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Journal articles on the topic 'Non-intrusive approach'

Author: Grafiati

Published: 24 June 2021

Last updated: 1 February 2022

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1

Panunzio, A. M., Loic Salles, and C. W. Schwingshackl. "Uncertainty propagation for nonlinear vibrations: A non-intrusive approach." Journal of Sound and Vibration 389 (February 2017): 309–25. http://dx.doi.org/10.1016/j.jsv.2016.09.020.

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2

Yin, Jianwei, Xinkui Zhao, Yan Tang, Chen Zhi, Zuoning Chen, and Zhaohui Wu. "CloudScout: A Non-Intrusive Approach to Service Dependency Discovery." IEEE Transactions on Parallel and Distributed Systems 28, no. 5 (May 1, 2017): 1271–84. http://dx.doi.org/10.1109/tpds.2016.2619715.

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3

Berveiller, Marc, Bruno Sudret, and Maurice Lemaire. "Stochastic finite element: a non intrusive approach by regression." European Journal of Computational Mechanics 15, no. 1-3 (January 2006): 81–92. http://dx.doi.org/10.3166/remn.15.81-92.

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4

Huang, Dongli, Jeongwon Seo, Salma Magdi, Alya Badawi, and Hany Abdel-Khalik. "Non-intrusive stochastic approach for nuclear cross-sections adjustment." Annals of Nuclear Energy 155 (June 2021): 108162. http://dx.doi.org/10.1016/j.anucene.2021.108162.

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5

Lu, Mengqi, and Zuyi Li. "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring." IEEE Transactions on Smart Grid 11, no. 1 (January 2020): 528–40. http://dx.doi.org/10.1109/tsg.2019.2924862.

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6

Cao, Gang, Yao Zhao, and Rongrong Ni. "Forensic identification of resampling operators: A semi non-intrusive approach." Forensic Science International 216, no. 1-3 (March 2012): 29–36. http://dx.doi.org/10.1016/j.forsciint.2011.08.012.

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7

Wahlsten, Markus, and Jan Nordström. "On Stochastic Investigation of Flow Problems Using the Viscous Burgers’ Equation as an Example." Journal of Scientific Computing 81, no. 2 (September 23, 2019): 1111–17. http://dx.doi.org/10.1007/s10915-019-01053-7.

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Abstract We consider a stochastic analysis of non-linear viscous fluid flow problems with smooth and sharp gradients in stochastic space. As a representative example we consider the viscous Burgers’ equation and compare two typical intrusive and non-intrusive uncertainty quantification methods. The specific intrusive approach uses a combination of polynomial chaos and stochastic Galerkin projection. The specific non-intrusive method uses numerical integration by combining quadrature rules and the probability density functions of the prescribed uncertainties. The two methods are compared in terms of error in the estimated variance, computational efficiency and accuracy. This comparison, although not general, provide insight into uncertainty quantification of problems with a combination of sharp and smooth variations in stochastic space. It suggests that combining intrusive and non-intrusive methods could be advantageous.

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8

Abade, Bruno, David Perez Abreu, and Marilia Curado. "A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments." Sensors 18, no. 11 (November 15, 2018): 3953. http://dx.doi.org/10.3390/s18113953.

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Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

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Buddhahai, Bundit, Waranyu Wongseree, and Pattana Rakkwamsuk. "A non-intrusive load monitoring system using multi-label classification approach." Sustainable Cities and Society 39 (May 2018): 621–30. http://dx.doi.org/10.1016/j.scs.2018.02.002.

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Liaskos, Christos, Xenofontas Dimitropoulos, and Leandros Tassiulas. "Backpressure on the Backbone: A Lightweight, Non-Intrusive Traffic Engineering Approach." IEEE Transactions on Network and Service Management 14, no. 1 (March 2017): 176–90. http://dx.doi.org/10.1109/tnsm.2016.2631477.

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He, Kanghang, Dusan Jakovetic, Bochao Zhao, Vladimir Stankovic, Lina Stankovic, and Samuel Cheng. "A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring." IEEE Transactions on Smart Grid 10, no. 6 (November 2019): 6472–80. http://dx.doi.org/10.1109/tsg.2019.2906012.

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Chakir, R., Y. Maday, and P. Parnaudeau. "A non-intrusive reduced basis approach for parametrized heat transfer problems." Journal of Computational Physics 376 (January 2019): 617–33. http://dx.doi.org/10.1016/j.jcp.2018.10.001.

