Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process

Practical secure information flow in programming languages

If we classify variables in a program into various security levels, then a secure information flow analysis aims to verify statically that information in a program can flow only in ways consistent with the specified security levels. One well-studied approach is to formulate the rules of the secure information flow analysis as a type system. A major trend of recent research focuses on how to accommodate various sophisticated modern language features. However, this approach often leads to overly complicated and restrictive type systems, making them unfit for practical use. Also, problems essential to practical use, such as type inference and error reporting, have received little attention. This dissertation identified and solved major theoretical and practical hurdles to the application of secure information flow. ^ We adopted a minimalist approach to designing our language to ensure a simple lenient type system. We started out with a small simple imperative language and only added features that we deemed most important for practical use. One language feature we addressed is arrays. Due to the various leaking channels associated with array operations, arrays have received complicated and restrictive typing rules in other secure languages. We presented a novel approach for lenient array operations, which lead to simple and lenient typing of arrays. ^ Type inference is necessary because usually a user is only concerned with the security types for input/output variables of a program and would like to have all types for auxiliary variables inferred automatically. We presented a type inference algorithm B and proved its soundness and completeness. Moreover, algorithm B stays close to the program and the type system and therefore facilitates informative error reporting that is generated in a cascading fashion. Algorithm B and error reporting have been implemented and tested. ^ Lastly, we presented a novel framework for developing applications that ensure user information privacy. In this framework, core computations are defined as code modules that involve input/output data from multiple parties. Incrementally, secure flow policies are refined based on feedback from the type checking/inference. Core computations only interact with code modules from involved parties through well-defined interfaces. All code modules are digitally signed to ensure their authenticity and integrity. ^

Essays on firm behavior and firm performance

This dissertation comprises three individual chapters in an effort to examine different explanatory variables that affect firm performance. Chapter Two proposes an additional determinant of firm survival. Based on a detailed examination of firm survival in the British automobile industry between 1895 and 1970, we conclude that a firm's selection of submarket (defined by quality level) influenced survival. In contrast to findings for the US automobile industry, there is no evidence of first-mover advantage in the market as a whole. However, we do find evidence of first-mover advantage after conditioning on submarket choice. Chapter Three examines the effects of product line expansion on firm performance in terms of survival time. Based on a detailed examination of firm survival time in the British automobile industry between 1895 and 1970, we find that diversification exerts a positive effect on firm survival. Furthermore, our findings support the literature with respect to the impacts of submarket types, pre-entry experience, and timing of entry on firm survival time. Chapter Four examines corporate diversification in U.S. manufacturing and service firms. We develop measures of how related a firm's diverse activities are using input-output data and the NAILS classification to construct indexes of "vertical relatedness" and "complementarity". Strong relationships between these two measures are found. We utilize profitability and excess value as the measure for firm performance. Econometric analysis reveals that there is no relationship between the degree of relatedness of diversification and firm performance for the study period.

BabelFuse data fusion unit with precision wireless clock synchronisation

A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fastmoving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment due to the use of non-deterministic readily-available hardware (such as 802.11-based wireless) and inaccurate clock synchronisation protocols (such as Network Time Protocol (NTP)). As a result, the synchronisation of the clocks between robots can be out by tens-to-hundreds of milliseconds making correlation of data difficult and preventing the possibility of the units performing synchronised actions such as triggering cameras or intricate swarm manoeuvres. In this thesis, a complete data fusion unit is designed, implemented and tested. The unit, named BabelFuse, is able to accept sensor data from a number of low-speed communication buses (such as RS232, RS485 and CAN Bus) and also timestamp events that occur on General Purpose Input/Output (GPIO) pins referencing a submillisecondaccurate wirelessly-distributed "global" clock signal. In addition to its timestamping capabilities, it can also be used to trigger an attached camera at a predefined start time and frame rate. This functionality enables the creation of a wirelessly-synchronised distributed image acquisition system over a large geographic area; a real world application for this functionality is the creation of a platform to facilitate wirelessly-distributed 3D stereoscopic vision. A ‘best-practice’ design methodology is adopted within the project to ensure the final system operates according to its requirements. Initially, requirements are generated from which a high-level architecture is distilled. This architecture is then converted into a hardware specification and low-level design, which is then manufactured. The manufactured hardware is then verified to ensure it operates as designed and firmware and Linux Operating System (OS) drivers are written to provide the features and connectivity required of the system. Finally, integration testing is performed to ensure the unit functions as per its requirements. The BabelFuse System comprises of a single Grand Master unit which is responsible for maintaining the absolute value of the "global" clock. Slave nodes then determine their local clock o.set from that of the Grand Master via synchronisation events which occur multiple times per-second. The mechanism used for synchronising the clocks between the boards wirelessly makes use of specific hardware and a firmware protocol based on elements of the IEEE-1588 Precision Time Protocol (PTP). With the key requirement of the system being submillisecond-accurate clock synchronisation (as a basis for timestamping and camera triggering), automated testing is carried out to monitor the o.sets between each Slave and the Grand Master over time. A common strobe pulse is also sent to each unit for timestamping; the correlation between the timestamps of the di.erent units is used to validate the clock o.set results. Analysis of the automated test results show that the BabelFuse units are almost threemagnitudes more accurate than their requirement; clocks of the Slave and Grand Master units do not di.er by more than three microseconds over a running time of six hours and the mean clock o.set of Slaves to the Grand Master is less-than one microsecond. The common strobe pulse used to verify the clock o.set data yields a positive result with a maximum variation between units of less-than two microseconds and a mean value of less-than one microsecond. The camera triggering functionality is verified by connecting the trigger pulse output of each board to a four-channel digital oscilloscope and setting each unit to output a 100Hz periodic pulse with a common start time. The resulting waveform shows a maximum variation between the rising-edges of the pulses of approximately 39¥ìs, well below its target of 1ms.

