Using a Markov-Chain Model for Assessing Accuracy Degradation and Developing Data Maintenance Policies

Accuracy reflects the extent of correctness of data. It is often evaluated by comparing the values recorded to a baseline perceived as correct. Even when data values are accurate at the time of recording – their accuracy may degrade over time, as certain properties of real-world entities may change, while the data values that reflect them are not being updated. This study uses the Markov-Chain model to develop an analytical framework that describes accuracy degradation over time – this by assessing the likelihood of certain data attributes to transition between states within a given time period. Evaluation of the framework with real-world data shows its potential contribution for key data-quality management tasks, such as the prediction of accuracy degradation, and the development of data auditing and maintenance policies

A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1) model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1) model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1) Markov chain model. The results show that the GM (1, 1) Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time

Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio

A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroecto-dermal tumours (PNETs), based on diffusionweighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research

Outlier robust system identification: a Bayesian kernel-based approach

In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. To build robustness to outliers, we model the measurement noise as realizations of independent Laplacian random variables. The identification problem is cast in a Bayesian framework, and solved by a new Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the representation of the Laplacian random variables as scale mixtures of Gaussians, we design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods.Comment: 5 figure

Markovian Characterisation of H.264/SVC scalable video

In this paper, a multivariate Markovian traffic: model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture. both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios

Land-cover change monitoring in Obuasi, Ghana: an integration of earth observation, geoinformation systems and stochastic modelling

For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely future land-cover. An integration of Earth Observation (or EO), Geographical Information Science (or GIS) and Stochastic Modelling was examined. Post-Classification Change Detection employed Landsat TM or ETM+ images from 1986, 2002 and 2008. Subsequently, Markov Chain Analysis projected the land-cover distribution for 2020. Seven broad land-use and land-cover classes were identified and mapped, namely: built-up areas; mine sites; tailing ponds; barren land; forestland; farmland; and, rangeland. The results obtained for the 2008 to 2020 projection revealed a continuous expansion of built-up areas (1.63%), mine sites (0.89%) and farmland (3.4%), and a reduction of forestland (4.17%) and rangeland (2.59%). Despite the advent of very high resolution satellite imagery, this use of EO and GIS technology focussed on low-cost and lower resolution satellite imagery, coupled with Markov Modelling and was found to be beneficial in describing and analysing land-cover change processes in the study area, and was hence potentially useful for strategic planning purposes