Journal articles: 'Non-Intrusive methods'

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Patil, Pooja R., and Subhash S. Kulkarni. "Survey of non-intrusive face spoof detection methods." Multimedia Tools and Applications 80, no. 10 (January 28, 2021): 14693–721. http://dx.doi.org/10.1007/s11042-020-10338-1.

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Nava, Martha X., Víctor H. Castillo, and Isabel M. Gómez. "Estimation of cognitive stress through the use of non-intrusive methods." Avances en Interacción Humano-Computadora, no. 1 (November 30, 2020): 94. http://dx.doi.org/10.47756/aihc.y5i1.73.

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Cerebral palsy (CP) is the most common cause of motor impairment in children and has increased globally. This health problem negatively impacts the quality of a patient's life and the people in their care. The literature reports the use of technological tools developed to support patients' rehabilitation; however, these are intrusive, so they are annoying and can generate stress. This work aims to analyze the existing developments regarding cognitive stress estimation through non-intrusive methods. This study identifies a few developments focused on rehabilitating children with CP by analyzing facial expressions with non-intrusive methods. From the above, the authors propose future lines of work that would eventually support patients with CP.

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Moruzzi, Rodrigo B., Pedro Grava da Silva, Soroosh Sharifi, Luiza C. Campos, and John Gregory. "Strength assessment of Al-Humic and Al-Kaolin aggregates by intrusive and non-intrusive methods." Separation and Purification Technology 217 (June 2019): 265–73. http://dx.doi.org/10.1016/j.seppur.2019.02.033.

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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 (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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Leblond, J., and D. Stepowski. "SOME NON-INTRUSIVE METHODS FOR DIAGNOSIS IN TWO-PHASE FLOWS." Multiphase Science and Technology 8, no. 1-4 (1994): 715–82. http://dx.doi.org/10.1615/multscientechn.v8.i1-4.130.

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Zhang, Wei, Bowen Yuan, Zhenyu Ma, and Qingling Lu. "Research on Non Intrusive Methods for Dynamic Monitoring of Software." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012052. http://dx.doi.org/10.1088/1757-899x/466/1/012052.

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Nguyen, Thanh Hoai, and Kyoungsik Chang. "Comparison of the Point-Collocation Non-Intrusive Polynomial (NIPC) and Non-Intrusive Spectral Projection (NISP) Methods for the γ-Rθ Transition Model." Applied Sciences 9, no. 7 (April 3, 2019): 1407. http://dx.doi.org/10.3390/app9071407.

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In the present work, a comparative study of two major non-intrusive polynomial chaos methods, Point-Collocation Non-Intrusive Polynomial Chaos (NIPC) and Non-Intrusive Spectral Projection (NISP), was conducted for the transitional transitional model. Three multiple model coefficients, Ca2, Ce1, and Ce2 were considered with multiple random inputs with the assumption of uniform distributions with ±10% deviation. The target transitional flows were one around a flat plate and Aerospatiale A-airfoil. Deterministic solutions were obtained by employing the open source software OpenFOAM. The results of two methods were compared to the results of Monte Carlo simulation with 500 runs. The order convergence of the mean value and the standard deviation (STD) were compared in terms of the quantities of interest, drag and lift coefficients. Further, the most effective model coefficient for each transitional flow could be found through the calculation of the Sobol index.

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Poette, Christopher, and Philippe Reynier. "Evaluation of 3D Mapping Experimental Non-Intrusive Methods for Multiphase Flows." International Journal of Multiphysics 8, no. 1 (March 2014): 69–90. http://dx.doi.org/10.1260/1750-9548.8.1.69.

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Jeong, D. U. "S45.C Non-intrusive methods for biological signal monitoring during sleep." Sleep Medicine 8 (February 2007): S42. http://dx.doi.org/10.1016/s1389-9457(07)70169-7.

