Advanced control methods for reducing nitrogen oxides in a fluidized bed boiler

Tämän työn tarkoituksena oli löytää keinoja erään leijukerroskattilan typenoksidipäästöjen vähentämiseksi. Koska päästöt olivat jo alunperin alhaiset leijukerrostekniikan ja hybridin SNCR/SCR –typenpoistolaitteiston ansiosta, päätettiin päästöjä lähteä vähentämään parantamalla ammoniakkiruiskutuksen säätöä. Alkuperäinen ammoniakkiruiskutuksen säätö oli liian hidas, jotta satunnaisten häiriöiden aiheuttamat typenoksidipiikit olisi pystytty poistamaan. Ammoniakkiruiskutusta parannettiin lisäämällä jokaiseen ammoniakkilinjaan mäntäpumput, joiden avulla ammoniakkia voidaan syöttää sinne, missä sitä eniten tarvitaan. Ammoniakkiruiskutuksen säätöön kehitettiin uusi sumeaan logiikkaan perustuva säätäjä. Myös muita kehittyneitä säätömenetelmiä kuten neuroverkkoa hyödynnettiin säätäjän kehityksessä. Ammoniakkiruiskutuksen säätäjää testattiin menestyksekkäästi Ruotsissa Brista Kraftin Märstassa sijaitsevalla voimalaitoksella

Ohjelmistoarkkitehtuurien vertailu ja valinta monimediatuotteelle. CASE: AVO

Työn tavoitteena oli tutkia ja vertailla komponenttipohjaisia ohjelmistoarkkitehtuureita (Microsoft .NET ja J2EE). Työn tarkoituksena oli valita ohjelmistoarkkitehtuuri uudelle neuroverkkopohjaiselle urasuunnittelupalvelulle. Tässä työssä selvitettiin myös, miten luodaan kansainvälistettäviä ja lokalisoitavia sovelluksia, sekä kuinka Web-, Windows-, mobiili-, puhe- ja Digi-TV -käyttöliittymät soveltuvat uudelle urasuunnittelupalvelulle. Tutkimustyössä käytettiin alan kirjallisuutta, Microsoftin ja Sun Microsystemsin Web-sivuja. Tutkimustyössä analysoitiin Microsoft Pet Shop- ja Sun Microsystemsin Java Pet Store -esimerkkisovellusten suorituskykyvertailua. Analyysituloksiin perustuen urasuunnittelupalvelussa suositellaan käytettäväksi J2EE-arkkitehtuuria. Uudelle urasuunnittelupalvelulle toimenpide-ehdotus on komponenttipohjainen järjestelmä Web-, puhe- ja Digi-TV -käyttöliittymillä ja personoidulla sisällöllä. Järjestelmä tehdään viisivaiheisena hankkeena, johon sisältyy pilottitestejä. Uuteen urasuunnittelupalveluun liitetään mukaan opiskelijat, oppilaitokset ja työnantajat sekä asiantuntijoita neuroverkon opetusdatan määrittämiseen. Palvelu perustuu integroituun tietokantaan. Eri osajärjestelmissä tuotettua tietoa voidaan hyödyntää kaikkialla urasuunnittelupalvelussa.

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

Quantifying insufficient coping behavior under chronic stress. A cross-cultural study of 1,303 students from Italy, Spain, and Argentina.

The question of how to quantify insufficient coping behavior under chronic stress is of major clinical relevance. In fact, chronic stress increasingly dominates modern work conditions and can affect nearly every system of the human body, as suggested by physical, cognitive, affective and behavioral symptoms. Since freshmen students experience constantly high levels of stress due to tight schedules and frequent examinations, we carried out a 3-center study of 1,303 students from Italy, Spain and Argentina in order to develop socioculturally independent means for quantifying coping behavior. The data analysis relied on 2 self-report questionnaires: the Coping Strategies Inventory (COPE) for the assessment of coping behavior and the Zurich Health Questionnaire which assesses consumption behavior and general health dimensions. A neural network approach was used to determine the structural properties inherent in the COPE instrument. Our analyses revealed 2 highly stable, socioculturally independent scales that reflected basic coping behavior in terms of the personality traits activity-passivity and defeatism-resilience. This replicated previous results based on Swiss and US-American data. The percentage of students exhibiting insufficient coping behavior was very similar across the study sites (11.5-18.0%). Given their stability and validity, the newly developed scales enable the quantification of basic coping behavior in a cost-efficient and reliable way, thus clearing the way for the early detection of subjects with insufficient coping skills under chronic stress who may be at risk of physical or mental health problems.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Sleep in vitro : Erk pathway regulates sleep duration and sleep related genes

