作者：
RAWLING, AGJOHNSON, NLArnold George Rawlings was born 20 December 1921 in Luke
Maryland. He received both a Bachelor's degree in Chemical Engineering and a Master's degree in Applied Mathematics from the University of Maryland. He served overseas as an LST officer in the Amphibious Forces of the United Navy participating in the Pacific campaigns of World War II. His professional experience includes theoretical mechanics at the Naval Ordnance Laboratory at White Oak (1949-52) digital computer design and large scale air defense system engineering at MIT (1952-54) Navy interceptor missile guidance and control system design at the Johns Hopkins Applied Physics Laboratory (1954-60) flight dynamics and currently submarine simulation as a consulting engineer with the General Electric Company. He is a member of the AIAA and Sigma Xi and is author of the papers “Automation and the Scientific Laboratory from a Systems Viewpoint” “Passive Determination of Homing Time” and “On Non-Zero Miss Distance.” Norman L. Johnson was born on 3 December 1937 in Three Falls
Michigan. He received a Bachelor's degree in Engineering Physics (1959) from Michigan Technological University and is currently studying towards a Master's degree. His professional experience at the General Electric Company includes analog and hybrid simulation engineering applications up to 1967. Since then he has held the post of Supervisory Engineer in the same area. Problems investigated during the past nine years include the body dynamics and control system design for interceptor missiles re-entry vehicles satellites and for the last two years submarines. He is co-author of the publication “Temperature Generated by the Flow of Liquids in Pipes.”
In designing the man-multiloop controlsystem for a small submersible, many different types of terminal equipment for manual intervention can interface with the human operator, including handlebars, wheels, joysticks ...
In designing the man-multiloop controlsystem for a small submersible, many different types of terminal equipment for manual intervention can interface with the human operator, including handlebars, wheels, joysticks and pedals. The proper choice of the means to control the boat in attitude and steering, as well as the actual implementation, constitute a major human factors design problem. This paper discusses a particularly promising concept of manual control that has shown superiority over several alternatives, and decribes the real-time simulation employed in verifying its advantages.
Fact verification task has emerged as an essential research topic recently due to abundant fake news spreading on the Internet. The task based on unstructured data (i.e., news) has achieved great development, but the ...
详细信息
Fact verification task has emerged as an essential research topic recently due to abundant fake news spreading on the Internet. The task based on unstructured data (i.e., news) has achieved great development, but the task based on structured data (i.e., table) is still in the primary development period. The existing methods usually construct complete heterogeneous graph networks around statement, table, and program subgraphs, and then infer to learn similar semantics on them for fact verification. However, they generally connect the nodes with the same content between subgraphs directly to frame a larger graph network, which has serious sparsity in connections, especially when subgraphs possess limited semantics. To this end, we propose tight-fitting graph inference network (TFGIN), which innovatively builds tight-fitting graphs (TF-graph) to strengthen the connections of subgraphs, and designs inference modeling layer (IML) to learn coherence evidence for fact verification. Specifically, different from traditional connection ways, the constructed TF-graph enhances inter-graph and intra-graph connections of subgraphs through subgraph segmentation and interaction guidance mechanisms. Inference modeling layer could reason the semantics with strong correlation and high consistency as explainable evidence. Experiments on three competitive datasets confirm the superiority and scalability of our TFGIN.
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