The Experts below are selected from a list of 101346 Experts worldwide ranked by ideXlab platform
Bernardete Ribeiro - One of the best experts on this subject based on the ideXlab platform.
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Electrical Signal source separation via nonnegative tensor factorization using on site measurements in a smart home
IEEE Transactions on Instrumentation and Measurement, 2014Co-Authors: Marisa B Figueiredo, Bernardete Ribeiro, Ana De AlmeidaAbstract:Measuring the Electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's Electrical Signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household Electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.
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home Electrical Signal disaggregation for non intrusive load monitoring nilm systems
Neurocomputing, 2012Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:Electrical load disaggregation for end-use recognition in the smart home has become an area of study of its own right. The most well-known examples are energy monitoring, health care applications, in-home activity modeling, and home automation. Real-time energy-use analysis for whole-home approaches needs to understand where and when the Electrical loads are spent. Studies have shown that individual loads can be detected (and disaggregated) from sampling the power at one single point (e.g. the electric service entrance for the house) using a non-intrusive load monitoring (NILM) approach. In this paper, we focus on the feature extraction and pattern recognition tasks for non-intrusive residential Electrical consumption traces. In particular, we develop an algorithm capable of determining the step-changes in Signals that occur whenever a device is turned on or off, and which allows for the definition of a unique signature (ID) for each device. This algorithm makes use of features extracted from active and reactive powers and power factor. The classification task is carried out by Support Vector Machines and 5-Nearest Neighbors methods. The results illustrate the effectiveness of the proposed signature for distinguishing the different loads.
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wavelet decomposition and singular spectrum analysis for Electrical Signal denoising
Systems Man and Cybernetics, 2011Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:The aggregated Electrical load of a household network contains relevant information. Specifically, which loads are related to Electrical appliances switched on. However, when real-world data is at stake, not only this specific data must be individually recognized, but also there is other non-relevant information that can be thought as noise in the Electrical Signal. Therefore, to extract the important information we need to use Signal denoising algorithms. This work presents a comparison for the application of an algorithm based on Wavelet Decomposition versus the Singular Spectrum Analysis to the denoise of aggregated Electrical Signal. These techniques were applied both in an artificially generated Signal as well as to the analysis of a Signal obtained from an ordinary household. For the latter, the experiments highlighted a small set of wavelet functions that are more suitable for the problem being tackled. Finally, the comparison of the performance of each of the approaches applied to the same sampled Signal data indicate the effectiveness of both denoising techniques for use over real data sets.
Michael C Schatz - One of the best experts on this subject based on the ideXlab platform.
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targeted nanopore sequencing by real time mapping of raw Electrical Signal with uncalled
Nature Biotechnology, 2021Co-Authors: Sam Kovaka, Yunfan Fan, Winston Timp, Michael C SchatzAbstract:Conventional targeted sequencing methods eliminate many of the benefits of nanopore sequencing, such as the ability to accurately detect structural variants or epigenetic modifications. The ReadUntil method allows nanopore devices to selectively eject reads from pores in real time, which could enable purely computational targeted sequencing. However, this requires rapid identification of on-target reads while most mapping methods require computationally intensive basecalling. We present UNCALLED ( https://github.com/skovaka/UNCALLED ), an open source mapper that rapidly matches streaming of nanopore current Signals to a reference sequence. UNCALLED probabilistically considers k-mers that could be represented by the Signal and then prunes the candidates based on the reference encoded within a Ferragina–Manzini index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species 4.46-fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6× coverage using one MinION flowcell, enabling accurate detection of single-nucleotide polymorphisms, insertions and deletions, structural variants and methylation in these genes. UNCALLED enables targeted sequencing in nanopores by mapping the Signal to large reference genomes.
