Modeling and Control Method for Multi-Layer Distributed Flow Based on WFCP-Net

2010 ◽  
Vol 97-101 ◽  
pp. 3682-3685
Author(s):  
Hai Wei Wang ◽  
Geng Liu ◽  
Dang Dang Zheng
Keyword(s):  
Control Mechanism ◽  
Control Method ◽  
Flow Modeling ◽  
Class Model ◽  
Modeling And Control ◽  
Collaborative Simulation ◽  
System Building ◽  
Management Flow ◽  
And Control ◽  
Entity Class

According to the simulation requirement for complex mechanical system, building flow modeling and control mechanism is the main content of development for the collaborative simulation platform. Based on research and analysis to WFCP-Net technique, we present multi-layer distributed flow. Management flow and simulation flow are defined, and a suit of entity class model and a kind of flow control mechanism are designed. Finally, the method is verified by the platform developed.

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

2021 ◽  
Vol 2 (4) ◽  
pp. 1-24
Author(s):  
Pratyay Banerjee ◽  
Kuntal Kumar Pal ◽  
Murthy Devarakonda ◽  
Chitta Baral
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Neural Models ◽  
Named Entity ◽  
Input Text ◽  
The Impact ◽  
Biomedical Named Entity Recognition ◽  
Entity Class

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

scholarly journals A Preliminary Study on the Mixed Learning Mode of Public English in Higher Vocational Colleges under the Interactive English Platform

2019 ◽  
Vol 3 (2) ◽  
pp. 94
Author(s):  
Benying Liu
Keyword(s):  
Teaching Method ◽  
Rapid Development ◽  
Higher Vocational Education ◽  
Teaching Mode ◽  
English Teaching ◽  
Multimedia Teaching ◽  
Internet Information ◽  
Methods Of Learning ◽  
Entity Class ◽  
Learning English

<p>In the past in the higher vocational education the entity class teaching mode is generally adopted in the process of public English teaching, in recent years due to the rapid development of Internet information technology and the emergence of multimedia teaching methods, to gradually change the traditional English teaching mode to entity class teaching and online mutual combination of Internet teaching. This new hybrid English teaching method has both the advantages of physical classroom teaching and the new advantages of online learning. It is a brand-new way of learning English. Interactive English platform is a new model of this new way of learning English. This paper will take this platform as an example and focus on the methods of learning public English in colleges and universities.</p>

scholarly journals Modeling simulation and virtual platform construction of crossbow making technology

2019 ◽  
Vol 136 ◽  
pp. 03032
Author(s):  
Cong Wen ◽  
Wu Xu ◽  
Wenquan Tang ◽  
Xing Guo ◽  
Bing Zhou
Keyword(s):  
Data Storage ◽  
Storage Management ◽  
3D Interaction ◽  
Data Set ◽  
Organization Model ◽  
Arithmetic Average ◽  
Modeling Simulation ◽  
Virtual Platform ◽  
Query Operations ◽  
Entity Class

In this paper, a set of virtual simulation platform for crossbow making is established. First, build a virtual model of Lisu crossbow, optimize the model, accelerate the organization, model generation, modification and editing of the database; then complete the database establishment of the corresponding model, nest the comment entity class and filemeta entity class into the file entity class, store them in a nested data set, and complete the data storage, management and query operations; finally, combined with Kalman filtering, arithmetic average filtering and KNN algorithm are used to build the quest 3D virtual platform to improve the 3D interaction effect with data gloves and complete the construction of the 3D platform.

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