Modelling, control and testing of internal combustion engines to minimize consumption and pollutant emissions

The objective of the PhD thesis was to research technologies and strategies to reduce fuel consumption and pollutants emission produced by internal combustion engines. In order to meet this objective my activity was focused on the research of advanced controls based on cylinder pressure feedback. These types of control strategies were studied because they present promising results in terms of engine efficiency enhancement. In the PhD dissertation two study cases are presented. The first case is relative to a control strategy to be used at the test bench for the optimisation of the spark advance calibration of motorcycle Engine. The second case is relative to a control strategy to be used directly on board of mining engines with the objective or reducing the engine consumption and correct ageing effects. In both cases the strategies proved to be effective but their implementation required the use of specific toolchains for the measure of the cylinder pressure feedback that for a matter of cost makes feasible the strategy use only for applications: • At test bench • In small-markets like large off-road engines The major bottleneck that prevents the implementation of these strategies on mass production is the cost of cylinder pressure sensor. In order to tackle this issue, during the PhD research, the development of a low-cost sensor for the estimation of cylinder pressure was studied. The prototype was a piezo-electric washer designed to replace the standard spark-plug washer or high-pressure fuel injectors gasket. From the data analysis emerged the possibility to use the piezo-electric prototype signal to evaluate with accuracy several combustion metrics compatible for the implementation of advanced control strategies in on-board applications. Overall, the research shows that advanced combustion controls are feasible and beneficial, not only at the test bench or on stationary engines, but also in mass-produced engines.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Development and testing of a safety module for diagnostics and protection from hazards in secondary woodworking machinery

The main theme covered by this dissertation is safety, set in the context of automatic machinery for secondary woodworking. The thesis describes in detail the project of a software module for CNC machining centers to protect the operator against hazards and to report errors in the machine safety management. Its design has been developed during an internship at SCM Group technical department. The development of the safety module is addressed step by step in a detailed way: first the company and the reference framework are introduced and then all the design choices are explained and justified. The discussion begins with a detailed analysis of the standards concerning woodworking machines and safety-related software. In this way, a clear and linear procedure can be established to develop and implement the internal structure of the module, its interface, and its application to specific safety-critical conditions. Afterwards, particular attention is paid to software testing, with the development of a comprehensive test procedure for the module, and to diagnostics, especially oriented towards signal management in IoT mode. Finally, the safety module is used as an anti-regression tool to initiate a design improvement of the machine control program. The refactoring steps performed in the process are explained in detail and the SCENT approach is introduced to test the result.

Development and testing of a virtual energy meter using the Labview Environment

The quantity of electric energy utilized by a home, a business, or an electrically powered device is measured by an electricity meter, also known as an electric meter, electrical meter, or energy meter. Electric meters located at customers' locations are used by electric providers for billing. They are usually calibrated in billing units, with the kilowatt hour being the most popular (kWh). Typically, they are read once each billing cycle. When energy savings are sought during specific times, some meters may monitor demand, or the highest amount of electricity used during a specific time. Additionally, some meters feature relays for load shedding in response to responses during periods of peak load. The amount of electrical energy consumed by users is measured by a Watt-hour meter, also known as an energy meter. To charge the electricity usage by loads like lights, fans, and other appliances, utilities put these gadgets everywhere, including in households, businesses, and organizations. Watts are a fundamental power unit. A kilowatt is equal to one thousand watts. One kilowatt is regarded as one unit of energy used if used for one hour. These meters calculate the product of the instantaneous voltage and current readings and provide instantaneous power. This power is distributed over a period and is used during that time. Depending on the supply used by home or commercial installations, these may be single or three phase meters. These can be linked directly between line and load for minor service measurements, such as home consumers. However, step-down current transformers must be installed for greater loads to handle their higher current demands.

Modelo de data mining para deteção de embolias pulmonares

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Effect of repeated restraint stress on memory in different tasks

The present study investigated the effect of repeated stress applied to female rats on memory evaluated by three behavioral tasks: two-way shuttle avoidance, inhibitory avoidance and habituation to an open field. Repeated stress had different effects on rat behavior when different tasks were considered. In the two-way active avoidance test the stressed animals presented memory of the task, but their memory scores were impaired when compared to all other groups. In the habituation to the open field, only the control group showed a significant difference in the number of rearings between training and testing sessions, which is interpreted as an adequate memory of the task. In the handled and chronically stressed animals, on the other hand, no memory was observed, suggesting that even a very mild repeated stress would be enough to alter habituation to this task. The performance in the inhibitory avoidance task presented no significant differences between groups. The findings suggest that repeated restraint stress might induce cognitive impairments that are dependent on the task and on stress intensity.