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13

Hasan, Md Mehedi, Dhiman Chowdhury, and Md Ziaur Rahman Khan. "Non-Intrusive Load Monitoring Using Current Shapelets." Applied Sciences 9, no. 24 (December 8, 2019): 5363. http://dx.doi.org/10.3390/app9245363.

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Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.

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14

Donat, Rosa, and Sergio López-Ureña. "High-accuracy approximation of piecewise smooth functions using the Truncation and Encode approach." Applied Mathematics and Nonlinear Sciences 2, no. 2 (September 6, 2017): 367–84. http://dx.doi.org/10.21042/amns.2017.2.00030.

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AbstractIn the present work, we analyze a technique designed by Geraci et al. in [1,11] named the Truncate and Encode (TE) strategy. It was presented as a non-intrusive method for steady and non-steady Partial Differential Equations (PDEs) in Uncertainty Quantification (UQ), and as a weakly intrusive method in the unsteady case.We analyze the TE algorithm applied to the approximation of functions, and in particular its performance for piecewise smooth functions. We carry out some numerical experiments, comparing the performance of the algorithm when using different linear and non-linear interpolation techniques and provide some recommendations that we find useful in order to achieve a high performance of the algorithm.

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15

Hu, Suyang, Li Wang, Chuang Gao, Bin Zhang, Zhichan Liu, and Shanshui Yang. "Non-Intrusive Cable Fault Diagnosis Based on Inductive Directional Coupling." Sensors 18, no. 11 (November 1, 2018): 3724. http://dx.doi.org/10.3390/s18113724.

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This paper presents and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to restrict the detection signal from transmitting to power source and to eliminate the effect of the power source impedance mismatch. The development, analysis, and implementation of the proposed approach are discussed in detail. A series of simulations and experiments on cables with different fault modes are conducted, along with comparison of existing capacitive coupling, to verify and demonstrate the effectiveness of the proposed method.

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16

Eigel, Martin, Johannes Neumann, Reinhold Schneider, and Sebastian Wolf. "Non-intrusive Tensor Reconstruction for High-Dimensional Random PDEs." Computational Methods in Applied Mathematics 19, no. 1 (January 1, 2019): 39–53. http://dx.doi.org/10.1515/cmam-2018-0028.

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AbstractThis paper examines a completely non-intrusive, sample-based method for the computation of functional low-rank solutions of high-dimensional parametric random PDEs, which have become an area of intensive research in Uncertainty Quantification (UQ). In order to obtain a generalized polynomial chaos representation of the approximate stochastic solution, a novel black-box rank-adapted tensor reconstruction procedure is proposed. The performance of the described approach is illustrated with several numerical examples and compared to (Quasi-)Monte Carlo sampling.

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YUN, Deokgyu, Hannah LEE, and Seung Ho CHOI. "A Deep Learning-Based Approach to Non-Intrusive Objective Speech Intelligibility Estimation." IEICE Transactions on Information and Systems E101.D, no. 4 (2018): 1207–8. http://dx.doi.org/10.1587/transinf.2017edl8225.

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18

Shahroz, Muhammad, Muhammad Shahzad Younis, and Hasan Arshad Nasir. "A Scenario-Based Stochastic Optimization Approach for Non-Intrusive Appliance Load Monitoring." IEEE Access 8 (2020): 142205–17. http://dx.doi.org/10.1109/access.2020.3013682.

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19

Kong, Weicong, Zhao Yang Dong, Jin Ma, David J. Hill, Junhua Zhao, and Fengji Luo. "An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data." IEEE Transactions on Smart Grid 9, no. 4 (July 2018): 3362–72. http://dx.doi.org/10.1109/tsg.2016.2631238.

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20

Baudin, Sophie, Didier Rémond, Jérôme Antoni, and Olivier Sauvage. "Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach." Journal of Sound and Vibration 366 (March 2016): 501–13. http://dx.doi.org/10.1016/j.jsv.2015.11.044.

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21

Salem, Hajer, Moamar Sayed-Mouchaweh, and Moncef Tagina. "Unsupervised Bayesian Non Parametric approach for Non-Intrusive Load Monitoring based on time of usage." Neurocomputing 435 (May 2021): 239–52. http://dx.doi.org/10.1016/j.neucom.2020.12.096.

