In this thesis, we integrate the memory subsystem, including cache and MMU¡]Memory Management Unit¡^ with the embedded 32 bits microprocessor SYS32TM-II to support the virtual memory mechanism of the operating system and make memory management effectively among multi-processes in the system. To provide the virtual to physical address translation with MMU and to improve the system performance with cache. We reuse the memory subsystem of the LEON2 SoC platform and design the communication interface to coordinate the processor core SYS32TM-II with the LEON2 memory subsystem, and modify the LEON2 memory subsystem to compatible with SYS32TM-II. After the integration of memory subsystem, a reusing cache for program address trace compression in real time is proposed. The advantage is that reusing cache with minor hardware modification can not only save the hardware compressor overhead but also obtain a high compression ratio. Experimental results show that the proposed approach causes few hardware area overhead but achieves approximately 90% compression ratio at real-time. Therefore, this thesis is the memory subsystem with parameterized design and with the ability to support system debugging. The role of the memory subsystem is not only to improve the system performance and to provide the hardware support requiring by the operating system, with minor modification, the memory susbsystem can also capture the dynamic program execution trace in parallel with microprocessor. The address trace compression mechanism will not effect the program execution and capable to compress at real-time.

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of black box approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the black box approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.<p>The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the pump only model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a pump only model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. <p>It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. <p>The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the error accumulation problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response.

The chapters of this dissertation explore related aspects of the procurement of conservation services from private landowners. In the first chapter, heuristic laboratory experiments reveal the impact of potential government regulation on strategic forces and efficiency properties in conservation procurement auctions. In the second chapter, data from past procurement auctions are analyzed to discover the existence and magnitude of premiums received by auction participants. The first Chapter, “Procurement Auctions Under Regulatory Threat,” examines how strategic forces and efficiency properties are impacted in auctions for the procurement of environmental services when a threat of regulation is levied. Laboratory experiments examining different regulatory environments demonstrate that a threat of regulation will reduce the amount of public funds necessary to purchase a given level of environmental services. However, adverse selection costs and equity are negatively impacted by threat implementation. The second Chapter, “Estimating Bid Inflation in Procurement of Environmental Services,” studies the size of premiums received by program participants in conservation programs. Predictions informed by economic literature and theory elicit the underlying value distribution for a unique dataset of procurement auctions. Average premiums for auction participants range from almost 50 percent to less than 1 percent across auction periods and institutions. The results demonstrate that both repetition and rule variation may improve the efficiency of procurement auctions. The auctions studied here are shown to yield efficiency improvements of more than 32 percent over standard fixed-payment schemes for service procurement.

Recently, some authors point to value creation from the structure and behaviours associated with competition and collaboration inside the organisation (Helfat and Eisenhardt, 2004; Birkinshaw and Lingblad, 2005). While both competition and collaboration have been studied extensively between organisations, less attention has been focused on them and their interaction between units inside the organisation, particularly within complex and heterogeneous multinational corporations. The question is how to achieve the coordination and collaboration that is necessary for a multinational organisation to reap the benefits that international expansion has to offer and yet balance the propensity for competition that exists as business units struggle for scarce resources or new opportunities. In order to answer this question, the aim of this review is to first of all know what the factors and mechanisms are that influence competition and collaboration between organisational units within multinational organisations. Methodology: This study has been conducted using a systematic review methodology with the aim of producing a search of extant literature which can be trusted by others as being thorough, transparent, replicable and clear. Both quantitative and qualitative techniques have been used to achieve this. Findings: This review finds that the there is minimal extant literature that addresses competition and collaboration between business units within the multinational corporation and that it also fails to provide a comprehensive understanding of the factors and mechanisms that influence the co-existence of intraorganisational competition and collaboration. They are typically viewed as mutually exclusive or at opposite ends of a continuum. While there has been some recent research attention given to intraorganisational collaboration and competition, each in their own right, there has not been an extensive review of the factors and mechanisms when looking at their coexistence within the multinational corporate environment. By bringing the two literatures into view and investigating the paradoxical nature of the influences on andthe interactions between competition and collaboration, insights into an optimal mix based on the corporations strategy and value creation logic can be gained for both academics and business unit leaders.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of black box approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the black box approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.<p>The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the pump only model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a pump only model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. <p>It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. <p>The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the error accumulation problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response.

