scholarly journals A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7410
Author(s):  
Netzah Calamaro ◽  
Moshe Donko ◽  
Doron Shmilovitz
Keyword(s):  
Sampling Rate ◽  
Identification Accuracy ◽  
Spectral Space ◽  
Data Set ◽  
Device Identification ◽  
Fast Training ◽  
Electrical Devices ◽  
Load Monitoring ◽  
Spectral Algorithm ◽  
Recorded Data

The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8×108 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.

scholarly journals Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

2019 ◽  
Vol 9 (17) ◽  
pp. 3558 ◽  
Author(s):  
Jinying Yu ◽  
Yuchen Gao ◽  
Yuxin Wu ◽  
Dian Jiao ◽  
Chang Su ◽  
...  
Keyword(s):  
Identification Accuracy ◽  
Data Set ◽  
Linear Discriminant ◽  
Practical Applications ◽  
Core Technology ◽  
Open Source Data ◽  
Source Data ◽  
Load Monitoring ◽  
Global Similarity ◽  
Linear Discriminant Classifier

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

scholarly journals Facing the needs for clean bicycle data – a bicycle-specific approach of GPS data processing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sven Lißner ◽  
Stefan Huber
Keyword(s):  
Data Processing ◽  
Gps Data ◽  
Data Set ◽  
Specific Data ◽  
Driving Mode ◽  
Mode Detection ◽  
Current State ◽  
Mode Recognition ◽  
Recorded Data ◽  
Gps Tracks

Abstract Background GPS-based cycling data are increasingly available for traffic planning these days. However, the recorded data often contain more information than simply bicycle trips. GPS tracks resulting from tracking while using other modes of transport than bike or long periods at working locations while people are still tracking are only some examples. Thus, collected bicycle GPS data need to be processed adequately to use them for transportation planning. Results The article presents a multi-level approach towards bicycle-specific data processing. The data processing model contains different steps of processing (data filtering, smoothing, trip segmentation, transport mode recognition, driving mode detection) to finally obtain a correct data set that contains bicycle trips, only. The validation reveals a sound accuracy of the model at its’ current state (82–88%).

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

Station Keeping Trials in Ice: Ice Load Monitoring System

2018 ◽  
Author(s):  
Håvard Nyseth ◽  
Anders Hansson ◽  
Johan Johansson Iseskär
Keyword(s):  
Data Sets ◽  
Station Keeping ◽  
Ice Load ◽  
Hull Structure ◽  
Load Monitoring ◽  
Ice Loads ◽  
Recorded Data ◽  
Measurement Concept ◽  
Ice Impacts ◽  
Basic Philosophy

In connection with the Statoil SKT project, DNV GL have developed a method for estimating ice loads on the ship hull structure and mooring tension of the anchor handling tug supply (AHTS) vessel Magne Viking by full scale measurements. In March 2017, the vessel was equipped with an extensive measurement system as a preparation for the dedicated station-keeping trial in drifting ice in the Bay of Bothnia. Data of the ice impacts acting on the hull were collected over the days of testing together with several other parameters from the ship propulsion system. Whilst moored, the tension in the mooring chain was monitored via a load cell and logged simultaneously to the other parameters. This paper presents the processes involved in developing the measurement concept, including the actual installation and execution phases. The basic philosophy behind the system is described, including the methods used to design an effective measurement arrangement, and develop procedures for estimation of ice loads based on strain measurements. The actual installation and the process of obtaining the recorded data sets are also discussed.

scholarly journals A Systematic Study on DNA Barcoding of Medicinally Important Genus Epimedium L. (Berberidaceae)

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 637 ◽  
Author(s):  
Mengyue Guo ◽  
Yanqin Xu ◽  
Li Ren ◽  
Shunzhi He ◽  
and Xiaohui Pang
Keyword(s):  
Dna Barcode ◽  
Morphological Characteristics ◽  
Identification Accuracy ◽  
Dna Barcodes ◽  
Success Rates ◽  
Data Set ◽  
Herba Epimedii ◽  
Potential Risks ◽  
Medicinal Properties ◽  
Species Being

Genus Epimedium consists of approximately 50 species in China, and more than half of them possess medicinal properties. The high similarity of species’ morphological characteristics complicates the identification accuracy, leading to potential risks in herbal efficacy and medical safety. In this study, we tested the applicability of four single loci, namely, rbcL, psbA-trnH, internal transcribed spacer (ITS), and ITS2, and their combinations as DNA barcodes to identify 37 Epimedium species on the basis of the analyses, including the success rates of PCR amplifications and sequencing, specific genetic divergence, distance-based method, and character-based method. Among them, character-based method showed the best applicability for identifying Epimedium species. As for the DNA barcodes, psbA-trnH showed the best performance among the four single loci with nine species being correctly differentiated. Moreover, psbA-trnH + ITS and psbA-trnH + ITS + rbcL exhibited the highest identification ability among all the multilocus combinations, and 17 species, of which 12 are medicinally used, could be efficiently discriminated. The DNA barcode data set developed in our study contributes valuable information to Chinese resources of Epimedium. It provides a new means for discrimination of the species within this medicinally important genus, thus guaranteeing correct and safe usage of Herba Epimedii.

The Earth's magnetic field variations at the Algerian magnetic observatory of Tamanrasset from 1932 to 2020.

