The advances and the development of vehicles and autobalance robots make necessary the investigation of controllers able to meet the various challenges related to the use of these systems. The focus of this work is to study the equilibrium and position control of one two-wheeled robot. The particular interest in this application comes from its structure and its rich physical dynamics. Since this is a complex and non trivial problem, there is great interest in to analyze intelligent controllers. The first part of this dissertation discusses the development of a classic PID controller. Then it is compared with two types of intelligent controllers: On-line Neural Fuzzy Control (ONFC) and Proportional-Integral-Derivative Neural-Network (PID-NN). Also it is presented the implementation of controllers in a hadware plataform using the LEGO Mindstorm kit and in a simulation plataform using the MATLAB-Simulink. Two case studies are developed. The first one investigates the control of equilibrium and position of two-wheeled robot on a flat terrain to observe the intrinsec performance in lack of external factors. The second case studies the equilibrium and position control of the robot in irregular terrains to investigate the system response under influence of hard conditions in its environment. Finally, the performance of each controller developed is discussed and competitive results in the control of two-wheeled robot are achieved. / Com o avanço no desenvolvimento e utilização de veículos e robôs autoequilibrantes, faz-se necessário a investigação de controladores capazes de atender os diversos desafios relacionados à utilização desses sistemas. Neste trabalho foi estudado o controle de equilíbrio e posição de um robô auto-equilibrante de duas rodas. O interesse particular nesta aplicação vem da sua estrutura e da riqueza de sua dinâmica física. Por ser um problema complexo e não trivial há grande interesse em avaliar os controladores inteligentes. A primeira parte da dissertação aborda o desenvolvimento de um controle clássico do tipo PID, para em seguida ser comparado com a implementação de dois tipos de controladores inteligentes: On-line Neuro Fuzzy Control (ONFC) e Proportional-Integral-Derivative Neural-Network (PIDNN). Também é apresentada a implementação dos controladores em uma plataforma de hardware, utilizando o kit LEGO Mindstorm, e numa plataforma de simulação utilizando o MATLAB-Simulink. Em seguida, dois estudos de casos são desenvolvidos visando comparar o desempenho dos controladores. O primeiro caso avalia o controle de equilíbrio e posição do robô auto-equilibrante de duas rodas sobre um terreno plano tendo como interesse observar o desempenho intrínseco do sistema sob ausência de fatores externos. O segundo caso estuda o controle de equilíbrio e posição do robô em terrenos irregulares visando investigar a resposta do sistema sob influência de condições adversas em seu ambiente. Finalmente, o desempenho de cada um dos controladores desenvolvidos é discutido, verificando-se resultados competitivos no controle do robô auto-equilibrante de duas rodas.

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

The objective of this thesis is to investigate the flow conditions that lead to coarse grain entrainment at near incipient motion conditions. Herein, a new conceptual approach is proposed, which in addition to the magnitude of hydrodynamic force or flow power, takes into account the duration of the flow event. Two criteria for inception of grain entrainment, namely the critical impulse and critical energy concepts, are proposed and compared. These frameworks adopt a force or energy perspective, considering the momentum or energy transfer from each flow event to the particle respectively, to describe the phenomenon. A series of conducted mobile particle experiments, are analyzed to examine the validity of the proposed approaches. First a set of bench-top experiments incorporates an electromagnet which applies pulses of known magnitude and duration to a steel spherical particle in a controlled fashion, so as to identify the critical level for entrainment. The utility of the above criteria is also demonstrated for the case of entrainment by the action of turbulent flow, via analysis of a series of flume experiments, where both the history of hydrodynamic forces exerted on the particle as well as its response are recorded simultaneously. Statistical modeling of the distribution of impulses, as well as conditional excess impulses, is performed using distributions from Extreme Value Theory to effectively model the episodic nature of the occurrence of these events. For the examined uniform and low mobility flow conditions, a power law relationship is proposed for describing the magnitude and frequency of occurrence of the impulse events. The Weibull and exponential distributions provide a good fit for the time between particle entrainments. In addition to these statistical tools, a number of Adaptive Neuro-Fuzzy Inference Systems employing different input representations are used to learn the nonlinear dynamics of the system and perform statistical prediction. The performance of these models is assessed in terms of their broad validity, efficiency and forecast accuracy. Even though the impulse and energy criteria are deeply interrelated, the latter is shown to be advantageous with regard to its performance, applicability and extension ability. The effect of single or multiple highly energetic events carried by certain coherent flow structures (mainly strong sweep events) with regard to the particle response is also investigated. / Ph. D.

