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3rd IEEE Global Conference on signal and Information processing (GlobalSIP)

作者： Zhao, Bochao Stankovic, Lina Stankovic, Vladimir Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

ISBN: (纸本)9781479975914

With ongoing massive smart energy metering deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased interest. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges. Indeed, the majority of approaches require training and are sensitive to appliance changes requiring regular re-training. In this paper, we tackle this challenge by proposing a "blind" NALM approach that does not require any training. The main idea is to build upon an emerging field of graph-based signal processing to perform adaptive thresholding, signal clustering and feature matching. Using two datasets of active power measurements with 1min and 8sec resolution, we demonstrate the effectiveness of the proposed method using a state-of-the-art NALM approaches as benchmarks.

关键词： Non-intrusive appliance load monitoring load disaggregation graph-based signal processing

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A graph-based signal processing Approach for Non-Intrusive Load Disaggregation

A Graph-Based Signal Processing Approach for Non-Intrusive L...

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2nd IEEE International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Liu, Xiao-feng Zhai, Ming-yue Xie, Zheng-yan North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781509044429

graph-based signal processing (GSP) is an emerging field that has been used in many domains. Getting inspiration from the successful applications of GSP in signal filtering and image processing, in this paper, we demonstrate how GSP can serve as a feasible approach to non-intrusive appliance load monitoring (NALM). Specifically, NALM means disaggregating household's gross energy consumption down to single appliances through purely software solutions. Since NALM was proposed over 30 years ago, it has got a lot of attention. However, despite the fact that many solutions has been proposed, the majority of approaches can't work well without training and are prone to appliance variations requiring re-training on a regular basis. In this paper, we tackle this challenge by applying a GSP-based NALM approach that can perform well with no training. This algorithm uses GSP three times, which represents the datasets of active power measurements with 1 min resolution using graphs to perform adaptive thresholding, signal clustering and feature matching respectively. Simulation results using publicly available REDD dataset demonstrate the feasibility and potential of the GSP for NALM.

关键词： graph-based signal processing Non-intrusive appliance load monitoring Energy disaggregation

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A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5495-5504页

作者： Zhai, Ming-Yue Guangdong Univ Petrochem Technol Sch Elect Informat Engn Maoming Peoples R China

Recently, there is a potential technology called graph-based signal processing (GSP) that is being used in many applications. GSP has been used successfully in the domains such as signal and image filtering and processing. In the paper, GSP is used as an applicable method to non-intrusive appliance load monitoring (NILM). In NILM, all of power consumption is disaggregated down to every appliance's consumption without hardware. Although there is over 30 years after NILM was proposed, there are still some problems faced by applications of NILM in real scenario if there is no training data. By combination of NILM with GSP concept, such a challenge is tackled with better performance over existing methods. As the first step, we propose a new graph learning algorithm to get a graph suitable for appliance load representation and for the disaggregation algorithm. In the following steps, graph-based signal processing method is used three times, from representation of the data sets of power measurements. Public datasets are used to demonstrate the proposed method's performance and feasibility.

关键词： graph-based signal processing Non-intrusive appliance load monitoring Energy disaggregation graph learning

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Blind Non-intrusive Appliance Load Monitoring using graph-based signal processing

Blind Non-intrusive Appliance Load Monitoring using Graph-ba...

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IEEE Global Conference on signal and Information processing

作者： Bochao Zhao Lina Stankovic Vladimir Stankovic Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1XW UK

ISBN: (纸本)9781479975921

With ongoing massive smart energy metering deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased interest. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges. Indeed, the majority of approaches require training and are sensitive to appliance changes requiring regular re-training. In this paper, we tackle this challenge by proposing a "blind" NALM approach that does not require any training. The main idea is to build upon an emerging field of graph-based signal processing to perform adaptive thresholding, signal clustering and feature matching. Using two datasets of active power measurements with lmin and 8sec resolution, we demonstrate the effectiveness of the proposed method using a state-of-the-art NALM approaches as benchmarks.

关键词： Non-intrusive appliance load monitoring load disaggregation graph-based signal processing

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Human Upper Limb Motion Analysis for Post-Stroke Impairment Assessment Using Video Analytics

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IEEE ACCESS 2016年 4卷 650-659页

作者： Yang, Cheng Kerr, Andrew Stankovic, Vladimir Stankovic, Lina Rowe, Philip Cheng, Samuel Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland Univ Strathclyde Dept Biomed Engn Glasgow G1 1XQ Lanark Scotland Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA

Stroke is a worldwide healthcare problem, which often causes long-term motor impairment, handicap, and disability. Optical motion analysis systems are commonly used for impairment assessment due to high accuracy. However, the requirement of equipment-heavy and large laboratory space together with operational expertise makes these systems impractical for local clinic and home use. We propose an alternative, cost-effective and portable, decision support system for optical motion analysis, using a single camera. The system relies on detecting and tracking markers attached to subject's joints, data analytics for calculating relevant rehabilitation parameters, visualization, and robust classification based on graph-based signal processing. Experimental results show that the proposed decision support system has the potential to offer stroke survivors and clinicians an alternative, affordable, accurate, and convenient impairment assessment option suitable for home healthcare and telerehabilitation.

关键词： Rehabilitation graph-based signal processing video analytics

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graph-based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2018年第10期37卷 2211-2223页

作者： Sanchez, Victor Hernandez-Cabronero, Miguel Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

The increasing availability of digital pathology images has motivated the design of tools to foster multi-disciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilitates the transmission and access to pathology images by multiple users. However, the huge file size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-lambda) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the High-Efficiency Video Coding standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms the other state-of-the-art-methods designed for single images by very accurately attaining the target bit rate of the non-RoI.

关键词： Rate-control HEVC pathology images graph-based signal processing region of interest coding

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graph-based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

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IEEE TRANSACTIONS ON IMAGE processing 2020年 29卷 2439-2451页

作者： Lanz, Daniela Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

Lossless compression of dynamic 2D & x002B;t and 3D & x002B;t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block- and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block- and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53dB and 0.28dB for CT data, as well as 1.04dB and 1.90dB for MR data, respectively, while the bit rate is reduced at the same time.

关键词： Image coding Biomedical imaging Computed tomography Transforms Encoding Motion compensation Scalability Scalability discrete wavelet transform motion compensation graph-based signal processing computed tomography magnetic resonance imaging

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