Behavioral characteristics of the offspring of adolescent rats

The aim of this study was to test the hypothesis that, during adulthood, the offspring of adolescent rats differ in emotionality, learning and memory from the offspring of adult rats. The behavior of the offspring of adolescent (age, 50-55 days) and adult rats (age, 90-95 days) was tested in the open field, activity cage, and passive and active avoidance apparatus. The latencies during training and testing in the passive avoidance apparatus of the offspring of adolescent parents were shorter than the latencies of control offspring (P<0.001 on both training and testing days). Offspring of adolescent parents showed shorter latency time in acquisition trials during active avoidance testing compared to control offspring (P<0.001). They also showed a higher number of active avoidance responses in the last four blocks of acquisition (P<0.001) and first two blocks of extinction trials (P<0.05 and P<0.001, respectively). The offspring of adolescent parents showed higher latency on the first day of testing in the open field (P<0.01) and a lower latency on the third day of testing (P<0.01). They also showed higher activity during all three days of testing (1st and 2nd day: P<0.01; 3rd day: P<0.05). The spontaneous activity of the offspring of adolescent parents in the activity cage was higher in the last three intervals of testing (P<0.001). In summary, the offspring of adolescent parents were less anxious and tended to be more active. The results of two learning and memory tests were opposite, but could be explained by a higher exploratory drive of the offspring of adolescent parents. This was probably due to chronic malnutrition stress and the disturbed mother-infant relationship in the litters of adolescent mothers.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Wood species identification by visual characterization of fibers

Kandidaatintyö tehtiin osana PulpVision-tutkimusprojektia, jonka tarkoituksena on kehittää kuvapohjaisia laskenta- ja luokittelumetodeja sellun laaduntarkkailuun paperin valmistuksessa. Tämän tutkimusprojektin osana on aiemmin kehitetty metodi, jolla etsittiin kaarevia rakenteita kuvista, ja tätä metodia hyödynnettiin kuitujen etsintään kuvista. Tätä metodia käytettiin lähtökohtana kandidaatintyölle. Työn tarkoituksena oli tutkia, voidaanko erilaisista kuitukuvista laskettujen piirteiden avulla tunnistaa kuvassa olevien kuitujen laji. Näissä kuitukuvissa oli kuituja neljästä eri puulajista ja yhdestä kasvista. Nämä lajit olivat akasia, koivu, mänty, eukalyptus ja vehnä. Jokaisesta lajista valittiin 100 kuitukuvaa ja nämä kuvat jaettiin kahteen ryhmään, joista ensimmäistä käytettiin opetusryhmänä ja toista testausryhmänä. Opetusryhmän avulla jokaiselle kuitulajille laskettiin näitä kuvaavia piirteitä, joiden avulla pyrittiin tunnistamaan testausryhmän kuvissa olevat kuitulajit. Nämä kuvat oli tuottanut CEMIS-Oulu (Center for Measurement and Information Systems), joka on mittaustekniikkaan keskittynyt yksikkö Oulun yliopistossa. Yksittäiselle opetusryhmän kuitukuvalle laskettiin keskiarvot ja keskihajonnat kolmesta eri piirteestä, jotka olivat pituus, leveys ja kaarevuus. Lisäksi laskettiin, kuinka monta kuitua kuvasta löydettiin. Näiden piirteiden eri yhdistelmien avulla testattiin tunnistamisen tarkkuutta käyttämällä k:n lähimmän naapurin menetelmää ja Naiivi Bayes -luokitinta testausryhmän kuville. Testeistä saatiin lupaavia tuloksia muun muassa pituuden ja leveyden keskiarvoja käytettäessä saavutettiin jopa noin 98 %:n tarkkuus molemmilla algoritmeilla. Tunnistuksessa kuitujen keskimäärinen pituus vaikutti olevan kuitukuvia parhaiten kuvaava piirre. Käytettyjen algoritmien välillä ei ollut suurta vaihtelua tarkkuudessa. Testeissä saatujen tulosten perusteella voidaan todeta, että kuitukuvien tunnistaminen on mahdollista. Testien perusteella kuitukuvista tarvitsee laskea vain kaksi piirrettä, joilla kuidut voidaan tunnistaa tarkasti. Käytetyt lajittelualgoritmit olivat hyvin yksinkertaisia, mutta ne toimivat testeissä hyvin.

