Simulation of Active Control Using Fuzzy Logic Applied to a Pulse Combustor

This work analyzes an active fuzzy logic control system in a Rijke type pulse combustor. During the system development, a study of the existing types of control for pulse combustion was carried out and a simulation model was implemented to be used with the package Matlab and Simulink. Blocks which were not available in the simulator library were developed. A fuzzy controller was developed and its membership functions and inference rules were established. The obtained simulation showed that fuzzy logic is viable in the control of combustion instabilities. The obtained results indicated that the control system responded to pulses in an efficient and desirable way. It was verified that the system needed approximately 0.2 s to increase the tube internal pressure from 30 to 90 mbar, with an assumed total delay of 2 ms. The effects of delay variation were studied. Convergence was always obtained and general performance was not affected by the delay. The controller sends a pressure signal in phase with the Rijke tube internal pressure signal, through the speakers, when an increase the oscillations pressure amplitude is desired. On the other hand, when a decrease of the tube internal pressure amplitude is desired, the controller sends a signal 180º out of phase.

Sistema de classificação fuzzy para o risco de infestação por plantas daninhas considerando a sua variabilidade espacial

Este artigo trata do problema de classificação do risco de infestação por plantas daninhas usando técnicas geoestatísticas, análise de imagens e modelos de classificação fuzzy. Os principais atributos utilizados para descrever a infestação incluem a densidade de sementes, bem como a sua extensão, a cobertura foliar e a agressividade das plantas daninhas em cada região. A densidade de sementes reflete a produção de sementes por unidade de área, e a sua extensão, a influência das sementes vizinhas; a cobertura foliar indica a extensão dos agrupamentos das plantas daninhas emergentes; e a agressividade descreve a porcentagem de ocupação de espécies com alta capacidade de produção de sementes. Os dados da densidade de sementes, da cobertura foliar e da agressividade para as diferentes regiões são obtidos a partir de simulação com modelos matemáticos de populações. Neste artigo propõe-se um sistema de classificação fuzzy utilizando os atributos descritos para inferir os riscos de infestação de regiões da cultura por plantas daninhas. Resultados de simulação são apresentados para ilustrar o uso desse sistema na aplicação localizada de herbicida.

Fuzzy ontology for knowledge mobilisation and decision support

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available information systems are not capable of handling imprecise data, even though it can offer various advantages. Expert knowledge stored as linguistic expressions is a good example of imprecise but valuable data, i.e. data that is hard to exactly pinpoint to a definitive value. There is an obvious concern among organisations on how this problem should be handled; finding new methods for processing and storing imprecise data are therefore a key issue. Additionally, it is equally important to show that tacit knowledge and imprecise data can be used with success, which encourages organisations to analyse their imprecise data. The objective of the research conducted was therefore to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. The thesis introduces both practical and theoretical advances on how fuzzy logic, ontologies (fuzzy ontologies) and OWA operators can be utilized for different decision making problems. It is demonstrated how a fuzzy ontology can model tacit knowledge which was collected from wine connoisseurs. The approach can be generalised and applied also to other practically important problems, such as intrusion detection. Additionally, a fuzzy ontology is applied in a novel consensus model for group decision making. By combining the fuzzy ontology with Semantic Web affiliated techniques novel applications have been designed. These applications show how the mobilisation of knowledge can successfully utilize also imprecise data. An important part of decision making processes is undeniably aggregation, which in combination with a fuzzy ontology provides a promising basis for demonstrating the benefits that one can retrieve from handling imprecise data. The new aggregation operators defined in the thesis often provide new possibilities to handle imprecision and expert opinions. This is demonstrated through both theoretical examples and practical implementations. This thesis shows the benefits of utilizing all the available data one possess, including imprecise data. By combining the concept of fuzzy ontology with the Semantic Web movement, it aspires to show the corporate world and industry the benefits of embracing fuzzy ontologies and imprecision.

De la sanidad del cuerpo a la sanidad del alma: estudio sobre la lógica de construcción de las identidades colectivas en el neo-pentecostalismo argentino

No presente artigo, o autor se propõe a estudar em grandes traços a lógica de construção das identidades coletivas que se desenvolvem dentro dos grupos evangélicos neo- pentecostais. Para isso analisa a síntese entre religião, psicanálise e auto-ajuda que expressa o discurso de "sanidade interior", desenvolvido no marco de uma igreja evangélica, localizada em um setor de altos ingressos na Cidade de Buenos Aires, Argentina. A teoria da hegemonia de Ernesto Laclau lhe permite analisar o lugar que os significantes flutuantes ocupam no trabalho religioso de construir uma identidade ampla que se nutre de diferentes universos simbólicos no momento de interpelar os sujeitos a partir da teologia da sanidade.