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Etezadifar, Mozaffar, Houshang Karimi, and Jean Mahseredjian. "Non-intrusive load monitoring: Comparative analysis of transient state clustering methods." Electric Power Systems Research 223 (October 2023): 109644. http://dx.doi.org/10.1016/j.epsr.2023.109644.

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Privitera, Salvatore, Giuseppe Manetto, Simone Pascuzzi, Domenico Pessina, and Emanuele Cerruto. "Drop Size Measurement Techniques for Agricultural Sprays:A State-of-The-Art Review." Agronomy 13, no. 3 (February 26, 2023): 678. http://dx.doi.org/10.3390/agronomy13030678.

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Plant protection control based on the spray application of plant protection products is a very complex task depending on a series of factors, among which droplet size is the most influential for deposition and pesticide effectiveness. In fact, the adoption of the correct droplet size can ensure that the required dose reaches the target area and is not wasted, minimizes the off-target losses due to evaporation, drift and run-off and, at the same time, enhances the operator’s safety in terms of inhalation, ingestion and dermal exposure. In this paper, after defining some mean characteristic diameters helpful for a description of a drop population and focusing on the main drop size distribution functions for the statistical characterization of sprays, a critical analysis of known methods, both intrusive and non-intrusive, for drop size measurement is carried out by reviewing the literature. Among intrusive methods, the liquid immersion method and the use of water-sensitive papers are discussed, whereas, among non-intrusive methods, laser-based systems (laser diffraction, phase Doppler particle analysis) and high-speed imaging (shadowgrapy) are presented. Both types of method, intrusive and non-intrusive, can be used in machine-learning-based approaches exploiting regression techniques and neural network analysis.

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Zhou, Mengran, Shuai Shao, Xu Wang, Ziwei Zhu, and Feng Hu. "Deep Learning-Based Non-Intrusive Commercial Load Monitoring." Sensors 22, no. 14 (July 13, 2022): 5250. http://dx.doi.org/10.3390/s22145250.

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Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.

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Mamchur, Dmytro, Janis Peksa, Soledad Le Clainche, and Ricardo Vinuesa. "Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection." Sensors 22, no. 6 (March 9, 2022): 2121. http://dx.doi.org/10.3390/s22062121.

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Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

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singh, Barath Kumar, and Ravi Kumar Chittoriya. "Hair Evaluation Methods." Indian Journal of Medical and Health Sciences 10, no. 1 (June 15, 2023): 31–38. http://dx.doi.org/10.21088/ijmhs.2347.9981.10123.5.

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The three main Hair assessment methods in alopecia are Non-invasive (questionnaire, daily hair counts, standardized wash test, 60-s hair count, global pictures, dermoscopy, hair weight, contrasting felt examination, phototrichogram, TrichoScan), semi-invasive (trichogram and unit area trichogram), and intrusive procedures (e.g., scalp biopsy). No method is ideal or realistic. These are useful for patient diagnosis and monitoring when interpreted carefully. Daily hair counts, wash tests, etc. are good ways to evaluate a patient's shedding. Hair clinics use procedures like global photography. Phototrichogram is exclusively used in clinical trials. These procedures (like scalp biopsy) require processing and interpretation expertise. In this review article, we discuss the various hair evaluation methods.

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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 (January 23, 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-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

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Santos, D. A., G. C. Alves, M. A. S. Barrozo, and Claudio Roberto Duarte. "Study of Measurement Methods of Particle Velocity in a Spouted Bed." Materials Science Forum 727-728 (August 2012): 1842–47. http://dx.doi.org/10.4028/www.scientific.net/msf.727-728.1842.

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Average particle velocity measurements were carried out in a conical-cylindrical spouted bed made of acrylic. In this study an intrusive fiber optical technique which is based on a cross-correlation function between signals from its two channels was used. For a non-intrusive measurement in order to compare with the intrusive technique, images of particle movement were recorded using a high-speed video camera. The experiments were conducted in differents air velocity conditions above the minimum spouting velocity. The latter method was limited in velocity measurement only near the spouted beds wall inasmuch as the spouted bed used was a three dimensions one. On the other hand, the fiber optical is a promising technique for measuring particle velocity distributions in a three dimensions spouted bed. To predict the minimum spouting velocity in order to use this result in the measurement techniques investigation, simulations were carried out using the Eulerian-Eulerian multiphase model.

