Online Risk Prediction for Indoor Moving Objects

Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency or inaccuracy at any step can make the bag risky, i.e. the bag may be delayed at the airport or sent to a wrong airport. In this paper, we propose a novel probabilistic approach for predicting the risk of an indoor moving object in real-time. We propose a probabilistic flow graph (PFG) and an aggregated probabilistic flow graph (APFG) that capture the historical object transitions and the durations of the transitions. In the graphs, the probabilistic information is stored in a set of histograms. Then we use the flow graphs for obtaining a risk score of an online object and use it for predicting its riskiness. The paper reports a comprehensive experimental study with multiple synthetic data sets and a real baggage tracking data set. The experimental results show that the proposed method can identify the risky objects very accurately when they approach the bottleneck locations on their paths and can significantly reduce the operation cost.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Copula Calibration

We propose notions of calibration for probabilistic forecasts of general multivariate quantities. Probabilistic copula calibration is a natural analogue of probabilistic calibration in the univariate setting. It can be assessed empirically by checking for the uniformity of the copula probability integral transform (CopPIT), which is invariant under coordinate permutations and coordinatewise strictly monotone transformations of the predictive distribution and the outcome. The CopPIT histogram can be interpreted as a generalization and variant of the multivariate rank histogram, which has been used to check the calibration of ensemble forecasts. Climatological copula calibration is an analogue of marginal calibration in the univariate setting. Methods and tools are illustrated in a simulation study and applied to compare raw numerical model and statistically postprocessed ensemble forecasts of bivariate wind vectors

Durability Analysis of Concrete Bridge Deck Exposed to the Chloride Ions Using Direct Optimized Probabilistic Calculation

Durability of reinforced concrete structures is a deeply discussed problem recently. Concrete structures in the external environment are very often affected by chloride ions from de-icing salt or sea water. Chloride ions penetrate through the concrete cover layer of the reinforcement and can cause eventually the corrosion of the steel. However, when estimating the durability of the structure, it is not sometimes possible to express the parameters by constant values; therefore, the probabilistic methods come in handy. Then, the variability of inputs and outputs can be expressed by histograms. Two probabilistic approaches were applied in this task – Monte Carlo simulation with Simulation-Based Reliability Assessment method, which is widely used for such type of problems, and the Direct Optimized Probabilistic Calculation, which is still relatively new type of approach. The result is a comparison of mentioned methods in terms of accuracy on the model of one-dimensional chloride penetration with time independent diffusion coefficient by using the Fick’s Second Law of Diffusion