期刊：
IEEE Transactions on Industrial Informatics,2020年16(1):544-554 ISSN：1551-3203
通讯作者：
Liu, Hai
作者机构：
[Liu, Sannyuya; Liu, Tingting; Chen, Zengzhao; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Tingting] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA.;[Li, You-Fu; Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.;[Li, You-Fu] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China.
通讯机构：
[Liu, Hai] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Infrared (IR) spectral imaging sensing is a powerful visual technique for industrial material recognition in robot vision systems. However, the imaging sensing data have issues of random noise and band overlap. Resolution enhancement is usually the first step in the preprocessing procedure of industrial robot vision sensing. In this article, we develop a resolution-enhancement algorithm with total variation (TV) constraints for the degraded Fourier transform IR (FTIR) spectrum due to overlap and noise degradation in the robot vision sensing. The kernel function is calculated using the spectrometer imaging systems and Fourier optical theory. The proposed model not only can remove noises effectively but also can estimate the kernel function because of the adaptive TV as constraint regularization. This model is examined by a set of simulated FTIR spectra with the Poisson noises and a series of real FTIR spectra. The proposed model is compared with the other state-of-the-art methods in terms of performance. Experimental results demonstrate that the proposed approach can split the overlap band effectively while the spectral structure details are retained satisfactorily. The enhanced high-resolution imaging spectrum data can raise the robot vision sensing accuracy in industrial intelligent systems.
作者机构：
[Chen, Dan; Wang, Lizhe] China Univ Geosci, Sch Comp, Wuhan 430074, Peoples R China.;[Wang, Lizhe] Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100864, Peoples R China.;[Streit, Achim; Tao, Jie; Marten, Holger] Karlsruhe Inst Technol, Steinbuch Ctr Comp, D-76021 Karlsruhe, Germany.;[Ranjan, Rajiv] CSIRO, ICT Ctr, Informat Engn Lab, Canberra, ACT, Australia.;[Chen, Jingying] Cent China Normal Univ, Natl Engn Ctr E Learning, Beijing, Peoples R China.
通讯机构：
[Wang, Lizhe] C;Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100864, Peoples R China.
关键词：
Cloud computing;Data-intensive computing;Hadoop;MapReduce;Massive data processing
摘要：
Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data is processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters.