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targeted nanopore sequencing by real time mapping of raw Electrical Signal with uncalled
Nature Biotechnology, 2021Co-Authors: Sam Kovaka, Yunfan Fan, Winston Timp, Michael C SchatzAbstract:Conventional targeted sequencing methods eliminate many of the benefits of nanopore sequencing, such as the ability to accurately detect structural variants or epigenetic modifications. The ReadUntil method allows nanopore devices to selectively eject reads from pores in real time, which could enable purely computational targeted sequencing. However, this requires rapid identification of on-target reads while most mapping methods require computationally intensive basecalling. We present UNCALLED ( https://github.com/skovaka/UNCALLED ), an open source mapper that rapidly matches streaming of nanopore current Signals to a reference sequence. UNCALLED probabilistically considers k-mers that could be represented by the Signal and then prunes the candidates based on the reference encoded within a Ferragina-Manzini index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species 4.46-fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6× coverage using one MinION flowcell, enabling accurate detection of single-nucleotide polymorphisms, insertions and deletions, structural variants and methylation in these genes.
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targeted nanopore sequencing by real time mapping of raw Electrical Signal with uncalled
bioRxiv, 2020Co-Authors: Sam Kovaka, Yunfan Fan, Winston Timp, Michael C SchatzAbstract:Abstract ReadUntil sequencing allows nanopore devices to selectively eject individual reads from the pore in real-time. This could enable purely computational targeted sequencing, however most mapping methods require basecalling, which is computationally intensive. Here we present UNCALLED (github.com/skovaka/UNCALLED), an open-source mapper that rapidly matches streaming nanopore current Signals to a reference sequence. UNCALLED probabilistically considers k-mers that the Signal could represent, and then prunes the candidates based on the reference encoded within an FM-index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species by 4.46 fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6x coverage using one MinION flowcell, enabling accurate detection of SNPs, indels, structural variants (SVs), and methylation in these genes. Twice as many SVs were detected compared to 50x coverage Illumina sequencing, all verified by whole-genome nanopore and PacBio HiFi sequencing.
Marisa B Figueiredo - One of the best experts on this subject based on the ideXlab platform.
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Electrical Signal source separation via nonnegative tensor factorization using on site measurements in a smart home
IEEE Transactions on Instrumentation and Measurement, 2014Co-Authors: Marisa B Figueiredo, Bernardete Ribeiro, Ana De AlmeidaAbstract:Measuring the Electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's Electrical Signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household Electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.
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home Electrical Signal disaggregation for non intrusive load monitoring nilm systems
Neurocomputing, 2012Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:Electrical load disaggregation for end-use recognition in the smart home has become an area of study of its own right. The most well-known examples are energy monitoring, health care applications, in-home activity modeling, and home automation. Real-time energy-use analysis for whole-home approaches needs to understand where and when the Electrical loads are spent. Studies have shown that individual loads can be detected (and disaggregated) from sampling the power at one single point (e.g. the electric service entrance for the house) using a non-intrusive load monitoring (NILM) approach. In this paper, we focus on the feature extraction and pattern recognition tasks for non-intrusive residential Electrical consumption traces. In particular, we develop an algorithm capable of determining the step-changes in Signals that occur whenever a device is turned on or off, and which allows for the definition of a unique signature (ID) for each device. This algorithm makes use of features extracted from active and reactive powers and power factor. The classification task is carried out by Support Vector Machines and 5-Nearest Neighbors methods. The results illustrate the effectiveness of the proposed signature for distinguishing the different loads.
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wavelet decomposition and singular spectrum analysis for Electrical Signal denoising
Systems Man and Cybernetics, 2011Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:The aggregated Electrical load of a household network contains relevant information. Specifically, which loads are related to Electrical appliances switched on. However, when real-world data is at stake, not only this specific data must be individually recognized, but also there is other non-relevant information that can be thought as noise in the Electrical Signal. Therefore, to extract the important information we need to use Signal denoising algorithms. This work presents a comparison for the application of an algorithm based on Wavelet Decomposition versus the Singular Spectrum Analysis to the denoise of aggregated Electrical Signal. These techniques were applied both in an artificially generated Signal as well as to the analysis of a Signal obtained from an ordinary household. For the latter, the experiments highlighted a small set of wavelet functions that are more suitable for the problem being tackled. Finally, the comparison of the performance of each of the approaches applied to the same sampled Signal data indicate the effectiveness of both denoising techniques for use over real data sets.