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data.

Integrated Environment for Launch Vehicle Telemetry Data Management

The telemetry data processing operation intended for a given mission are pre-defined by an onboard telemetry configuration, mission trajectory and overall telemetry methodology have stabilized lately for ISRO vehicles. The given problem on telemetry data processing is reduced through hierarchical problem reduction whereby the sequencing of operations evolves as the control task and operations on data as the function task. The function task Input, Output and execution criteria are captured into tables which are examined by the control task and then schedules when the function task when the criteria is being met.

Vom Input zum Output

Infolge der durch die internationalen Schulvergleichstests eingeleiteten empirischen Wende in der Erziehungswissenschaft hat sich die Aufmerksamkeit vom Input schulischen Lehrens und Lernens zunehmend auf die Ergebnisse (Output) bzw. Wirkungen (Outcomes) verlagert. Die Kernfrage lautet nun: Was kommt am Ende in der Schule bzw. im Unterricht eigentlich heraus? Grundlegende Voraussetzung ergebnisorienterter Steuerung schulischen Unterrichts ist die Formulierung von Bildungsstandards. Wie Bildungsstandards mit Kompetenzmodellen und konkreten Aufgabenstellungen im Unterricht des Faches "Politik & Wirtschaft" verknüpft werden können, wird in diesem Beitrag einer genaueren Analyse unterzogen. Vor dem Hintergrund bildungstheoretischer Vorstellungen im Anschluss an Immanuel Kant kommen dabei das Literacy-Konzept der Pisa-Studie sowie die "Dokumentarische Methode" nach Karl Mannheim zur Anwendung.

Modeling Stock Order Flows and Learning Market-Making from Data

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling the market as a dynamic system and a reinforcement learning algorithm that learns profitable market-making strategies when run on this model. The sequence of buys and sells for a particular stock, the order flow, we model as an Input-Output Hidden Markov Model fit to historical data. When combined with the dynamics of the order book, this creates a highly non-linear and difficult dynamic system. Our reinforcement learning algorithm, based on likelihood ratios, is run on this partially-observable environment. We demonstrate learning results for two separate real stocks.

UK construction orders and output: A comparison of government and commercially available data 1995-2001

Because of the importance and potential usefulness of construction market statistics to firms and government, consistency between different sources of data is examined with a view to building a predictive model of construction output using construction data alone. However, a comparison of Department of Trade and Industry (DTI) and Office for National Statistics (ONS) series shows that the correlation coefcient (used as a measure of consistency) of the DTI output and DTI orders data and the correlation coefficient of the DTI output and ONS output data are low. It is not possible to derive a predictive model of DTI output based on DTI orders data alone. The question arises whether or not an alternative independent source of data may be used to predict DTI output data. Independent data produced by Emap Glenigan (EG), based on planning applications, potentially offers such a source of information. The EG data records the value of planning applications and their planned start and finish dates. However, as this data is ex ante and is not correlated with DTI output it is not possible to use this data to describe the volume of actual construction output. Nor is it possible to use the EG planning data to predict DTI construc-tion orders data. Further consideration of the issues raised reveal that it is not practically possible to develop a consistent predictive model of construction output using construction statistics gathered at different stages in the development process.

