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Institution

Beihang University

EducationBeijing, China
About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.


Papers
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Yu Wu1, Wei Wu2, Chen Xing3, Ming Zhou2, Zhoujun Li1 
TL;DR: This paper proposed a sequential matching network (SMN) to match a response with each utterance in the context on multiple levels of granularity, and distill important matching information from each pair as a vector with convolution and pooling operations.
Abstract: We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among utterances. The final matching score is calculated with the hidden states of the RNN. An empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.

243 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: An overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings is provided and it is observed that CI methods outperform traditional methods on online and 3D problems.
Abstract: The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

242 citations

Journal ArticleDOI
TL;DR: The critical design criteria and the state-of-the-art fabrication strategies of nanocomposite hydrogels with highly ordered structures are systemically reviewed and recent progress in applications in the fields of soft actuators, tissue engineering, and sensors is highlighted.
Abstract: In the human body, many soft tissues with hierarchically ordered composite structures, such as cartilage, skeletal muscle, the corneas, and blood vessels, exhibit highly anisotropic mechanical strength and functionality to adapt to complex environments. In artificial soft materials, hydrogels are analogous to these biological soft tissues due to their "soft and wet" properties, their biocompatibility, and their elastic performance. However, conventional hydrogel materials with unordered homogeneous structures inevitably lack high mechanical properties and anisotropic functional performances; thus, their further application is limited. Inspired by biological soft tissues with well-ordered structures, researchers have increasingly investigated highly ordered nanocomposite hydrogels as functional biological engineering soft materials with unique mechanical, optical, and biological properties. These hydrogels incorporate long-range ordered nanocomposite structures within hydrogel network matrixes. Here, the critical design criteria and the state-of-the-art fabrication strategies of nanocomposite hydrogels with highly ordered structures are systemically reviewed. Then, recent progress in applications in the fields of soft actuators, tissue engineering, and sensors is highlighted. The future development and prospective application of highly ordered nanocomposite hydrogels are also discussed.

241 citations


Authors

Showing all 67500 results

NameH-indexPapersCitations
Yi Chen2174342293080
H. S. Chen1792401178529
Alan J. Heeger171913147492
Lei Jiang1702244135205
Wei Li1581855124748
Shu-Hong Yu14479970853
Jian Zhou128300791402
Chao Zhang127311984711
Igor Katkov12597271845
Tao Zhang123277283866
Nicholas A. Kotov12357455210
Shi Xue Dou122202874031
Li Yuan12194867074
Robert O. Ritchie12065954692
Haiyan Wang119167486091
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023205
20221,178
20216,767
20206,916
20197,080