De Almeidaana - One of the best experts on this subject based on the ideXlab platform.
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home Electrical Signal disaggregation for non intrusive load monitoring nilm systems
Neurocomputing, 2012Co-Authors: De AlmeidaanaAbstract:Electrical load disaggregation for end-use recognition in the smart home has become an area of study of its own right. The most well-known examples are energy monitoring, health care applications, ...
Ana De Almeida - One of the best experts on this subject based on the ideXlab platform.
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Electrical Signal source separation via nonnegative tensor factorization using on site measurements in a smart home
IEEE Transactions on Instrumentation and Measurement, 2014Co-Authors: Marisa B Figueiredo, Bernardete Ribeiro, Ana De AlmeidaAbstract:Measuring the Electrical consumption of individual appliances in a household has recently received renewed interest in the area of energy efficiency research and sustainable development. The unambiguous acquisition of information by a single monitoring point of the whole house's Electrical Signal is known as energy disaggregation or nonintrusive load monitoring. A novel way to look into the issue of energy disaggregation is to interpret it as a single-channel source separation problem. To this end, we analyze the performance of source modeling based on multiway arrays and the corresponding decomposition or tensor factorization. First, with the proviso that a tensor composed of the data for the several devices in the house is given, nonnegative tensor factorization is performed in order to extract the most relevant components. Second, the outcome is later embedded in the test step, where only the measured consumption over the whole home is available. Finally, the disaggregated data by the device is obtained by factorizing the associated matrix considering the learned models. In this paper, we compare this method with a recent approach based on sparse coding. The results are obtained using real-world data from household Electrical consumption measurements. The analysis of the comparison results illustrates the relevance of the multiway array-based approach in terms of accurate disaggregation, as further endorsed by the statistical analysis performed.
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home Electrical Signal disaggregation for non intrusive load monitoring nilm systems
Neurocomputing, 2012Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:Electrical load disaggregation for end-use recognition in the smart home has become an area of study of its own right. The most well-known examples are energy monitoring, health care applications, in-home activity modeling, and home automation. Real-time energy-use analysis for whole-home approaches needs to understand where and when the Electrical loads are spent. Studies have shown that individual loads can be detected (and disaggregated) from sampling the power at one single point (e.g. the electric service entrance for the house) using a non-intrusive load monitoring (NILM) approach. In this paper, we focus on the feature extraction and pattern recognition tasks for non-intrusive residential Electrical consumption traces. In particular, we develop an algorithm capable of determining the step-changes in Signals that occur whenever a device is turned on or off, and which allows for the definition of a unique signature (ID) for each device. This algorithm makes use of features extracted from active and reactive powers and power factor. The classification task is carried out by Support Vector Machines and 5-Nearest Neighbors methods. The results illustrate the effectiveness of the proposed signature for distinguishing the different loads.
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wavelet decomposition and singular spectrum analysis for Electrical Signal denoising
Systems Man and Cybernetics, 2011Co-Authors: Marisa B Figueiredo, Ana De Almeida, Bernardete RibeiroAbstract:The aggregated Electrical load of a household network contains relevant information. Specifically, which loads are related to Electrical appliances switched on. However, when real-world data is at stake, not only this specific data must be individually recognized, but also there is other non-relevant information that can be thought as noise in the Electrical Signal. Therefore, to extract the important information we need to use Signal denoising algorithms. This work presents a comparison for the application of an algorithm based on Wavelet Decomposition versus the Singular Spectrum Analysis to the denoise of aggregated Electrical Signal. These techniques were applied both in an artificially generated Signal as well as to the analysis of a Signal obtained from an ordinary household. For the latter, the experiments highlighted a small set of wavelet functions that are more suitable for the problem being tackled. Finally, the comparison of the performance of each of the approaches applied to the same sampled Signal data indicate the effectiveness of both denoising techniques for use over real data sets.