AN EXPERT SYSTEM FOR TALENT SEARCHING IN LONG DISTANCE RUNNING

NI

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

Dynamic Reliability Indices in Reliability Analysis of Multi-State System

The reliability of Multi-State System (MSS) is analysed in this paper. In a MSS, both the system and its components may experience more than two reliability states. Many practical and theoretical problems needs still to be solved in this area. One of the crucial ones is to identify how a change in a state of an individual component or changes in states of several ones affect(s) the system reliability. The Multiple-Valued Logic (MVL) tools are employed for handling this problem in this paper. In the paper the structure function and Logical Differential Calculus of MVL function are combined to evaluate the dynamic behaviour of a MSS. The Logical Differential Calculus extends potentialities of structure function tool to analyse also the MSS dynamical properties. The evaluation of MSS components changes is considered in this paper

Towards the generic framework for utility considerations in data mining research

Rigor data mining (DM) research has successfully developed advanced data mining techniques and algorithms, and many organizations have great expectations to take more benefit of their vast data warehouses in decision making. Even when there are some success stories the current status in practice is mainly including great expectations that have not yet been fulfilled. DM researchers have recently become interested in utility-based DM (UBDM) starting to consider some of the economic utility factors (like cost of data, cost of measurement, cost of class label and so forth), but yet many other utility factors are left outside the main directions of UBDM. The goal of this position paper is (1) to motivate researchers to consider utility from broader perspective than usually done in UBDM context and (2) to introduce a new generic framework for these broader utility considerations in DM research. Besides describing our multi-criteria utility based framework (MCUF) we present a few hypothetical examples showing how the framework might be used to consider utilities of some potential DM research stakeholders

Interval-Based Parameter Recognition with the Trends in Multiple

This paper considers the context sensitive approach to handle interval knowledge acquired from multiple knowledge sources. Each source gives its estimation of the value of some parameter x. The goal is to process all the intervals in a context of trends caused by some noise and derive resulting estimation that is more precise than the original ones and also takes into account the context noise. The main assumption used is that if a knowledge source guarantees smaller measurement error (estimated interval is shorter) then this source in the same time is more resistant against the effect of noise. This assumption allows us to derive and process trends among intervals and end up to shorter resulting estimated interval than any of the original ones

Towards more relevance-oriented data mining research

Data mining (DM) research has successfully developed advanced DM techniques and algorithms over the last few decades, and many organisations have great expectations to take more benefit of their data warehouses in decision making. Currently, the strong focus of most DM-researchers is still only on technology-oriented topics. Commonly the DM research has several stakeholders, the major of which can be divided into internal and external ones each having their own point of view, and which are at least partly conflicting. The most important internal groups of stakeholders are the DM research community and academics in other disciplines. The most important external stakeholder groups are managers and domain experts who have their own utility-based interests to DM and DM research results. In this paper we discuss these practice-oriented points of view towards DM research and suggest broader discussions inside the DM research community about who should do that kind of research. We bring in the discussion several topics developed in the information systems (IS) discipline and show some similarities between IS and DM systems. DM systems have also their own peculiarities and we conclude that researchers who take into account human and organisational aspects related to DM systems need to have also some understanding about DM. This makes us suggest that the research area inside the DM community should be made broader than the current heavily technology-oriented one

Does relevance matter to data mining research?

Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of several DM algorithms from different paradigms in order to produce a better solution elevating the importance of practice-oriented aspects also in DM research. In this chapter we strongly emphasize that DM research should also take into account the relevance of research, not only the rigor of it. Under relevance of research in general, we understand how good this research is in terms of the utility of its results. This chapter motivates development of such a new framework for DM research that would explicitly include the concept of relevance. We introduce the basic idea behind such framework and propose one sketch for the new framework for DM research based on results achieved in the information systems area having some tradition related to the relevance aspects of research