Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Characterization of multinucleated giant cells in synovium and subchondral bone in knee osteoarthritis and rheumatoid arthritis

Background: Multinucleated giant cells have been noticed in diverse arthritic conditions since their first description in rheumatoid synovium. However, their role in the pathogenesis of osteoarthritis (OA) or rheumatoid arthritis (RA) still remains broadly unknown. We aimed to study the presence and characteristics of multinucleated giant cells (MGC) both in synovium and in subchondral bone tissues of patients with OA or RA. Methods: Knee synovial and subchondral bone samples were from age-matched patients undergoing total joint replacement for OA or RA, or non-arthritic post mortem (PM) controls. OA synovium was stratified by histological inflammation grade using index tissue sections. Synovitis was assessed by Krenn score. Histological studies employed specific antibodies against macrophage markers or cathepsin K, or TRAP enzymatic assay. Results: Inflamed OA and RA synovia displayed more multinucleated giant cells than did non-inflamed OA and PM synovia. There was a significant association between MGC numbers and synovitis severity. A TRAP negative/cathepsin K negative Langhans-like subtype was predominant in OA, whereas both Langhans-like and TRAP-positive/ cathepsin K negative foreign-body-like subtypes were most commonly detected in RA. Plasma-like and foam-like subtypes also were observed in OA and RA synovia, and the latter was found surrounding adipocytes. TRAP positive/ cathepsin K positive osteoclasts were only identified adjacent to subchondral bone surfaces. TRAP positive osteoclasts were significantly increased in subchondral bone in OA and RA compared to PM controls. Conclusions: Multinucleated giant cells are associated with synovitis severity, and subchondral osteoclast numbers are increased in OA, as well as in RA. Further research targeting multinucleated giant cells is warranted to elucidate their contributions to the symptoms and joint damage associated with arthritis

Evaluation of discretization and integration methods for the analysis of finite hydrodynamic bearings with surface texturing

Efficient numerical methods are essential in the analysis of finite hydrodynamic bearings with surface texturing. This is especially evident in optimization and parametric studies where the discretization and integration methods are used to solve the governing two-dimensional Reynolds equation multiple times. Performance comparison studies of the methods are thus required to select the method that is most suitable for a particular bearing geometry. In this work, we conduct a comprehensive and systematic comparison of typical implementations of the finite difference, finite volume, finite element and spectral element discretization methods together with the Newton-Cotes formula and Gauss quadratures for hydrodynamic bearings governed by the two-dimensional Reynolds equation. The methods were evaluated by comparing the approximation errors in the calculation of the maximum pressure, load capacity, coefficient of friction, and minimum film thickness for parallel and convergent bearings textured with elliptical grooves or trapezoidal dimples. The number of degrees of freedom required by the methods to achieve the error cut-off values of 5%, 1% and 0.1% were calculated. Our results demonstrate that the spectral element method uses up to 72 times fewer degrees of freedom than the other methods for the same cut-off values. Also, our study revealed that the shape of the groove/dimple and the bearing convergence ratio can have a significant effect on the approximation errors of the numerical methods used. Specifically, for piecewise-linear texture features (e.g. trapezoidal dimples) and positive convergence ratio it is easier, for the methods, to accurately approximate the solution. In such cases, the finite volume and finite element methods are reasonable choices and provide a good trade-off between the ease of implementation and approximation errors. The worse performance was observed for the finite difference and thus this method is not recommended when the computational efficiency and the accuracy of results are of importance.Our results demonstrate that the spectral element method uses up to 72 times fewer degrees of freedom than the other methods for the same cut-off values. Also, our study revealed that the shape of the groove/dimple and the bearing convergence ratio can have a significant effect on the approximation errors of the numerical methods used. Specifically, for piecewise-linear texture features (e.g. trapezoidal dimples) and positive convergence ratio it is easier, for the methods, to accurately approximate the solution. In such cases, the finite volume and finite element methods are reasonable choices and provide a good trade-off between the ease of implementation and approximation errors. The worse performance was observed for the finite difference and thus this method is not recommended when the computational efficiency and the accuracy of results are of importance

Efficient Solution to the Cavitation Problem in Hydrodynamic Lubrication

Hydrodynamic lubrication is present in the majority of machinery where load is transmitted between two contacting surfaces in relative motion. Cavitation in liquid lubricating films is common and directly affects the pressure distribution and subsequently the load-carrying capacity and friction force transmitted across the contact. By reformulating the Elrod-Adams implementation of the Jakobsson-Floberg-Olsson cavitation conditions, we developed an efficient algorithm, called Fischer-Burmeister-Newton-Schur (FBNS), for calculating the pressure distribution that combines two attractive properties. First, the system of discretized equations arising from the reformulation is continuously differentiable and unconstrained, thus allowing for the use of gradient-based methods to solve it. Second, the computational cost of solving the system is similar to that when cavitation is not considered. With the new algorithm, the transient analysis and optimisation of contacts with complex shapes becomes computationally feasible. A comparison of the FBNS with the established algorithms and an application to the transient analysis of a hydrodynamic contact with surface texturing are reported. The results show that the FBNS yields roughly two orders of magnitude reduction in computational time when compared against other algorithms

Evaluation of inertia effect in finite hydrodynamic bearings with surface texturing using spectral element solver

Fluid inertia can have a major effect on the pressure distribution in hydrodynamic bearings with surface texturing. So far, however, the effect has been ignored in the simulation of finite bearings. In this work, we develop a spectral element solver for the Navier-Stokes equations specifically tailored to slider finite bearings textured with multiple spherical dimples. Using the solver, we studied the effect of inertia on the load-carrying capacity for 90 different bearing configurations. Our results show that the spatial arrangement of dimples has a significant impact on the inertia effect. For the Reynolds number 50 and the ratio 10 of dimple length to dimple depth, a change from full to partial texturing reduced the effect by roughly 85%