Enabling Richer Insight Into Runtime Executions Of Systems

Systems software of very large scales are being heavily used today in various important scenarios such as online retail, banking, content services, web search and social networks. As the scale of functionality and complexity grows in these software, managing the implementations becomes a considerable challenge for developers, designers and maintainers. Software needs to be constantly monitored and tuned for optimal efficiency and user satisfaction. With large scale, these systems incorporate significant degrees of asynchrony, parallelism and distributed executions, reducing the manageability of software including performance management. Adding to the complexity, developers are under pressure between developing new functionality for customers and maintaining existing programs. This dissertation argues that the manual effort currently required to manage performance of these systems is very high, and can be automated to both reduce the likelihood of problems and quickly fix them once identified. The execution logs from these systems are easily available and provide rich information about the internals at runtime for diagnosis purposes, but the volume of logs is simply too large for today\u27s techniques. Developers hence spend many human hours observing and investigating executions of their systems during development and diagnosis of software, for performance management. This dissertation proposes the application of machine learning techniques to automatically analyze logs from executions, to challenging tasks in different phases of the software lifecycle. It is shown that the careful application of statistical techniques to features extracted from instrumentation, can distill the rich log data into easily comprehensible forms for the developers

Blue Nile Runoff Sensitivity to Climate Change

This study describes implementation of hydrological climate change impact assessment tool utilising a combination of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System. A spatial rainfall generator was used to produce high-resolution (daily, 20km) gridded rainfall data required by the distributed hydrological model from monthly GCM outputs. The combined system was used to assess the sensitivity of upper Blue Nile flows at Diem flow gauging station to changes in future rainfall during the June-September rainy season based on output from three GCMs. The assessment also incorporated future evapotranspiration changes over the basin. The climate change scenarios derived in this study were broadly in line with other studies, with the majority of scenarios indicating wetter conditions in the future. Translating the impacts into runoff in the basin showed increased future mean flows, although these would be offset to some degree by rising evapotranspiration. Impacts on extreme runoff indicated the possibility of more severe floods in future. These are likely to be exacerbated by land-use changes including overgrazing, deforestation, and improper farming practices. Blue Nile basin flood managers therefore need to continue to prepare for the possibility of more frequent floods by adopting a range of measures to minimise loss of life and guard against other flood damage

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Self-regulated learning and knowledge sharing in the workplace

This study explores how experts in a global multinational company self-regulate their learning. It investigates experts‟ perceptions of the impact of knowledge sharing on their learning and work. Findings indicate that self-regulated learning (SRL) is a highly social process that is structured by and deeply integrated with work tasks. Experts tend to draw heavily upon their personal networks of trusted colleagues in the process of diagnosing and attaining their learning goals. In contradiction to existing models, SRL in the workplace does not appear to be a clearly delineated, linear process comprised of discrete stages. Further research is needed to understand tacit practices of SRL in the workplace

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

[EN] Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.This work is supported by a Project (AA-ELACERO, P211LH021-023) of the National Key Research and Development Program of Automatic, Robotic and Artificial Intelligence of Cuba.González-Yero, G.; Ramírez Leyva, R.; Ramírez Mendoza, M.; Albertos, P.; Crespo, A.; Reyes Alonso, JM. (2021). Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control. Metals. 11(1):1-21. https://doi.org/10.3390/met1101005612111