Decision making to support an integrated infrastructure supply chain industrial ecological approach within public private partnerships

Supply chains are complex adaptive systems for which final performance depends upon numerous interdependent decisions made by numerous firms which synthesise inputs from various resources systems.  The dynamic interdependent behaviour of social, economic, material and informational resource systems within eco-industrial settings that support the built environment life cycle supply chains can be studied at the supply chain level.  The impact of megaprojects is significant and holds promise to explore the impact of decisions on various systems as it combines project and system boundaries.  Megaoprojects considered as major events within systems can produce critical revolutionary impacts on the systems within which they are embedded.  The decisions that are made on megaprojects are central to risk management.  typically major infrastructure projects are procured through a form of public private partnership (PPP).  The core principle of PPP is value for money which refers to the best available outcome attempting to take account of all benefits, costs and risks over the whole life of the procurement.  In this paper the focus is on Australia where there has been considerable acitivity in the use of PPPs.  With recent national infrastucture packages proposed to stimulate the economy due to the global financial crisis, decision modelling on risks is a revelant and critical matter not only in practice but also in the research community.  PPPs encourage the whole-of-lifecycle approach in the procurement and management of public sector assets by transparently recognising the costs and risks associated with the whole life of the required service or facility, thus integrated whole of life supply chains can be considered.  By creating a single point of responsibility for an entire project from inception through operation, a strong incentive is created for thinking about the effects that a design or construction decision will have on the effectiveness and efficiency of managing and maintaining a facility during its operational life.  The decision to procure holistic supply chains becomes a much more viable commercial reality in the PPP environment than previously considered in the usual commercial construction spot transactional approach.  These types of decisions tend to be imprecise, approximate and complex requireing justification and reasoning logic rather than the classical 'truth' logic.  The purpose of this paper is to develop a theoretical decision framework which combines interdependency and multi-values logic for supply chain procurement modelling.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Extending robust industrial optimisation using simulation and reinforcement learning

Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.

Improvements in teleoperation of industrial robots without low-level access

 This paper proposes a method to improve motion smoothness and decrease latency using existing ABB IRC5 robot controllers without access to any low level interface. The proposed control algorithm includes a high-level PID controller used to dynamically generate reference velocities for different travel ranges of the tool centre point (TCP) of the robot. Communication with the ABB IRC5 controller was performed utilising the ABB PC software development kit (SDK). The multitasking feature of the IRC5 controller was used in order to enhance the communication frequency between the controller and the remote application. Trajectory tracking experiments of a predefined 3D trajectory were carried out and the benefits of the proposed algorithm was demonstrated. The robot was intentionally installed on a wobbly table and its vibrations were recorded using a six degrees of freedom (DOF) force/torque sensor fitted to the tool mounting interface of the robot. The robot vibrations were used as a measure of the smoothness of the tracking movements. Experimental results demonstrating the robot tool centre point (TCP), tracking errors, and robot vibrations for different control approaches were provided and analysed. It was demonstrated that the proposed approach results in the smoothest motion with less than 0.2 mm tracking errors.

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.

Fuzzy logic controller optimized by particle swarm optimization for DC motor speed control

In this paper, we presented an optimized fuzzy logic controller using particle swarm optimization for DC motor speed control. The controller model is simulated using MATLAB software and also experimentally tested on a laboratory DC motor. A comparison of the performance of different controllers such as PID controller, fuzzy logic controller and optimized fuzzy logic controller is presented as well. With reference to the results of digital simulations and experiment, the designed FLC-PSO speed controller obtains much better dynamic behavior compared to PID and the normal FLC designed. Moreover, it can acquire superior performance of the DC motor, and also perfect speed tracking with no overshoot. The optimized membership functions (MFs) are obviously proved to be able to provide a better performance and higher robustness in comparison with a regular fuzzy model, when the MFs were heuristically defined. Besides, experimental results verify the ability of proposed FLC under sudden change of the load torque which leads to speed variances.