Facilitation of the main generator source of earthworm muscle contraction by a peripheral neuron

A constant facilitation of responses evoked in the earthworm muscle contraction generator neurons by responses evoked in the neurons of its peripheral nervous system was demonstrated. It is based on the proposal that these two responses are bifurcations of an afferent response evoked by the same peripheral mechanical stimulus but converging again on this central neuron. A single-peaked generator response without facilitation was demonstrated by sectioning the afferent route of the peripheral facilitatory modulatory response, or conditioning response (CR). The multipeaked response could be restored by restimulating the sectioned modulatory neuron with an intracellular substitutive conditioning stimulus (SCS). These multi-peaked responses were proposed to be the result of reverberating the original single peaked unconditioned response (UR) through a parallel (P) neuronal circuit which receives the facilitation of the peripheral modulatory neuron. This peripheral modulatory neuron was named "Peri-Kästchen" (PK) neuron because it has about 20 peripheral processes distributed on the surface of a Kästchen of longitudinal muscle cells on the body wall of this preparation as revealed by the Lucifer Yellow-CH-filling method.

Persistent attenuation and enhancement of the earthworm main muscle contraction generator response induced by repeated stimulation of a peripheral neuron

Responses evoked in the earthworm, Amynthas hawayanus, main muscle contraction generator M-2 (postsynaptic mechanical-stimulus-sensitive) neuron by threshold mechanical stimuli in 2-s intertrial intervals (ITI) were used as the control or unconditioned responses (UR). Their attenuation induced by decreasing these intervals in non-associative conditioning and their enhancement induced by associating the unconditioned stimuli (US) to a train of short (0.1 s) hyperpolarizing electrical substitutive conditioning stimuli (SCS) in the Peri-Kästchen (PK) neuron were measured in four parameters, i.e., peak numbers (N) and amplitude ()averaged from 120 responses, sum of these amplitudes (SAMP) and the highest peak amplitude (V) over a period of 4 min. Persistent attenuation similar to habituation was induced by decreasing the control ITI to 0.5 s and 2.0 s in non-associative conditioning within less than 4 min. Dishabituation was induced by randomly pairing one of these habituated US to an electrical stimulus in the PK neuron. All four parameters of the UR were enhanced by forward (SCS-US), but not backward (US-SCS), association of the US with 25, 100 and 250-Hz trains of SCS with 40-ms interstimulus intervals (ISI) for 4 min and persisted for another 4 min after turning off the SCS. The enhancement of these parameters was proportional to the SCS frequencies in the train. No UR was evoked by the SCS when the US was turned off after 4 min of classical conditioning.

Dietary sodium intake induced myenteric neuron hypertrophy in Wistar rats

In the present study we investigated the effect of salt intake on myenteric neuron size of the colon of adult male Wistar rats. The animals were placed on either a high-salt (HS; 8%; 12 animals) or a low-salt diet (LS; 0.15%; 12 animals) for 15 or 52 weeks and blood pressure was measured. The sizes of myenteric neurons of the distal colon from both groups were measured. No difference in neuron size was observed between the HS and LS groups after 15 weeks. After 52 weeks on HS, neuron size was increased (P<0.005) when compared with the LS group. The rats also presented hypertension, which was significantly different at 52 weeks (142 ± 11 vs 119 ± 7 mmHg). These results suggest that a long time on an HS diet can significantly increase myenteric nerve cell size.

Fuzzy tolerance/equivalence relations from intersection, union and complement operators and their application to a clustering method

This master thesis work introduces the fuzzy tolerance/equivalence relation and its application in cluster analysis. The work presents about the construction of fuzzy equivalence relations using increasing generators. Here, we investigate and research on the role of increasing generators for the creation of intersection, union and complement operators. The objective is to develop different varieties of fuzzy tolerance/equivalence relations using different varieties of increasing generators. At last, we perform a comparative study with these developed varieties of fuzzy tolerance/equivalence relations in their application to a clustering method.

