Multi Agent Diagnosis: an analysis

The paper analyzes the use of a Multi Agent System for Model Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may also be physically be distributed. A Multi Agent System of diagnostic agents may offer solutions for establishing a global diagnosis. If we use a separate agent for each incomplete model of the system, establishing a global diagnosis becomes a problem cooperation and negotiation between the diagnostic agents. This raises the question whether `a set of diagnostic agents, each having an incomplete model of the system, can (efficiently) determine the same global diagnosis as an ideal single diagnostic agent having the combined knowledge of the diagnostic agents?''economics of technology ;

Uncertainty Quantification of a Nonlinear Aeroelastic System Using Polynomial Chaos Expansion With Constant Phase Interpolation

The present study focuses on the uncertainty quantification of an aeroelastic instability system. This is a classical dynamical system often used to model the flow induced oscillation of flexible structures such as turbine blades. It is relevant as a preliminary fluid-structure interaction model, successfully demonstrating the oscillation modes in blade rotor structures in attached flow conditions. The potential flow model used here is also significant because the modern turbine rotors are, in general, regulated in stall and pitch in order to avoid dynamic stall induced vibrations. Geometric nonlinearities are added to this model in order to consider the possibilities of large twisting of the blades. The resulting system shows Hopf and period-doubling bifurcations. Parametric uncertainties have been taken into account in order to consider modeling and measurement inaccuracies. A quadrature based spectral uncertainty tool called polynomial chaos expansion is used to quantify the propagation of uncertainty through the dynamical system of concern. The method is able to capture the bifurcations in the stochastic system with multiple uncertainties quite successfully. However, the periodic response realizations are prone to time degeneracy due to an increasing phase shifting between the realizations. In order to tackle the issue of degeneracy, a corrective algorithm using constant phase interpolation, which was developed earlier by one of the authors, is applied to the present aeroelastic problem. An interpolation of the oscillatory response is done at constant phases instead of constant time and that results in time independent accuracy levels

Cost-effectiveness of shifting breast cancer surveillance from a hospital setting to a community-based national screening programme setting

Background: In the Netherlands, breast cancer surveillance after breast conserving surgery (BCS) takes place in a hospital setting for at least five years to detect possible recurrences in early stage. As breast cancer incidence rises and mortality decreases, surveillance expenses increase. This study explores the effectiveness and cost-effectiveness of BCS surveillance as delivered in a hospital setting versus providing BCS surveillance as part of the community-based National Breast Cancer Screening Program (NBCSP). We hypothesise that the NBCSP-based strategy leads to lower costs and a lower proportion of true test results (TTR) compared to the hospital-based strategy and determine to what extent potential lower effectiveness may be balanced with expected cost savings. Materials and Methods: Both strategies are compared on effectiveness and cost-effectiveness in a decision tree from a healthcare perspective over a 5-year time horizon. Women aged 50–75 without distant metastases that underwent BCS in the years 2003–2006 with complete 5 year follow-up were selected from the Netherlands Cancer Registry (n = 14,093). Key input variables were mammography sensitivity and specificity, risk of loco regional recurrence (LRR), and direct healthcare costs. The primary outcome measure was the overall predictive value (measured in true test results). Secondary effectiveness measures were the positive predictive value (PPV) (LRRs detected or true positive test results) and the negative predictive value (NPV) (true negative test results) detected within five years post-treatment. Results are presented for low and high risk patients separately and expressed in incremental cost-effectiveness ratios (ICERs). Results: For low risk patients (with grade 1 tumours, no node involvement, and hormonal treatment), the PPV and NPV for the NBCSP strategy were 3.31% and 99.88%, and 2.74% and 99.95% for the hospital strategy respectively. For high risk patients (grade 3 tumours, over three nodes involved, without hormonal treatment), the PPV and NPV for the NBCSP strategy were 64.1% and 98.9%; and 51.0% and 99.7% for the hospital strategy respectively. For low risk patients the NBCSP saves €202 per patient leading to an ICER of €109/accurate test result. For high risk patients the cost savings are €72 per patient, leading to an ICER of €43/accurate test result. Conclusion: Although the NBCSP-based strategy is cheaper, it cannot replace the hospital-based strategy, since it leads to only half of the accurate test results compared to hospital-based strategy. This contradicts the goal of early detection of LRRs and improving outcomes

Uncertainty Quantification of Heavy Gas Release Over a Barrier

In this study a procedure for input uncertainty quantification (UQ) in computational fluid dynamics (CFD) simulations is proposed. The suggested procedure has been applied to a test case. The test case concerns the modeling of a heavy gas release into an atmospheric boundary layer over a barrier. The following uncertain parameters are investigated in their respective intervals: release velocity (18 m/s, 22 m/s), release temperature (270 K, 310 K) and the atmospheric boundary layer velocity (3 m/s, 7 m/s). The Stochastic Collocation (SC) method is used to perform the probabilistic propagation of the uncertain parameters. The uncertainty analysis was performed with two sets of sampling grids (full and sparse grids) for the uncertain parameters. The results show which of the selected uncertain parameters have the largest impact on the dispersed gas plume and the local concentrations in the gas cloud. Additionally, using sparse grids shows potential to reduce the computational effort of the uncertainty analysis

Stochastic analysis of the impact of freestream conditions on the aerodynamics of a rectangular 5:1 cylinder

Uncertainty plays a significant role in the Benchmark on the Aerodynamics of a Rectangular Cylinder (BARC) with a chord-to-depth ratio of 5. In particular, besides modeling and numerical errors, in numerical simulations it is difficult to exactly reproduce the experimental conditions due to uncertainties in the set-up parameters, which sometimes cannot be exactly controlled or characterized. In this study, the impact of the uncertainties in the inflow conditions of the BARC configuration is investigated by using probabilistic methods and two-dimensional URANS simulations. The following uncertain set-up parameters are investigated: the angle of incidence, the freestream longitudinal turbulence intensity and the freestream turbulence length scale. The stochastic collocation method is employed to perform the probabilistic propagation of the uncertainty in the three set-up parameters. This results in 25 URANS simulations based on the Smolyak sparse grid extension of the level-2 Clenshaw-Curtis quadrature points. The discretization error is estimated by repeating the same analysis on different grid sizes. Similarly, the effect of turbulence modeling is appraised by carrying out the uncertainty quantification for the Reynolds stress and the SST k-. ω models. Finally, the results obtained for different assumed probability density functions of the set-up parameters are compared. The propagation of the considered uncertainties does not explain alone the dispersion of the BARC experimental data. For certain quantities of interest, the effect of turbulence modeling is more important than the impact of the uncertainties in inflow conditions. The sensitivity to the considered uncertainties also varies with the turbulence model, with a larger variability of the results obtained with the Reynolds stress model. The inflow turbulence length scale is in all cases the least important parameter

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a variation operator that is designed to extensively exploit linkage models by effectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having little problem-specific knowledge, and in an effort to design easy-to-use EAs, GP-GOMEA requires no parameter specification. On a set of well-known benchmark problems we find that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on effects in performance. On the difficult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour