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RISS 인기검색어
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- 저자 : Yidong Shi (검색결과 3 건)
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Hardware Implementation of Two-level Scheduling Algorithm in μC/OS-II
Guangwu Zhang,Yan Li,Yidong Chen,Huaiguo Dong,Huanhuan Chi,Min Shi,Junfeng Gao 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.4
Aiming at the problem that μC/OS-II does not support round-robin scheduling of the same priority task, a two-level hybrid task scheduling strategy was proposed. In the first level, by putting the task priority as criterion for task scheduling, a preemptive scheduling of different priority task was implemented. And in the second level, adopting time slice circulars scheduling strategy, round-robin scheduling of same priority task was implemented. The waiting list of tasks was designed by on-chip registers of FPGA and the ready list of tasks was designed by RAM of FPGA, and to implement time slice circulars scheduling, hardware circuit for finding successor of task was designed. The system adopted VHDL, and simulated by the software ISE10.1. The simulation results show that the hardware implementation of the system is well-worked.
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A Sense Embedding of Deep Convolutional Neural Networks for Sentiment Classification
Zhijian Cui,Xiaodong Shi,Yidong Chen,Yinmei Guo 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
Sentiment classification task has attracted considerable interest as sentiment information is crucial for many natural language processing (NLP) applications. The goal of sentiment classification is to predict the overall emotional polarity of a given text. Previous work has demonstrate the remarkable performance of Convolutional Neural Network (CNN). However, nearly all this work assumes a single word embedding for each word type, ignoring polysemy and thus inevitably casting negative impact on the downstream tasks. We extend the Skip-gram model to learn multiple sense embeddings for the word types, catering to introduce sense-based embeddings for CNN during sentiment classification. Instead of using the pipeline method to learn multiple sense embeddings of a word type, the sense discrimination and sense embedding learning for each word type are performed jointly based upon the semantics of its contextual words. We validate the effectiveness of the method on the commonly used datasets. Experiment results show that our method are able to improve the quality of sentiment classification when comparing with several competitive baselines.
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Ya Wen,Ruifang Zhao,Xueqian Yin,Yidong Shi,Haojun Fan,Yi Zhou,Lin Tang 한국섬유공학회 2020 Fibers and polymers Vol.21 No.1
Antibacterial and antioxidant properties exert an indispensable effect on biomedical and packaging materialswhich are mainly made of fiber-based fabrics. The current research illustrates a novel type of composite fiber which wasprepared from thermoplastic polyurethane (PU), polyacrylonitrile (PAN) and tea polyphenols (TP) through wet-spinningtechnology. The PU-PAN mass ratio was optimized upon the mechanical property; TP was incorporated into the compositefiber with different contents. The obtained fibers were fully characterized in terms of mechanical, thermal, morphologicalproperties as well as surface wettability. Additionally, in vitro TP release, antioxidant and antibacterial activity of suchcomposite fibers before and after washing were under investigation systematically and orderly. The findings showed that TPloadedcomposite fibers with an average diameter around 120 μm performed a sustained TP release behavior and desirablecytocompatibility. According to the capacity on scavenging 2, 2-diphenyl-1-picrylhydrazyl (DPPH) free radicals, theobservation of scavenging ratio reaching to 97 % proved the excellent antioxidant performance. Furthermore, the idealantibacterial activity against Gram-negative E. coli and Gram-positive S. aureus was also demonstrated. Collectively, thisstudy provides a facile strategy to design and prepare a novel type of composite fibers and fabrics, which may be put intoapplication as wound dressing and tissue conduit.
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