A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems

Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.

     

High-performance simulations of turbulent boundary layer flow using Intel Xeon Phi many-core processors

The Journal of Supercomputing - Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also...     

DM-CTSA: a discriminative multi-focused and complementary temporal/spatial attention framework for action recognition

Neural Computing and Applications - Video-based human action recognition remains a challenging task. There are three main limitations: (1) Most works are only restricted to single temporal scale...     

双目视觉系统在风洞伞摆角测量中的研究与应用

伞系统是飞行器的重要组成部分,其摆角参数是系统安全性的重要指标之一;针对目前风洞试验中伞摆角难以测量的问题,提出了一种基于双目视觉系统的试验方法,该系统主要由两部CCD相机、图像采集卡和控制计算机组成,通过立体视觉标定和数字图像处理技术解算出空间特征标记的三维坐标,根据坐标的空间几何关系计算出伞摆角参数;试验结果表明该方法应用成功,能够同时获取伞运动过程中投影在正交平面上的竖直和水平摆角,且具有系统组成简单、通用性强的优点,有较高的应用价值。     

从“超级网银”看我国PKI交叉认证体系建设

"超级网银"是我国第一个大规模跨CA的交叉认证平台,采用的是桥接交叉认证模式。不同的领域,应根据其自身特点,采用不同形式的交叉认证模型。最终的目的,是要在全国范围内建立一个交叉认证、相互融合又相互独立、相互协调的PKI整体体系,在这个过程中,国家和政府应发挥更加积极的作用。     

Data-driven facial expression synthesis via Laplacian deformation

Realistic talking heads have important use in interactive multimedia applications. This paper presents a novel framework to synthesize realistic facial animations driven by motion capture data using Laplacian deformation. We first capture the facial expression from a performer, then decompose the motion data into two components: the rigid movement of the head and the change of the facial expression. By making use of the local-detail preserving property of the Laplacian coordinate, we clone the captured facial expression onto a neutral 3D facial model using Laplacian deformation. We choose some expression “independent points” in the facial model as the fixed points when solving the Laplacian deformation equations. Experimental results show that our approach can synthesize realistic facial expressions in real time while preserving the facial details. We compare our method with the state-of-the-art facial expression synthesis methods to verify the advantages of our method. Our approach can be applied in real-time multimedia systems.     

Performance of TZCD-MBOK watermarking scheme in T-DMB systems

Recently, watermarking based data transmission techniques using terrestrial digital TV signal have been proposed since they are also cost-free and can overcome the limitations of GPS. However, in the previous watermarking based methods, the detection accuracy is low and additional data rate is too low. Thus, we propose the throughput enhancement method by employing the TZCD-MBOK watermarking technique in T-DMB system. By applying the proposed scheme to T-DMB, it allows additional data transmission for disaster broadcasting and improves efficiency of data transmission in shadow region and indoor to mobile environment through watermarking spread code. From the simulation results, we confirm the proposed watermarking scheme affected on the existing T-DMB signal. Moreover, it was also confirmed that the system capacity increases as the power of additional watermarking signal rose. The results of the paper can be applied to wireless multimedia digital broadcasting systems.     

Analysis of emerging technology adoption for the digital content market

The digital content market is undergoing an evolution in networking and digitalization technologies, offering diverse information and services. Due to the characteristics of these emerging technologies, the digital content market is growing rapidly and traditional content providers face service transformation decisions. While a majority of the previous technology adoption studies have focused on the viewpoints of users and customers, cost reduction, or electronic channel related technologies, in this research we analyze the emerging technology adoption decisions of competing firms for providing new content services from a strategic perspective. Utilizing game theoretical models, we examine the effects of market environments (technology cost, channel cannibalization, brand power, brand extension, information asymmetry and market uncertainty) on firms’ adoption decisions. This research contributes a number of unique and interesting implications for the issues of emerging technology adoption for new content service provision.     

Fuzzy grammar theory based on lattices

In this paper, on the basis of breadth-first and depth-first ways, we establish a fundamental framework of fuzzy grammars based on lattices, which provides a necessary tool for the analysis of fuzzy automata. The relationship among finite automata with membership values in lattices (l-VFAs), lattice-valued regular grammars (l-RGs) and lattice-valued deterministic regular grammars (l-DRGs) is investigated. It is demonstrated that, based on each semantic way, l-VFAs and l-RGs are equivalent in the sense that they accept or generate the same classes of fuzzy languages. Furthermore, it is proved that l-VFAs,?l-valued deterministic finite automata, l-RGs and l-DRGs are equivalent based on depth-first way. For any l-RG,?the language based on breadth-first way coincides with the language based on depth-first way if and only if the truth-valued domain l is a distributive lattice.     

Adaptive synchronization of master-slave large-scale systems against bias actuators and network attenuations

This paper utilizes the adaptive technique to design a class of active synchronizing masterslave large scale systems against imperfect actuators and networked interconnections with bias faults and signal attenuations, respectively. Without the requirement of knowledge of eventual faulty factors of bias-actuators on systems, and attenuation factors of connected networks, an adaptive mechanism is designed to estimate each unknown faulty factor on-line for constructing a class of distributed adaptive controllers. Then based on the adaptive adjustment parameters and Lyapunov stability theory, Lyapunov functions are addressed to prove that the proposed adaptive master-slave large-scale system can be guaranteed to be asymptotic synchronization with the improper actuator and faulty transmitted signals. Finally, a multiple vehicle large-scale system is used to verify the efficiency of the method.