To date, for most biological and physiological phenomena, the scientific community has reach a consensus on their related function, except for sleep, which has an undetermined, albeit mystery, function. To further our understanding of sleep function(s), we first focused on the level of complexity at which sleep-like phenomenon can be observed. This lead to the development of an in vitro model. The second approach was to understand the molecular and cellular pathways regulating sleep and wakefulness, using both our in vitro and in vivo models. The third approach (ongoing) is to look across evolution when sleep or wakefulness appears. (1) To address the question as to whether sleep is a cellular property and how this is linked to the entire brain functioning, we developed a model of sleep in vitro by using dissociated primary cortical cultures. We aimed at simulating the major characteristics of sleep and wakefulness in vitro. We have shown that mature cortical cultures display a spontaneous electrical activity similar to sleep. When these cultures are stimulated by waking neurotransmitters, they show a tonic firing activity, similar to wakefulness, but return spontaneously to the "sleep-like" state 24h after stimulation. We have also shown that transcriptional, electrophysiological, and metabolic correlates of sleep and wakefulness can be reliably detected in dissociated cortical cultures. (2) To further understand at which molecular and cellular levels changes between sleep and wakefulness occur, we have used a pharmacological and systematic gene transcription approach in vitro and discovered a major role played by the Erk pathway. Indeed, pharmacological inhibition of this pathway in living animals decreased sleep by 2 hours per day and consolidated both sleep and wakefulness by reducing their fragmentation. (3) Finally, we tried to evaluate the presence of sleep in one of the most primitive species with a neural network. We set up Hydra as a model organism. We hypothesized that sleep as a cellular (neuronal) property may occur with the appearance of the most primitive nervous system. We were able to show that Hydra have periodic rest phases amounting to up to 5 hours per day. In conclusion, our work established an in vitro model to study sleep, discovered one of the major signaling pathways regulating vigilance states, and strongly suggests that sleep is a cellular property highly conserved at the molecular level during evolution. -- Jusqu'à ce jour, la communauté scientifique s'est mise d'accord sur la fonction d'une majorité des processus physiologiques, excepté pour le sommeil. En effet, la fonction du sommeil reste un mystère, et aucun consensus n'est atteint le concernant. Pour mieux comprendre la ou les fonctions du sommeil, (1) nous nous sommes d'abord concentré sur le niveau de complexité auquel un état ressemblant au sommeil peut être observé. Nous avons ainsi développé un modèle du sommeil in vitro, (2) nous avons disséqué les mécanismes moléculaires et cellulaires qui pourraient réguler le sommeil, (3) nous avons cherché à savoir si un état de sommeil peut être trouvé dans l'hydre, l'animal le plus primitif avec un système nerveux. (1) Pour répondre à la question de savoir à quel niveau de complexité apparaît un état de sommeil ou d'éveil, nous avons développé un modèle du sommeil, en utilisant des cellules dissociées de cortex. Nous avons essayé de reproduire les corrélats du sommeil et de l'éveil in vitro. Pour ce faire, nous avons développé des cultures qui montrent les signes électrophysiologiques du sommeil, puis quand stimulées chimiquement passent à un état proche de l'éveil et retournent dans un état de sommeil 24 heures après la stimulation. Notre modèle n'est pas parfait, mais nous avons montré que nous pouvions obtenir les corrélats électrophysiologiques, transcriptionnels et métaboliques du sommeil dans des cellules corticales dissociées. (2) Pour mieux comprendre ce qui se passe au niveau moléculaire et cellulaire durant les différents états de vigilance, nous avons utilisé ce modèle in vitro pour disséquer les différentes voies de signalisation moléculaire. Nous avons donc bloqué pharmacologiquement les voies majeures. Nous avons mis en évidence la voie Erkl/2 qui joue un rôle majeur dans la régulation du sommeil et dans la transcription des gènes qui corrèlent avec le cycle veille-sommeil. En effet, l'inhibition pharmacologique de cette voie chez la souris diminue de 2 heures la quantité du sommeil journalier et consolide l'éveil et le sommeil en diminuant leur fragmentation. (3) Finalement, nous avons cherché la présence du sommeil chez l'Hydre. Pour cela, nous avons étudié le comportement de l'Hydre pendant 24-48h et montrons que des périodes d'inactivité, semblable au sommeil, sont présentes dans cette espèce primitive. L'ensemble de ces travaux indique que le sommeil est une propriété cellulaire, présent chez tout animal avec un système nerveux et régulé par une voie de signalisation phylogénétiquement conservée.

