No description available.

The generation of electricity from tidal currents is an emerging industry with the potential to contribute to the UK energy supply in a predictable and sustainable way. The development of the technology requires the cost effective subsea installation of energy conversion systems in an energetic and challenging marine environment. One concept developed for the fastening of tidal energy converters to the seabed is the Active Gravity Base (AGB), which offers potential reductions in installation cost and time, relative to existing fastening methods. The performance of this concept in response to unsteady flow conditions is explored within this thesis. The dynamic behaviour of a tidal current is driven by a range of factors from gravitational forces of celestial bodies to high-frequency fluctuations of turbulent eddies. The response of the AGB concept to the unsteadiness of tidal currents is herein considered under the two broad time-scales; the directionality of the mean semi-diurnal cycle and the high frequency variations from a given mean flow velocity. The correlation between the direction and velocity of the tidal flow was assessed using hourly averaged data provided by the Admiralty Charts in the northern UK waters. The resulting directionality model was used to predict the performance of the AGB under a range of quasi-steady flow conditions. High frequency velocity measurements of a potential tidal energy site were obtained through collaboration with the University of Washington and the Pacific Northwest National Laboratory. This data was used to estimate the maximum perturbation from the mean velocity that can be expected on an annual basis. An experimental facility was developed within the re-circulating water flume at the University of Edinburgh to examine the dynamic loads generated by controllable two-dimensional flow perturbations. This was successfully achieved using a configuration of twin pitching foils with independent motion control. A relationship between the foil pitch angle and velocity perturbation time series was predicted using a vortex model of the foil wakes. This configuration was shown to be able to generate significant flow fluctuations within the range of reduced frequencies 0:06 ≤ k ≤ 1:9, with a peak gust intensity of Ig = 0:5. The numerical solution was validated against experimental results.

Multi-homing is a technology used by Internet Service Provider (ISP) to connect to the Internet via multiple networks. This connectivity enhances the network reliability and service quality of the ISP. However, using multi-networks may imply multiple costs on the ISP. To make full use of the underlying networks with minimum cost, a routing strategy is requested by ISPs. Of course, this optimal routing strategy depends on the pricing regime used by network providers. In this study we investigate a relatively new pricing regime – top-percentile pricing. Under top-percentile pricing, network providers divide the charging period into several fixed length time intervals and calculate their cost according to the traffic volume that has been shipped during the θ-th highest time interval. Unlike traditional pricing regimes, the network design under top-percentile pricing has not been fully studied. This paper investigates the optimal routing strategy in case where network providers charge ISPs according to top-percentile pricing. We call this problem the Top-percentile Traffic Routing Problem (TpTRP). As the ISP cannot predict next time interval’s traffic volume in real world application, in our setting up the TpTRP is a multi-stage stochastic optimisation problem. Routing decisions should be made at the beginning of every time period before knowing the amount of traffic that is to be sent. The stochastic nature of the TpTRP forms the critical difficulty of this study. In this paper several approaches are investigated in either the modelling or solving steps of the problem. We begin by exploring several simplifications of the original TpTRP to get an insight of the features of the problem. Some of these allow analytical solutions which lead to bounds on the achievable optimal solution. We also establish bounds by investigating several “naive” routing policies. In the second part of this work, we build the multi-stage stochastic programming model of the TpTRP, which is hard to solve due to the integer variables introduced in the calculation of the top-percentile traffic. A lift-and-project based cutting plane method is investigated in solving the SMIP for very small examples of TpTRP. Nevertheless it is too inefficient to be applicable on large sized instances. As an alternative, we explore the solution of the TpTRP as a Stochastic Dynamic Programming (SDP) problem by a discretization of the state space. This SDP model gives us achievable routing policies on small size instances of the TpTRP, which of course improve the naive routing policies. However, the solution approach based on SDP suffers from the curse of dimensionality which restricts its applicability. To overcome this we suggest using Approximate Dynamic Programming (ADP) which largely avoids the curse of dimensionality by exploiting the structure of the problem to construct parameterized approximations of the value function in SDP and train the model iteratively to get a converged set of parameters. The resulting ADP model with discrete parameter for every time interval works well for medium size instances of TpTRP, though it still requires too long to be trained for large size instances. To make the realistically sized TpTRP problem solvable, we improve on the ADP model by using Bezier Curves/Surfaces to do the aggregation over time. This modification accelerates the efficiency of parameter training in the solution of the ADP model, which makes the realistically sized TpTRP tractable.

