Techno-economic Appraisal of Concentrating Solar Power Systems (CSP)

The diffusion of Concentrating Solar Power Systems (CSP) systems is currently taking place at a much slower pace than photovoltaic (PV) power systems. This is mainly because of the higher present cost of the solar thermal power plants, but also for the time that is needed in order to build them. Though economic attractiveness of different Concentrating technologies varies, still PV power dominates the market. The price of CSP is expected to drop significantly in the near future and wide spread installation of them will follow. The main aim of this project is the creation of different relevant case studies on solar thermal power generation and a comparison betwwen them. The purpose of this detailed comparison is the techno-economic appraisal of a number of CSP systems and the understanding of their behaviour under various boundary conditions. The CSP technologies which will be examined are the Parabolic Trough, the Molten Salt Power Tower, the Linear Fresnel Mirrors and the Dish Stirling. These systems will be appropriatly sized and simulated. All of the simulations aim in the optimization of the particular system. This includes two main issues. The first is the achievement of the lowest possible levelized cost of electricity and the second is the maximization of the annual energy output (kWh). The project also aims in the specification of these factors which affect more the results and more specifically, in what they contribute to the cost reduction or the power generation. Also, photovoltaic systems will be simulated under same boundary conditions to facolitate a comparison between the PV and the CSP systems. Last but not leats, there will be a determination of the system which performs better in each case study.

Use of geographical information systems as a tool for predicting fish yield in tropical reservoirs : case study on Sri Lankan reservoirs

Use of geographical information systems (GIS) in inland fisheries has hitherto been essentially restricted to site evaluation for aquaculture development and assessment of limnological changes in time and space in individual water bodies. The present GIS study was conducted on the land-use pattern of the catchments of nine reservoirs in Sri Lanka, for which detailed fishery data, viz. yield, fishing intensity, landing size of major constituent species, together with selected limnological data such as conductivity and chlorophyll-a, were available. Potential statistical relationships (linear, curvilinear, exponential and second-order polynomial) of fish yield (FY, in kg ha−1 yr−1) to different land-use patterns, such as forest cover (FC, in km2) and shrub-land (SL, in km2), either singly, or in combination, and/or the ratio of each land type to reservoir area (RA in km2) and reservoir capacity (RC in km3), were explored. Highly significant relationships were evident between FY to the ratio of SL and/or FC+SL to RA and/or RC. Similarly, the above land-use types to RA and RC ratios were significantly related to limnological features of the reservoirs. The relationships of FY to various parameters obtained in this study were much better correlated than those relationships of FY to limnological and biological parameters used in yield prediction in tropical and temperate lacustrine waters previously.

Improving the performance of equalization in communication systems

In this paper, research on exploring the potential of several popular equalization techniques while overcoming their disadvantages has been conducted. First, extensive literature survey on equalization is conducted. The focus has been placed on several popular linear equalization algorithm such as the conventional least-mean-square (LMS) algorithm, the recursive least squares (RLS) algorithm, the fi1tered-X LMS algorithm and their development. The approach in analysing the performance of the filtered-X LMS Algorithm, a heuristic method based on linear time-invariant operator theory is provided to analyse the robust perfonnance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filtered X structure. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

An observer design procedure for a class of nonlinear time-delay systems

This paper considers a class of uncertain, nonlinear differential state delayed control systems and presents a reduced-order observer design procedure to asymptotically estimate any vector state functionals. The method proposed involves decomposition of the delayed portion of the system into two parts: a matched and mismatched part. Provided that the rank of the mismatched part is less than the number of the outputs, a reduced-order linear functional observer, with any prescribed stability margin, can be constructed by using a simple procedure. A numerical example is given to illustrate the new design procedure and its features.

Study of ensemble strategies in discovering linear casual models

Determining the causal structure of a domain is frequently a key task in the area of Data Mining and Knowledge Discovery. This paper introduces ensemble learning into linear causal model discovery, then examines several algorithms based on different ensemble strategies including Bagging, Adaboost and GASEN. Experimental results show that (1) Ensemble discovery algorithm can achieve an improved result compared with individual causal discovery algorithm in terms of accuracy; (2) Among all examined ensemble discovery algorithms, BWV algorithm which uses a simple Bagging strategy works excellently compared to other more sophisticated ensemble strategies; (3) Ensemble method can also improve the stability of parameter estimation. In addition, Ensemble discovery algorithm is amenable to parallel and distributed processing, which is important for data mining in large data sets.

Partial-state observers for nonlinear systems

This note deals with the design of reduced-order observers for a class of nonlinear systems. The order reduction of the observer is achieved by only estimating a required partial set of the state vector. Necessary and sufficient conditions are derived for the existence of reduced-order observers. An observer design procedure based on linear matrix inequalities is given. A numerical example is given to illustrate the design method.

