Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

Subscribe

- Donate
- Personal Sign In

Institutional Sign In

Institutional Sign In

ADVANCED SEARCH

Search History

Showing 1-2 of 2 results

Conferences (2)

Sort

Filter Results

Show

All Results

Open Access Only

Range
Single Year
Jie Zhang(2)
Yi Gong(1)
Sen Yu(1)
Lina Zhao(1)
Yongji Cao(1)
State Grid Shandong Electric Power Company, Jinan, China(2)
Academy of Intelligent Innovation, Shandong University, Jinan, China(1)
Nanjing University of Finance and Economics, Nanjing, China(1)
School of Electrical Engineering, Shandong University, Jinan, China(1)
School of Integrated Circuits, Shandong University, Jinan, China(1)
2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)(1)
2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)(1)
IEEE(2)
Guilin, China(1)
Shenyang, China(1)
Access Control(1)
Agglomerative Clustering(1)
Bag-of-words(1)
Basic Characteristics(1)
Client Software(1)

Select All on Page

Sort By

Results

Feature Extraction for Post-Disturbance Frequency Prediction Based on Hierarchical Agglomerative Clustering

Jie Zhang;Hongji Shi;Yongji Cao;Changgang Li;Sen Yu;Yanlin Yi;Wenbo Li;Yi Gong;Yang Liu

2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

Year: 2024 | Conference Paper |

HTML

The prediction of post-disturbance frequency dynamic response plays a significant role in decision making for stability control. This paper proposes a feature extraction scheme for post-disturbance frequency prediction based on hierarchical agglomerative clustering (HAC). The post-disturbance dynamic response process of power system frequency is analyzed, on which the basic input features for pred...Show More

Feature Extraction for Post-Disturbance Frequency Prediction Based on Hierarchical Agglomerative Clustering

Jie Zhang;Hongji Shi;Yongji Cao;Changgang Li;Sen Yu;Yanlin Yi;Wenbo Li;Yi Gong;Yang Liu

2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

Year: 2024 | Conference Paper |

Semantic Integration based User Behavior Monitoring Framework in Zero Trust Network

Yansheng Qu;Hua Huang;Lin Chen;Lina Zhao;Yuxiang Zhao;Jie Zhang;Bo Mao

2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

User behavior monitoring is an essential task in Zero Trust Network. In this paper, a semantic analysis framework is proposed to generate an integrated network user behavior which can be applied for abnormal detection. The framework first defines an unified network behavior structure which covers different network layers; then a knowledge graph of the network is created based on the entity detecti...Show More

Semantic Integration based User Behavior Monitoring Framework in Zero Trust Network

Yansheng Qu;Hua Huang;Lin Chen;Lina Zhao;Yuxiang Zhao;Jie Zhang;Bo Mao

2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)

Year: 2022 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.

Learn More