Designing single event upset mitigation techniques for large SRAM-Based FPGA components

This thesis presents the study and development of fault-tolerant techniques for programmable architectures, the well-known Field Programmable Gate Arrays (FPGAs), customizable by SRAM. FPGAs are becoming more valuable for space applications because of the high density, high performance, reduced development cost and re-programmability. In particular, SRAM-based FPGAs are very valuable for remote missions because of the possibility of being reprogrammed by the user as many times as necessary in a very short period. SRAM-based FPGA and micro-controllers represent a wide range of components in space applications, and as a result will be the focus of this work, more specifically the Virtex® family from Xilinx and the architecture of the 8051 micro-controller from Intel. The Triple Modular Redundancy (TMR) with voters is a common high-level technique to protect ASICs against single event upset (SEU) and it can also be applied to FPGAs. The TMR technique was first tested in the Virtex® FPGA architecture by using a small design based on counters. Faults were injected in all sensitive parts of the FPGA and a detailed analysis of the effect of a fault in a TMR design synthesized in the Virtex® platform was performed. Results from fault injection and from a radiation ground test facility showed the efficiency of the TMR for the related case study circuit. Although TMR has showed a high reliability, this technique presents some limitations, such as area overhead, three times more input and output pins and, consequently, a significant increase in power dissipation. Aiming to reduce TMR costs and improve reliability, an innovative high-level technique for designing fault-tolerant systems in SRAM-based FPGAs was developed, without modification in the FPGA architecture. This technique combines time and hardware redundancy to reduce overhead and to ensure reliability. It is based on duplication with comparison and concurrent error detection. The new technique proposed in this work was specifically developed for FPGAs to cope with transient faults in the user combinational and sequential logic, while also reducing pin count, area and power dissipation. The methodology was validated by fault injection experiments in an emulation board. The thesis presents comparison results in fault coverage, area and performance between the discussed techniques.

Automação industrial e cultura técnica: o caso do petróleo brasileiro S/A - PETROBRÁS

A PETROBRAS, conhecedora das mudanças ocorridas no ambiente de negócios durante a década de 90, esforça-se em aproveitar as oportunidades surgidas ou aquelas que ainda estão por aparecer no setor petroleiro. Tal esforço traduziu-se na formulação do Plano Estratégico do Sistema PETROBRAS 199212001. Este plano define objetivos e estratégias decenais. Inserida no Plano Estratégico, está a questão da automação da companhia, considerada de suma importância para o seu desempenho no setor. Dentre os 14 Projetos Estratégicos do Plano temos o Projeto Estratégico de Automação (PEA), e para a implementação de seus objetivos é fundamental o desempenho dos funcionários neste Projeto. O desempenho do quadro funcional irá influenciar decisivamente o caminho a ser percorrido pela empresa rumo à modernidade, em um ambiente de negócios altamente renovado pela tecnologia de base microeletrônica. Uma das variáveis , que permitirá ou não a efetividade da PETROBRAS está relacionada à sua Cultura Técnica, ou seja, ao conjuntod,e conhecimentos que os agentes técnicos da Companhia .,.~ I-r '\ .. utilizam no processo de tomada de decisão e que depende dos paradigmas tecnológicos internacionais, das normas internas da empresa e das características societais brasileiras. Investigar criticamente a situação do PEA, por meio da identificação da Cultura Técnica predominante, é o foco do trabalho ora proposto para apreciação. Este não é um tema que diz respeito somente aos engenheiros e técnicos diretamente envolvidos com o PEA, mas a todos aqueles que fazem a empresa, como também a todos os estudiosos do tema.

Trabalho e educação da classe operaria: a perspectiva politica da escola tecnica do Sindicato dos Metalurgicos - RJ

Esta dissertação procura refletir os pressupostos teóricos da relação trabalho-educação, a partir da vida real da classe operária, bem como a partir da prática político-pedagógica da escola técnica do Sindicato dos Metalúrgicos do Rio de Janeiro. Contextualizando o nexo entre a escola e a entidade de trabalhadores nos diferentes momentos do movimento sindical, procura resgatar a lógica da educação promovida pelos próprios operários-metalúrgicos. Ao tomar o trabalho como princípio educativo, analisa a experiência vivida por professores, operários-estudantes, dirigentes e ativistas sindicais na reconstrução do processo de produção pedagógica. Reflete então, a formação dos trabalhadores tendo como perspectiva o encontro entre o saber escolar e o saber prático do operariado adquirido no cotidiano da fábrica. Analisando a dicotomia trabalho manual/trabalho intelectual, o processo de automação da produção, a condição de classe do técnico industrial, entre outras questões, enfatiza a necessidade de se resgatar a unidade entre educação técnica e educação política da classe operária.

