An investigation into the use of knowledge representation in computer-integrated manufacturing

In a certain automobile factory, batch-painting of the body types in colours is controlled by an allocation system. This tries to balance production with orders, whilst making optimally-sized batches of colours. Sequences of cars entering painting cannot be optimised for easy selection of colour and batch size. `Over-production' is not allowed, in order to reduce buffer stocks of unsold vehicles. Paint quality is degraded by random effects. This thesis describes a toolkit which supports IKBS in an object-centred formalism. The intended domain of use for the toolkit is flexible manufacturing. A sizeable application program was developed, using the toolkit, to test the validity of the IKBS approach in solving the real manufacturing problem above, for which an existing conventional program was already being used. A detailed statistical analysis of the operating circumstances of the program was made to evaluate the likely need for the more flexible type of program for which the toolkit was intended. The IKBS program captures the many disparate and conflicting constraints in the scheduling knowledge and emulates the behaviour of the program installed in the factory. In the factory system, many possible, newly-discovered, heuristics would be awkward to represent and it would be impossible to make many new extensions. The representation scheme is capable of admitting changes to the knowledge, relying on the inherent encapsulating properties of object-centres programming to protect and isolate data. The object-centred scheme is supported by an enhancement of the `C' programming language and runs under BSD 4.2 UNIX. The structuring technique, using objects, provides a mechanism for separating control of expression of rule-based knowledge from the knowledge itself and allowing explicit `contexts', within which appropriate expression of knowledge can be done. Facilities are provided for acquisition of knowledge in a consistent manner.

Predicting and diagnosing faults in wastewater treatment process by Bayesian Belief Networks

Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.

The development of an expert system for the identification anodic coating process defects as a contribution to the dissemination of anodizing technology

Initially this thesis examines the various mechanisms by which technology is acquired within anodizing plants. In so doing the history of the evolution of anodizing technology is recorded, with particular reference to the growth of major markets and to the contribution of the marketing efforts of the aluminium industry. The business economics of various types of anodizing plants are analyzed. Consideration is also given to the impact of developments in anodizing technology on production economics and market growth. The economic costs associated with work rejected for process defects are considered. Recent changes in the industry have created conditions whereby information technology has a potentially important role to play in retaining existing knowledge. One such contribution is exemplified by the expert system which has been developed for the identification of anodizing process defects. Instead of using a "rule-based" expert system, a commercial neural networks program has been adapted for the task. The advantages of neural networks over 'rule-based' systems is that they are better suited to production problems, since the actual conditions prevailing when the defect was produced are often not known with certainty. In using the expert system, the user first identifies the process stage at which the defect probably occurred and is then directed to a file enabling the actual defects to be identified. After making this identification, the user can consult a database which gives a more detailed description of the defect, advises on remedial action and provides a bibliography of papers relating to the defect. The database uses a proprietary hypertext program, which also provides rapid cross-referencing to similar types of defect. Additionally, a graphics file can be accessed which (where appropriate) will display a graphic of the defect on screen. A total of 117 defects are included, together with 221 literature references, supplemented by 48 cross-reference hyperlinks. The main text of the thesis contains 179 literature references. (DX186565)

Multinationality-performance relationship:a meta-analysis

- We conduct a Meta-analysis of 54 papers that study the relationship between multinationality and firm performance. The aim is to understand if any systematic relationships exist between the characteristics of each study and the reported results of linear and curvilinear regressions to examine the multinationality-performance relationship. - Our main finding, robust to different specifications and to different weights for each observation, is that when analysis is based on non-US data, the reported return to multinationality is higher. However, this relationship for non-US firms is usually U-shaped rather than inverted U-shaped. This indicates that US firms face lower returns to internationalization than other firms but are less likely to incur losses in the early stages of internationalization. - The findings also highlight the differences that are reported when comparing regression and non-regression based techniques. Our results suggest that in this area regression based analysis is more reliable than say ANOVA or other related approaches. - Other characteristics that influence the estimated rate of return and its shape across different studies are: the measure of multinationality used; size distribution of the sample; and the use of market-based indicators to measure firm performance. Finally, we find no evidence of publication bias.