A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent alpha. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for alpha may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Measuring the promise of open data: Development of the Impact Monitoring Framework

It is a challenge to measure the impact of releasing data to the public since the effects may not be directly linked to particular open data activities or substantial impact may only occur several years after publishing the data. This paper proposes a framework to assess the impact of releasing open data by applying the Social Return on Investment (SROI) approach. SROI was developed for organizations intended to generate social and environmental benefits thus fitting the purpose of most open data initiatives. We link the four steps of SROI (input, output, outcome, impact) with the 14 high-value data categories of the G8 Open Data Charter to create a matrix of open data examples, activities, and impacts in each of the data categories. This Impact Monitoring Framework helps data providers to navigate the impact space of open data laying out the conceptual basis for further research.

Comparing Input- and Output-Oriented Measures of Technical Efficiency to Determine Local Returns to Scale in DEA Models

This paper shows how one can infer the nature of local returns to scale at the input- or output-oriented efficient projection of a technically inefficient input-output bundle, when the input- and output-oriented measures of efficiency differ.

CompactRIO: advanced data acquisition systems integration in CODAC Core System

Las herramientas de configuración basadas en lenguajes de alto nivel como LabVIEW permiten el desarrollo de sistemas de adquisición de datos basados en hardware reconfigurable FPGA muy complejos en un breve periodo de tiempo. La estandarización del ciclo de diseño hardware/software y la utilización de herramientas como EPICS facilita su integración con la plataforma de adquisición y control ITER CODAC CORE SYSTEM (CCS) basada en Linux. En este proyecto se propondrá una metodología que simplificará el ciclo completo de integración de plataformas novedosas, como cRIO, en las que el funcionamiento del hardware de adquisición puede ser modificado por el usuario para que éste se amolde a sus requisitos específicos. El objetivo principal de este proyecto fin de master es realizar la integración de un sistema cRIO NI9159 y diferentes módulos de E/S analógica y digital en EPICS y en CODAC CORE SYSTEM (CCS). Este último consiste en un conjunto de herramientas software que simplifican la integración de los sistemas de instrumentación y control del experimento ITER. Para cumplir el objetivo se realizarán las siguientes tareas: • Desarrollo de un sistema de adquisición de datos basado en FPGA con la plataforma hardware CompactRIO. En esta tarea se realizará la configuración del sistema y la implementación en LabVIEW para FPGA del hardware necesario para comunicarse con los módulos: NI9205, NI9264, NI9401.NI9477, NI9426, NI9425 y NI9476 • Implementación de un driver software utilizando la metodología de AsynDriver para integración del cRIO con EPICS. Esta tarea requiere definir todos los records necesarios que exige EPICS y crear las interfaces adecuadas que permitirán comunicarse con el hardware. • Implementar la descripción del sistema cRIO y del driver EPICS en el sistema de descripción de plantas de ITER llamado SDD. Esto automatiza la creación de las aplicaciones de EPICS que se denominan IOCs. SUMMARY The configuration tools based in high-level programing languages like LabVIEW allows the development of high complex data acquisition systems based on reconfigurable hardware FPGA in a short time period. The standardization of the hardware/software design cycle and the use of tools like EPICS ease the integration with the data acquisition and control platform of ITER, the CODAC Core System based on Linux. In this project a methodology is proposed in order to simplify the full integration cycle of new platforms like CompactRIO (cRIO), in which the data acquisition functionality can be reconfigured by the user to fits its concrete requirements. The main objective of this MSc final project is to develop the integration of a cRIO NI-9159 and its different analog and digital Input/Output modules with EPICS in a CCS. The CCS consists of a set of software tools that simplifies the integration of instrumentation and control systems in the International Thermonuclear Reactor (ITER) experiment. To achieve such goal the following tasks are carried out: • Development of a DAQ system based on FPGA using the cRIO hardware platform. This task comprehends the configuration of the system and the implementation of the mandatory hardware to communicate to the I/O adapter modules NI9205, NI9264, NI9401, NI9477, NI9426, NI9425 y NI9476 using LabVIEW for FPGA. • Implementation of a software driver using the asynDriver methodology to integrate such cRIO system with EPICS. This task requires the definition of the necessary EPICS records and the creation of the appropriate interfaces that allow the communication with the hardware. • Develop the cRIO system’s description and the EPICS driver in the ITER plant description tool named SDD. This development will automate the creation of EPICS applications, called IOCs.