A situation awareness interface for a bi-wheeled industrial hovercraft: design, development and evaluation

The intention of this thesis is to develop a prototype interface that enables an operator to control a bi-wheeled industrial hovercraft that will work within a fusion power plant if the automation system fails. This fusion power plant is part of the ITER project a conjoint effort of various industrialized countries to develop cleaner sources of energy. The development of the interface prototype will be based on situation awareness concepts, which provide a means to understand how human operators perceive the world around, then process that information and make decisions based on the knowledge that they already have and the projected knowledge of the reactions that will occur in the world in response to the actions the operator makes. Two major situation awareness methods will be used, GDTA as a means to discover the requirements the interface needs to solve, and SAGAT to conduct the evaluation on the three interfaces. This technique can isolate the differences an operator has in situation awareness when presented with relevant information given by each of the three interfaces that were built for this thesis. Where the first interface presents the information within the operator’s focal point of view in a pictorial style, the second interface shows the same information within the same point of view has the first interface but only shows it in a textual manner. While the third interface shows the relevant information in the operator’s peripheral field of view. Also SAGAT can provide insight on the question to know if providing the operator with feed-forward information about the stoppage distances of the bi-wheeled industrial hovercraft has any effect on the operator’s decision making.

Implementação e análise de desempenho dos protocolos de criptografia neural e Diffie-Hellman em sistemas RFID utilizando uma plataforma embarcada

RFID (Radio Frequency Identification) identifies object by using the radio frequency which is a non-contact automatic identification technique. This technology has shown its powerful practical value and potential in the field of manufacturing, retailing, logistics and hospital automation. Unfortunately, the key problem that impacts the application of RFID system is the security of the information. Recently, researchers have demonstrated solutions to security threats in RFID technology. Among these solutions are several key management protocols. This master dissertations presents a performance evaluation of Neural Cryptography and Diffie-Hellman protocols in RFID systems. For this, we measure the processing time inherent in these protocols. The tests was developed on FPGA (Field-Programmable Gate Array) platform with Nios IIr embedded processor. The research methodology is based on the aggregation of knowledge to development of new RFID systems through a comparative analysis between these two protocols. The main contributions of this work are: performance evaluation of protocols (Diffie-Hellman encryption and Neural) on embedded platform and a survey on RFID security threats. According to the results the Diffie-Hellman key agreement protocol is more suitable for RFID systems

Simulador computacional para poços de petróleo com método de elevação artificial por bombeio mecânico

This work proposes a computer simulator for sucker rod pumped vertical wells. The simulator is able to represent the dynamic behavior of the systems and the computation of several important parameters, allowing the easy visualization of several pertinent phenomena. The use of the simulator allows the execution of several tests at lower costs and shorter times, than real wells experiments. The simulation uses a model based on the dynamic behavior of the rod string. This dynamic model is represented by a second order partial differencial equation. Through this model, several common field situations can be verified. Moreover, the simulation includes 3D animations, facilitating the physical understanding of the process, due to a better visual interpretation of the phenomena. Another important characteristic is the emulation of the main sensors used in sucker rod pumping automation. The emulation of the sensors is implemented through a microcontrolled interface between the simulator and the industrial controllers. By means of this interface, the controllers interpret the simulator as a real well. A "fault module" was included in the simulator. This module incorporates the six more important faults found in sucker rod pumping. Therefore, the analysis and verification of these problems through the simulator, allows the user to identify such situations that otherwise could be observed only in the field. The simulation of these faults receives a different treatment due to the different boundary conditions imposed to the numeric solution of the problem. Possible applications of the simulator are: the design and analysis of wells, training of technicians and engineers, execution of tests in controllers and supervisory systems, and validation of control algorithms

Simplified architecture of a neurofuzzy controller applied on industrial systems for fluid flow control

This work presents a simplified architecture of a neurofuzzy controller for general purpose applications that tries to minimize the processing used in the several stages of hazy modeling of systems. The basic procedures of fuzzification and defuzzification are simplified to the maximum while the inference procedures are computed in a private way. The simplified architecture allows a fast and easy configuration of the neurofuzzy controller and the structuring rules that define the control actions is automatic. Th controller's Limits and performance are standardized and the control actions are previously calculated. For application, the industrial systems of fluid flow control will be considered.