Estudo do estado fundamental de aglomerados de silício via redes neurais

We introduce a global optimization method based on the cooperation between an Artificial Neural Net (ANN) and Genetic Algorithm (GA). We have used ANN to select the initial population for the GA. We have tested the new method to predict the ground-state geometry of silicon clusters. We have described the clusters as a piling of plane structures. We have trained three ANN architectures and compared their results with those of pure GA. ANN strongly reduces the total computational time. For Si10, it gained a factor of 5 in search speed. This method can be easily extended to other optimization problems.

Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Aplicação de métodos de calibração multivariada para a determinação simultânea de riboflavina (VB2), tiamina (VB1), piridoxina (VB6) e nicotinamida (VPP)

In this work, the artificial neural networks (ANN) and partial least squares (PLS) regression were applied to UV spectral data for quantitative determination of thiamin hydrochloride (VB1), riboflavin phosphate (VB2), pyridoxine hydrochloride (VB6) and nicotinamide (VPP) in pharmaceutical samples. For calibration purposes, commercial samples in 0.2 mol L-1 acetate buffer (pH 4.0) were employed as standards. The concentration ranges used in the calibration step were: 0.1 - 7.5 mg L-1 for VB1, 0.1 - 3.0 mg L-1 for VB2, 0.1 - 3.0 mg L-1 for VB6 and 0.4 - 30.0 mg L-1 for VPP. From the results it is possible to verify that both methods can be successfully applied for these determinations. The similar error values were obtained by using neural network or PLS methods. The proposed methodology is simple, rapid and can be easily used in quality control laboratories.

A tabela periódica dos elementos químicos prevista por redes neurais artificiais de Kohonen

Although several chemical elements were not known by end of the 18th century, Mendeleyev came up with an astonishing achievement: the periodic table of elements. He was not only able to predict the existence of (then) new elements but also to provide accurate estimates of their chemical and physical properties. This is certainly a relevant example of the human intelligence. Here, we intend to shed some light on the following question: Can an artificial intelligence system yield a classification of the elements that resembles, in some sense, the periodic table? To achieve our goal, we have fed a self-organized map (SOM) with information available at Mendeleyev's time. Our results show that similar elements tend to form individual clusters. Thus, SOM generates clusters of halogens, alkaline metals and transition metals that show a similarity with the periodic table of elements.

Aplicação de redes neurais artificiais na identificação de gasolinas adulteradas comercializadas na região de Londrina - Paraná

The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance nº 309 of the Agência Nacional do Petróleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

Retrieval of kinetic rates in reactions with semi batch liquid phase using ill-posed inverse problem theory

A neural network procedure to solve inverse chemical kinetic problems is discussed in this work. Rate constants are calculated from the product concentration of an irreversible consecutive reaction: the hydrogenation of Citral molecule, a process with industrial interest. Simulated and experimental data are considered. Errors in the simulated data, up to 7% in the concentrations, were assumed to investigate the robustness of the inverse procedure. Also, the proposed method is compared with two common methods in nonlinear analysis; the Simplex and Levenberg-Marquardt approaches. In all situations investigated, the neural network approach was numerically stable and robust with respect to deviations in the initial conditions or experimental noises.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

Determinação de constituintes químicos em madeira de eucalipto por Pi-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte

Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.