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22

Yu, Jian, Chao Yan, and Mengwu Guo. "Non-intrusive reduced-order modeling for fluid problems: A brief review." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 16 (December 2019): 5896–912. http://dx.doi.org/10.1177/0954410019890721.

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Despite tremendous progress seen in the computational fluid dynamics community for the past few decades, numerical tools are still too slow for the simulation of practical flow problems, consuming thousands or even millions of computational core-hours. To enable feasible multi-disciplinary analysis and design, the numerical techniques need to be accelerated by orders of magnitude. Reduced-order modeling has been considered one promising approach for such purposes. Recently, non-intrusive reduced-order modeling has drawn great interest in the scientific computing community due to its flexibility and efficiency and undergoes rapid development at present with different approaches emerging from various perspectives. In this paper, a brief review of non-intrusive reduced-order modeling in the context of fluid problems is performed involving three key aspects: i.e. dimension reduction of the solution space, surrogate models, and sampling strategies. Furthermore, non-intrusive reduced-order modelings regarding to some interesting topics such as unsteady flows, shock-dominating flows are also discussed. Finally, discussions on future development of non-intrusive reduced-order modeling for fluid problems are presented.

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23

Escobar Grisales, Daniel, Juan C. Vásquez-Correa, Jesús F. Vargas-Bonilla, and Juan Rafael Orozco-Arroyave. "Identity Verification in Virtual Education Using Biometric Analysis Based on Keystroke Dynamics." TecnoLógicas 23, no. 47 (January 30, 2020): 197–211. http://dx.doi.org/10.22430/22565337.1475.

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Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.

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24

Hall, Eric R. "Non-Intrusive Estimation of Active Volume in Anaerobic Reactors." Water Quality Research Journal 20, no. 2 (May 1, 1985): 44–54. http://dx.doi.org/10.2166/wqrj.1985.017.

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Abstract A stimulus-response and flow modelling technique was developed for the examination of internal mixing patterns in high rate anaerobic reactors. This method allowed the effects of biomass accumulation to be monitored in a non-intrusive manner which avoided interruption of the reactor operation. The analysis was applied to three pilot scale systems which utilized different reactor designs for anaerobic wastewater treatment. The results of multiple tracer studies demonstrated that high rate anaerobic processes most closely resemble well mixed reactors. However, significant deviations from ideal behaviour were noted. A flow model incorporating bypass and dead volume was found to adequately describe the measured tracer results. Estimates of reactor actively mixed working volumes obtained by the modelling approach were highly correlated to void volumes measured by an intrusive technique. The stimulus-response methodology is suitable for routine application in full scale facilities where an assessment of biomass accumulation effects is required.

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25

Asaithambi, Suriya, Sitalakshmi Venkatraman, and Ramanathan Venkatraman. "Big Data and Personalisation for Non-Intrusive Smart Home Automation." Big Data and Cognitive Computing 5, no. 1 (January 30, 2021): 6. http://dx.doi.org/10.3390/bdcc5010006.

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With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

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26

SÓSKUTHY, MÁRTON. "Analogy in the emergence of intrusive-r in English." English Language and Linguistics 17, no. 1 (February 11, 2013): 55–84. http://dx.doi.org/10.1017/s1360674312000329.

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This article presents a novel approach to the phenomenon of intrusive-r in English based on analogy. The main claim of the article is that intrusive-r in non-rhotic dialects of English is the result of the analogical extension of the r~zero alternation shown by words such as far, more and dear. While this idea has been around for a long time, this is the first study that explores this type of analysis in detail. Specifically, I provide an overview of the developments that led to the emergence of intrusive-r and show that they are fully compatible with an analogical approach. This includes the analysis of frequency data taken from an eighteenth-century corpus of English compiled specifically for the purposes of this article and the discussion of a related development, namely intrusive-l. The article also presents a review of the evidence about the variability of intrusive-r, which serves as the basis of an evaluation of previous approaches. Once the notion of analogy is made formally explicit, the analogical approach becomes capable of providing a unified account of the historical development and the variability of intrusive-r. This is demonstrated through a computer simulation of the emergence of the phenomenon based on the eighteenth-century corpus mentioned above. The results of the simulation confirm the predictions of the analogical approach.

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27

Parise, Alec, Miguel A. Manso-Callejo, Hung Cao, and Monica Wachowicz. "Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors." AGILE: GIScience Series 2 (June 4, 2021): 1–13. http://dx.doi.org/10.5194/agile-giss-2-9-2021.

