Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Verification and post-processing of ensemble weather forecasts for renewable energy applications

The energy transition taking place in Germany encourages a large scale penetration of weather-dependent energy sources into the power grid. The grid integration of intermittent sources increases the need for balancing demand and supply in order to ensure the reliability and safety of the power system. In this context, forecasts are essential for the cost-effective management of reserves and trading activities. Solar and wind power forecasts with a time horizon of few hours up to several days are usually based on outputs of numerical weather prediction systems routinely provided by weather centres. At the German Weather Service, the high-resolution ensemble prediction system COSMO-DE-EPS is called to support renewable energy applications which require dealing with the intermittency and uncertainty in the energy production. In this study, ensemble forecast verification and post-processing are addressed focusing on global radiation, which is the main weather variable affecting solar power production. First, the ensemble forecast performances are assessed from the user’s and developer’s perspectives. New tools are proposed for the verification of quantile forecasts which are probabilistic products appropriate for many renewable energy applications. Forecast discrimination ability and value are assessed considering users with different aversions to under- and over-forecasting. Moreover, a new measure is introduced in order to summarize the added value of the ensemble approach with respect to a single run approach. The new skill score is conditioned on calibration, that is, statistical consistency between the distributional forecasts and observations. Second, an enhanced framework for the post-processing of ensemble forecasts is proposed. The aim is to provide the users with calibrated consistent scenarios which are required for the optimization of complex decision-making processes. Therefore, a two-step procedure is developed starting with the marginal calibration of the forecasts based on quantile regression and the selection of appropriate predictors. Next, consistent scenarios are generated using a dual ensemble copula coupling approach which combines information from past error statistics and the dependence structure in the original ensemble forecast

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies