Search Results for author: Xiao Lu

Found 13 papers, 3 papers with code

Pseudo-supervised Deep Subspace Clustering

1 code implementation8 Apr 2021 Juncheng Lv, Zhao Kang, Xiao Lu, Zenglin Xu

To tackle these problems, we use pairwise similarity to weigh the reconstruction loss to capture local structure information, while a similarity is learned by the self-expression layer.

Clustering

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

no code implementations20 Jun 2018 Zhao Kang, Xiao Lu, Jin-Feng Yi, Zenglin Xu

There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms.

Clustering General Classification

Submodular Load Clustering with Robust Principal Component Analysis

no code implementations20 Feb 2019 Yishen Wang, Xiao Lu, Yiran Xu, Di Shi, Zhehan Yi, Jiajun Duan, Zhiwei Wang

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS).

Clustering Load Forecasting

Probabilistic Load Forecasting via Point Forecast Feature Integration

no code implementations26 Mar 2019 Qicheng Chang, Yishen Wang, Xiao Lu, Di Shi, Haifeng Li, Jiajun Duan, Zhiwei Wang

In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.

energy management Feature Importance +3

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

no code implementations26 Mar 2019 Yayu Peng, Yishen Wang, Xiao Lu, Haifeng Li, Di Shi, Zhiwei Wang, Jie Li

Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.

Load Forecasting

Structure Learning with Similarity Preserving

no code implementations3 Dec 2019 Zhao Kang, Xiao Lu, Yiwei Lu, Chong Peng, Zenglin Xu

Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications.

Clustering

Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms

no code implementations8 Dec 2019 Guangxia Lia, Yulong Shena, Peilin Zhaob, Xiao Lu, Jia Liu, Yangyang Liu, Steven C. H. Hoi

Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information.

Continuous Control Intrusion Detection

Relation-Guided Representation Learning

no code implementations11 Jul 2020 Zhao Kang, Xiao Lu, Jian Liang, Kun Bai, Zenglin Xu

In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning.

Clustering Relation +1

Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey

no code implementations26 Jul 2020 Nguyen Cong Luong, Xiao Lu, Dinh Thai Hoang, Dusit Niyato, Dong In Kim

First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems.

Management

Ambient Backscatter-Assisted Wireless-Powered Relaying

no code implementations2 Feb 2021 Xiao Lu, Dusit Niyato, Hai Jiang, Ekram Hossain, Ping Wang

With different mode selection protocols, we characterize the success probability and ergodic capacity of a dual-hop relaying system with the hybrid relay in the field of randomly located ambient transmitters.

Information Theory Networking and Internet Architecture Information Theory

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

no code implementations18 Nov 2022 Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du

In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).

Image Super-Resolution

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