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  • Open Access

    ARTICLE

    Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

    Mengwei Hou1, Rong Wei1,*, Tiangang Wang1, Yu Cheng2, Buyue Qian3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI:10.3970/cmc.2018.02438

    Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect… More >

  • Open Access

    ARTICLE

    A Distributed LRTCO Algorithm in Large-Scale DVE Multimedia Systems

    Hangjun Zhou1,2,*, Guang Sun1, Sha Fu1, Wangdong Jiang1, Tingting Xie3, Danqing Duan1

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 73-89, 2018, DOI:10.3970/cmc.2018.02411

    Abstract In the large-scale Distributed Virtual Environment (DVE) multimedia systems, one of key challenges is to distributedly preserve causal order delivery of messages in real time. Most of the existing causal order control approaches with real-time constraints use vector time as causal control information which is closely coupled with system scales. As the scale expands, each message is attached a large amount of control information that introduces too much network transmission overhead to maintain the real-time causal order delivery. In this article, a novel Lightweight Real-Time Causal Order (LRTCO) algorithm is proposed for large-scale DVE multimedia More >

  • Open Access

    ARTICLE

    A Novel Quantum Stegonagraphy Based on Brown States

    Zhiguo Qu1,*, Tiancheng Zhu2, Jinwei Wang1, Xiaojun Wang3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 47-59, 2018, DOI:10.3970/cmc.2018.02215

    Abstract In this paper, a novel quantum steganography protocol based on Brown entangled states is proposed. The new protocol adopts the CNOT operation to achieve the transmission of secret information by the best use of the characteristics of entangled states. Comparing with the previous quantum steganography algorithms, the new protocol focuses on its anti-noise capability for the phase-flip noise, which proved its good security resisting on quantum noise. Furthermore, the covert communication of secret information in the quantum secure direct communication channel would not affect the normal information transmission process due to the new protocol’s good… More >

  • Open Access

    ARTICLE

    Determination of the Normal Contact Stiffness and Integration Time Step for the Finite Element Modeling of Bristle-Surface Interaction

    Libardo V. Vanegas-Useche1, Magd M. Abdel-Wahab2,3,4,*, Graham A. Parker5

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 169-184, 2018, DOI:10.3970/cmc.2018.01827

    Abstract In finite element modeling of impact, it is necessary to define appropriate values of the normal contact stiffness, Kn, and the Integration Time Step (ITS). Because impacts are usually of very short duration, very small ITSs are required. Moreover, the selection of a suitable value of Kn is a critical issue, as the impact behavior depends dramatically on this parameter. In this work, a number of experimental tests and finite element analyses have been performed in order to obtain an appropriate value of Kn for the interaction between a bristle of a gutter brush for road sweeping… More >

  • Open Access

    ARTICLE

    Data Mining and Machine Learning Methods Applied to 3 A Numerical Clinching Model

    Marco Götz1,*, Ferenc Leichsenring1, Thomas Kropp2, Peter Müller2, Tobias Falk2, Wolfgang Graf1, Michael Kaliske1, Welf-Guntram Drossel2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

    Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The More >

  • Open Access

    ARTICLE

    A Survey of Image Information Hiding Algorithms Based on Deep Learning

    Ruohan Meng1,2,*, Qi Cui1,2, Chengsheng Yuan1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 425-454, 2018, DOI:10.31614/cmes.2018.04765

    Abstract With the development of data science and technology, information security has been further concerned. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding algorithms has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensuring that the stego image cannot be distinguished from the cover image, and sending secret information to receiver through the transmission of the stego image. At present, the model based on deep learning is also widely applied to the More >

  • Open Access

    ARTICLE

    Lattice Boltzmann Simulation of a Gas-to-Solid Reaction and Precipitation Process in a Circular Tube

    Matthew D. Lindemer1, Suresh G. Advani2,*, Ajay K. Prasad2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 527-553, 2018, DOI:10.31614/cmes.2018.00481

    Abstract The lattice Boltzmann method (LBM) is used to simulate the growth of a solid-deposit on the walls of a circular tube resulting from a gas-to-solid reaction and precipitation process. This process is of particular interest for the design of reactors for the production of hydrogen by the heterogeneous hydrolysis of steam with Zn vapor in the Zn/ZnO thermochemical cycle. The solid deposit of ZnO product on the tube wall evolves in time according to the temporally- and axially-varying convective-diffusive transport and reaction of Zn vapor with steam on the solid surface. The LBM is well-suited… More >

  • Open Access

    ARTICLE

    A Method for Rapidly Determining the Optimal Distribution Locations of GNSS Stations for Orbit and ERP Measurement Based on Map Grid Zooming and Genetic Algorithm

    Qianxin Wang1,2,3, Chao Hu1,2,*, Ya Mao1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 509-525, 2018, DOI:10.31614/cmes.2018.04098

    Abstract Designing the optimal distribution of Global Navigation Satellite System (GNSS) ground stations is crucial for determining the satellite orbit, satellite clock and Earth Rotation Parameters (ERP) at a desired precision using a limited number of stations. In this work, a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed, named the minimum Orbit and ERP Dilution of Precision Factor (OEDOP) criterion. To quickly identify the specific station locations for the optimal station distribution on a map, a method for the rapid determination of the selected station locations is developed,… More >

  • Open Access

    ARTICLE

    Machine Learning Models of Plastic Flow Based on Representation Theory

    R. E. Jones1,*, J. A. Templeton1, C. M. Sanders1, J. T. Ostien1

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 309-342, 2018, DOI:10.31614/cmes.2018.04285

    Abstract We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen inputoutput map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the More >

  • Open Access

    ARTICLE

    On the Robustness of the xy-Zebra-Gauss-Seidel Smoother on an Anisotropic Diffusion Problem

    Michely Laís de Oliveira1,*, Marcio Augusto Villela Pinto2, Simone de Fátima Tomazzoni Gonçalves2, Grazielli Vassoler Rutz3

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.2, pp. 251-270, 2018, DOI:10.31614/cmes.2018.04237

    Abstract Studies of problems involving physical anisotropy are applied in sciences and engineering, for instance, when the thermal conductivity depends on the direction. In this study, the multigrid method was used in order to accelerate the convergence of the iterative methods used to solve this type of problem. The asymptotic convergence factor of the multigrid was determined empirically (computer aided) and also by employing local Fourier analysis (LFA). The mathematical model studied was the 2D anisotropic diffusion equation, in which ε > 0 was the coefficient of a nisotropy. The equation was discretized by the Finite… More >

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