Cost-sensitive learning classification strategy for predicting product failures

In the current era of Industry 4.0, sensor data used in connection with machine learning algorithms can help manufacturing industries to reduce costs and to predict failures in advance. This paper addresses a binary classification problem found in manufacturing engineering, which focuses on how to ensure product quality delivery and at the same time to reduce production costs. The aim behind this problem is to predict the number of faulty products, which in this case is extremely low. As a result of this characteristic, the problem is reduced to an imbalanced binary classification problem. The authors contribute to imbalanced classification research in three important ways. First, the industrial application coming from the electronic manufacturing industry is presented in detail, along with its data and modelling challenges. Second, a modified cost-sensitive classification strategy based on a combination of Voronoi diagrams and genetic algorithm is applied to tackle this problem and is compared to several base classifiers. The results obtained are promising for this specific application. Third, in order to evaluate the flexibility of the strategy, and to demonstrate its wide range of applicability, 25 real-world data sets are selected from the KEEL repository with different imbalance ratios and number of features. The strategy, in this case implemented without a predefined cost, is compared with the same base classifiers as those used for the industrial problem

Stratigraphy and palaeoceanography of upper Maastrichtian chalks, southern Danish Central Graben

Upper Maastrichtian chalks form important hydrocarbon reservoirs in the Danish sector of the North Sea and have been intensively studied, yet their lithological uniformity can frustrate attempts to develop a high-resolution stratigraphic subdivision and a genetic understanding of the factors controlling production and sedimentation of the pelagic carbonate ooze. Recent research into these topics, supported by the Danish Energy Authority, was carried out by the Geological Survey of Denmark and Greenland (GEUS) in collaboration with the Geological Institute, University of Copenhagen by means of a multidisciplinary study involvingquantitative/semiquantitative palynology, micropalaeontology (nannofossils, foraminifers) and isotope geochemistry, integrated with detailed sedimentology. Two key wells were selected, the M-10X well from the Dan Field and the E-5X well from the Tyra SE Field (Fig. 1), based on the extensive core coverage in these wells and on their position in the southern part of the Danish Central Graben where evidence of large-scale resedimentation (and consequent stratigraphic complexity) is uncommon within the Maastrichtian section. In focusing on such a pelagic carbonate system, the ultimate aim is a holistic understanding of the marine system including temperature variation, nutrient supply and distribution, salinity, watermass layering, circulation and oxygen distribution. All these factors influence organic productivity and thus the accumulation of biogenic sediment. This study concentrated on a number of palaeoceanographic signals that can be derived from the sedimentary record, summarised in Fig. 2. Planktonic organisms, both phytoplankton (e.g. coccolithophores, some dinoflagellates) and zooplankton (e.g. foraminifers) provide a record of conditions in the upper water masses, largely within the photic zone, while bottom conditions are indicated by epifaunal/infaunal organisms (e.g. benthic foraminifers) and bioturbation, and by the sedimentological evidence of depositional processes at the sea floor. On a larger scale, the input of terrestrial organic material relative to the marine component can provide an indirect measure of shoreline migration and thus relative sea-level change, a factor that is also reflected in the δ13C isotopic composition of the seawater, as recorded by the biogenic carbonate ooze

Dengue contingency planning: from research to policy and practice

Background Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Methodology/Principal findings Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Conclusions/Significance Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan

Wind Farm Dispatch Control for Demand Tracking and Minimized Fatigue

Abstract: This work presents a strategy for dispatching production references to the individual turbinesinawindfarm,suchthatanoverallproductiondemandforthefarmisobeyed,whilethe fatigue experienced by the turbines is minimized. Using a turbine fatigue model for simulating the aging across the farm, we show that a 17 % reduction of the turbine aging can be obtained compared to a commonly employed industrial dispatcher, without degrading the power demand tracking