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Yang, Chuan Choong, Chit Siang Soh, and Vooi Voon Yap. "Comparative Study of Event Detection Methods for Non-intrusive Appliance Load Monitoring." Energy Procedia 61 (2014): 1840–43. http://dx.doi.org/10.1016/j.egypro.2014.12.225.

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Xiao, D., J. Du, F. Fang, C. C. Pain, and J. Li. "Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation." Computers & Fluids 177 (November 2018): 69–77. http://dx.doi.org/10.1016/j.compfluid.2018.10.006.

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Beritelli, Francesco, Salvatore Casale, Alfredo Cavallaro, and Roberto Montagna. "Network Performance and Fault Detection in a PSTN Using Non-Intrusive Methods." European Transactions on Telecommunications 10, no. 5 (September 1999): 487–96. http://dx.doi.org/10.1002/ett.4460100503.

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Biezemans, Rutger A., Claude Le Bris, Frédéric Legoll, and Alexei Lozinski. "Non-intrusive implementation of a wide variety of Multiscale Finite Element Methods." Comptes Rendus. Mécanique 351, S1 (July 27, 2023): 1–46. http://dx.doi.org/10.5802/crmeca.178.

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Glaser, Steven D., and Riley M. Chung. "Estimation of Liquefaction Potential by in Situ Methods." Earthquake Spectra 11, no. 3 (August 1995): 431–55. http://dx.doi.org/10.1193/1.1585822.

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This report examines the state-of-the-art of in situ methods of estimating liquefaction potential in sands. In situ methods are especially important since “undisturbed” samples of loose sand for laboratory testing are virtually unobtainable. Various penetration test methods are examined, such as the SPT, DMT, and the CPT and variants. These methods are completely empirical in nature, and have worked well to date. The current state-of-practice is an SPT-based method. Intrusive, seismic-based tests are also examined: the cross-hole, down-hole tests, and down-hole logger. The seismic velocity-based predictors have a stronger physical basis than the penetration test-based estimation methods, but need a larger database. A non-intrusive test, the Spectral Analysis of Surface Waves technique, seems especially suited for examining sites of large areal extent.

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Mariscotti, Andrea. "Non-Intrusive Load Monitoring Applied to AC Railways." Energies 15, no. 11 (June 4, 2022): 4141. http://dx.doi.org/10.3390/en15114141.

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Non-intrusive load monitoring takes place in residential and industrial contexts to disaggregate and identify loads connected to a distribution grid. This work studies the applicability and effectiveness for AC railways, considering the highly dynamic behavior of rolling stock as an electric load, immersed in varying contexts of moving loads. Both voltage–current diagrams and harmonic spectra were considered for identification and extraction of features relevant to classification and clustering. Principal components were extracted, approaching the problem using principal component analysis (PCA) and partial least square regression (PLSR). Clustering methods were then discussed, verifying separability performance and applicability to the railway context, checking the performance by means of the balanced accuracy index. Based on more than one hundred measured spectra, PLSR has been confirmed with superior performance and lower complexity. Independent verification based on dispersion and correlation were used to spot relevant spectrum components to use as clustering features and confirm the PLSR outcome.

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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 (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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Ghaffar, Muzzamil, Shakil R. Sheikh, Noman Naseer, Zia Mohy Ud Din, Hafiz Zia Ur Rehman, and Muhammad Naved. "Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering." Sensors 22, no. 11 (May 26, 2022): 4036. http://dx.doi.org/10.3390/s22114036.

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With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.

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Massidda, Luca, and Marino Marrocu. "A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation." Sensors 22, no. 12 (June 14, 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 environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

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Kumar, Satish, Sunanda Gupta, and Sakshi Arora. "A comparative simulation of normalization methods for machine learning-based intrusion detection systems using KDD Cup’99 dataset." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1749–66. http://dx.doi.org/10.3233/jifs-211191.

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Network Intrusion detection systems (NIDS) detect malicious and intrusive information in computer networks. Presently, commercial NIDS is based on machine learning approaches that have complex algorithms and increase intrusion detection efficiency and efficacy. These machine learning-based NIDS use high dimensional network traffic data from which intrusive information is to be detected. This high-dimensional network traffic data in NIDS needs to be preprocessed and normalized to make it suitable for machine learning tools. A machine learning approach with appropriate normalization and prepossessing increases NIDS performance. This paper presents an empirical study on various normalization methods implemented on a benchmark network traffic dataset, KDD Cup’99, that has been used to evaluate the NIDS model. The present study shows decimal normalization has a better prediction performance than non-normalized traffic data categorized into ‘normal’ or ‘intrusive’ classes.

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Haibel, M., Franz Mayinger, and G. Strube. "APPLICATION OF NON-INTRUSIVE DIAGNOSTIC METHODS TO SUB- AND SUPERSONIC H-AIR-FLAMES." International Journal of Energetic Materials and Chemical Propulsion 3, no. 1-6 (1994): 449–64. http://dx.doi.org/10.1615/intjenergeticmaterialschemprop.v3.i1-6.470.

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Škerjanec, Mateja, Klemen Kregar, Gašper Štebe, and Gašper Rak. "Analysis of floating objects based on non-intrusive measuring methods and machine learning." Geomorphology 408 (July 2022): 108254. http://dx.doi.org/10.1016/j.geomorph.2022.108254.

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Kantzas, Apostolos, Ian Wright, Amit Bhargava, Fan Li, and Kelly Hamilton. "Measurement of hydrodynamic data of gas-phase polymerization reactors using non-intrusive methods." Catalysis Today 64, no. 3-4 (January 20, 2001): 189–203. http://dx.doi.org/10.1016/s0920-5861(00)00523-x.

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Giri, Suman, and Mario Bergés. "An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring." Energy Conversion and Management 90 (January 2015): 488–98. http://dx.doi.org/10.1016/j.enconman.2014.11.047.

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Basu, Kaustav, Vincent Debusschere, Ahlame Douzal-Chouakria, and Seddik Bacha. "Time series distance-based methods for non-intrusive load monitoring in residential buildings." Energy and Buildings 96 (June 2015): 109–17. http://dx.doi.org/10.1016/j.enbuild.2015.03.021.

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Wang, Guohao, Yantao Yu, and Heng Li. "Automated activity recognition of construction workers using single in-pocket smartphone and machine learning methods." IOP Conference Series: Earth and Environmental Science 1101, no. 7 (November 1, 2022): 072008. http://dx.doi.org/10.1088/1755-1315/1101/7/072008.

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Abstract Automatic recognition of construction workers’ activities contributes to improving productivity and reducing the potential risk of injury. Kinematics sensors have been proved feasible and efficient to recognize construction activities. However, most of the sensors need to be tightly tied to workers’ bodies, which might result in uncomfortableness and workers’ reluctance to wear the sensors. To solve the problem, this paper proposes a less physically intrusive construction activities recognition method with a single in-pocket smartphone. The smartphone was placed in the pocket in a natural and non-fixed manner, with its built-in accelerometer and gyroscope collecting motion data. Machine learning-based classifiers were trained to recognize construction activities. An experiment simulating rebar activities was designed to verify the effectiveness of the proposed method. The experiment results showed that the proposed method could identify rebar activities (with an accuracy over 94%) in a non-intrusive manner.

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Trüb, Roman, Reto Da Forno, Lukas Daschinger, Andreas Biri, Jan Beutel, and Lothar Thiele. "Non-Intrusive Distributed Tracing of Wireless IoT Devices with the FlockLab 2 Testbed." ACM Transactions on Internet of Things 3, no. 1 (February 28, 2022): 1–31. http://dx.doi.org/10.1145/3480248.

