A scalable and open source linear positioning system controller

This paper is on the implementation of a dual axis positioning system controller. The system was designed to be used for space-dependent ultrasound signal acquisition problems, such as pressure field mapping. The work developed can be grouped in two main subjects: hardware and software. Each axis includes one stepper motor connected to a driver circuit, which is then connected to a processing unit. The graphical user interface is simple and clear for the user. The system resolution was computed as 127 mu m with an accuracy of 2.44 mu m. Although the target application is ultrasound signal acquisition, the controller can be applied to other devices that has up to four stepper motors. The application was developed as an open source software, thus it can be used or changed to fit different purposes.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

A mechanical design approach to testing fluid turbulence over a rough surface

Senior thesis written for Oceanography 444

A psychosocial approach to understanding pilot and controller acceptance of change in ATM, based on three CDA case studies

Next generation ATM systems cannot be implemented in a technological vacuum. The further ahead we look, the greater the likely impact of societal factors on such changes, and how they are prioritised and promoted. The equitable sustainability of travel behaviour is rising on the political agenda in Europe in an unprecedented manner. This paper examines pilot and controller attitudes towards Continuous Descent Approaches (CDAs). It aims to promote a better understanding of acceptance of change in ATM. The focus is on the psychosocial context and the relationships between perceived societal and system benefits. Behavioural change appeared more correlated with such benefit perceptions in the case of the pilots. For the first time in the study of ATM implementation, and acceptance of change, this paper incorporates the Seven Stages of Change model, based on the constructs of the Theory of Planned Behaviour. It employs a principal components (factor) analysis, and further explores the intercorrelations of benefit perceptions, known in psychology as the ‘halo effect’. Disbenefit perceptions may break down this effect, it appears. For implementers of change, this evidence suggests an approach in terms of reinforcing the dominant benefit(s) perceived, for sub-groups within which a halo effect is evident. In the absence of such an effect, perceived disbenefits, such as with respect to workload and capacity, should be off-set against specific, perceived benefits of the change, as far as possible. This methodology could be equally applied to other stakeholders, from strategic planners to the public. The set of three case studies will be extended beyond CDA trials. A set of concise guidelines will be published with a strong focus on practical advice, in addition to continued work enabling a better understanding of the expected, increasing psychosocial contributions to successful and unsuccessful efforts at ATM innovation and change.

Energy-aware Workflow Scheduling and Fuzzy Logic Based Capacity Provisioning in Cloud Environment

Thesis (Master's)--University of Washington, 2015

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Network air gap controller

As ameaças à segurança da informação, (INFOSEC) atentam contra a perda da respectiva confidencialidade, integridade e disponibilidade, pelo que as organizações são impelidas a implementar políticas de segurança, quer ao nível físico quer ao nível lógico, utilizando mecanismos específicos de defesa. O projecto Network Air Gap Controller (NAGC) foi concebido no sentido de contribuir para as questões da segurança, designadamente daquelas que se relacionam directamente com a transferência de informação entre redes de classificação de segurança diferenciadas ou de sensibilidades distintas, sem requisitos de comunicação em tempo real, e que mereçam um maior empenho nas condições de robustez, de disponibilidade e de controlo. Os organismos que, em razão das atribuições e competências cometidas, necessitam de fazer fluir informação entre este tipo de redes, são por vezes obrigados a realizar a transferência de dados com recurso a um processo manual, efectuado pelo homem e não pela máquina, que envolve dispositivos amovivéis, como sejam o CD, DVD, PEN, discos externos ou switches manuais. Neste processo, vulgarmente designado por Network Air Gap (NAG), o responsável pela transferência de dados deverá assumir de forma infalível, como atribuições intrínsecas e inalienáveis da função exercida, as garantias do cumprimento de um vasto conjunto de normas regulamentares. As regras estabelecidas desdobram-se em ferramentas e procedimentos que se destinam, por exemplo, à guarda em arquivo de todas as transferências efectuadas; à utilização de ferramentas de segurança (ex: antivírus) antes da colocação da informação na rede de classificação mais elevada; ao não consentimento de transferência de determinados tipos de ficheiro (ex: executáveis) e à garantia de que, em consonância com a autonomia que normalmente é delegada no elemento responsável pela operação das comunicações, apenas se efectuam transferências de informação no sentido da rede de classificação inferior para a rede de classificação mais elevada. Face ao valor da informação e do impacto na imagem deste tipo de organizações, o operador de comunicações que não cumpra escrupulosamente o determinado é inexoravelmente afastado dessas funções, sendo que o processo de apuramento de responsabilidades nem sempre poderá determinar de forma inequívoca se as razões apontam para um acto deliberado ou para factores não intencionais, como a inépcia, o descuido ou a fadiga. Na realidade, as actividades periódicas e rotineiras, tornam o homem propenso à falha e poderão ser incontornavelmente asseguradas, sem qualquer tipo de constrangimentos ou diminuição de garantias, por soluções tecnológicas, desde que devidamente parametrizadas, adaptadas, testadas e amadurecidas, libertando os recursos humanos para tarefas de manutenção, gestão, controlo e inspecção. Acresce que, para este tipo de organizações, onde se multiplicam o número de redes de entrada de informação, com diferentes classificações e actores distintos, e com destinatários específicos, a utilização deste tipo de mecanismos assume uma importância capital. Devido a este factor multiplicativo, impõe-se que o NAGC represente uma opção válida em termos de oferta tecnológica, designadamente para uma gama de produtos de baixíssimo custo e que possa desenvolver-se por camadas de contributo complementar, em função das reais necessidades de cada cenário.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.