<p>U doktorskoj disertaciji rešavaju se sledeći problemi: problem opisivanja sistema masovnog opsluživanja kada teorija masovnog opsluživanja nailazi na ograničenja primene, problem predviđanja vremena čekanja, problem modelovanja odnosa na tržištu ekspres usluga kao izvora uticaja na redove čekanja, problem upravljanja brojem aktivnih kanala sistema masovnog opsluživanja i problem uticaja na subjektivno vreme čekanja. Primenom elemenata veštačke inteligencije i statističkih metoda razvijen je model za predviđanje parametra vremena čekanja u realnom vremenu pri jedinicama poštanske mreže za pružanje usluga korisnicima.</p> / <p>The dissertation provides answers to the following issues: the problem of describing the queueing system when the queueing theory encounters limitations in its use, predicting the waiting time, the problem of modeling relations in the market of express services as a source of influence on the queues, managing the number of active channels in the queueing systems and the impact on subjective waiting time. Through application of artificial intelligence and statistical methods, a model has been developed which in real time predicts the parameters of waiting time at the units of postal network that provide service to customers.</p>

Submitted by Luciclea Silva (luci@ufpa.br) on 2018-10-01T14:39:49Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Previsaodistorcaoharmonica.pdf: 4236129 bytes, checksum: bb47a1edb3151361639a5867d6c2c545 (MD5) / Approved for entry into archive by Luciclea Silva (luci@ufpa.br) on 2018-10-01T14:40:33Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Previsaodistorcaoharmonica.pdf: 4236129 bytes, checksum: bb47a1edb3151361639a5867d6c2c545 (MD5) / Made available in DSpace on 2018-10-01T14:40:33Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Previsaodistorcaoharmonica.pdf: 4236129 bytes, checksum: bb47a1edb3151361639a5867d6c2c545 (MD5) Previous issue date: 2018-07-11 / Este trabalho apresenta uma modelagem para DHTv%, DHTi% e harmônicos individuais utilizando previsões de um sistema ANFIS que aprende com dados medidos e prevê o comportamento da rede para valores futuros. Estas previsões podem ajudar a atender as normas nacionais de DHTv% estipuladas pela Agência Nacional de Energia Elétrica (ANEEL) através dos Procedimentos de Distribuição (PRODIST), como as normas internacionais de DHTi%., desta forma se antecipando às normas que atualmente são recomendativas, mas em um futuro próximo serão punitivas. A modelagem é realizada por meio de um sistema Neuro-Fuzzy denominado ANFIS, o qual utiliza rede neural para aprender o comportamento do sistema e ajuste dos parâmetros e regra Fuzzy para a determinação dos valores de saída do sistema levando em consideração o aprendizado da rede Neural. A grande vantagem desta ferramenta é o poder de se modelar padrões utilizando uma previsão de estado harmônico das cargas conectadas na baixa tensão, o que ajuda na criação de pseudomedidas para as redes de distribuição, onde é difícil e oneroso a obtenção de medições reais. Entre as aplicações práticas para esta ferramenta pode-se destacar a utilização dos valores previstos em substituição a valores anômalos medidos, a utilização em medidores de energia para prever e evitar a ultrapassagem dos valores de Distorção Harmônico estipulados em norma e a utilização como base para a previsão de harmônicas individuais, que podem ser utilizadas em estudos de fluxo de carga harmônicos. / This work presents a modeling for THDv%, THDi% and individual harmonics using predictions from an ANFIS system that learns with measured data and predicts the behavior of the network for future values. These forecasts can help meet national THDv% standards stipulated by the Agência Nacional de Energia Elétrica (ANEEL) through Distribution Procedures (PRODIST), such as THDi% international standards, thus anticipating the currently recommended standards, but in the near future will be punitive. The modeling is performed by means of a Neuro-Fuzzy system called ANFIS, which uses neural network to learn the behavior of the system and adjustment of the parameters and Fuzzy rule for the determination of the system output values taking into account the learning of the Neural network. The great advantage of this tool is the power of modeling standards using a prediction of the harmonic state of the connected loads in the low voltage, which helps in the creation of pseudomedidas for the distribution networks, where it is difficult and costly to obtain real measurements. Among the practical applications for this tool is the use of the predicted values instead of measured anomalous values, the use in energy meters to predict and avoid exceeding the values of Harmonic Distortion stipulated in standard and the use as a basis for the prediction of individual harmonics that can be used in harmonic load flow studies.

In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.

