LCG-1 Deployment and usage experience

LCG-1 is the second release of the software framework for the LHC Computing Grid project. In our work we describe the installation process, arising problems and their solutions, and configuration tuning details of the complete LCG-1 site, including all LCG elements required for the self-sufficient site.Comment: To be published in the proceedings of the XIX International Symposium on Nuclear Electronics and Computing (NEC'2003), Bulgaria, Varna, 15-20 September, 200

Implementation of self-tuning control for turbine generators

PhD ThesisThis thesis documents the work that has been done towards the development of a 'practical' self-tuning controller for turbine generator plant. It has been shown by simulation studies and practical investigations using a micro-alternator system that a significant enhancement in the overall performance in terms of control and stability can be achieved by improving the primary controls of a turbine generator using self-tuning control. The self-tuning AVR is based on the Generalised Predictive Control strategy. The design of the controller has been done using standard off-the-shelf microprocessor hardware and structured software design techniques. The proposed design is thus flexible, cost-effective, and readily applicable to 'real' generating plant. Several practical issues have been tackled during the design of the self-tuning controller and techniques to improve the robustness of the measurement system, controller, and parameter estimator have been proposed and evaluated. A simple and robust measurement system for plant variables based on software techniques has been developed and its suitability for use in the self-tuning controller has been practically verified. The convergence, adaptability, and robustness aspects of the parameter estimator have been evaluated and shown to be suitable for long-term operation in 'real' self-tuning controllers. The self-tuning AVR has been extensively evaluated under normal and fault conditions of the turbine generator. It has been shown that this new controller is superior in performance when compared with a conventional lag-lead type of fixed-parameter digital AVR. The use of electrical power as a supplementary feedback signal in the new AVR is shown to further improve the dynamic stability of the system. The self-tuning AVR has been extended to a multivariable integrated self-tuning controller which combines the AVR and EHG functions. The flexibility of the new AVR to enable its expansion for more complex control applications has thus been demonstrated. Simple techniques to incorporate constraints on control inputs without upsetting the loop decoupling property of the multivariable controller have been proposed and evaluated. It is shown that a further improvement in control performance and stability can be achieved by the integrated controller.Parsons Turbine Generators Ltd

Self-tuning of software systems through goal-based feedback control loop

Quality requirements of a software system cannot be optimally met, especially when it is running in an uncertain and changing environment. In principle, a controller at runtime can monitor the change impact on quality requirements of the system, update the expectations and priorities from the environment, and take reasonable actions to improve the overall satisfaction. In practice, however, existing controllers are mostly designed for tuning low- level performance indicators rather than high-level requirements. By maintaining a live goal model to represent the runtime requirements and linking the overall satisfaction to an earned business value indicator as feedback, we propose a control-theoretic self-tuning method that can dynamically tune the preferences of different quality requirements, and can autonomously make the tradeoff decisions among different quality requirements through our preference-based goal reasoning. The reasoning result is involved to reconfigure the variation points of the goal model, and accordingly mapped to the system architecture reconfiguration. The effectiveness of our self-tuning method is evaluated by comparing the earned business value with the static and ad-hoc methods and analysing the self-tuning process

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Transient Stability Enhancement of Power System Using TCSC

This project presents the variable effective fundamental equivalent reactance capability of TCSC for enhancing the transient stability of power systems. For obtaining the varying effective fundamental equivalent reactance, two different controllers namely a speed deviation based Self-tuning Fuzzy PID Controller and a nonlinear controller are used. To validate the performance of the control schemes, the simulation studies are carried out on a single machine infinite bus system using MATLAB/ SIMULINK software package. The results of computer simulation indicate that Self-tuning Fuzzy PID controlled TCSC can not only improve the static stability of system, but also effectively damp power oscillation and enhance the transient stability of system when the power system suffers small disturbance and short circuit. In addition, it also illuminates that Self-tuning Fuzzy PID Controlled TCSC is more effective than nonlinear control, traditional PID control and fixed series compensation.DOI:http://dx.doi.org/10.11591/ijece.v2i3.24

Software Implementation of Self-Tuning Controllers

This chapter deals mainly with software implementation of selected digital self-tuning control algorithms into the Matlab and Pascal environment for the purpose of possible industrial utilization. The work was motivated by co-operation with a manufacturer of aluminium-based rolled products and packaging materials. His project has supposed primarily the application of discrete-time adaptive compensator to control of a metal smelting furnace. Other requirements were the plant model with ?a2b3? structure and final implementation in Borland Pascal (because of integration into the existing system). However the paper presents not only derived relations applicable to Pascal environment but also program for simulative purposes and testing created under Matlab and some preliminary simulation results. In the first stage, the applied methods have included a polynomial approach to discrete-time control design and recursive least-squares identification algorithm LDDIF, but subsequently also two alternative techniqI, Z(MSM7088352102

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201