Tong Zhao on Muck Rack

Tong Zhao

Washington, D.C.
As seen in: Apple Podcasts, CNN, The New York Times, Color Research & Application, Nature, Wiley Online Library, New Phytologist, PLOS, ScienceDirect, The Atlantic and
Senior fellow @CarnegieEndow @carnegiechina @carnegienpp. Nuclear, arms control, nonproliferation, new mil tech, Asia-Pacific security, US-China. Views my own.
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Articles

360° Tunable Balanced Filtering Phase Shifter With Reflectionless and Wide Stopband Characteristics

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By Tong Zhao, Zhongbao Wang, Chaoxiang Bao
| Wiley Online Library Verified
, , , and , “360° Tunable Balanced Filtering Phase Shifter With Reflectionless and Wide Stopband Characteristics,” International Journal of Circuit Theory and Applications (2026): 1–11, https://doi.org/10.1002/cta.70524. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Computer Science > Multimedia

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By Lin Chen, Chenyang Xu, Tong Zhao
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (493) Issue with article?

Participant-level anomaly detection for generation and load data using dual-side LSTMs

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By Qi Liu, Jiaxun Liu, Liming Yao, Tianze Yu, Tong Zhao, Qianya He, Xueliang Gong
| Nature Verified
Abstract Reliable anomaly detection for generation and load metering data is essential to market settlement. In practice, anomalies are diverse and sparse, and the load side contains a large and time-varying number of participants, which makes fine-grained participant-level localization difficult.
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