Use of neuron-specific enolase for assessing the severity and outcome of neurological disorders in patients

Neuron-specific enolase (NSE) is a glycolytic enzyme present almost exclusively in neurons and neuroendocrine cells. NSE levels in cerebrospinal fluid (CSF) are assumed to be useful to estimate neuronal injury and clinical outcome of patients with serious clinical manifestations such as those observed in stroke, head injury, anoxic encephalopathy, encephalitis, brain metastasis, and status epilepticus. We compared levels of NSE in serum (sNSE) and in CSF (cNSE) among four groups: patients with meningitis (N = 11), patients with encephalic injuries associated with impairment of consciousness (ENC, N = 7), patients with neurocysticercosis (N = 25), and normal subjects (N = 8). Albumin was determined in serum and CSF samples, and the albumin quotient was used to estimate blood-brain barrier permeability. The Glasgow Coma Scale score was calculated at the time of lumbar puncture and the Glasgow Outcome Scale (GOS) score was calculated at the time of patient discharge or death. The ENC group had significantly higher cNSE (P = 0.01) and albumin quotient (P = 0.005), but not sNSE (P = 0.14), levels than the other groups (Kruskal-Wallis test). Patients with lower GOS scores had higher cNSE levels (P = 0.035) than patients with favorable outcomes. Our findings indicate that sNSE is not sensitive enough to detect neuronal damage, but cNSE seems to be reliable for assessing patients with considerable neurological insult and cases with adverse outcome. However, one should be cautious about estimating the severity of neurological status as well as outcome based exclusively on cNSE in a single patient.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Neo-Timm staining in the thalamus of chronically epileptic rats

The thalamus is an important modulator of seizures and is severely affected in cholinergic models of epilepsy. In the present study, chronically epileptic rats had their brains processed for neo-Timm and acetylcholinesterase two months after the induction of status epilepticus with pilocarpine. Both controls and pilocarpine-treated animals presented neo-Timm staining in the anterodorsal nucleus, laterodorsal nucleus, reticular nucleus, most intralaminar nuclei, nucleus reuniens, and rhomboid nucleus of the thalamus, as well as in the zona incerta. The intensity of neo-Timm staining was similar in control and pilocarpine-treated rats, except for the nucleus reuniens and the rhomboid nucleus, which had a lower intensity of staining in the epileptic group. In animal models of temporal lobe epilepsy, zinc seems to modulate glutamate release and to decrease seizure activity. In this context, a reduction of neo-Timm-stained terminals in the midline thalamus could ultimately result in an increased excitatory activity, not only within its related nuclei, but also in anatomical structures that receive their efferent connections. This might contribute to the pathological substrate observed in chronic pilocarpine-treated epileptic animals.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Fuzzy real option analysis in patent related decision making and patent valuation

The shift towards a knowledge-based economy has inevitably prompted the evolution of patent exploitation. Nowadays, patent is more than just a prevention tool for a company to block its competitors from developing rival technologies, but lies at the very heart of its strategy for value creation and is therefore strategically exploited for economic pro t and competitive advantage. Along with the evolution of patent exploitation, the demand for reliable and systematic patent valuation has also reached an unprecedented level. However, most of the quantitative approaches in use to assess patent could arguably fall into four categories and they are based solely on the conventional discounted cash flow analysis, whose usability and reliability in the context of patent valuation are greatly limited by five practical issues: the market illiquidity, the poor data availability, discriminatory cash-flow estimations, and its incapability to account for changing risk and managerial flexibility. This dissertation attempts to overcome these impeding barriers by rationalizing the use of two techniques, namely fuzzy set theory (aiming at the first three issues) and real option analysis (aiming at the last two). It commences with an investigation into the nature of the uncertainties inherent in patent cash flow estimation and claims that two levels of uncertainties must be properly accounted for. Further investigation reveals that both levels of uncertainties fall under the categorization of subjective uncertainty, which differs from objective uncertainty originating from inherent randomness in that uncertainties labelled as subjective are highly related to the behavioural aspects of decision making and are usually witnessed whenever human judgement, evaluation or reasoning is crucial to the system under consideration and there exists a lack of complete knowledge on its variables. Having clarified their nature, the application of fuzzy set theory in modelling patent-related uncertain quantities is effortlessly justified. The application of real option analysis to patent valuation is prompted by the fact that both patent application process and the subsequent patent exploitation (or commercialization) are subject to a wide range of decisions at multiple successive stages. In other words, both patent applicants and patentees are faced with a large variety of courses of action as to how their patent applications and granted patents can be managed. Since they have the right to run their projects actively, this flexibility has value and thus must be properly accounted for. Accordingly, an explicit identification of the types of managerial flexibility inherent in patent-related decision making problems and in patent valuation, and a discussion on how they could be interpreted in terms of real options are provided in this dissertation. Additionally, the use of the proposed techniques in practical applications is demonstrated by three fuzzy real option analysis based models. In particular, the pay-of method and the extended fuzzy Black-Scholes model are employed to investigate the profitability of a patent application project for a new process for the preparation of a gypsum-fibre composite and to justify the subsequent patent commercialization decision, respectively; a fuzzy binomial model is designed to reveal the economic potential of a patent licensing opportunity.