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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 发表日期：2023     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Solar Observation with the Fourier Transform Spectrometer. II. Preliminary Results of Solar Spectrum near the CO 4.66 μm and Mg i 12.32 μm 期刊论文 RESEARCH IN ASTRONOMY AND ASTROPHYSICS, 2023, 卷号: 23, 期号: 12 作者:  Bai, Xianyong;  Feng, Zhiwei;  Zhang, Zhiyong;  Deng, Yuanyong;  Zhu, Xiaoming 收藏  |  浏览/下载：1/0  |  提交时间：2024/03/29 Sun: infrared  methods: observational  instrumentation: interferometers   Investigation on anti-wear and corrosion-resistance behavior of steel-steel friction pair enhanced by ionic liquid additives under conductive conditions 期刊论文 Tribology International, 2023, 卷号: 177, 期号: 无, 页码: 108002 作者:  Wenhao Chen;  Yange Feng;  Yong Wan;  Liqiang Zhang;  Di Yang 收藏  |  浏览/下载：31/0  |  提交时间：2023/01/09 current-carrying friction  ionic liquid  anti-wear  anti-corrosion   Investigation on anti-wear and corrosion-resistance behavior of steel-steel friction pair enhanced by ionic liquid additives under conductive conditions 期刊论文 Tribology International, 2023, 卷号: 177, 期号: 无, 页码: 108002 作者:  Wenhao Chen;  Yange Feng;  Yong Wan;  Liqiang Zhang;  Di Yang 收藏  |  浏览/下载：2/0  |  提交时间：2023/12/11 Clickable periodic mesoporous organosilicas core-shell particles with radially-oriented mesopores for high-performance liquid chromatography 期刊论文 Microporous and Mesoporous Materials, 2023, 期号: 362, 页码: 112785 作者:  Bei Liu;  Kaijun Quan;  Xiaoming Yang;  Jia Chen;  Hongdeng Qiu 收藏  |  浏览/下载：0/0  |  提交时间：2023/11/03 Network-Wide Traffic Signal Control Based on MARL With Hierarchical Nash-Stackelberg Game Model 期刊论文 IEEE ACCESS, 2023, 卷号: 11, 页码: 145085-145100 作者:  Shen, Hui;  Zhao, Hongxia;  Zhang, Zundong;  Yang, Xun;  Song, Yutong 收藏  |  浏览/下载：0/0  |  提交时间：2024/02/22 Games  Roads  Approximation algorithms  Q-learning  Multi-agent systems  Process control  Optimization  Reinforcement learning  Traffic control  Network-wide traffic signal control  hierarchical game model  multi-agent reinforcement learning