Image classification for a large number of object categories

L'increment de bases de dades que cada vegada contenen imatges més difícils i amb un nombre més elevat de categories, està forçant el desenvolupament de tècniques de representació d'imatges que siguin discriminatives quan es vol treballar amb múltiples classes i d'algorismes que siguin eficients en l'aprenentatge i classificació. Aquesta tesi explora el problema de classificar les imatges segons l'objecte que contenen quan es disposa d'un gran nombre de categories. Primerament s'investiga com un sistema híbrid format per un model generatiu i un model discriminatiu pot beneficiar la tasca de classificació d'imatges on el nivell d'anotació humà sigui mínim. Per aquesta tasca introduïm un nou vocabulari utilitzant una representació densa de descriptors color-SIFT, i desprès s'investiga com els diferents paràmetres afecten la classificació final. Tot seguit es proposa un mètode par tal d'incorporar informació espacial amb el sistema híbrid, mostrant que la informació de context es de gran ajuda per la classificació d'imatges. Desprès introduïm un nou descriptor de forma que representa la imatge segons la seva forma local i la seva forma espacial, tot junt amb un kernel que incorpora aquesta informació espacial en forma piramidal. La forma es representada per un vector compacte obtenint un descriptor molt adequat per ésser utilitzat amb algorismes d'aprenentatge amb kernels. Els experiments realitzats postren que aquesta informació de forma te uns resultats semblants (i a vegades millors) als descriptors basats en aparença. També s'investiga com diferents característiques es poden combinar per ésser utilitzades en la classificació d'imatges i es mostra com el descriptor de forma proposat juntament amb un descriptor d'aparença millora substancialment la classificació. Finalment es descriu un algoritme que detecta les regions d'interès automàticament durant l'entrenament i la classificació. Això proporciona un mètode per inhibir el fons de la imatge i afegeix invariança a la posició dels objectes dins les imatges. S'ensenya que la forma i l'aparença sobre aquesta regió d'interès i utilitzant els classificadors random forests millora la classificació i el temps computacional. Es comparen els postres resultats amb resultats de la literatura utilitzant les mateixes bases de dades que els autors Aixa com els mateixos protocols d'aprenentatge i classificació. Es veu com totes les innovacions introduïdes incrementen la classificació final de les imatges.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Funcionalidade em pacientes após acidente vascular encefálico: relação com o sono e ritmo de atividade-repouso

It is known that sleep plays an important role in the process of motor learning. Recent studies have shown that the presence of sleep between training a motor task and retention test promotes a learning task so than the presence of only awake between training and testing. These findings also have been reported in stroke patients, however, there are few studies that investigate the results of this relationship on the functionality itself in this population. The objective of this study was to evaluate the relationship between functionality and sleep in patients in the chronic stage of stroke. A cross-sectional observational study was conducted. The sample was composed of 30 stroke individuals in chronic phase, between 6 and 60 months after injury and aged between 55 and 75 years. The volunteers were initially evaluated for clinical data of disease and personal history, severity of stroke, through the National Institute of Health Stroke Scale, and mental status, the Mini-Mental State Examination. Sleep assessment tools were Pittsburgh Sleep Quality Index, the Questionnaire of Horne and Ostberg, Epworth Sleepiness Scale, the Berlin questionnaire and actigraphy, which measures were: real time of sleep, waking after sleep onset, percentage of waking after sleep onset, sleep efficiency, sleep latency, sleep fragmentation index, mean activity score. Other actigraphy measures were intraday variability, stability interdiária, a 5-hour period with minimum level of activity (L5) and 10-hour period with maximum activity (M10), obtained to evaluate the activity-rest rhythm. The Functional Independence Measure (FIM) and the Berg Balance Scale (BBS) were the instruments used to evaluate the functional status of participants. The Spearman correlation coefficient and comparison tests (Student's t and Mann-Whitney) were used to analyze the relationship of sleep assessment tools and rest-activity rhythm to measures of functional assessment. The SPSS 16.0 was used for analysis, adopting a significance level of 5%. The main results observed were a negative correlation between sleepiness and balance and a negative correlation between the level of activity (M10) and sleep fragmentation. No measurement of sleep or rhythm was associated with functional independence measure. These findings suggest that there may be an association between sleepiness and xii balance in patients in the chronic stage of stroke, and that obtaining a higher level of activity may be associated with a better sleep pattern and rhythm more stable and less fragmented. Future studies should evaluate the cause-effect relationship between these parameters

How narrow is the spectrum of submaximal speeds in swimming?

The purpose of this study was to identify the boundary of submaximal speed zones (i.e., exercise intensity domains) between maximal aerobic speed (S-400) and lactate threshold (LT) in swimming. A 400-m all-out test, a 7 × 200 m incremental step test, and two to four 30-minute submaximal tests were performed by 12 male endurance swimmers (age = 24.5 ± 9.6 years; body mass = 71.3 ± 9.8 kg) to determine S-400, speed corresponding to LT, and maximal lactate steady state (MLSS). S-400 was 1.30 ± 0.09 m·s -1 (400 m-5:08 minutes:seconds). The speed at LT (1.08 ± 0.02 m·s-1; 83.1 ± 2.2 %S-400) was lower than the speed at MLSS (1.14 ± 0.02 m·s-1; 87.5 ± 1.9 %S-400). Maximal lactate steady state occurred at 26 ± 10% of the difference between the speed at LT and S-400. Mean blood lactate values at the speeds corresponding to LT and MLSS were 2.45 ± 1.13 mmol·L-1 and 4.30 ± 1.32 mmol·L-1, respectively. The present findings demonstrate that the range of intensity zones between LT and MLSS (i.e., heavy domain) and between MLSS and S-400 (i.e., severe domain) are very narrow in swimming with LT occurring at 83% S-400 in trained swimmers. Precision and sensitivity of the measurement of aerobic indexes (i.e., LT and MLSS) should be considered when conducting exercise training and testing in swimming. © 2013 National Strength and Conditioning Association.