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Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.

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28

Azizi, Elnaz, Mohammad T. H. Beheshti, and Sadegh Bolouki. "Event Matching Classification Method for Non-Intrusive Load Monitoring." Sustainability 13, no. 2 (January 12, 2021): 693. http://dx.doi.org/10.3390/su13020693.

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Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

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29

Vitter, J. S., and M. E. Webber. "A non-intrusive approach for classifying residential water events using coincident electricity data." Environmental Modelling & Software 100 (February 2018): 302–13. http://dx.doi.org/10.1016/j.envsoft.2017.11.029.

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30

David, L., T. Jardin, and A. Farcy. "On the non-intrusive evaluation of fluid forces with the momentum equation approach." Measurement Science and Technology 20, no. 9 (July 3, 2009): 095401. http://dx.doi.org/10.1088/0957-0233/20/9/095401.

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31

AL-Akhras, M., H. Zedan, R. John, and I. ALMomani. "Non-intrusive speech quality prediction in VoIP networks using a neural network approach." Neurocomputing 72, no. 10-12 (June 2009): 2595–608. http://dx.doi.org/10.1016/j.neucom.2008.10.019.

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32

Henao, Nilson, Kodjo Agbossou, Sousso Kelouwani, Sayed Hosseini, and Michael Fournier. "Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach." Energies 11, no. 1 (January 1, 2018): 88. http://dx.doi.org/10.3390/en11010088.

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33

Günther, S., N. R. Gauger, and J. B. Schroder. "A non-intrusive parallel-in-time approach for simultaneous optimization with unsteady PDEs." Optimization Methods and Software 34, no. 6 (August 6, 2018): 1306–21. http://dx.doi.org/10.1080/10556788.2018.1504050.

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34

Acharjee, Swagato, and Nicholas Zabaras. "A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in deformation processes." Computers & Structures 85, no. 5-6 (March 2007): 244–54. http://dx.doi.org/10.1016/j.compstruc.2006.10.004.

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35

Paquet-Mercier, F., M. Parvinzadeh Gashti, J. Bellavance, S. M. Taghavi, and J. Greener. "Through thick and thin: a microfluidic approach for continuous measurements of biofilm viscosity and the effect of ionic strength." Lab on a Chip 16, no. 24 (2016): 4710–17. http://dx.doi.org/10.1039/c6lc01101b.

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36

Szychta, Elzbieta, and Leszek Szychta. "Collective Losses of Low Power Cage Induction Motors—A New Approach." Energies 14, no. 6 (March 22, 2021): 1749. http://dx.doi.org/10.3390/en14061749.

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Energy efficiency of systems of water pumping is a complex problem since efficiency of two distinct interacting systems needs to be combined: water and power supply. This paper introduces a non-intrusive method of calculating the so-called “collective losses” of a cage induction motor. The term “collective losses”, which the authors define, allows for accurate estimation of motor efficiency. Control system of a pump determines operating point of a pumping station, and thus its efficiency. General estimated performance characteristics of a motor, components of a control system, are assumed to serve selection of a range of pumping speed variations. Rotational speed has a direct effect on motor load torque, pump power and head, and thus on motor performance. Hellwig’s statistical method was used to specify characteristics of estimated collective losses on the basis of experimental studies of 21 motors rated at up to 2.2 kW. The results of simulations and experiments are used to verify validity and efficiency of the suggested method. The method is non-intrusive, simple to use, and requires minimum data.

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37

Raisee, M., D. Kumar, and C. Lacor. "A non-intrusive model reduction approach for polynomial chaos expansion using proper orthogonal decomposition." International Journal for Numerical Methods in Engineering 103, no. 4 (March 26, 2015): 293–312. http://dx.doi.org/10.1002/nme.4900.

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38

Kim, Jung Soo, Young Joon Chee, Ju Wan Park, Jin Wook Choi, and Kwang Suk Park. "A new approach for non-intrusive monitoring of blood pressure on a toilet seat." Physiological Measurement 27, no. 2 (January 6, 2006): 203–11. http://dx.doi.org/10.1088/0967-3334/27/2/010.

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Demo, Nicola, Marco Tezzele, and Gianluigi Rozza. "A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces." Comptes Rendus Mécanique 347, no. 11 (November 2019): 873–81. http://dx.doi.org/10.1016/j.crme.2019.11.012.

