Nonlinear penalized estimation of true Q-matrix in cognitive diagnostic models

A key issue of cognitive diagnostic models (CDMs) is the correct identification of Q-matrix which indicates the relationship between attributes and test items. Previous CDMs typically assumed a known Q-matrix provided by domain experts such as those who developed the questions. However, misspecifications of Q-matrix had been discovered in the past studies. The primary purpose of this research is to set up a mathematical framework to estimate the true Q-matrix based on item response data. The model considers all Q-matrix elements as parameters and estimates them through EM algorithm. Two simulation designs are conducted to evaluate the feasibility and performance of the model. An empirical study is addressed to compare the estimated Q-matrix with the one designed by experts. The results show that the model performs well and is able to identify 60% to 90% of correct elements of Q-matrix. The model also indicates possible misspecifications of the designed Q-matrix in the fraction subtraction test

Sampling-based Nystr\"om Approximation and Kernel Quadrature

We analyze the Nystr\"om approximation of a positive definite kernel associated with a probability measure. We first prove an improved error bound for the conventional Nystr\"om approximation with i.i.d. sampling and singular-value decomposition in the continuous regime; the proof techniques are borrowed from statistical learning theory. We further introduce a refined selection of subspaces in Nystr\"om approximation with theoretical guarantees that is applicable to non-i.i.d. landmark points. Finally, we discuss their application to convex kernel quadrature and give novel theoretical guarantees as well as numerical observations.Comment: 27 page

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

A hybrid generalised linear and Levenberg-Marquardt artificial neural network approach for downscaling future rainfall in North Western England

This paper describes a novel technique for downscaling daily rainfall which uses a combination of a generalised linear model (GLM) and artificial neural network (ANN) to downscale rainfall. A two-stage process is applied, an occurrence process which uses the GLM model and an amount process which uses an ANN model trained with a Levenberg–Marquardt approach. The GLM-ANN was compared with other three downscaling models, the traditional neural network (ANN), multiple linear regression (MLR) and Poisson regression (PR). The models are applied for downscaling daily rainfall at three locations in the North West of England during the winter and summer. Model performances with respect to reproduction of various statistics such as correlation coefficient, autocorrelation, root mean square errors (RMSE), standard deviation and the mean rainfall are examined. It is found that the GLM-ANN model performs better than the other three models in reproducing most daily rainfall statistics, with slight difficulties in predicting extremes rainfall event in summer. The GLM-ANN model is then used to project future rainfall at the three locations employing three different general circulation models (GCMs) for SRES scenarios A2 and B2. The study projects significant increases in mean daily rainfall at most locations for winter and decreases in summe

Application of learning algorithms to traffic management in integrated services networks.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A Real-Time Adaptive Sampling Strategy Optimized by Uncertainty for Spatial Data Analysis on Complex Domains

Environmental monitoring is used to reveal the state of the environment, to inform experts and help them to prioritise actions in the context of environmental policies. Environmental sampling is the way the environment is interrogated to get measures of environmental (e.g., physical, chemical) parameters in a limited set of locations (samples). The environmental properties varies from place to place in continuum and there are infinitely many places at which we might record what they are like, but practically we can measure them at only a finite number by sampling. The role of the location in which samples are collected is very crucial. The focus of the thesis is the study of a mathematical framework that supports a reasoned and non-random sampling of environmental variables, with the aim of defining a methodological approach to optimise the number of sampling required while maintaining a target precision. The arrangement of points is not selected or known a priori; conversely, we propose an iterative process where the next-sample location is determined on-the-fly on the basis of the environmental scenario that is delineated more and more accurately at each iteration. At each iteration, the distribution map is updated with the new incoming data. The geostatistical analysis we implement provides a predicted value and the related uncertainty about that value, actually providing an uncertainty map beside the predicted distribution. The system responds to the current state by requiring a measurement in the area with highest uncertainty, to reduce uncertainty and increase accuracy. Environmental survey areas to monitor are often characterised by very complex boundaries. Unstructured grids are more flexible to faithfully represent complex geometries compared to structured grids. The usage of unstructured grids introduces another innovation aspect studied in the thesis, which is the change of support model

Automated mapping of climatic variables using spatio-temporal geostatistical methods