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Testbeds for wireless IoT devices facilitate testing and validation of distributed target nodes. A testbed usually provides methods to control, observe, and log the execution of the software. However, most of the methods used for tracing the execution require code instrumentation and change essential properties of the observed system. Methods that are non-intrusive are typically not applicable in a distributed fashion due to a lack of time synchronization or necessary hardware/software support. In this article, we present a tracing system for validating time-critical software running on multiple distributed wireless devices that does not require code instrumentation, is non-intrusive and is designed to trace the distributed state of an entire network. For this purpose, we make use of the on-chip debug and trace hardware that is part of most modern microcontrollers. We introduce a testbed architecture as well as models and methods that accurately synchronize the timestamps of observations collected by distributed observers. In a case study, we demonstrate how the tracing system can be applied to observe the distributed state of a flooding-based low-power communication protocol for wireless sensor networks. The presented non-intrusive tracing system is implemented as a service of the publicly accessible open source FlockLab 2 testbed.

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Yang, Xiao, Ramesh Bahadur Bist, Bidur Paneru, and Lilong Chai. "Deep Learning Methods for Tracking the Locomotion of Individual Chickens." Animals 14, no. 6 (March 15, 2024): 911. http://dx.doi.org/10.3390/ani14060911.

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Poultry locomotion is an important indicator of animal health, welfare, and productivity. Traditional methodologies such as manual observation or the use of wearable devices encounter significant challenges, including potential stress induction and behavioral alteration in animals. This research introduced an innovative approach that employs an enhanced track anything model (TAM) to track chickens in various experimental settings for locomotion analysis. Utilizing a dataset comprising both dyed and undyed broilers and layers, the TAM model was adapted and rigorously evaluated for its capability in non-intrusively tracking and analyzing poultry movement by intersection over union (mIoU) and the root mean square error (RMSE). The findings underscore TAM’s superior segmentation and tracking capabilities, particularly its exemplary performance against other state-of-the-art models, such as YOLO (you only look once) models of YOLOv5 and YOLOv8, and its high mIoU values (93.12%) across diverse chicken categories. Moreover, the model demonstrated notable accuracy in speed detection, as evidenced by an RMSE value of 0.02 m/s, offering a technologically advanced, consistent, and non-intrusive method for tracking and estimating the locomotion speed of chickens. This research not only substantiates TAM as a potent tool for detailed poultry behavior analysis and monitoring but also illuminates its potential applicability in broader livestock monitoring scenarios, thereby contributing to the enhancement of animal welfare and management in poultry farming through automated, non-intrusive monitoring and analysis.

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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 (April 11, 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 transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity’s superiority compared to several state-of-the-art methods.

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de Aguiar, Everton Luiz, André Eugenio Lazzaretti, Bruna Machado Mulinari, and Daniel Rodrigues Pipa. "Scattering Transform for Classification in Non-Intrusive Load Monitoring." Energies 14, no. 20 (October 18, 2021): 6796. http://dx.doi.org/10.3390/en14206796.

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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

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Zapevalov, D. N., and R. K. Vagapov. "ANALYSIS OF THE USE OF ULTRASONIC TESTING METHODS IN THE FRAMEWORK OF CORROSION MONITORING OF INTERNAL CORROSION AT GAS PRODUCTION FACILITIES IN THE PRESENCE OF CARBON DIOXIDE." Kontrol'. Diagnostika, no. 261 (March 2020): 30–35. http://dx.doi.org/10.14489/td.2020.03.pp.030-035.