2021 ◽  
Author(s):  
Lemgharbi Abdenaceur ◽  
Hamoudi Mohamed ◽  
Abtout Abdeslam ◽  
Abdelhamid Bendekken ◽  
Ener Aganou ◽  
...  
Keyword(s):  
Magnetic Field ◽  
International Association ◽  
Sampling Rate ◽  
Magnetic Data ◽  
Magnetic Observatory ◽  
Earth’S Magnetic Field ◽  
Data Set ◽  
Secular Variations ◽  
The World ◽  
Earth's Magnetic Field

<p>In order to understand the spatial and temporal behavior of the Earth's magnetic field, scientists, following C.F. Gauss initiative in 1838 have established observatories around the world. More than 200 observatories aiming to continuously record, the time variations of the magnetic field vector and to maintain the best standard of the accuracy and resolution of the measurements.</p><p>This study focused on the acquisition and analysis of the magnetic data provided by the Algerian magnetic observatory of Tamanrasset (labelled TAM by the International Association of Geomagnetism and Aeronomy). This observatory is located in southern Algeria at 5.53°E longitude, 22.79°N Latitude. Its altitude is 1373 meters above msl. TAM is continuously running since 1932, using old brand variometers, like Mascart and La Cour with photographic recording at the very beginning. Nowadays modern electronic equipment are used in the framework of INTERMAGNET project. Very large geomagnetic database collected over a century is available. We will describe the history and the various improvement of the methods and instrumentation.</p><p>Preliminary analysis of time series of the observatory data allowed to distinguish two kinds of data: the first type, with low resolution, collected between 1932 and 1992. This data set comes from the annual, monthly, daily and hourly means. The second one with high resolution is represented by minutes and seconds sampling rate since 1993 when TAM was integrated to the world observatory network, INTERMAGNET. Part of the second dataset contains many gaps. We try to fill these gaps thanks to mathematical methods. Absolute measurements and repeat station data allow better accuracy in the secular variations and an improved regional model.</p><p>Keywords: TAM observatory, temporal variation, terrestrial magnetic field, secular variations, INTERMAGNET.</p>

scholarly journals Smartphone Location Recognition: A Deep Learning-Based Approach

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 214 ◽  
Author(s):  
Itzik Klein
Keyword(s):  
Deep Learning ◽  
Sampling Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Point Of View ◽  
Learning Approaches ◽  
Location Recognition ◽  
Length Estimation ◽  
Recorded Data ◽  
Definition Of

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.

Black aspergilli: A remaining challenge in fungal taxonomy?

2018 ◽  
Vol 57 (6) ◽  
pp. 773-780 ◽  
Author(s):  
Elizabet D’hooge ◽  
Pierre Becker ◽  
Dirk Stubbe ◽  
Anne-Cécile Normand ◽  
Renaud Piarroux ◽  
...  
Keyword(s):  
Antifungal Susceptibility ◽  
Identification Accuracy ◽  
Clinical Isolates ◽  
Reference Database ◽  
Data Set ◽  
Ionization Time ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Flight Mass Spectrometry ◽  
Tof Ms

AbstractAspergillus section Nigri is a taxonomically difficult but medically and economically important group. In this study, an update of the taxonomy of A. section Nigri strains within the BCCM/IHEM collection has been conducted. The identification accuracy of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was tested and the antifungal susceptibilities of clinical isolates were evaluated. A total of 175 strains were molecularly analyzed. Three regions were amplified (ITS, benA, and caM) and a multi-locus phylogeny of the combined loci was created by using maximum likelihood analysis. The in-house MALDI-TOF MS reference database was extended and an identification data set of 135 strains was run against a reference data set. Antifungal susceptibility was tested for voriconazole, itraconazole, and amphotericin B, using the EUCAST method. Phylogenetic analysis revealed 18 species in our data set. MALDI-TOF MS was able to distinguish between A. brasiliensis, A. brunneoviolaceus, A. neoniger, A. niger, A. tubingensis, and A. welwitschiae of A. sect. Nigri. In the routine clinical lab, isolates of A. sect. Nigri are often identified as A. niger. However, in the clinical isolates of our data set, A. tubingensis (n = 35) and A. welwitschiae (n = 34) are more common than A. niger (n = 9). Decreased antifungal susceptibility to azoles was observed in clinical isolates of the /tubingensis clade. This emphasizes the importance of identification up to species level or at least up to clade level in the clinical lab. Our results indicate that MALDI-TOF MS can be a powerful tool to replace classical morphology.

Automatic Detection of Manufacturing Equipment Cycles Using Time Series

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Jan-Peter Seevers ◽  
Kristina Jurczyk ◽  
Henning Meschede ◽  
Jens Hesselbach ◽  
John W. Sutherland
Keyword(s):  
Time Series ◽  
Energy Demand ◽  
Manufacturing Industry ◽  
Electrical Power ◽  
Sampling Rate ◽  
Real Life ◽  
Recognition Algorithm ◽  
Sensor Data ◽  
Data Set ◽  
Manufacturing Equipment

Abstract Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy demand, energy/power data are required. All too often, however, energy data are either not available, or available but too aggregated to be useful, or in a form that makes information difficult to access. Attention herein is focused on this last point. As a step toward greater energy information transparency and smart energy-monitoring systems, this paper introduces a novel, robust time series-based approach to automatically detect and analyze the electrical power cycles of manufacturing equipment. A new pattern recognition algorithm including a power peak clustering method is applied to a large real-life sensor data set of various machine tools. With the help of synthetic time series, it is shown that the accuracy of the cycle detection of nearly 100% is realistic, depending on the degree of measurement noise and the measurement sampling rate. Moreover, this paper elucidates how statistical load profiling of manufacturing equipment cycles as well as statistical deviation analyses can be of value for automatic sensor and process fault detection.

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