Process” sono ancora ampiamente sconosciuti. Lo scopo di questa tesi è stato quello di chiarire la possibile organizzazione della rete, e i meccanismi neurali che scatenano l’illusione di Weber e il “Rescaling Process”, usando un modello di rete neurale. La maggior parte del lavoro è stata svolta nel Dipartimento di Scienze Psicologiche della Birkbeck University of London, sotto la supervisione del Dott. M. Longo, il quale ha contribuito principalmente all’interpretazione dei risultati del modello, dando suggerimenti sull’elaborazione dei risultati in modo da ottenere un’informazione più chiara; inoltre egli ha fornito utili direttive per la validazione dei risultati durante l’implementazione di test statistici. Per replicare l’illusione di Weber ed il “Rescaling Proess”, la rete neurale è stata organizzata con due strati principali di neuroni corrispondenti a due differenti aree funzionali corticali: • Primo strato di neuroni (il quale dà il via ad una prima elaborazione degli stimoli esterni): questo strato può essere pensato come parte della Corteccia Primaria Somato-Sensoriale affetta da Magnificazione Corticale (homunculus). • Secondo strato di neuroni (successiva elaborazione delle informazioni provenienti dal primo strato): questo strato può rappresentare un’Area Corticale più elevata coinvolta nell’implementazione del “Rescaling Process”. Le reti neurali sono state costruite includendo connessioni sinaptiche all’interno di ogni strato (Sinapsi Laterali), e connessioni sinaptiche tra i due strati neurali (Sinapsi Feed-Forward), assumendo inoltre che l’attività di ogni neurone dipenda dal suo input attraverso una relazione sigmoidale statica, cosi come da una dinamica del primo ordine. In particolare, usando la struttura appena descritta, sono state implementate due differenti reti neurali, per due differenti regioni corporee (per esempio, Mano e Braccio), caratterizzate da differente risoluzione tattile e differente Magnificazione Corticale, in modo da replicare l’Illusione di Weber ed il “Rescaling Process”. Questi modelli possono aiutare a comprendere il meccanismo dell’illusione di Weber e dare così una possibile spiegazione al “Rescaling Process”. Inoltre, le reti neurali implementate forniscono un valido contributo per la comprensione della strategia adottata dal cervello nell’interpretazione della distanza sulla superficie della pelle. Oltre allo scopo di comprensione, tali modelli potrebbero essere impiegati altresì per formulare predizioni che potranno poi essere verificate in seguito, in vivo, su soggetti reali attraverso esperimenti di percezione tattile. E’ importante sottolineare che i modelli implementati sono da considerarsi prettamente come modelli funzionali e non intendono replicare dettagli fisiologici ed anatomici. I principali risultati ottenuti tramite questi modelli sono la riproduzione del fenomeno della “Weber’s Illusion” per due differenti regioni corporee, Mano e Braccio, come riportato nei tanti articoli riguardanti le illusioni tattili (per esempio “The perception of distance and location for dual tactile pressures” di Barry G. Green). L’illusione di Weber è stata registrata attraverso l’output delle reti neurali, e poi rappresentata graficamente, cercando di spiegare le ragioni di tali risultati.

Neural network activity in the neonatal acute slice, slice culture and cell culture

Information processing and storage in the brain may be presented by the oscillations and cell assemblies. Here we address the question of how individual neurons associate together to assemble neural networks and present spontaneous electrical activity. Therefore, we dissected the neonatal brain at three different levels: acute 1-mm thick brain slice, cultured organotypic 350-µm thick brain slice and dissociated neuronal cultures. The spatio-temporal properties of neural activity were investigated by using a 60-channel Micro-electrode arrays (MEA), and the cell assemblies were studied by using a template-matching method. We find local on-propagating as well as large- scale propagating spontaneous oscillatory activity in acute slices, spontaneous network activity characterized by synchronized burst discharges in organotypic cultured slices, and autonomous bursting behaviour in dissociated neuronal cultures. Furthermore, repetitive spike patterns emerge after one week of dissociated neuronal culture and dramatically increase their numbers as well as their complexity and occurrence in the second week. Our data indicate that neurons can self-organize themselves, assembly to a neural network, present spontaneous oscillations, and emerge spatio-temporal activation patterns. The spontaneous oscillations and repetitive spike patterns may serve fundamental functions for information processing and storage in the brain.

Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

Neural network activity in the neonatal rat barrel cortex in vivo

Coordinated patterns of electrical activity are important for the early development of sensory systems. The spatiotemporal dynamics of these early activity patterns and the role of the peripheral sensory input for their generation are essentially unknown. There are two projects in this thesis. In project1, we performed extracellular multielectrode recordings in the somatosensory cortex of postnatal day 0 to 7 rats in vivo and observed three distinct patterns of synchronized oscillatory activity. (1) Spontaneous and periphery-driven spindle bursts of 1–2 s in duration and ~10 Hz in frequency occurred approximately every 10 s. (2) Spontaneous and sensory-driven gamma oscillations of 150–300 ms duration and 30–40 Hz in frequency occurred every 10–30 s. (3) Long oscillations appeared only every ~20 min and revealed the largest amplitude (250–750 µV) and longest duration (>40 s). These three distinct patterns of early oscillatory activity differently synchronized the neonatal cortical network. Whereas spindle bursts and gamma oscillations did not propagate and synchronized a local neuronal network of 200–400 µm in diameter, long oscillations propagated with 25–30 µm/s and synchronized 600-800 µm large ensembles. All three activity patterns were triggered by sensory activation. Single electrical stimulation of the whisker pad or tactile whisker activation elicited neocortical spindle bursts and gamma activity. Long oscillations could be only evoked by repetitive sensory stimulation. The neonatal oscillatory patterns in vivo depended on NMDAreceptor-mediated synaptic transmission and gap junctional coupling. Whereas spindle bursts and gamma oscillations may represent an early functional columnar-like pattern, long oscillations may serve as a propagating activation signal consolidating these immature neuronal networks. In project2, Using voltage-sensitive dye imaging and simultaneous multi-channel extracellular recordings in the barrel cortex and somatosensory thalamus of newborn rats in vivo, we found that spontaneous and whisker stimulation induced activity patterns were restricted to functional cortical columns already at the day of birth. Spontaneous and stimulus evoked cortical activity consisted of gamma oscillations followed by spindle bursts. Spontaneous events were mainly generated in the thalamus or by spontaneous whisker movements. Our findings indicate that during early developmental stages cortical networks self-organize in ontogenetic columns via spontaneous gamma oscillations triggered by the thalamus or sensory periphery.

Long-term potentiation and neural network activity in the neonatal rat cerebral cortex in vivo

Long-term potentiation in the neonatal rat rnbarrel cortex in vivo rnLong-term potentiation (LTP) is important for the activity-dependent formation of early cortical circuits. In the neonatal rodent barrel cortex LTP has been so far only studied in vitro. I combined voltage-sensitive dye imaging with extracellular multi-electrode recordings to study whisker stimulation-induced LTP for both the slope of field potential and the number of multi-unit activity in the whisker-to-barrel cortex pathway of the neonatal rat barrel cortex in vivo. Single whisker stimulation at 2 Hz for 10 min induced an age-dependent expression of LTP in postnatal day (P) 0 to P14 rats with the strongest expression of LTP at P3-P5. The magnitude of LTP was largest in the stimulated barrel-related column, smaller in the surrounding septal region and no LTP could be observed in the neighboring barrel. Current source density analyses revealed an LTP-associated increase of synaptic current sinks in layer IV / lower layer II/III at P3-P5 and in the cortical plate / upper layer V at P0-P1. This study demonstrates for the first time an age-dependent and spatially confined LTP in the barrel cortex of the newborn rat in vivo. These activity-dependent modifications during the critical period may play an important role in the development and refinement of the topographic map in the barrel cortex. (An et al., 2012)rnEarly motor activity triggered by gamma and spindle bursts in neonatal rat motor cortexrnSelf-generated neuronal activity generated in subcortical regions drives early spontaneous motor activity, which is a hallmark of the developing sensorimotor system. However, the neuronal activity patterns and functions of neonatal primary motor cortex (M1) in the early movements are still unknown. I combined voltage-sensitive dye imaging with simultaneous extracellular multi-electrode recordings in the neonatal rat S1 and M1 in vivo. At P3-P5, gamma and spindle bursts observed in M1 could trigger early paw movements. Furthermore, the paw movements could be also elicited by the focal electrical stimulation of M1 at layer V. Local inactivation of M1 could significantly attenuate paw movements, suggesting that the neonatal M1 operates in motor mode. In contrast, the neonatal M1 can also operate in sensory mode. Early spontaneous movements and sensory stimulations of paw trigger gamma and spindle bursts in M1. Blockade of peripheral sensory input from the paw completely abolished sensory evoked gamma and spindle bursts. Moreover, both sensory evoked and spontaneously occurring gamma and spindle bursts mediated interactions between S1 and M1. Accordingly, local inactivation of the S1 profoundly reduced paw stimulation-induced and spontaneously occurring gamma and spindle bursts in M1, indicating that S1 plays a critical role in generation of the activity patterns in M1. This study proposes that both self-generated and sensory evoked gamma and spindle bursts in M1 may contribute to the refinement and maturation of corticospinal and sensorimotor networks required for sensorimotor coordination.rn

Simulating event related potentials in tasks involving conflict resolution: a neural network

In this thesis, the main Executive Control theories are exposed. Methods typical of Cognitive and Computational Neuroscience are introduced and the role of behavioural tasks involving conflict resolution in the response elaboration, after the presentation of a stimulus to the subject, are highlighted. In particular, the Eriksen Flanker Task and its variants are discussed. Behavioural data, from scientific literature, are illustrated in terms of response times and error rates. During experimental behavioural tasks, EEG is registered simultaneously. Thanks to this, event related potential, related with the current task, can be studied. Different theories regarding relevant event related potential in this field - such as N2, fERN (feedback Error Related Negativity) and ERN (Error Related Negativity) – are introduced. The aim of this thesis is to understand and simulate processes regarding Executive Control, including performance improvement, error detection mechanisms, post error adjustments and the role of selective attention, with the help of an original neural network model. The network described here has been built with the purpose to simulate behavioural results of a four choice Eriksen Flanker Task. Model results show that the neural network can simulate response times, error rates and event related potentials quite well. Finally, results are compared with behavioural data and discussed in light of the mentioned Executive Control theories. Future perspective for this new model are outlined.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.