There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated (sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics.

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.

Intuitionistic fuzzy preference relations can take membership degrees, non-membership degrees, and hesitancy degrees into account during decision making. It has good practicability and flexibility in dealing with fuzzy and uncertain information. As for analytic network process, it is performed by thinking over the interaction and feedback relationships between criteria and indices, so that an effective method is provided for multi-criteria decision making. An index system with network structure for evaluating the bonds is presented, and a comprehensive method by combining the advantages of intuitionistic fuzzy preference relations and analytic network process is proposed to select and rank the bonds. A case study is given by the proposed method as well.

This paper puts forward a new color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform for the purpose of removing uncertainty when choosing sub-band coefficients in the smooth regions. Luminance component is the weighted average of the three color channels in the IHS color space and it is not sensitive to noise. According to the characteristics, luminance component was chosen as the measurement to calculate the focus degree. After separating the luminance component and spectrum component, Fisher classification and fuzzy theory were chosen as the fusion rules to conduct the choice of the coefficients after the dual-tree complex wavelet transform. So fusion color image could keep the natural color information as much as possible. This method could solve the problem of color distortion in the traditional algorithms. According to the simulation results, the proposed algorithm obtained better visual effects and objective quantitative indicators.

Although the network worm propagation models are able to predict trends in the spread of the worm, these papers did not consider the economic losses caused by the worm propagation. This paper uses the Hamiltonian approach of the optimal control theory and proposes the cost optical control system based on the Kermack–Mckendrick worm propagation model. In the cost optical control system, we control and process the infectious hosts. In this paper, we present an optimization model that takes into account infection cost and treatment cost. The simulation on the cost optical control system is carried out, and then the results of the analysis are given. In conclusion, the cost optical control system of the Kermack–Mckendrick model can provide the optical control of the worm propagating and effectively reduce the worm propagation with minimum cost.

In contrast with the large diesel engine fuel system operational condition complexity, the chaotic fractal characteristics in the combustion process of the diesel engine fuel system are systematically analyzed. Then, based on chaotic fractal method, an effective fault diagnosis method is further proposed. And under the two kinds of typical failure modes, the two important characteristic parameters variation trend for chaotic system, namely, correlation dimensions and the calculation methods of the maximum Lyapunov exponent are discussed. On the process of chaotic parameters calculation the wavelet noise reduction method is used to handle the original signal to conquer the linear scaling region properly. The results show that the maximum Lyapunov exponent method is a more effective method on the diesel engine fuel system fault diagnosis than the correlation dimension method, which can be further used in health condition assessment for this type fuel engine.

The wavelet transform de-noising method based on the threshold shrinkage is widely used, however, the classical threshold shrinkage method maybe exists constant deviation or appears additional concussion after reconfiguration. If the threshold value is chosen too large, part of useful signal points will be eliminated; if chosen too small, part of the noise will be kept. In addition, the “first generation wavelet” de-noising method only can choose one type of wavelet base, and can't be changed during the de-noising process. Due to the different wavelet has its own characteristics and applicable signal, It can achieve local optimum then overall optimization that choosing wavelet in a small wave focus according to the partial feature of the signal, namely using the multiwavelets to comprehensively decompose signal. This paper puts forward a new threshold function, then improves the selection scheme of threshold value, achieves that it can select dynamically according to the wavelet decomposition levels; Finally, on the basis of the lifting theory, this paper puts forward an adaptive method, which can select wavelet dynamically according to the partial feature of the signal, comprehensively making use of the advantages of each wavelet, to achieve better de-noising effect and a certain degree of adaptability. Experiments show the effectiveness of our optimization.

We have derived a novel approach to simulate free surfaces. For two-dimensional finite-element flow simulations of the incompressible Navier-Stokes equations we integrate out the velocities on the surface obtained from the FEM-simulations with integrators for ordinary differential equations. The advance of the fluid front does not need additional data structures or interpolated mesh points as in the front tracking for finite difference methods, the adaptive mesh of the FEM-simulation is sufficient. As we perform the time-integration of the FEM-code, the second-order Adams-Bashforth turns out to be the most suitable integrator for the surface motion. For the speed of the wavefronts, we get excellent agreement for large viscosity with the lubrication approximation by Huppert, and for small viscosity, we get very good agreement with the experimental data for water by Martin and Moyce.

Wafer dicing is a basic step in building electronic devices such as CPU or GPU. It is automatically accomplished in the wafer Dicing Machine in which a little misalignment of wafers may lead to damaged chips. The information of misalignment may be obtained by detecting the paddle vat to calculate the misalignment between the blade and the part of the wafer which has been diced in real-time. In this paper, methods for detecting the paddle vat are studied. For most of simple wafer images where the intensities of paddle vats are quite different from that of the background, a binary segmentation approach is used to detect the paddle vat of the wafer image. The threshold for binarization is calculated by the grey-scale histogram of the wafer image which is smoothed by using exponentially smoothed method. For wafer images with complex backgrounds, the improved Canny detection algorithm with anisotropic diffusion is used to detect the edges of paddle vats. Experimental results show the validity of the methods.