It has been broadly recognised that today’s manufacturing organisations face increasing pressures from continuous and unexpected changes in the business environment such as changes in product types, changes in demand pattern, changes in manufacturing technologies etc. To enable manufacturing organisations to rapidly and timely deal with these changes, operational decisions (e.g., process planning and production scheduling) have to be integrated with dynamic system restructure or reconfiguration so that manufacturing organisations do not only use the flexible resource utilisations to deal with these changes, but also can dynamically reconfigure their existing system structures in response these changes. A manufacturing system concept and implementation methodology is proposed by the Exeter Manufacturing Enterprise Centre (XMEC), which is called the Dynamically Integrated Manufacturing System (DIMS). The overall aim of DIMS is to provide a systematic modelling and control framework in which operational decisions can be integrated with the dynamic system restructuring decisions so as to help manufacturing systems to dynamically deal with changes in the business environment. This PhD research is a part of DIMS research, which focuses on the investigation on operational control in DIMS. Based on the established agent-based modelling architecture in DIMS, this research develops two agent bidding mechanisms for the hierarchical control of production planning and scheduling. These two mechanisms work together to assist manufacturing systems in making optimal and flexible operational decisions in response to changes in the business environment. The first mechanism is the iterative agent bidding mechanism based on a Genetic Algorithm (GA) which facilitates the determination of the optimal or near optimal allocation of a production job containing a set of sub-jobs to a pool of heterarchical resources. The second mechanism is the hierarchical agent bidding mechanism which enables product orders to be cost-efficiently and flexibly planned and scheduled to meet the orders’ due dates. The novelty of this mechanism is that it enables orders to be fulfilled within structural constraints of manufacturing systems as far as possible and however enables resources to be regrouped flexibly across system boundaries when orders cannot be fulfilled within structural constraints of manufacturing systems.

The phenomenal growth of Information Technology requires us to elicit, store and maintain huge volumes of data. Analyzing this data for various purposes is becoming increasingly important. Data mining consists of applying data analysis and discovery algorithms that under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data. We present two techniques based on using Logic programming tools for data mining. Data mining analyzes data by extracting patterns which describe its structure and discovers co-relations in the form of rules. We distinguish analysis methods as visual and non-visual and present one application of each. We explain that our focus on the field of Logic Programming makes some of the very complex tasks related to Web based data mining and dynamic content generation, simple and easy to implement in a uniform framework.

Resources are said to be fragmented in the network when they are available in non-contiguous blocks, and calls are dropped as they may not end sufficient resources. Hence, available resources may remain unutilized. In this thesis, the effect of resource fragmentation (RF) on RSVP-controlled networks was studied and new algorithms were proposed to reduce the effect of RF. In order to minimize the effect of RF, resources in the network are dynamically redistributed on different paths to make them available in contiguous blocks. Extra protocol messages are introduced to facilitate resource redistribution in the network. The Dynamic Resource Redistribution (DRR) algorithm when used in conjunction with RSVP, not only increased the number of calls accommodated into the network but also increased the overall resource utilization of the network. Issues such as how many resources need to be redistributed and of which call(s), and how these choices affect the redistribution process were investigated. Further, various simulation experiments were conducted to study the performance of the DRR algorithm on different network topologies with varying traffic characteristics.

Designing Dynamic Textile Patterns Progress in chemistry, fibres and polymers technology provides textile designers with new expressive materials, making it possible to design dynamic textile patterns, where several different expressions are inherent in the same textile, textiles that, for example, could alternate between a striped and checkered pattern. Textiles are traditionally designed and produced to keep a given, static expression during their life cycle; a striped pattern is supposed to keep its stripes. In the same way textile designers are trained to design for static expressions, where patterns and decorations are meant to last in a specific manner. However, things are changing. The textile designer now deals also with a new raw material, a dynamic textile, ready to be further designed, developed and/or programmed, depending on functional context. This transformation in practice is not an easy one for the designers. Designers need to learn how to design with these new materials and their specific qualities, to be able to develop the full expressional potential inherent in “smart textiles design”. The aim of this thesis is to display, and discuss, a methodology for designing dynamic textile patterns. So far, something that mainly has been seen in different experimental and conceptual prototypes, in artistic expressions and for commercial efforts etc. In terms of basic experimental research this thesis explores the turn in textile design practice through a series of design experiments with focus on contributing to identifying and characterizing new design variables, new design methods and new design techniques as a foundation for dynamic textile patterns. / <p>Disputationen sker den 1:a juni 2010, kl. 13.00 i Textilmuseet, Druveforsvägen 8, Borås. Opponent: Senior Lecturer, Mary- Ann Hansen, Danmarks Designskole, Denmark</p>

Digital disruptions are changing the healthcare ecosystem, requiring organizations to rethink IT strategies and develop new IT competencies. This study focuses on the exploitation and exploration tension that managers face within an IT organization of a global pharmaceutical company, and their response to the related environmental exigencies in healthcare. Dynamic capability theory (DC) provides the overall framing, while ambidexterity provides an understanding of top management’s response to the exploit–explore tensions that arise. This engaged scholarship longitudinal case study takes a shifting stories methodological approach to elicit participants’ reflections and interpretations of significant events, including their own role in evolving the ambidextrous posture of the IT organization. Through rich description stories, process related decisions have been revealed, and have provided an understanding into organizational reconfiguration of IT resources. Subsequently, this resulted in a situated grounded model for understanding DC and OA for this case. Practical insights are offered on how dynamic capability theory could be applied for IT management to be smarter at becoming more ambidextrous.

This thesis describes the study of a series of platinum complexes, with particular emphasis towards hydrosilation. Platinum bis(phosphine) azodicarbonyl complexes Pt(PRI 3)2(R20CNNCOR2) (RI = Ph, Me; R2 = Ph, Me, OEt, Pri) were synthesised and studied. Multinuclear NMR spectroscopy on Pt(PRI3)2(R20CNNCOR2) revealed that the dicarbonyl substituted azo ligand is co-ordinated asymmetrically, consistent with a five membered, Pt-N-N-C-O ring. The crystal structure of Pt(PPh3)2(Pri02CNNC02Pri) shows that the co-ordination sphere of platinum is essentially square planar and co-planar with the five-membered, Pt(1)-0(1)-C(5)-N(2)-N(1) ring. The Pt(PRI 3)lR20CNNCOR2) complexes show sensitivity towards chlorinated solvents (CH2CI2, CHCI3) under photolysis conditions forming the corresponding platinum bis(phosphine) dichloride complexes; the same products are formed in a slower thermal reaction but only for complexes with azodicarboxylate ligands. Complexes with azodicarboxylate ligands also react photochemically with ethylene in ds-THF yielding Pt(PPh3)2(C2H4) but the azodiacyl analogues are inert in this respect. Azodicarboxylate compounds R02CNNC02R (R = Et, Pri, But) are inhibitors of the catalytic activity of [(Pt {174 _(CH2=CHSiMe2hO }h {.u-( CH2=CHSiMe2)20}] for the hydrosilation reaction. The inhibited species can be decomposed thermally or photoch~mically to give active hydrosilation catalysts. It was found that the bulky azo compound But02CNNC02But was the least effective inhibitor of [(Pt{ 174 - '(CH2=CHSiMe2hO} )2(P-( CH2=CHSiMe2)20)]. The photochemistry of platinum bis(phosphine) malonates and phthalates was found to be limited, and their reactivities were much lower compared to the analogous oxalate complexes. Silyl hydride complexes, cis-Pt(PCY3)2(H)(SiR3), were synthesised from the reaction of Pt(PCY3)2 and the corresponding silane. These complexes were undergo dynamic exchange in solution. Two exchange processes were identified; the first involves mutual phosphine exchange, i.e. positional interchange between the hydride and the silyl ligands. The second process occurs at higher temperatures (above 290 K) and involves the elimination and re-addition of the silane ligand HSiR3. Thermodynamic and activation parameters are obtained for cis-Pt(PCY3)2(SiR3) (R = Ph, SiR3 = SiMe2CH2CH=CH2, SiMe2Et). The reaction of Pt(PCY3)2 with the disilane HSiMe2(l,2-C6~)SiMe2H is thought to form a Pt(IV) bis(silyl) dihydride trigonal bipyramidal species of the form, Pt(PCY3)(H)2[SiMe2(1,2-C6~)SiMe2]' where the hydride ligands are in the axial positions. All of the platinum silyl hydride complexes studied degrade thermally to form trans-Pt(PCY3)2(H)2 at, or above, room temperature.

The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and values.