Living with uncertainty in the age of runtime models

Uncertainty can be defined as the difference between information that is represented in an executing system and the information that is both measurable and available about the system at a certain point in its life-time. A software system can be exposed to multiple sources of uncertainty produced by, for example, ambiguous requirements and unpredictable execution environments. A runtime model is a dynamic knowledge base that abstracts useful information about the system, its operational context and the extent to which the system meets its stakeholders' needs. A software system can successfully operate in multiple dynamic contexts by using runtime models that augment information available at design-time with information monitored at runtime. This chapter explores the role of runtime models as a means to cope with uncertainty. To this end, we introduce a well-suited terminology about models, runtime models and uncertainty and present a state-of-the-art summary on model-based techniques for addressing uncertainty both at development- and runtime. Using a case study about robot systems we discuss how current techniques and the MAPE-K loop can be used together to tackle uncertainty. Furthermore, we propose possible extensions of the MAPE-K loop architecture with runtime models to further handle uncertainty at runtime. The chapter concludes by identifying key challenges, and enabling technologies for using runtime models to address uncertainty, and also identifies closely related research communities that can foster ideas for resolving the challenges raised. © 2014 Springer International Publishing.

A high sensitivity long period grating Mach-Zehnder refractometer

A refractive index sensing system has been demonstrated, which is based upon an in-line fibre long period grating Mach-Zehnder interferometer with a heterodyne interrogation technique. This sensing system has comparable accuracy to laboratory-based techniques used in industry such as high performance liquid chromatography and UV spectroscopy. The advantage of this system is that measurements can be made in-situ for applications in continuous process control. Compared to other refractive index sensing schemes using LPGs, this approach has two main advantages. Firstly, the system relies on a simple optical interrogation system and therefore has the real potential for being low cost, and secondly, so far as we are aware it provides the highest refractive index resolution reported for any fibre LPG device.

A multi-modular second life hybrid battery energy storage system for utility grid applications

The modern grid system or the smart grid is likely to be populated with multiple distributed energy sources, e.g. wind power, PV power, Plug-in Electric Vehicle (PEV). It will also include a variety of linear and nonlinear loads. The intermittent nature of renewable energies like PV, wind turbine and increased penetration of Electric Vehicle (EV) makes the stable operation of utility grid system challenging. In order to ensure a stable operation of the utility grid system and to support smart grid functionalities such as, fault ride-through, frequency response, reactive power support, and mitigation of power quality issues, an energy storage system (ESS) could play an important role. A fast acting bidirectional energy storage system which can rapidly provide and absorb power and/or VARs for a sufficient time is a potentially valuable tool to support this functionality. Battery energy storage systems (BESS) are one of a range suitable energy storage system because it can provide and absorb power for sufficient time as well as able to respond reasonably fast. Conventional BESS already exist on the grid system are made up primarily of new batteries. The cost of these batteries can be high which makes most BESS an expensive solution. In order to assist moving towards a low carbon economy and to reduce battery cost this work aims to research the opportunities for the re-use of batteries after their primary use in low and ultra-low carbon vehicles (EV/HEV) on the electricity grid system. This research aims to develop a new generation of second life battery energy storage systems (SLBESS) which could interface to the low/medium voltage network to provide necessary grid support in a reliable and in cost-effective manner. The reliability/performance of these batteries is not clear, but is almost certainly worse than a new battery. Manufacturers indicate that a mixture of gradual degradation and sudden failure are both possible and failure mechanisms are likely to be related to how hard the batteries were driven inside the vehicle. There are several figures from a number of sources including the DECC (Department of Energy and Climate Control) and Arup and Cenex reports indicate anything from 70,000 to 2.6 million electric and hybrid vehicles on the road by 2020. Once the vehicle battery has degraded to around 70-80% of its capacity it is considered to be at the end of its first life application. This leaves capacity available for a second life at a much cheaper cost than a new BESS Assuming a battery capability of around 5-18kWhr (MHEV 5kWh - BEV 18kWh battery) and approximate 10 year life span, this equates to a projection of battery storage capability available for second life of >1GWhrs by 2025. Moreover, each vehicle manufacturer has different specifications for battery chemistry, number and arrangement of battery cells, capacity, voltage, size etc. To enable research and investment in this area and to maximize the remaining life of these batteries, one of the design challenges is to combine these hybrid batteries into a grid-tie converter where their different performance characteristics, and parameter variation can be catered for and a hot swapping mechanism is available so that as a battery ends it second life, it can be replaced without affecting the overall system operation. This integration of either single types of batteries with vastly different performance capability or a hybrid battery system to a grid-tie 3 energy storage system is different to currently existing work on battery energy storage systems (BESS) which deals with a single type of battery with common characteristics. This thesis addresses and solves the power electronic design challenges in integrating second life hybrid batteries into a grid-tie energy storage unit for the first time. This study details a suitable multi-modular power electronic converter and its various switching strategies which can integrate widely different batteries to a grid-tie inverter irrespective of their characteristics, voltage levels and reliability. The proposed converter provides a high efficiency, enhanced control flexibility and has the capability to operate in different operational modes from the input to output. Designing an appropriate control system for this kind of hybrid battery storage system is also important because of the variation of battery types, differences in characteristics and different levels of degradations. This thesis proposes a generalised distributed power sharing strategy based on weighting function aims to optimally use a set of hybrid batteries according to their relative characteristics while providing the necessary grid support by distributing the power between the batteries. The strategy is adaptive in nature and varies as the individual battery characteristics change in real time as a result of degradation for example. A suitable bidirectional distributed control strategy or a module independent control technique has been developed corresponding to each mode of operation of the proposed modular converter. Stability is an important consideration in control of all power converters and as such this thesis investigates the control stability of the multi-modular converter in detailed. Many controllers use PI/PID based techniques with fixed control parameters. However, this is not found to be suitable from a stability point-of-view. Issues of control stability using this controller type under one of the operating modes has led to the development of an alternative adaptive and nonlinear Lyapunov based control for the modular power converter. Finally, a detailed simulation and experimental validation of the proposed power converter operation, power sharing strategy, proposed control structures and control stability issue have been undertaken using a grid connected laboratory based multi-modular hybrid battery energy storage system prototype. The experimental validation has demonstrated the feasibility of this new energy storage system operation for use in future grid applications.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.

