Institution
China Aerospace Science and Industry Corporation
Company•Beijing, China•
About: China Aerospace Science and Industry Corporation is a company organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Computer science. The organization has 595 authors who have published 505 publications receiving 5136 citations. The organization is also known as: CASIC.
Topics: Control theory, Computer science, Finite element method, Filter (signal processing), Radar imaging
Papers published on a yearly basis
Papers
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TL;DR: The LSTM cell and its variants are reviewed and their variants are explored to explore the learning capacity of the LSTm cell and the L STM networks are divided into two broad categories:LSTM-dominated networks and integrated LSTS networks.
Abstract: Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are...
1,561 citations
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TL;DR: In this article, a supercritical drying or freeze drying of hydrogel precursors synthesized from reduction of graphene oxide with Lascorbic acid was used to obtain a specific capacitance of 128 F g−1 with superior rate performance.
Abstract: Mechanically strong and electrically conductive graphene aerogels can be prepared by either supercritical drying or freeze drying of hydrogel precursors synthesized from reduction of graphene oxide with L-ascorbic acid, and the resulting graphene aerogels possess the specific capacitance of 128 F g−1 with superior rate performance.
889 citations
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TL;DR: WPC/MNPs-80 has an excellent EM wave absorbency with a wide absorption band at a relatively low loading and thin absorber thickness, and the design strategy could be extended as a general method to synthesize other high-performance absorbers.
Abstract: A method combining liquid–liquid phase separation and the pyrolysis process has been developed to fabricate the wormhole-like porous carbon/magnetic nanoparticles composite with a pore size of about 80 nm (WPC/MNPs-80). In this work, the porous structure was designed to enhance interaction between the electromagnetic (EM) wave and the absorber, while the magnetic nanoparticles were used to bring about magnetic loss ability. The structure, morphology, porosity and magnetic properties of WPC/MNPs-80 were investigated in detail. To evaluate its EM wave attenuation performance, the EM parameters of the absorber and wax composite were measured at 2–18 GHz. WPC/MNPs-80 has an excellent EM wave absorbency with a wide absorption band at a relatively low loading and thin absorber thickness. At the absorber thickness of 1.5 and 2.0 mm, minimum RL values of −29.2 and −47.9 dB were achieved with the RL below −10 dB in 12.8–18 and 9.2–13.3 GHz, respectively. The Co and Fe nanoparticles derived from the chemical reduction of Co0.2Fe2.8O4 can enhance the graphitization process of carbon and thus improve dielectric loss ability. Polarizations in the nanocomposite absorber also play an important role in EM wave absorption. Thus, EM waves can be effectively attenuated by dielectric loss and magnetic loss through multiple reflections and absorption in the porous structure. WPC/MNPs-80 could be an excellent absorber for EM wave attenuation; and the design strategy could be extended as a general method to synthesize other high-performance absorbers.
292 citations
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TL;DR: A four-process structure is proposed to describe the typical scenario in cloud manufacturing, hoping to provide a theoretical reference for practical applications and the key characteristics of cloud manufacturing are presented in order to clarify the cloud manufacturing concept.
Abstract: Cloud manufacturing is emerging as a new manufacturing paradigm as well as an integrated technology, which is promising in transforming today’s manufacturing industry towards service-oriented, highly collaborative and innovative manufacturing in the future. In order to better understand cloud manufacturing, this paper provides a critical review of relevant concepts and ideas in cloud computing as well as advanced manufacturing technologies that contribute to the evolution of cloud manufacturing. The key characteristics of cloud manufacturing are also presented in order to clarify the cloud manufacturing concept. Furthermore, a four-process structure is proposed to describe the typical scenario in cloud manufacturing, hoping to provide a theoretical reference for practical applications. Finally, an application case of a private cloud manufacturing system for a conglomerate is presented.
264 citations
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TL;DR: A new perspective for cloud manufacturing, as well as a cloud-to-ground solution, including the terminology, MfgCloud, and applications, can push forward this new paradigm from concept to practice.
Abstract: The concept of cloud manufacturing is emerging as a new promising manufacturing paradigm, as well as a business model, which is reshaping the service-oriented, highly collaborative, knowledge-intensive and eco-efficient manufacturing industry. However, the basic concepts about cloud manufacturing are still in discussion. Both academia and industry will need to have a commonly accepted definition of cloud manufacturing, as well as further guidance and recommendations on how to develop and implement cloud manufacturing. In this paper, we review some of the research work and clarify some fundamental terminologies in this field. Further, we developed a cloud manufacturing systems which may serve as an application example. From a systematic and practical perspective, the key requirements of cloud manufacturing platforms are investigated, and then we propose a cloud manufacturing platform prototype, MfgCloud. Finally, a public cloud manufacturing system for small- and medium-sized enterprises (SME) is presented...
242 citations
Authors
Showing all 597 results
Name | H-index | Papers | Citations |
---|---|---|---|
Tao Zhang | 123 | 2772 | 83866 |
Rong Chen | 65 | 582 | 22888 |
Wei Xue | 34 | 368 | 5973 |
Weiye Hu | 11 | 13 | 493 |
Ning Wang | 11 | 22 | 513 |
Changsheng Gao | 9 | 33 | 324 |
Erqi Liu | 8 | 13 | 141 |
Yuanzhen Ren | 7 | 11 | 148 |
Sicong Zhao | 7 | 9 | 261 |
Dong Shao | 7 | 11 | 297 |
Min Du | 7 | 10 | 165 |
Xudong Chai | 6 | 9 | 95 |
Shenlin Hu | 6 | 8 | 176 |
Tongda Wang | 6 | 14 | 106 |
Yuanyuan Liu | 6 | 9 | 166 |