Digital control system programming from process control diagrams

A graphical process control language has been developed as a means of defining process control software. The user configures a block diagram describing the required control system, from a menu of functional blocks, using a graphics software system with graphics terminal. Additions may be made to the menu of functional blocks, to extend the system capability, and a group of blocks may be defined as a composite block. This latter feature provides for segmentation of the overall system diagram and the repeated use of the same group of blocks within the system. The completed diagram is analyzed by a graphics compiler which generates the programs and data structure to realise the run-time software. The run-time software has been designed as a data-driven system which allows for modifications at the run-time level in both parameters and system configuration. Data structures have been specified to ensure efficient execution and minimal storage requirements in the final control software. Machine independence has been accomodated as far as possible using CORAL 66 as the high level language throughout the entire system; the final run-time code being generated by a CORAL 66 compiler appropriate to the target processor.

Design of an expandable manufacturing simulator through the application of object-oriented principles

In analysing manufacturing systems, for either design or operational reasons, failure to account for the potentially significant dynamics could produce invalid results. There are many analysis techniques that can be used, however, simulation is unique in its ability to assess detailed, dynamic behaviour. The use of simulation to analyse manufacturing systems would therefore seem appropriate if not essential. Many simulation software products are available but their ease of use and scope of application vary greatly. This is illustrated at one extreme by simulators which offer rapid but limited application whilst at the other simulation languages which are extremely flexible but tedious to code. Given that a typical manufacturing engineer does not posses in depth programming and simulation skills then the use of simulators over simulation languages would seem a more appropriate choice. Whilst simulators offer ease of use their limited functionality may preclude their use in many applications. The construction of current simulators makes it difficult to amend or extend the functionality of the system to meet new challenges. Some simulators could even become obsolete as users, demand modelling functionality that reflects the latest manufacturing system design and operation concepts. This thesis examines the deficiencies in current simulation tools and considers whether they can be overcome by the application of object-oriented principles. Object-oriented techniques have gained in popularity in recent years and are seen as having the potential to overcome any of the problems traditionally associated with software construction. There are a number of key concepts that are exploited in the work described in this thesis: the use of object-oriented techniques to act as a framework for abstracting engineering concepts into a simulation tool and the ability to reuse and extend object-oriented software. It is argued that current object-oriented simulation tools are deficient and that in designing such tools, object -oriented techniques should be used not just for the creation of individual simulation objects but for the creation of the complete software. This results in the ability to construct an easy to use simulator that is not limited by its initial functionality. The thesis presents the design of an object-oriented data driven simulator which can be freely extended. Discussion and work is focused on discrete parts manufacture. The system developed retains the ease of use typical of data driven simulators. Whilst removing any limitation on its potential range of applications. Reference is given to additions made to the simulator by other developers not involved in the original software development. Particular emphasis is put on the requirements of the manufacturing engineer and the need for Ihe engineer to carrv out dynamic evaluations.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Is there life in the old dogs yet? Making break-tests work on financial contagion

Many tests of financial contagion require a definition of the dates separating calm from crisis periods. We propose to use a battery of break search procedures for individual time series to objectively identify potential break dates in relationships between countries. Applied to the biggest European stock markets and combined with two well established tests for financial contagion, this approach results in break dates which correctly identify the timing of changes in cross-country transmission mechanisms. Application of break search procedures breathes new life into the established contagion tests, allowing for an objective, data-driven timing of crisis periods.

One date, one break?

This paper demonstrates that the conventional approach of using official liberalisation dates as the only existing breakdates could lead to inaccurate conclusions as to the effect of the underlying liberalisation policies. It also proposes an alternative paradigm for obtaining more robust estimates of volatility changes around official liberalisation dates and/or other important market events. By focusing on five East Asian emerging markets, all of which liberalised their financial markets in the late, and by using recent advances in the econometrics of structural change, it shows that (i) the detected breakdates in the volatility of stock market returns can be dramatically different to official liberalisation dates and (ii) the use of official liberalisation dates as breakdates can readily entail inaccurate inference. In contrast, the use of data-driven techniques for the detection of multiple structural changes leads to a richer and inevitably more accurate pattern of volatility evolution emerges in comparison with focussing on official liberalisation dates.

