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Your search keyword '"Yang, Xue"' showing total 330 results
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330 results on '"Yang, Xue"'

7. Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor

Author

Yanlin Peng, Yining Yang, Yuejie Xu, Yang Xue, Runan Song, Jinping Kang, and Haisen Zhao

Subjects
Clustering, data mining, electricity theft detection, local outlier factor, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

As one of the key components of smart grid, advanced metering infrastructure (AMI) provides an immense number of data, making technologies such as data mining more suitable for electricity theft detection. However, due to the unbalanced dataset in the field of electricity theft, many AI-based methods such as deep learning are prone to under-fitting. To evade this problem and to detect as many types of theft attacks as possible, an outlier detection method based on clustering and local outlier factor (LOF) is proposed in this study. We firstly analyze the load profiles with $k$ -means. Then, customers whose load profiles are far from the cluster centers are selected as outlier candidates. After that, the LOF is utilized to calculate the anomaly degrees of outlier candidates. Corresponding framework for practical application is then designed. Finally, numerical experiments based on realistic dataset show the good performance of the presented method.

Published
2021
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45. RSDet++: Point-Based Modulated Loss for More Accurate Rotated Object Detection.

Author

Qian, Wen, Yang, Xue, Peng, Silong, Zhang, Xiujuan, and Yan, Junchi

Subjects
*OBJECT recognition (Computer vision), *MINIATURE objects, *UNITS of measurement, *FEATURE extraction
Abstract

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration. We introduce a novel modulated rotation loss to alleviate the problem and a rotation sensitivity detection network (RSDet) which consists of an eight-param single-stage rotated object detector and the modulated rotation loss. Our proposed RSDet has several advantages: 1) it reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-param-based regression method with different measurement units. 2) modulated rotation loss achieves consistent improvement on both five-param and eight-param rotated object detection methods by solving the discontinuity of loss. To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++ which consists of a point-based anchor-free rotated object detector and a modulated rotation loss. Extensive experiments demonstrate the effectiveness of both RSDet and RSDet++, which achieve competitive results on rotated object detection in the challenging benchmarks DOTA-v1.0, DOTA-v1.5, and DOTA-v2.0. We hope the proposed method can provide a new perspective for designing algorithms to solve rotated object detection and pay more attention to tiny objects. The codes and models are available at: https://github.com/yangxue0827/RotationDetection. [ABSTRACT FROM AUTHOR]

Published
2022
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47. Revisiting the Feld’s Friendship Paradox in Online Social Networks

Author

Xiaoping Zhou, Xun Liang, Jichao Zhao, Haiyan Zhang, and Yang Xue

Subjects
social computing, social network, lcsh:Electrical engineering. Electronics. Nuclear engineering, Computer science, lcsh:TK1-9971, Feld’s friendship paradox
Abstract

Feld's friendship paradox is a widely accepted observation that states that the friends of any particular individual tend to have more friends on average than the individual has. Due to its implications for information transmission, the friendship paradox has become a generalized paradigm in many disciplines. However, how many of an individual's friends have more friends than the individual does is still unknown, yet interesting. In this paper, we revisited the Feld's friendship paradox and we found that only a limited number of a person's friends have more friends than the person herself has. This conclusion was reached from empirical studies using real-world networks and is true regardless of the number of one's neighbours, which contradicts the intuitive deduction from the friendship paradox. For one thing, if a person is unpopular, the number of her friends that have more friends than she does grows with the number of friends that she makes. This observation crystallizes the tenable margin of the friendship paradox. In another case, if a person is popular, as she acquires more friends, fewer of her friends have more friends than she does. This finding suggests an observation bias in the friendship paradox, which makes individuals feel less popular than their friends. Besides enriching our knowledge of the friendship paradox in psychological science, the findings reported here are also beneficial for technical areas such as large-scale triangle discovery. Although the friendship paradox was proposed from the perspective of the number of neighbours, the findings reported here can shed light on theories and applications from different disciplines like information, happiness, obesity, and extroversion, to name a few.

Published
2020

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