"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

Supply chain systems have become a vital component of successful networked business firms/organisations. Over the last three decades, there has been a dramatic growth globally in the formation of supply chain networks. Research, however, indicates that there has been an increase in reported supply chains failures, and the incompatibility issues between participated organisations. Yet, these incompatibility issues are not just technical, but encompass wider cultural, organisational, and economical factors. Whilst research has shown the effect of such factors on supply chain performance, the influence of achieving corporate culture compatibility to the success of supply chains remains poorly understood. This is because it is widely accepted that organisations that operate in the same region possess a similar culture. In contrast, this research will examine the existence of corporate culture diversity between organisations in the same region, rather than diversity of national culture across different regions. Specifically, the study described the development of corporate culture compatibility between supply chains’ organisations and its influences on supply chain performance. Therefore, the thesis focus is the complex interrelationships between corporate culture compatibility of member organisations and supply chain performance. This research identifies cultural norms and beliefs of supply chain members within key organisational factors, rather than national or multi-national organisations factors, as in Hofstede (1983). A multi-method research design (combining case study, simulation, and neuro-fuzzy methods) was used to provide a rounded perspective on the phenomena studied. The multiple case studies helped to explore how corporate culture compatibility influences supply chain performance and develop a conceptual model for this association. The simulation experiments were conducted to verify the obtained conceptual framework from the multiple case studies, and investigate the effects of changing the corporate culture compatibility level on supply chain performance. The simulation is designed based on a Multi-Agent System (MAS) approach, in which each organisation in a supply chain is represented as an intelligent agent. Finally, a neuro-fuzzy approach is presented to assess corporate culture on supply chains context using real data. The analysis of the quantitative neuro-fuzzy study confirmed and validated the theoretical findings and adds depth to our understanding of the influences of corporate culture compatibility on supply chain performance. The study confirmed that organisations within the same supply chain in the same region possess different corporate cultures that consequently need the achievement of corporate culture compatibility as it is indicated by the literature. Moreover, the study revealed two types of corporate culture in supply chains’ context: individual culture and common culture. Individual culture refers to the internal beliefs within the organisation’s boundary, while common culture refers to beliefs when trading with partners across the organisation’s boundary. However, the study shows that common culture has more influences on supply chain performance than individual culture. In addition, the study highlighted bi-directional association between individual culture and common culture that helps the supply chain’s organisations developing their corporate culture compatibility. The results from the current study also showed that supply chain performance was shown to arise dramatically in response to corporate culture compatibility level increases. Yet, this increase in performance is diminished at a higher level of corporate culture compatibility, because more corporate culture compatibility increases are not cost effective for the organisations. In addition, organisations at a higher level of compatibility have more preferences to preserve their individual culture because it represents their identity. Furthermore, the study complements the gap in the literature related to the assessment of corporate culture of individual organisations in supply chains for sustaining a higher supply chain performance. While current culture assessment models observe individual organisations’ culture, the proposed approach describes a single concentrated model that integrates both individual and common culture in measuring influences of culture compatibility on supply chain performance. The findings from this study provide scholars, consultants, managers, and supply chain systems vendors with valuable information. This research thesis contributes to supply chain configuration and partnership formation theory, along with corporate culture theory, and is the first of its kind to establish the use of intelligent methods to model corporate culture compatibility. It is also one of the first empirical studies to compare corporate culture compatibility of supply chains’ organisations from organisational perspectives, rather than national perspectives.

We propose a novel approach for building a type-2 neuro-fuzzy system from a given set of input-output training data. For an input pattern, a corresponding crisp output of the system is obtained by combining the inferred results of all the rules into a type-2 fuzzy set which is then defuzzified by applying a type reduction algorithm. Karnik and Mendel proposed an algorithm, called KM algorithm, to compute the centroid of an interval type-2 fuzzy set efficiently. Based on this algorithm, Liu developed a centroid type-reduction strategy to do type reduction for type-2 fuzzy sets. A type-2 fuzzy set is decomposed into a collection of interval type-2 fuzzy sets by £\-cuts. Then the KM algorithm is called for each interval type-2 fuzzy set iteratively. However, the initialization of the switch point in each application of the KM algorithm is not a good one. In this thesis, we present an improvement to Liu's algorithm. We employ the result previously obtained to construct the starting values in the current application of the KM algorithm. Convergence in each iteration except the first one can then speed up and type reduction for type-2 fuzzy sets can be done faster. The efficiency of the improved algorithm is analyzed mathematically and demonstrated by experimental results. Constructing a type-2 neuro-fuzzy system involves two major phases, structure identification and parameter identification. We propose a method which incorporates self-constructing fuzzy clustering algorithm and a SVD-based least squares estimator for structure identification of type-2 neuro-fuzzy modeling. The self-constructing fuzzy clustering method is used to partition the training data set into clusters through input-similarity and output-similarity tests. The membership function associated with each cluster is defined with the mean and deviation of the data points included in the cluster. Then applying SVD-based least squares estimator, a type-2 fuzzy TSK IF-THEN rule is derived from each cluster to form a fuzzy rule base. After that a fuzzy neural network is constructed. In the parameter identification phase, the parameters associated with the rules are then refined through learning. We propose a hybrid learning algorithm which incorporates particle swarm optimization and a SVD-based least squares estimator to refine the antecedent parameters and the consequent parameters, respectively. We demonstrate the effectiveness of our proposed approach in constructing type-2 neuro-fuzzy systems by showing the results for two nonlinear functions and two real-world benchmark datasets. Besides, we use the proposed approach to construct a type-2 neuro-fuzzy system to forecast the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Experimental results show that our forecasting system performs better than other methods.