Event tracking for real-time unaware sensitivity analysis (EventTracker)

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method.EPSR

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Feed Speed Control to Assure Accuracy of Complex Profile Surfaces Processing

AbstractThis document describes how cutting force components influence the accuracy of processing complex profile surfaces of parts on CNC machines. It demonstrates how changing cutting zone geometrical parameters influences cutting force components. The article includes dependences for determining cutting force components. We propose a mathematical model to identify dynamic size-setting errors for 2D machining of complex profile surfaces. We suggest a processing method to reduce error occurrence rate by controlling feed speed. This document includes a method of setting feed motion speed depending on the geometrical parameters of the machined surface. We recommend changing feed motion speed depending on machined surface pitch against the feed motion vector. We demonstrate that feed motion speed control allows to increase machining efficiency while assuring the set accuracy of the shape and dimensions of the components’ complex profile surfaces

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

A study of the very high order natural user language (with AI capabilities) for the NASA space station common module

The requirements are identified for a very high order natural language to be used by crew members on board the Space Station. The hardware facilities, databases, realtime processes, and software support are discussed. The operations and capabilities that will be required in both normal (routine) and abnormal (nonroutine) situations are evaluated. A structure and syntax for an interface (front-end) language to satisfy the above requirements are recommended

Responsible Data Governance of Neuroscience Big Data

Open access article.Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP)

Measurements, Models, Systems and Design

531 s.