Formal methods @ runtime

Heuristics, simulation, artificial intelligence techniques and combinations thereof have all been employed in the attempt to make computer systems adaptive, context-aware, reconfigurable and self-managing. This paper complements such efforts by exploring the possibility to achieve runtime adaptiveness using mathematically-based techniques from the area of formal methods. It is argued that formal methods @ runtime represents a feasible approach, and promising preliminary results are summarised to support this viewpoint. The survey of existing approaches to employing formal methods at runtime is accompanied by a discussion of their challenges and of the future research required to overcome them. © 2011 Springer-Verlag.

Investigating the ability of antibodies to recognize specific oxidized protein epitopes

There is a growing awareness that inflammatory diseases have an oxidative pathology, which can result in specific oxidation of amino acids within proteins. Antibody-based techniques for detecting oxidative posttranslational modifications (oxPTMs) are often used to identify the level of protein oxidation. There are many commercially available antibodies but some uncertainty to the potential level of cross reactivity they exhibit; moreover little information regarding the specific target epitopes is available. The aim of this work was to investigate the potential of antibodies to distinguish between select peptides with and without oxPTMs. Two peptides, one containing chlorotyrosine (DY-Cl-EDQQKQLC) and the other an unmodified tyrosine (DYEDQQKQLC) were synthesized and complementary anti-sera were produced in sheep using standard procedures. The anti-sera were tested using a half-sandwich ELISA and the anti-serum raised against the chloro-tyrosine containing peptide showed increased binding to the chlorinated peptide, whereas the control anti-serum bound similarly to both peptides. This suggested that antibodies can discriminate between similar peptide sequences with and without an oxidative modification. A peptide (STSYGTGC) and its variants with chlorotyrosine or nitrotyrosine were produced. The anti-sera showed substantially less binding to these alternative peptides than to the original peptides the anti-sera were produced against. Work is ongoing to test commercially available antibodies against the synthetic peptides as a comparison to the anti-sera produced in sheep. In conclusion, the antisera were able to distinguish between oxidatively modified and unmodified peptides, and two different sequences around the modification site.