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The use of various intrusive and non-intrusive methods of corrosion monitoring makes it possible to assess the corrosion situation and the effectiveness of the applied corrosion protection agents in conditions of internal corrosion at gas production facilities due to the presence of aggressive gases. The analysis of the application of ultrasonic testing methods as part of corrosion monitoring of internal corrosion at gas production facilities in the presence of corrosive components is carried out. Ultrasonic thickness measurement is widely used as a non-intrusive method for monitoring internal corrosion and detecting corrosion defects in promising gas fields. Many gas fields (Bovanenkovskoye oil and gas condensate field, Urengoy oil and gas field and others) revealed corrosion defects due to cases of internal corrosion due to the presence of increased amounts of carbon dioxide in the produced hydrocarbons. Under conditions of corrosion in the presence of carbon dioxide, ultrasonic methods for measuring the thickness of a metal have certain limitations associated with the unpredictable local nature of carbon dioxide corrosion, which should be considered when used in gas facilities. The main method for measuring thickness under operational conditions is ultrasonic thickness measurement, which is used in conjunction with radiographic monitoring. Using these two main non-intrusive methods, corrosion monitoring monitors the thinning of the metal, the size and depth of local defects and the dynamics of their change over time. Based on the results of measuring the residual wall thickness of the pipe and equipment, the possibility of their further work is determined, and recommendations are made on extending the safe life of gas facilities. The authors analyzed the literature data on new options and technical solutions for the use of ultrasonic methods in the measurement of the thickness of a metal surface.

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Bortolotti, Pietro, Helena Canet, Carlo L. Bottasso, and Jaikumar Loganathan. "Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines." Wind Energy Science 4, no. 3 (July 11, 2019): 397–406. http://dx.doi.org/10.5194/wes-4-397-2019.

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Abstract. The present paper characterizes the performance of non-intrusive uncertainty quantification methods for aeroservoelastic wind turbine analysis. Two different methods are considered, namely non-intrusive polynomial chaos expansion and Kriging. Aleatory uncertainties are associated with the wind inflow characteristics and the blade surface state, on account of soiling and/or erosion, and propagated throughout the aeroservoelastic model of a large conceptual offshore wind turbine. Results are compared with a brute-force extensive Monte Carlo sampling, which is used as benchmark. Both methods require at least 1 order of magnitude less simulations than Monte Carlo, with a slight advantage of Kriging over polynomial chaos expansion. The analysis of the solution space clearly indicates the effects of uncertainties and their couplings, and highlights some possible shortcomings of current mostly deterministic approaches based on safety factors.

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Houidi, Sarra, Dominique Fourer, François Auger, Houda Ben Attia Sethom, and Laurence Miègeville. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning." Energies 14, no. 9 (May 10, 2021): 2726. http://dx.doi.org/10.3390/en14092726.

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Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.

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Mocayd, Nabil El, M. Shadi Mohamed, and Mohammed Seaid. "Non-intrusive polynomial chaos methods for uncertainty quantification in wave problems at high frequencies." Journal of Computational Science 53 (July 2021): 101344. http://dx.doi.org/10.1016/j.jocs.2021.101344.

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Bindu, S., S. David Sumam, and Thomas V. Vinod. "Non-Intrusive Methods to Detect Air-Gap Eccentricity Faults in Three-Phase Induction Motor." International Review of Electrical Engineering (IREE) 15, no. 1 (February 29, 2020): 41. http://dx.doi.org/10.15866/iree.v15i1.17805.

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Roy, James W., Jasen M. Robillard, Susan B. Watson, and Masaki Hayashi. "Non-intrusive characterization methods for wastewater-affected groundwater plumes discharging to an alpine lake." Environmental Monitoring and Assessment 149, no. 1-4 (February 6, 2008): 201–11. http://dx.doi.org/10.1007/s10661-008-0194-9.

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BEULAH DAVID, D., and M. A.DORAIRANGASWAMY. "Gait Recognition as Non-Intrusive Biometric Using View Invariant Methods in Multi Temporal Images." International Journal of Engineering & Technology 7, no. 4.7 (September 27, 2018): 127. http://dx.doi.org/10.14419/ijet.v7i4.7.20527.

