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While lean manufacturing has greatly improved the efficiency of production operations, it has left US enterprises in an increasingly risky environment. Causes of manufacturing disruptions continue to multiply, and today, seemingly minor disruptions can cause cascading sequences of capacity losses. Historically, enterprises have lacked viable tools for addressing operational volatility. As a result, each year US companies forfeit billions of dollars to unpredictable capacity disruptions and insurance premiums. In this dissertation we develop a number of stochastic models that capture the dynamics of capacity disruptions in complex multi-tier flow-matching feed-forward networks (FFN). In particular, we relax basic structural assumptions of FFN, introduce random propagation times, study the impact of inventory buffers on propagation times, and make initial efforts to model random network topology. These stochastic models are central to future methodologies supporting strategic risk management and enterprise network design.

The purposes of this study were to: 1) evaluate how movement performance of the thumb and fingers were modified during manipulation of objects with and without fluids and 2) to quantify movement performance and accuracy during manipulation of objects, in two different modes of manipulation, i.e., pendulum and inverted pendulum. Twenty young healthy adults (age 24-35) were recruited and performed two predictable cyclic tracking tasks and episodic short-duration precision movement task. No change in movement performance observed in open-loop or episodic tasks. However, in closed-loop task, mode of manipulation (IP versus P) had a significant effect on amplitude consistency (P<0.001), and temporal accuracy (P<0.050).Fluid motion had a significant effect on RMS of index finger contact forces (p < 0.01) in episodic task. In conclusion, fluid motion had no significant effect on movement performance and accuracy. The quality of movement was better in pendulum mode than inverted pendulum movement.

The purposes of this study were to: 1) evaluate how movement performance of the thumb and fingers were modified during manipulation of objects with and without fluids and 2) to quantify movement performance and accuracy during manipulation of objects, in two different modes of manipulation, i.e., pendulum and inverted pendulum. Twenty young healthy adults (age 24-35) were recruited and performed two predictable cyclic tracking tasks and episodic short-duration precision movement task. No change in movement performance observed in open-loop or episodic tasks. However, in closed-loop task, mode of manipulation (IP versus P) had a significant effect on amplitude consistency (P<0.001), and temporal accuracy (P<0.050).Fluid motion had a significant effect on RMS of index finger contact forces (p < 0.01) in episodic task. In conclusion, fluid motion had no significant effect on movement performance and accuracy. The quality of movement was better in pendulum mode than inverted pendulum movement.

In this dissertation harmonic current, harmonic torque originated at the load and harmonic torque originated at the motor, where modeled and treated via closed loop control. The dissertation propose a remedy for cancelling harmonic current by placing the proposed adaptive feedforward controller (AFC) in parallel with the FOC current control. Similarly, harmonic torque load was cancelled by proposing an AFC in parallel with the speed control loop. Harmonic torque originated in the motor mainly due to harmonic flux where cancelled through the estimation of harmonic flux, which was achieved by a novel Minimal Parameter Harmonic Flux Estimator (MPHFE). The latter is formulated such that the inductance, resistance, and stator current and its derivative are not necessary for the estimation of the harmonic eflux. This was achieved by forcing the harmonic current induced by the harmonic flux component to zero through the combined action of a Field-Oriented Controller (FOC) and a feed-forward controller. Subsequently, the harmonic flux can be obtained directly from the estimated harmonic back-EMF without the involvement of other motor parameters. Finally, the estimated flux is used in conjunction with a comprehensive analysis of the motor harmonic torque to determine the stator current compensation to eliminate the torque harmonic. A systematic approach to assign the parameter of the AFC controller were developed in this dissertation. Furthermore, multiple experiments were conducted to demonstrate the efficacy of the proposed control schemes harmonics.