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40

Abgrall, Rémi, and Pietro Marco Congedo. "A semi-intrusive deterministic approach to uncertainty quantification in non-linear fluid flow problems." Journal of Computational Physics 235 (February 2013): 828–45. http://dx.doi.org/10.1016/j.jcp.2012.07.041.

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41

Piscitelli, Marco Savino, Silvio Brandi, and Alfonso Capozzoli. "Recognition and classification of typical load profiles in buildings with non-intrusive learning approach." Applied Energy 255 (December 2019): 113727. http://dx.doi.org/10.1016/j.apenergy.2019.113727.

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42

Massidda, Luca, Marino Marrocu, and Simone Manca. "Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification." Applied Sciences 10, no. 4 (February 21, 2020): 1454. http://dx.doi.org/10.3390/app10041454.

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Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

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Kundinger, Thomas, Nikoletta Sofra, and Andreas Riener. "Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection." Sensors 20, no. 4 (February 14, 2020): 1029. http://dx.doi.org/10.3390/s20041029.

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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Nasri, Nadia, Sergio Orts-Escolano, Francisco Gomez-Donoso, and Miguel Cazorla. "Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor." Sensors 19, no. 2 (January 17, 2019): 371. http://dx.doi.org/10.3390/s19020371.

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Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures.

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Lin, Chien Cheng, Chin Chun Chang, De Ron Liang, and Ching Han Yang. "A Preliminary Study on Non-Intrusive User Authentication Method Using Smartphone Sensors." Applied Mechanics and Materials 284-287 (January 2013): 3270–74. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3270.

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This paper proposes a non-intrusive authentication method based on two sensitive apparatus of smartphones, namely, the orientation sensor and the touchscreen. We have found that these two sensors are capable of capturing behavioral biometrics of a user while the user is engaged in relatively stationary activities. The experimental results with respect to two types of flick operating have an equal error rate of about 3.5% and 5%, respectively. To the best of our knowledge, this work is the first publicly reported study that simultaneously adopts the orientation sensor and the touchscreen to build an authentication model for smartphone users. Finally, we show that the proposed approach can be used together with existing intrusive mechanisms, such as password and/or fingerprints, to build a more robust authentication framework for smartphone users.

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Rehman, Attique Ur, Tek Tjing Lie, Brice Vallès, and Shafiqur Rahman Tito. "Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques." Inventions 5, no. 4 (November 23, 2020): 57. http://dx.doi.org/10.3390/inventions5040057.

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The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

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Wu, Sheng, and Kwok L. Lo. "Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic." Processes 8, no. 11 (October 30, 2020): 1385. http://dx.doi.org/10.3390/pr8111385.

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Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

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Perron, Christian, Dushhyanth Rajaram, and Dimitri N. Mavris. "Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2253 (September 2021): 20210495. http://dx.doi.org/10.1098/rspa.2021.0495.

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This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance.

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Sharrock, Rémi, Thierry Monteil, Patricia Stolf, Daniel Hagimont, and Laurent Broto. "Non-Intrusive Autonomic Approach with Self-Management Policies Applied to Legacy Infrastructures for Performance Improvements." International Journal of Adaptive, Resilient and Autonomic Systems 2, no. 1 (January 2011): 58–76. http://dx.doi.org/10.4018/jaras.2011010104.

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The growing complexity of large IT facilities involves important time and effort costs to operate and maintain. Autonomic computing gives a new approach in designing distributed architectures that manage themselves in accordance with high-level objectives. The main issue is that existing architectures do not necessarily follow this new approach. The motivation is to implement a system that can interface heterogeneous components and platforms supplied by different vendors in a non-intrusive and generic manner. The goal is to increase the intelligence of the system by actively monitoring its state and autonomously taking corrective actions without the need to modify the managed system. In this paper, the authors focus on modeling software and hardware architectures as well as describing administration policies using a graphical language inspired from UML. The paper demonstrates that this language is powerful enough to describe complex scenarios and evaluates some self-management policies for performance improvement on a distributed computational jobs load balancer over a grid.

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Abdallah, Fatima, Shadi Basurra, and Mohamed Medhat Gaber. "A Non-Intrusive Heuristic for Energy Messaging Intervention Modeled Using a Novel Agent-Based Approach." IEEE Access 7 (2019): 1627–46. http://dx.doi.org/10.1109/access.2018.2886146.

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