Javno dostupni meteorološki podaci, kako sa stanica tako i iz daljinske detekcije, korišćeni su za prostorno vremensku interpolaciju temperature vazduha iznad površine Zemlje. Zastupljenost i pogodnost javno dostupnih podataka je ocenjena, kroz tri aspekta kontrole kvaliteta: (a) zastupljenost u geografskom i prostornom domenu, (b) zastupljenost u karaktestičnom prostoru (feature space; bazirano na MaxEnt metodi), kao i (c) pogodnost korišćenja podataka za prostorno-vremensku predikciju (na osnovu kros-validacije prostorno-vremnskog regresionog kriginga). Rezultati pokazuju da je kombinovani set podataka (GSOD i ECA&D) značajno klasteriran i u geografskom i u karakterističnom prostoru. Uprkos klasteriranju, preliminarni rezultati globalne interpolacije primenom prostorno-vremenskog regresionog kriginga koristeći merenja sa stanica i snimke daljinske detekcije su pokazali da se tako mogu dobiti precizne globalne karte dnevne temperature. Oko 9000 stanica kombinovanog seta podataka (GSOD i ECA&D) je korišćeno za prostorno-vremensko geostatističko modeliranje i predikciju dnevnih temperatura u rezoluciji 1 km, iznad površine Zemlje. Za predikciju srednjih, minimalnih i maksimalnih temperatura korišćen je regresioni kriging uz pomoćne prediktore: MODIS LST 8-dnevni snimci, topografski lejeri (DEM i TWI) i geometrijski temperaturni trend. Model i predikcija se odnose na 2011 godinu, ali ista metodologija bi se mogla primeniti od 2001 godine do danas (od kada su dostupni MODIS snimci). Rezultati pokazuju da je prosečna tačnost predikcije za srednju, minimalnu i maksimalnu temperaturu vazduha oko ±2°C za oblasti gusto pokrivene stanicama i između ±2°C i ±4°C za oblasti koje su slabo pokrivene stanicama. Najniža tačnost predikcije je dobijena u planinskim predelima i na Antartiku, oko 6°C. R softverski paket, meteo, je razvijen kao resenje za automatsko kartiranje. Razvijen je i paket plotGoogleMaps za automatsku vizuelizaciju na Web-u, koristeći Google Maps API.Publicly available global meteorological data sets, from ground stations and remote sensing, are used for spatio-temporal interpolation of air temperature data for global land areas. Publicly available data sets were assessed for representation and usability for global spatio-temporal analysis. Three aspects of data quality were considered: (a) representation in the geographical and temporal domains, (b) representation in the feature space (based on the MaxEnt method), and (c) usability i.e. fitness of use for spatio-temporal interpolation (based on cross-validation of spatio-temporal regression-kriging models). The results show that clustering of meteorological stations in the combined data set (GSOD and ECA&D) is significant in both geographical and feature space. Despite the geographical and feature space clustering, preliminary tested global spatio-temporal model using station observations and remote sensing images, shows this method can be used for accurate mapping of daily temperature. Around 9000 stations from merged GSOD and ECA&D daily meteorological data sets were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions were made for the mean, maximum and minimum temperature using spatio-temporal regression-kriging with a time series of MODIS 8 day images, topographic layers (DEM and TWI) and a geometrical temperature trend as covariates. The model and predictions were built for the year 2011 only, but the same methodology can be extended for the whole range of the MODIS LST images (2001–today). The results show that the average accuracy for predicting mean, maximum and minimum daily temperatures is RMSE = ± 2°C for areas densely covered with stations, and between ± 2°C and ± 4°C for areas with lower station density. The lowest prediction accuracy was observed in highlands (> 1000 m) and in Antarctica with a RMSE around 6°C. Automated mapping framework is developed and implemented as R package meteo. Likewise, package plotGoogleMaps for automated visualisation on the Web, base on Google Maps API is developed

Very short-term photovoltaic power forecasting with cloud modeling: A review

This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide whereas it exceeds by far humans' energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indeed, the number of utility-scale PV farms is currently fast increasing globally, with planned capacities in excess of several hundred megawatts. This makes the cost of PV-generated electricity quickly plummet and reach parity with non-renewable resources. However, like many other renewable energy sources, PV power depends highly on weather conditions. This particularity makes PV energy difficult to dispatch unless a properly sized and controlled energy storage system (ESU) is used. An accurate power forecasting method is then required to ensure power continuity but also to manage the ramp rates of the overall power system. In order to perform these actions, the forecasting timeframe also called horizon must be first defined according to the grid operation that is considered. This leads to define both spatial and temporal resolutions. As a second step, an adequate source of input data must be selected. As a third step, the input data must be processed with statistical methods. Finally, the processed data are fed to a precise PV model. It is found that forecasting the irradiance and the cell temperature are the best approaches to forecast precisely swift PV power fluctuations due to the cloud cover. A combination of several sources of input data like satellite and land-based sky imaging also lead to the best results for very-short term forecasting

Optimising non-destructive examination of newbuilding ship hull structures by developing a data-centric risk and reliability framework based on fracture mechanics

This thesis was previously held under moratorium from 18/11/19 to 18/11/21Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure.Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure