Modelling the linkages between the Australian and G7 stock markets : common stochastic trends and regime shifts

This paper examines whether the Australian equity market is integrated with the equity markets of the G7 economies by applying both the Johansen (Statistical analysis of conintegrating vectors, Journal of Economic Dynamics and Control, 12, 231-54, 1988) and Gregory and Hansen (Residual-based tests for cointegration in models with regime shifts, Journal of Econometrics, 70, 99-126, 1996) approaches to cointegration. Some evidence of a pairwise long-run relationship between the Australian stock market and the stock markets of Canada, Italy, Japan and the United Kingdom is found, but the Australian equity market is not pairwise cointegrated with the equity markets of France, Germany or the USA.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

An optimal transportation routing approach using GIS-based dynamic traffic flows

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

Power-cycle-library-based control strategy for plug-in hybrid electric vehicles

It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.

Integrated modelling of cost-effective siting and operation of flow-control infrastructure for river ecosystem conservation

Wetland and floodplain ecosystems along many regulated rivers are highly stressed, primarily due to a lack of environmental flows of appropriate magnitude, frequency, duration, and timing to support ecological functions. In the absence of increased environmental flows, the ecological health of river ecosystems can be enhanced by the operation of existing and new flow-control infrastructure (weirs and regulators) to return more natural environmental flow regimes to specific areas. However, determining the optimal investment and operation strategies over time is a complex task due to several factors including the multiple environmental values attached to wetlands, spatial and temporal heterogeneity and dependencies, nonlinearity, and time-dependent decisions. This makes for a very large number of decision variables over a long planning horizon. The focus of this paper is the development of a nonlinear integer programming model that accommodates these complexities. The mathematical objective aims to return the natural flow regime of key components of river ecosystems in terms of flood timing, flood duration, and interflood period. We applied a 2-stage recursive heuristic using tabu search to solve the model and tested it on the entire South Australian River Murray floodplain. We conclude that modern meta-heuristics can be used to solve the very complex nonlinear problems with spatial and temporal dependencies typical of environmental flow allocation in regulated river ecosystems. The model has been used to inform the investment in, and operation of, flow-control infrastructure in the South Australian River Murray.

Congestion and optimal immigration policy

This paper presents a model to explain the stylized fact that many countries have a low ratio of migrants in their population while some countries have a high ratio of migrants. Immigration improves the income of the domestic residents, but migrants also increase the congestion of public services. If migrants are unskilled and therefore pay low taxes, and the government does not limit access to these services, then the welfare of the domestic residents decreases with the number of migrants. Visa auctions can lower the cost of immigration control and substitute legal migrants for illegal migrants. If the government decides to limit the access of migrants to public services, immigration control becomes unnecessary and the optimal number of migrants can be very large.

Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

A method and its optimal parameters for the measurement of mass transfer coefficient in heat treatment furnaces

The mass transfer coefficient is an important kinetic factor to control the thermo-chemical treatment processes of metals and alloys. More importantly, the mass transfer coefficient is different at different surface positions of a metallic part treated, which depends on the dynamic characteristics of the atmosphere close to the treated surface. Understanding the local mass transfer coefficient would be significant to approach the expected physical and mechanical properties of treated surfaces. In this paper, a reverse method was proposed to measure the mass transfer coefficient at component surface and the diffusivity in metal during heat treatment. The methodology of the reverses method and the optimal parame-ters are discussed in some detail. This method was successfully used to determine the car-bon transfer coefficient at the surface of a part in a carburizing furnace and carbon diffusiv-ity from the carbon distribution within the diffusion layer.

Control of crystallization in supramolecular soft materials engineering

As one class of the most important supramolecular functional materials, gels formed by low molecular weight gelators (LMWGs) have many important applications. The key important parameters affecting the in-use performance of a gel are determined by the hierarchical fiber network structures. Fiber networks consisting of weakly interacting multiple domains are commonly observed in gels formed by LMWGs. The rheological properties, particularly the elasticity, of a gel with such a fiber network are weak due to the weak interactions between the individual domains. As achieving desirable rheological properties of such a gel is practically relevant, in this work, we demonstrate the engineering of gels with such a type of fiber network by controlling crystallization of the gelator. Two example gels formed by a glutamic acid derivative in a non-ionic surfactant Tween 80 and in propylene glycol were engineered by controlling the thermodynamic driving force for crystallization. For a fixed gelator concentration, the thermodynamic driving force was manipulated by controlling the temperature for fiber crystallization. It was observed that there exists an optimal temperature at which a gel with maximal elasticity can be fabricated. This will hopefully provide guidelines for producing high performance soft materials by engineering their fiber network structures.

D-STATCOM control in distribution networks with composite loads to ensure grid code compatible performance of photovoltaic generators

This paper proposes an optimal linear quadratic Gaussian (LQG) controller for D-STATCOM to improve the dynamic performance of distribution networks with photovoltaic generators. The controller is designed based on the H∞ norm of the uncertain system. The change in system model due to the variation of load compositions in the composite load is considered as an uncertain term in the design algorithm. The performance of the designed controller is demonstrated on a widely used test system. Simulation results indicate that the proposed controller can be a potential solution for improving the voltage stability of distribution networks.

An analytical model for optimal spectrum leasing under constraints of quality of service in CRNs

Cognitive radio improves spectrum efficiency and mitigates spectrum scarcity by allowing cognitive users to opportunistically access idle chunks of the spectrum owned by licensed users. In long-term spectrum leasing markets, secondary network operators make a decision about how much spectrum is optimal to fulfill their users' data transmission requirements. We study this optimization problem in multiple channel scenarios. Under the constrains of expected user admission rate and quality of service, we model the secondary network into a dynamic data transportation system. In this system, the spectrum accesses of both primary users and secondary users are in accordance with stochastic processes, respectively. The main metrics of quality of service we are concerned with include user admission rate, average transmission delay and stability of the delay. To quantify the relationship between spectrum provisioning and quality of service, we propose an approximate analytical model. We use the model to estimate the lower and upper bounds of the optimal amount of the spectrum. The distance between the bounds is relatively narrow. In addition, we design a simple algorithm to compute the optimum by using the bounds. We conduct numerical simulations on a slotted multiple channel dynamic spectrum access network model. Simulation results demonstrate the preciseness of the proposed model. Our work sheds light on the design of game and auction based dynamic spectrum sharing mechanisms in cognitive radio networks.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Global finite-time stabilisation for a class of stochastic nonlinear systems by output feedback

In this paper, the problem of global finite-time stabilisation by output feedback is considered for a class of stochastic nonlinear systems. First, based on homogeneous systems theory and the adding a power integrator technique, a homogeneous reduced order observer and control law are constructed in a recursive manner for the nominal system. Then, the homogeneous domination approach is used to deal with the nonlinearities in drift and diffusion terms; it is shown that the proposed output-feedback control law can guarantee that the closed-loop system is global finite-time stable in probability. Finally, simulation examples are carried out to demonstrate the effectiveness of the proposed control scheme.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.