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Journal articles on the topic 'Non-intrusive approach'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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1

Ling-Zhi Yi, Ling-Zhi Yi, Xi-Meng Liu Ling-Zhi Yi, Guo-Yong Zhang Xi-Meng Liu, Hui-Na Song Guo-Yong Zhang, and Ning Liu Hui-Na Song. "An Non-Intrusive Load Event Detection Approach Based on CEEMDAN-WTD-F Test." 電腦學刊 33, no. 6 (2022): 021–36. http://dx.doi.org/10.53106/199115992022123306002.

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<p>To improve the perception of the switch state of the electrical equipment and realize the identification of the non-intrusive load switching process more accurately, a non-intrusive load event detection method based on the CEEMDAN-WTD-F test is proposed in this paper. Firstly, the adaptive median filter is used to reduce the noise fluctuation of power data of electric equipment and the discrete sequence derivative is used to reduce turn-on transition time for individual loads. Then, based on the principle of decomposition denoising and statistical testing, Complete Ensemble Empirical

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Bajaj, Jaspreet Singh, Naveen Kumar, Rajesh Kumar Kaushal, H. L. Gururaj, Francesco Flammini, and Rajesh Natarajan. "System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures." Sensors 23, no. 3 (2023): 1292. http://dx.doi.org/10.3390/s23031292.

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The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusiv

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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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Massidda, Luca, and Marino Marrocu. "A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation." Sensors 22, no. 12 (2022): 4481. http://dx.doi.org/10.3390/s22124481.

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Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmenta

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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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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 (2017): 1271–84. http://dx.doi.org/10.1109/tpds.2016.2619715.

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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 (2006): 81–92. http://dx.doi.org/10.3166/remn.15.81-92.

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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 (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 ter

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

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10

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

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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 (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 interp

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Liu, Yu, Yan Wang, Yu Hong, Qianyun Shi, Shan Gao, and Xueliang Huang. "Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method." Sensors 21, no. 21 (2021): 7272. http://dx.doi.org/10.3390/s21217272.

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As a pivotal technological foundation for smart home implementation, non-intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non-intrusive load disaggregation-oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding-based approach is capable of provi

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Kroener, Julia, Caroline Schaitz, and Zrinka Sosic-Vasic. "Prospective Mental Images: A Transdiagnostic Approach to Negative Affectivity and Mood Dysregulation among Borderline Personality Disorder and Depression." Behavioral Sciences 14, no. 2 (2024): 81. http://dx.doi.org/10.3390/bs14020081.

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There is initial evidence that patients diagnosed with Borderline Personality Disorder (BPD) experience intrusive prospective mental images about non-suicidal self-injury (NSSI). These images, in turn, are associated with the conduct of NSSI. As the negative emotional valence of intrusive images has been established across clinical disorders, negative affectivity might play a key role linking mental imagery and psychopathology. Therefore, the present study aimed to investigate the possible mediating role of symptoms of depression as a proxy for negative affectivity linking intrusive prospectiv

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Hasan, Md Mehedi, Dhiman Chowdhury, and Md Ziaur Rahman Khan. "Non-Intrusive Load Monitoring Using Current Shapelets." Applied Sciences 9, no. 24 (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 su

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Abade, Bruno, David Perez Abreu, and Marilia Curado. "A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments." Sensors 18, no. 11 (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

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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 (2017): 176–90. http://dx.doi.org/10.1109/tnsm.2016.2631477.

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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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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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19

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 (2019): 6472–80. http://dx.doi.org/10.1109/tsg.2019.2906012.

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20

Li, Chuyi, Kedi Zheng, Hongye Guo, and Qixin Chen. "A mixed-integer programming approach for industrial non-intrusive load monitoring." Applied Energy 330 (January 2023): 120295. http://dx.doi.org/10.1016/j.apenergy.2022.120295.

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Abadleh, Ahmad H. "Landmark-Based Indoor Positioning: a Non-Intrusive and Cost-Effective Approach." International Journal on Communications Antenna and Propagation (IRECAP) 13, no. 1 (2023): 55. http://dx.doi.org/10.15866/irecap.v13i1.23209.

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Xiao, Yong, Zhukui Tan, Bin Qian, et al. "A non-intrusive load monitoring data generation method." Journal of Physics: Conference Series 2963, no. 1 (2025): 012016. https://doi.org/10.1088/1742-6596/2963/1/012016.

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Abstract Non-intrusive load monitoring (NILM) is a technique that does not require access to the interior of the system or to the interior of each electrical appliance. Instead, it only installs monitoring equipment at the entrance of the user bus. In the course of investigating non-intrusive load identification techniques, it is often necessary to collect extensive user load data in order to confirm the applicability of the proposed load identification methods across various situations. This requirement inevitably entails a significant burden of data collection and organization. In order to o

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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 (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

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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 (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 flexibilit

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SÓSKUTHY, MÁRTON. "Analogy in the emergence of intrusive-r in English." English Language and Linguistics 17, no. 1 (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

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Liu, Yu, Qianyun Shi, Yan Wang, Xin Zhao, Shan Gao, and Xueliang Huang. "An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity." Sensors 21, no. 22 (2021): 7750. http://dx.doi.org/10.3390/s21227750.