Breaking down the non-normality of daily stock returns

This paper investigates whether the non-normality typically observed in daily stock-market returns could arise because of the joint existence of breaks and GARCH effects. It proposes a data-driven procedure to credibly identify the number and timing of breaks and applies it on the benchmark stock-market indices of 27 OECD countries. The findings suggest that a substantial element of the observed deviations from normality might indeed be due to the co-existence of breaks and GARCH effects. However, the presence of structural changes is found to be the primary reason for the non-normality and not the GARCH effects. Also, there is still some remaining excess kurtosis that is unlikely to be linked to the specification of the conditional volatility or the presence of breaks. Finally, an interesting sideline result implies that GARCH models have limited capacity in forecasting stock-market volatility.

Monetary variability and monetary variables in the Franc zone

Failure to detect or account for structural changes in economic modelling can lead to misleading policy inferences, which can be perilous, especially for the more fragile economies of developing countries. Using three potential monetary policy instruments (Money Base, M0, and Reserve Money) for 13 member-states of the CFA Franc zone over the period 1989:11-2002:09, we investigate the magnitude of information extracted by employing data-driven techniques when analyzing breaks in time-series, rather than the simplifying practice of imposing policy implementation dates as break dates. The paper also tests Granger's (1980) aggregation theory and highlights some policy implications of the results.

Stock market efficiency before and after a financial liberalisation reform:do breaks in volatility dynamics matter?

This article focuses on the deviations from normality of stock returns before and after a financial liberalisation reform, and shows the extent to which inference based on statistical measures of stock market efficiency can be affected by not controlling for breaks. Drawing from recent advances in the econometrics of structural change, it compares the distribution of the returns of five East Asian emerging markets when breaks in the mean and variance are either (i) imposed using certain official liberalisation dates or (ii) detected non-parametrically using a data-driven procedure. The results suggest that measuring deviations from normality of stock returns with no provision for potentially existing breaks incorporates substantial bias. This is likely to severely affect any inference based on the corresponding descriptive or test statistics.

Intonation contour realisation for Standard Yorùbá text-to-speech synthesis:a fuzzy computational approach

This paper presents a novel intonation modelling approach and demonstrates its applicability using the Standard Yorùbá language. Our approach is motivated by the theory that abstract and realised forms of intonation and other dimensions of prosody should be modelled within a modular and unified framework. In our model, this framework is implemented using the Relational Tree (R-Tree) technique. The R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. Our R-Tree for an utterance is generated in two steps. First, the abstract structure of the waveform, called the Skeletal Tree (S-Tree), is generated using tone phonological rules for the target language. Second, the numerical values of the perceptually significant peaks and valleys on the S-Tree are computed using a fuzzy logic based model. The resulting points are then joined by applying interpolation techniques. The actual intonation contour is synthesised by Pitch Synchronous Overlap Technique (PSOLA) using the Praat software. We performed both quantitative and qualitative evaluations of our model. The preliminary results suggest that, although the model does not predict the numerical speech data as accurately as contemporary data-driven approaches, it produces synthetic speech with comparable intelligibility and naturalness. Furthermore, our model is easy to implement, interpret and adapt to other tone languages.

Dynamic Ontologies in Information Security Systems

Different types of ontologies and knowledge or metaknowledge connected to them are considered and analyzed aiming at realization in contemporary information security systems (ISS) and especially the case of intrusion detection systems (IDS) or intrusion prevention systems (IPS). Human-centered methods INCONSISTENCY, FUNNEL, CALEIDOSCOPE and CROSSWORD are algorithmic or data-driven methods based on ontologies. All of them interact on a competitive principle ‘survival of the fittest’. They are controlled by a Synthetic MetaMethod SMM. It is shown that the data analysis frequently needs an act of creation especially if it is applied to knowledge-poor environments. It is shown that human-centered methods are very suitable for resolutions in case, and often they are based on the usage of dynamic ontologies

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Class Size Reduction: A Significant Option for Improving Student Learning

Raising academic standards is a driving force behind public school initiatives. True educational reform requires a data-driven approach to choosing valid options for student improvement. We discuss current and continuing research that provides evidence that class size reduction, with related variables, is a significant option for improving student learning.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.