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pro vyhledávání: '"Ting-Hsiang Wang"'
Autor:
Ting-Hsiang Wang, 汪鼎翔
103
The purpose of this study is to analyze the system characteristic of coupling multiple power unit in hybrid power trains and to evaluate the system efficiency of varies kind of powertrains. The goal is to identify the proper coupling type to
The purpose of this study is to analyze the system characteristic of coupling multiple power unit in hybrid power trains and to evaluate the system efficiency of varies kind of powertrains. The goal is to identify the proper coupling type to
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/16802313988504979248
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:1258-1274
Item concept modeling is commonly achieved by leveraging textual information. However, many existing models do not leverage the inferential property of concepts to capture word meanings, which therefore ignores the relatedness between correlated conc
Autor:
Chipan Hwang, Ting-Hsiang Wang
Publikováno v:
2020 International Symposium on Computer, Consumer and Control (IS3C).
This study focused on controlling the heating temperature of the electric oven and made it more consistent and effective. There are three methods to control temperature and each one has different advantages and disadvantages. The traditional rotary o
Publikováno v:
RecSys
Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec 1 2, an open-source automated machine learning (AutoML) platform extended
Publikováno v:
RecSys
In the Age of Big Data, graph embedding has received increasing attention for its ability to accommodate the explosion in data volume and diversity, which challenge the foundation of modern recommender systems. Respectively, graph facilitates fusing
Publikováno v:
SIGIR
This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a ge