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Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous desig

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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 (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 exp

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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 (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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Zhang, Xiao, Sijin Cheng, Yi Wang, Shenzheng Wang, Xinyi Li, and Yang Gu. "Non-intrusive load monitoring based on equipment operation state." Journal of Physics: Conference Series 2835, no. 1 (2024): 012058. http://dx.doi.org/10.1088/1742-6596/2835/1/012058.

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Abstract Energy disaggregation, known as Non-Intrusive Load Monitoring (NILM), is a practical approach to providing device-level electrical information and can be applied to enhance various scenarios in smart grids. In recent years, with the emergence of large-scale energy consumption datasets, a growing multitude of deep learning methods have been employed to address energy disaggregation problems. However, these methods face challenges in resolving the disaggregation of multi-state devices and devices with overlapping operation cycles, especially during startup and shutdown periods. This pap

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Parson, Oliver, Siddhartha Ghosh, Mark Weal, and Alex Rogers. "Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 356–62. http://dx.doi.org/10.1609/aaai.v26i1.8162.

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Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instan

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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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Athanasiadis, Christos L., Theofilos A. Papadopoulos, and Dimitrios I. Doukas. "Real-time non-intrusive load monitoring: A light-weight and scalable approach." Energy and Buildings 253 (December 2021): 111523. http://dx.doi.org/10.1016/j.enbuild.2021.111523.

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Raguraman, T. B., Sini Raj Pulari, and Shriram K. Vasudevan. "Curtailing insomnia in a non-intrusive hardware less approach with machine learning." International Journal of Medical Engineering and Informatics 14, no. 6 (2022): 537. http://dx.doi.org/10.1504/ijmei.2022.10050773.

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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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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 (2018): 3362–72. http://dx.doi.org/10.1109/tsg.2016.2631238.

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36

Pujić, Dea, Nikola Tomašević, and Marko Batić. "A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring." Sensors 23, no. 3 (2023): 1444. http://dx.doi.org/10.3390/s23031444.

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Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods an

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Vasudevan, Shriram K., T. B. Raguraman, and Sini Raj Pulari. "Curtailing insomnia in a non-intrusive hardware less approach with machine learning." International Journal of Medical Engineering and Informatics 14, no. 6 (2022): 537. http://dx.doi.org/10.1504/ijmei.2022.126524.

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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 o

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P C, Shimjith. "Real-Time Power Usage Analyzing Device: A Non-Intrusive Approach to Energy Monitoring." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47349.

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Abstract Many gadgets with different power ratings are frequently used in middle-class Indian homes, but consumers can only obtain information about power use from their monthly electricity bill. There is insufficient information in this bill about incidents of inappropriate usage or power consumption by equipment. By creating a cost-effective, non-intrusive gadget that gathers electrical energy usage data from conventional energy meters, saves it, and displays it in an easy-to-use interface for monitoring and analysis, this study fills this knowledge gap. The technology is generally applicabl

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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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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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Sáenz-Adán, Carlos, Francisco J. García-Izquierdo, Beatriz Pérez, Trung Dong Huynh, and Luc Moreau. "Automated and non-intrusive provenance capture with UML2PROV." Computing 104, no. 4 (2021): 767–88. http://dx.doi.org/10.1007/s00607-021-01012-x.

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AbstractData provenance is a form of knowledge graph providing an account of what a system performs, describing the data involved, and the processes carried out over them. It is crucial to ascertaining the origin of data, validating their quality, auditing applications behaviours, and, ultimately, making them accountable. However, instrumenting applications, especially legacy ones, to track the provenance of their operations remains a significant technical hurdle, hindering the adoption of provenance technology. UML2PROV is a software-engineering methodology that facilitates the instrumentatio

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He, Xi, Heng Dong, Wanli Yang, and Jun Hong. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring." Energies 15, no. 6 (2022): 2290. http://dx.doi.org/10.3390/en15062290.

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Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only de

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Hall, Eric R. "Non-Intrusive Estimation of Active Volume in Anaerobic Reactors." Water Quality Research Journal 20, no. 2 (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 fro

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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 (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 di

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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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Azizi, Elnaz, Mohammad T. H. Beheshti, and Sadegh Bolouki. "Event Matching Classification Method for Non-Intrusive Load Monitoring." Sustainability 13, no. 2 (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

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Sykiotis, Stavros, Maria Kaselimi, Anastasios Doulamis, and Nikolaos Doulamis. "ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring." Sensors 22, no. 8 (2022): 2926. http://dx.doi.org/10.3390/s22082926.

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Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes tra

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Szychta, Elzbieta, and Leszek Szychta. "Collective Losses of Low Power Cage Induction Motors—A New Approach." Energies 14, no. 6 (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 s

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García, Álvaro, Anibal Bregon, and Miguel A. Martínez-Prieto. "A non-intrusive Industry 4.0 retrofitting approach for collaborative maintenance in traditional manufacturing." Computers & Industrial Engineering 164 (February 2022): 107896. http://dx.doi.org/10.1016/j.cie.2021.107896.

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