Results 111 to 120 of about 12,752 (151)

An Unsupervised NILM approach based on Graph Signal Processing with Feature Fusion

Annual Meeting of the IEEE Industry Applications Society
Non-intrusive load monitoring (NILM) is an advanced technology for intelligent energy management. Although graph signal processing (GSP) concepts have been applied to NILM in an unsupervised way, the performance of such solutions remains unstable and ...
Ruitao Feng, Bochao Zhao, Wenpeng Luan, Yang Han, Bo Liu, Hanju Cai   +5 more
semanticscholar   +1 more source

Equivalence of Graph Signal Processing Using a Hermitian Graph Laplacian and its Corresponding Graph Laplacian with Duplicated Nodes

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
In our previous work, we introduced a novel graph Laplacian for directed graphs, referred to as the Hermitian graph Laplacian, which enables a unitary graph Fourier transform (GFT) for signals defined on directed graphs. We also showed that the Hermitian
Akira Tanaka
semanticscholar   +1 more source

Unsupervised Multi-Feature Non-Intrusive Load Monitoring Based on Graph Signal Processing

International Conference on Electrical Machines and Systems
Non-intrusive load monitoring (NILM) is an advanced technique for demand side management. While graph signal processing (GSP) based unsupervised methods have been explored in NILM, such solutions remain insufficient for practical deployment due to their ...
Wenpeng Luan, Ruitao Feng, Bochao Zhao, Yang Han, Bo Liu, Hanju Cai   +5 more
semanticscholar   +1 more source

Integrating Graph Signal Processing and Multitask Temporal Convolutional Networks for Household Nonintrusive Load Monitoring

IEEE Transactions on Instrumentation and Measurement
Highly accurate nonintrusive load monitoring (NILM) models are essential for energy management, optimization decisions, and system monitoring. However, the sparsity of load features and spatio-temporal relationships hidden in loads have not been fully ...
Yongxin Su, Haotian Peng, Mao Tan, Jie Chen   +3 more
semanticscholar   +1 more source

Hierarchical Graph Signal Processing for Collaborative Filtering

The Web Conference
Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users ...
Jiafeng Xia, Dongsheng Li, H. Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu   +6 more
semanticscholar   +1 more source

Frequency-aware Graph Signal Processing for Collaborative Filtering

The Web Conference
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention for high efficiency. However, these methods failed to utilize user/item unique characteristics, as well as user and item high-order neighborhood ...
Jiafeng Xia, Dongsheng Li, H. Gu, T. Lu, Peng Zhang, Li Shang, Ning Gu   +6 more
semanticscholar   +1 more source

Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

IEEE Transactions on Signal Processing
Graph spectral analysis has emerged as an important tool to extract underlying structures among data samples. Central to graph signal processing (GSP) and graph neural networks (GNN), graph spectrum is often derived via eigen-decomposition (ED) of graph ...
Qinwen Deng, Yangwen Zhang, Mo Li, Songyang Zhang, Zhi Ding   +4 more
semanticscholar   +1 more source

Graph Signal Processing: Frequency Analysis for Similar Matrices

Asilomar Conference on Signals, Systems and Computers
Spectral analysis is a fundamental part of both Discrete Signal Processing (DSP) and Graph Signal Processing (GSP). Many signal processing applications such as sampling, modulation, denoising, and filtering rely on performing a spectral analysis by ...
John Shi, José M. F. Moura
semanticscholar   +1 more source

Byzantine Attack Identification using Graph Signal Processing in Cooperative Spectrum Sensing

2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)
Graph signal processing (GSP) paradigm generalizes the existing one-dimensional signal processing concept and focuses on multi-dimensional signals residing on a structure that can be represented by a graph.
Ayu Oktaviani Dewi, D. D. Ariananda, S. B. Wibowo   +2 more
semanticscholar   +1 more source

Utilizing Graph Signal-Processing-Based Spectrum to Classify Mental Tasks With Multichannel EEG Signals

IEEE Sensors Journal
The precise identification of mental tasks through the analysis of electroencephalogram (EEG) signals plays an important role in the field of brain–computer interfaces (BCIs).
Ramnivas Sharma, Hemant Kumar Meena
semanticscholar   +1 more source