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  • Open Access

    ARTICLE

    Efficient Computation Offloading in Mobile Cloud Computing for Video Streaming Over 5G

    Bokyun Jo1, Md. Jalil Piran2,*, Daeho Lee3, Doug Young Suh4,*

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 439-463, 2019, DOI:10.32604/cmc.2019.08194

    Abstract In this paper, we investigate video quality enhancement using computation offloading to the mobile cloud computing (MCC) environment. Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs. To do so, we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation (5G) cellular networks. In the proposed framework, the mobile client offloads the computational burden for the video enhancement to the cloud, which renders the side information needed to… More >

  • Open Access

    ABSTRACT

    Probabilistic Floor Response Spectrum of Nonlinear Nuclear Power Plant Structure using Latin Hypercube Sampling Method

    Heekun Ju, Hyung-Jo Jung*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.1, pp. 7-7, 2019, DOI:10.32604/icces.2019.05846

    Abstract Latin hypercube sampling (LHS) is widely applied to estimate a probabilistic floor response spectrum (FRS) of nonlinear nuclear power plant (NPP) structure. ASCE 4-16 Standards recommend that the minimum number of simulations should be larger than 30 when using LHS. Although this recommendation is commonly used for the minimum number of the simulation, there is no theoretical background. The variability of the estimations may exist according to the number of the simulation. Stated differently, the minimum number of the simulation may be varied depending on the characteristics of the problem (i.e., problem-dependent). In this context,… More >

  • Open Access

    ABSTRACT

    Small Vibration Measurement of Plant Equipment Using Sampling Moire Camera

    Motoharu Fujigaki*, Tomoaki Nakajima

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.1, pp. 2-2, 2019, DOI:10.32604/icces.2019.05156

    Abstract A sampling moire camera was developed by authors to measure the displacement of large structures effectively. The sampling moire camera was applied to small vibration measurement of a plant equipment in this paper. An algorithm of a sampling moire method is assembled into the sampling moire camera. It is very convenient to use the sampling moire camera in a practical field outdoors because any calibration is not necessary. This camera can measure the 2-D displacement in real-time. An experiment to measure small vibration of a moving conveyor belt to carry materials in a steel plant More >

  • Open Access

    ARTICLE

    Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem

    Shuangsheng Zhang1,5, Hanhu Liu1, Jing Qiang2,*, Hongze Gao3,*, Diego Galar4, Jing Lin4

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825

    Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X0 , Y0) and release time (T0), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in… More >

  • Open Access

    ARTICLE

    Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

    Chen Ding1, Guizhi Wang1,*, Qi Liu2

    Journal on Big Data, Vol.1, No.2, pp. 55-69, 2019, DOI:10.32604/jbd.2019.06110

    Abstract Haze concentration prediction, especially PM2.5, has always been a significant focus of air quality research, which is necessary to start a deep study. Aimed at predicting the monthly average concentration of PM2.5 in Beijing, a novel method based on Monte Carlo model is conducted. In order to fully exploit the value of PM2.5 data, we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively. The results show that these data are both approximately normal distribution. On the basis… More >

  • Open Access

    ARTICLE

    Seismic Vulnerability Analysis of Single-Story Reinforced Concrete Industrial Buildings with Seismic Fortification

    Jieping Liu1, Lingxin Zhang1,*, Haohao Zhang2, Tao Liu1

    Structural Durability & Health Monitoring, Vol.13, No.2, pp. 123-142, 2019, DOI:10.32604/sdhm.2019.04486

    Abstract As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed… More >

  • Open Access

    ARTICLE

    Stream-Based Data Sampling Mechanism for Process Object

    Yongzheng Lin1, Hong Liu1, ∗, Zhenxiang Chen2, Kun Zhang2, Kun Ma2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 245-257, 2019, DOI:10.32604/cmc.2019.04322

    Abstract Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive More >

  • Open Access

    ARTICLE

    Effective Piecewise Linear Skeletonization of Sparse Shapes

    Wenyu Qu1, Zhiyang Li2,*, Junfeng Wu2, Yinan Wu3, Zhaobin Liu2

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 115-123, 2018, DOI:10.32604/csse.2018.33.115

    Abstract Conventional image skeletonization techniques implicitly assume the pixel level connectivity. However, noise inside the object regions destroys the connectivity and exhibits sparseness in the image. We present a skeletonization algorithm designed for these kinds of sparse shapes. The skeletons are produced quickly by using three operations. First, initial skeleton nodes are selected by farthest point sampling with circles containing the maximum effective information. A skeleton graph of these nodes is imposed via inheriting the neighborhood of their associated pixels, followed by an edge collapse operation. Then a skeleton tting process based on feature-preserving Laplacian smoothing More >

  • Open Access

    ARTICLE

    The SLAM Algorithm for Multiple Robots Based on Parameter Estimation

    MengYuan Chen1,2

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 593-602, 2018, DOI:10.31209/2018.100000026

    Abstract With the increasing number of feature points of a map, the dimension of systematic observation is added gradually, which leads to the deviation of the volume points from the desired trajectory and significant errors on the state estimation. An Iterative Squared-Root Cubature Kalman Filter (ISR-CKF) algorithm proposed is aimed at improving the SR-CKF algorithm on the simultaneous localization and mapping (SLAM). By introducing the method of iterative updating, the sample points are re-determined by the estimated value and the square root factor, which keeps the distortion small in the highly nonlinear environment and improves the… More >

  • Open Access

    ARTICLE

    Neural Network-Based Second Order Reliability Method (NNBSORM) for Laminated Composite Plates in Free Vibration

    Mena E. Tawfik1, 2, Peter L. Bishay3, *, Edward A. Sadek1

    CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.1, pp. 105-129, 2018, DOI:10.3970/cmes.2018.115.105

    Abstract Monte Carlo Simulations (MCS), commonly used for reliability analysis, require a large amount of data points to obtain acceptable accuracy, even if the Subset Simulation with Importance Sampling (SS/IS) methods are used. The Second Order Reliability Method (SORM) has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures, when compared to the slower MCS techniques. However, SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation. The most suitable approach to do this is to use a symbolic solver, which renders… More >

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