This investigation has analysed the impact on energy consumption for heating and cooling in UK housing stock, from the predicted future global warming effect, up to the 2080s. It was found that, up to the 2050s, it is likely that heating rather than cooling will remain the main source of energy use, in a pessimistic business-as-usual scenario. Micro-CHP (domestic scale combined heat and power plant) offers a medium term low carbon solution for the replacement of heating equipment. The operation of a micro-CHP unit in a time-of-use electricity tariff scenario for existing UK housing stock was simulated. The investigation analysed whether electrical export earnings could be increased by the use of feed forward control in comparison to a conventional control strategy of timed operation with optimised start, without serious compromise to fuel consumption. It has been found that the use of an auto-regressive model with exogenous input (ARX) model can adequately describe thermal characteristics of a typical house for a model based predictive controller. It was demonstrated that export earnings could be increased significantly without additional fuel consumption by accepting dynamic heating operation within a comfort range, rather than a static setpoint value. Compared to a conventional control strategy, overall CO2 emissions and peak network electricity demand were reduced. This approach increased electrical export earnings without additional fuel consumption during theoretical testing and has potential for further development. These finding confirm a positive case for utilising time-of-use electricity tariffs and could contribute significantly towards increasing the productivity of micro-CHP.

Biological systems exhibit complex behaviors through coordinated responses of individual biological components. With the advent of genome-scale techniques, one focus has been to develop methods to model interactions between components to accurately describe intact system function. Mathematical modeling techniques such as constraint-based modeling, agent-based modeling, cellular automata (CA) modeling and differential equation modeling are employed as computational tools to study biological phenomenon. We have shown that cellular automata simulations can be used as a computational tool for 12 predicting the dynamics of biological systems with stochastic behavior. The basic premise for the research was the observations made during a study of biologically important feed-forward motifs where CA simulations were compared with differential equation simulations. It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. The study which comprised of CA simulations compared with differential equation modeling show reasonable agreement in the predictability of system dynamics, which provided enough support to model biological systems at cellular level to observe dynamic system evolution. The great promise shown by CA simulations to model biochemical systems was then employed to elucidate evolutionary clues as to why biological networks show preference for certain types of motifs and preserve them with higher frequency during evolution. It was followed by modeling apoptotic networks to shed light on the efficacy of inhibitors and to model cellulose hydrolysis to evaluate efficiency of different enzyme systems used by cellulytic bacteria.

Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.

The first loudspeaker was invented almost 150 years ago and even though much has changed regarding the manufacturing, the main idea is still the same. To produce clean sound, modern loudspeaker consist of expensive materials that often need advanced manufacturing equipment. The relatively newly established company Actiwave AB uses digital signal processing to enhance the audio for loudspeakers with poor acoustic properties. Their algorithms concentrate on attenuating the linear distortion but there is no compensation for the loudspeakers' non-linear distortion, such as harmonic distortion. To attenuate the harmonic distortion, this thesis presents controllers based on exact input-output linearisation. This type of controller needs an accurate model of the system. A loudspeaker model has been derived based on the LR-2 model, an extension of the more common Thiele-Small model. A controller based on exact input-output linearisation also needs full state feedback, but since feedback risk being expensive, state estimators were used. The state estimators were based on feed-forward or observers using the extended Kalman filter or the unscented Kalman filter. A combination of feed-forward state estimation and a PID controller were designed as well. In simulations, the total harmonic distortion was attenuated for all controllers up to 180 Hz. The simulations also showed that the controllers are sensitive to inaccurate parameter values in the loudspeaker model. During real-life experiments, the controllers needed to be extended with a model of the used amplifier to function properly. The controllers that were able to attenuate the harmonic distortion were the two methods using feed-forward state estimation. Both controllers showed improvement compared to the uncontrolled case for frequencies up to 40 Hz.

I detta arbete som fokuserar på hur neurala nätverk kan appliceras på och hur väl de presterar i en spelmiljö undersöks två nätverksarkitekturer applicerat på en simulation av ett så kallat urvalsbaserat spel. I arbetet så är ett urvalsbaserat spel ett spel som går ut på att en spelare skall göra en mängd val innan spelet börjar, och de två nätverksarkitekturerna som utvärderas är Feed Forward och NEAT. Experimenten låter nätverken skapa lag för en förenklad version av spelet Pokemon och kommer sedan att låta dessa lag tävla emot varandra i en deterministisk testmiljö för att bedöma hur bra nätverken presterar.