For the power market participators, there are competition and more trade opportunities in the power industry under the deregulation. In the electricity market, the bidding model is adopted instead of the cost model. GenCos try to maximize the profit under bidding model according to the power demand. Electricity becomes commodity and its price varies with power demand, bidding strategy and the grid. GenCos perform the unit commitment in a price volatile environment to reach the maximal profit. In a deregulation environment, Independent System Operator (ISO) is very often responsible for the electricity auction and secured power scheduling. The ISO operation may involve all kinds of risks. These risks include price volatility risk, bidding risk, congestion risk, and so on. For some markets, it is very important how GenCos determine the optimal unit commitment schedule considering risk management. A good risk analysis will help GenCo maximize profit and purse sustainable development. In this study, price forecasting is developed to provide information for power producers to develop bidding strategies to maximize profit. Profit-Based Unit Commitment (PBUC) model was also derived. An Enhanced Immune Algorithm (EIA) is developed to solve the PBUC problem. Finally, the Value-at-Risk (VAR) of GenCos is found with a present confident level. Simulation results provide a risk management rule to find an optimal risk control strategy to maximize profit and raise its compatibility against other players.

Empirical performance models of fan airflow, primary airflow and power consumption were developed for series and parallel variable air volume fan powered terminal units. An experimental setup and test procedure were created to test the terminal units at typical design pressures and airflows. Each terminal unit observed in this study used an 8 in (20.3 cm) primary air inlet. Two fan motor control methods were considered. The primary control of interest was the electronically commutated motor (ECM) controller. Data collected were compared with previous research regarding silicon rectified control (SCR) units. Generalized models were developed for both series and parallel terminal units. Coefficients for performance models were then compared with comparable SCR controlled units. Non-linear statistical modeling was performed using SPSS software (2008). In addition to airflow and power consumption modeling, power quality was also quantified. Relationships between real power (watts) and apparent power (VA) were presented as well as harmonic frequencies and total harmonic distortion. Power quality was recorded for each ECM controlled terminal unit tested. Additional tests were also made to SCR controlled terminal units used in previous research (Furr 2006). The airflow and power consumption performance models had an R2 equal to 0.990 or greater for every terminal unit tested. An air leakage model was employed to account for leakage in the parallel designed VAV terminal units when the internal fan was turned off. For the leakage model, both ECM and SCR controlled units achieved an R2 greater than or equal to 0.918.

A percentage of insulating glass (IG) units break each year due to thermally induced perimeter stresses. The glass industry has known about this problem for many years and an ASTM standard has recently been developed for the design of monolithic glass plates for thermal stresses induced by solar irradiance. It is believed that a similar standard can be developed for IG units if a proper understanding of IG thermal stresses can be developed. The objective of this research is to improve understandings of IG thermal stresses and compare the IG thermal stresses with those that develop in monolithic glass plates given similar environmental conditions. The major difference between the analysis of a monolithic glass plate and an IG unit is energy exchange due to conduction, natural convection, and long wave radiation through the gas space cavity. In IG units, conduction, natural convection, and long wave radiation combine in a nonlinear fashion that frequently requires iterative numerical analyses for determining thermal stresses in certain situations. To simplify the gas space energy exchange, a numerical propagation procedure was developed. The numerical propagation procedure combines the nonlinear effects of conduction, natural convection, and long wave radiation into a single value. Use of this single value closely approximates the nonlinear nature of the gas space energy exchange and simplifies the numerical analysis. The numerical propagation procedure was then coupled with finite element analysis to estimate thermal stresses for both monolithic glass plates and IG units. It is shown that the maximum thermal stresses that develop in IG units increase linearly with input solar irradiance during the transient phase. It is shown that an initial preload stress develops under equilibrium conditions due to the thermal bridge effects of the spacer. It is shown that IG units develop larger thermal stresses than monolithic glass plates under similar environmental conditions. Finally, it is shown that the use of low-e coatings increase IG thermal stresses and that the location of low-e coating as well as environmental conditions affect which glass plate develops larger thermal stresses.