Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Chenhao Niu"'
Publikováno v:
Crystals, Vol 14, Iss 11, p 1002 (2024)
In order to improve the electrochemical activity and discharge performance of aluminum–air batteries and to reduce self-corrosion of the anode, an SLM-manufactured aluminum alloy was employed as the anode of the Al-air battery, and the influence of
Externí odkaz:
https://doaj.org/article/5bf3f2b295d449379f014a45e69a1b20
Publikováno v:
Crystals, Vol 14, Iss 9, p 784 (2024)
To improve the discharge performance of aluminum–air batteries, CeO2/Al6061 composites were prepared as an anode using selective laser melting (SLM). Response surface methodology (RSM) was employed, and the test results were linearly fitted. A pred
Externí odkaz:
https://doaj.org/article/0acbae6fae594f5aa3d4da2a0d757b6c
Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::362f583747d347f54114f85a72972929
Publikováno v:
KDD
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::959230e0025516a80ec6369936394d7b
https://doi.org/10.1145/3292500.3330672
https://doi.org/10.1145/3292500.3330672
Autor:
Wentao Xia, Zhaogang Wang, Yiyin Zhang, Dan Pei, Haibin Li, Jiahao Bu, Ya Su, Jing Zhu, Yuanpu Cao, Rong Liu, Youjian Zhao, Chenhao Niu
Publikováno v:
IWQoS
Internet-based service companies monitor a large number of KPIs (Key Performance Indicators) to ensure their service quality and reliability. Correlating KPIs by fluctuations reveals interactions between KPIs under anomalous situations and can be ext
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdc7c0ee436d314e0db04cc3e30a76f6
https://doi.org/10.1145/3326285.3329048
https://doi.org/10.1145/3326285.3329048
Publikováno v:
The Open Cybernetics & Systemics Journal. 8:1129-1133
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd2dfaf0ee9e895e5ba2464a8cb29fdd
https://doi.org/10.2174/1874110x01408011129
https://doi.org/10.2174/1874110x01408011129
Publikováno v:
The Open Electrical & Electronic Engineering Journal. 8:723-728
Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68707efc95d61117a8033465ca85b077
https://doi.org/10.2174/1874129001408010723
https://doi.org/10.2174/1874129001408010723
Autor:
Ling Ji, Chenhao Niu
Publikováno v:
Journal of Convergence Information Technology. 7:540-547
Power load is a typical time series with the characters of nonlinearity and volatility. Accuracy load forecasting can help ensure the security of power grid operation. In this paper, a new hybrid method based on wavelet analysis for short-time load f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96068ac4d3bcc1313da4bf04347f8e3a
https://doi.org/10.4156/jcit.vol7.issue17.63
https://doi.org/10.4156/jcit.vol7.issue17.63
Autor:
Bingjie Li, Chenhao Niu
Publikováno v:
Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science.
Recently, the proportion of distributed generation in the power system is growing with the increasing development and of distributed generation technologies. The application of distributed generation brings many benefits to the grid, such as improvin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31bb71438ad05a07fa88e758b0734930
https://doi.org/10.2991/lemcs-15.2015.353
https://doi.org/10.2991/lemcs-15.2015.353