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Predictive analytics and data fusion techniques are being regularly used for analysis in Quantitative Risk Management (QRM). The primary risk metric of interest, Value-at-Risk (VaR), has always been difficult to robustly estimate for different data types. The classical Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach) assumes the independence of loss severity and loss...
Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and...
Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and...
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath...
We present a method for developing executable algorithms for quantitative cyber-risk assessment. Exploiting techniques from security risk modeling and actuarial approaches, the method pragmatically combines use of available empirical data and expert judgments. The input to the algorithms are indicators providing information about the target of analysis, such as suspicious events observed in the network...
This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other...
In this paper, a method to implement a platform of failure diagnosis and prognosis, and health monitoring based on data using Input Output Hidden Markov Models (IOHMM) is proposed. Several sensors on a diesel generator system give information such as on-line operating conditions. The goal of this work is to use on-line collected data in order to determine degradation state of the diesel generator...
This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According...
In this paper we present the results of experimental observation from October till November 2016. Variations in angles-of-arrival (AoA) on two mid-latitude paths with different orientation were calculated and the average accuracy of single-station location (SSL) was estimated. The comparison of obtained data with radio channel simulation results was conducted in terms of naturally disturbed ionosphere...
The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied. In the second stage,...
Our previous work proposed hand posture estimation technique. The hand region is first extracted using depth image, and then the initial features, such as fingertip, hand center point, and palm size, have been calculated. The concept of active contour using energy function is implemented in order to track fingertip position in the frame image sequence. To discriminate the hand posture sets, a hand...
Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a critical role in Prognostics and Health Management(PHM). Data driven approaches for RUL estimation use sensor data and operational data to estimate RUL. Traditional regression based approaches and recent Convolutional Neural Network (CNN)...
The service technology and crowdsourcing movement have spawned a host of successful efforts that promote the rapid development of the human service ecosystem. In this ecosystem, a large number of globally-distributed freelancers are organized to tackle a range of tasks over the web. These crowdsourcing services provide convenience for civilians with lower price and shorter response time. However,...
Consider a set of random sequences, each consisting of independent and identically distributed random variables. Each sequence is generated according to one of the two possible distributions F0 or F1 with unknown prior probabilities (1 − ∊) and ∊, respectively. The objective is to design a sequential decision-making procedure that identifies a sequence generated according to F1 with the fewest number...
Precise radius estimation is of high interest for rebar and pipe characterization but very challenging. In this work, we present a novel 3D frequency-domain full-waveform inversion (FWI) approach with which the geometrical information of subsurface cylindrical objects and the dielectric properties of the penetrating medium are simultaneously extracted from ground penetrating radar (GPR) data. The...
Data from vehicles instrumented with GPS or other localization technologies are increasingly becoming widely available due to the investments in Connected and Automated Vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. Tracking or trajectory data from these probe vehicles are already being used in practice for travel time or speed estimation and for monitoring network...
This paper describes a method of separation (degarbling) of individual Secondary Surveillance Radar replies from a mixture (garble) received using a multi-channel antenna system. The aim of described method is to increase the number of correctly decoded messages used for aircraft surveillance by Air-Traffic Control center. Firstly, described method is evaluated on the model data simulation and afterwards...
An approach for modeling in-depth destination and mode choice is to deploy discrete choice models. Large scale transportation demand models usually are macroscopic, thus using aggregated demand data. In the new transportation model for the federal state of Upper Austria discrete choice models for the mode and destination choice have been implemented. Considering the special characteristics when applying...
The jackknife resampling procedure is a technique to reduce the bias of a statistic. As with other resampling techniques, the jackknife procedure is motivated by and is well understood in the i.i.d. regime. However, analysis of the procedure when samples have memory is limited, and is predominantly restricted to cases with strong mixing or memory constraints. In this paper, we analyze a natural jackknife...
Current procedure in travel demand estimation models is to separately deal with attraction, production and trip distribution, where the latter typically assumes inverse distance proportionality. We show that this procedure leads to errors in the demand estimation, particularly when dealing with very specific zones and heterogeneous travel behavior. We argue that this traditional procedure is rooted...
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