Dynamic output feedback sliding-mode control using pole placement and linear functional observers

This paper presents a methodological approach to design dynamic output feedback sliding-mode control for a class of uncertain dynamical systems. The control action consists of the equivalent control and robust control components. The design of the equivalent control and the sliding function are based on the pole-placement technique. Linear functional observers are developed to implement the sliding function and the equivalent control. Stability of the resulting system under the proposed control scheme is guaranteed. A numerical example is given to demonstrate its efficacy.

State and input simultaneous estimation for a class of time-delay systems with uncertainties

This brief addresses the problem of estimation of both the states and the unknown inputs of a class of systems that are subject to a time-varying delay in their state variables, to an unknown input, and also to an additive uncertain, nonlinear disturbance. Conditions are derived for the solvability of the design matrices of a reduced-order observer for state and input estimation, and for the stability of its dynamics. To improve computational efficiency, a delay-dependent asymptotic stability condition is then developed using the linear matrix inequality formulation. A design procedure is proposed and illustrated by a numerical example.

Design of minimal-order stabilisers for multi-machine unstable power systems

A reduced dynamics stabiliser for multi-machine power systems is presented in this paper. The design of the stabiliser is based on the theory of linear functional observers and the solution of a simple parameter optimisation problem. The order of the stabiliser could be as low as the number of machines in the system. The design is applied to an open-loop unstable multi-machine power system.

Towards optimization of a real-world robotic-sensor system of systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.

Design of reduced-order observers for neutral time-delay systems

This paper presents a method for the design of reduced-order observers for a class of linear time-delay systems of the neutral-type. Conditions for the existence of reduced-order observers that are capable of asymptotically estimating any given function of the state vector are derived. A step-by-step design procedure is given for the determination of the observer parameters. A numerical example is given to illustrate the design procedure.

Protecting information systems from DDoS attack using multicore methodology

Previous work, in the area of defense systems has focused on developing a firewall like structure, in order to protect applications from attacks. The major drawback for implementing security in general, is that it affects the performance of the application they are trying to protect. In fact, most developers avoid implementing security at all. With the coming of new multicore systems, we might at last be able to minimize the performance issues that security places on applications. In our bodyguard framework we propose a new kind of defense that acts alongside, not in front, of applications. This means that performance issues that effect system applications are kept to a minimum, but at the same time still provide high grade security. Our experimental results demonstrate that a ten to fifteen percent speedup in performance is possible, with the potential of greater speedup.

Fitting fuzzy measures by linear programming. Programming library fmtools

We discuss the problem of learning fuzzy measures from empirical data. Values of the discrete Choquet integral are fitted to the data in the least absolute deviation sense. This problem is solved by linear programming techniques. We consider the cases when the data are given on the numerical and interval scales. An open source programming library which facilitates calculations involving fuzzy measures and their learning from data is presented.

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.

Maximising the benefits of radio frequency identification technology (RFID) integration in clinical contexts : a linear conduit

Radio Frequency Identification Technology (RFIO) has been explored for various process enhancements in clinical contexts, particularly hospitals, for asset tracking. The technology has been accepted in such environments, as it is inexpensive and, in principle, uncomplicated to integrate with other clinical support systems. It is perceived to offer many benefits to currently resource critical/strained clinical environments. This research investigation focuses on the exploitation of the potential of the technology, to enhance processes in clinical environments. In this paper, the researchers aimed to uncover if the technology, as presently deployed, has been able to achieve its potential and, in particular, if it has been fully integrated into processes in a way that maximises the benefits that were perceived. This research is part of a larger investigation that aims to develop a meta-model for integration of RFIO into processes in a form that will maximise benefits that may be achievable in clinical environments. As the first phase of the investigation, the key learning from a clinical context (hospital), which has deployed RFIO and attempted to integrate it into the processes, to enable better efficiencies, is presented in this paper. The case method has been used as a methodological framework. Two clinical contexts (hospitals) are involved in the larger project, which constitutes two phases. In Phase 1, semi structured interviews were conducted with a selected number of participants involved with the RFIO deployment project, before and after, in clinical context 1 (hereinafter named as CCl). The results were then synthesised drawing a set of key learning, from different viewpoints (implementers and users), as reported in this paper. These results outline a linear conduit for a new proposed implementation (CC2). On completion of the phase II, the researchers aim to construct a meta-model for maximising the potential of RFIO in clinical contexts. This paper is limited to the first phase that aims to draw key learning to inform the linear conduit.