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Gait patterns have been used widely in recent years to authenticate users. Because it doesn’t require user intrusion, it is often used as a biometric to make authentication processes easier and hassle free. But there are various issues with this process. To start with, the viewing angle has to be constant which is quite difficult to achieve with limited number of cameras. Speed too can alter the way a person walks and cause inconsistencies in identification. Furthermore, complications might arise if the subject is carrying something. The weight can affect his walking pattern. Besides, a recent accident could also transform a person’s walking pattern and lead to wrong identification. Other biometrics such as face detection can be combined with this technique to reduce the issues leading to erroneous identification. In this paper, we propose a system to overcome the viewing angle discrepancies. The system takes in walking sequences as input and processes them to create images. This is converted into 3D images by means of stereovision algorithms. Using which, we can effectively match the real-time image with various image sequences in the database. Side face detection can enhance the accuracy further..

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Boiko, Andrei, Natividad Madrid, and Ralf Seepold. "Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review." Sensors 23, no. 11 (May 24, 2023): 5038. http://dx.doi.org/10.3390/s23115038.

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Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.

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Tangrand, Kristoffer, and Bernt Bremdal. "Using Deep Learning Methods to Monitor Non-Observable States in a Building." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5159.

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This paper presents results from ongoing research with a goal to use a combination of time series from non-intrusive soft sensors and deep recurrent neural networks to predict room usage at a university campus. Training data was created by collecting measurements from sensors measuring room CO2, humidity, temperature, light, motion and sound, while the labels was created manually by human inspection. Results include analyses of relationships between different sensor data sequences and recommendations for a prototype predictive model using deep recurrent neural networks.

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Zaman, Qummar, Senan Alraho, and Andreas König. "Low-cost Indirect Measurement Methods for Self-x Integrated Sensory Electronics for Industry 4.0." tm - Technisches Messen 87, s1 (September 25, 2020): s79—s84. http://dx.doi.org/10.1515/teme-2020-0020.

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AbstractThe conventional method for testing the performance of reconfigurable sensory electronics of industry 4.0 relies on the direct measurement methods. This approach gives higher accuracy but at the price of extremely high testing cost and does not utilize the new degrees of freedom for measurement methods enabled by industry 4.0. In order to reduce the test cost and use available resources more efficiently, a primary approach, called indirect measurements or alternative testing has been proposed using a non-intrusive sensor. Its basic principle consists in using the indirect measurements, in order to estimate the sensory electronics performance parameters without measuring directly. The non-intrusive property of the proposed method offers better performance of the sensing electronics and virtually applicable to any sensing electronics. Efficiency is evaluated in terms of model accuracy by using six different classical metrics. It uses an indirect current-feedback instrumentation amplifier (InAmp) as a test vehicle to evaluate the performance parameters of the circuit. The device is implemented using CMOS 0.35 μm technology. The achieved maximum value of average expected error metrics is 0.24, and the lowest value of correlation performance metrics is 0.91, which represent an excellent efficiency of InAmp performance predictor.

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Honig, R., D. Theisen, R. Fink, G. Kappler, D. Rist, and P. Andresen. "DIAGNOSTICS OF NON-REACTING AND REACTING SUPERSONIC FLOWS IN A SCRAMJET MODEL COMBUSTOR USING NON-INTRUSIVE SPECTROSCOPIC METHODS." International Journal of Energetic Materials and Chemical Propulsion 4, no. 1-6 (1997): 837–48. http://dx.doi.org/10.1615/intjenergeticmaterialschemprop..v4.i1-6.780.

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Wickramathilaka, Manjula, Md Pauzi Abdullah, Mohammad Yusri Hassan, and Hayati Abdullah. "Detection of occupancy status from internet connectivity for non-intrusive load monitoring." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (June 1, 2023): 1678. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1678-1688.

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Non-intrusive load monitoring (NILM) methods are widely used for appliance level energy disaggregation in residential buildings. These methods mostly depend on electrical features, and they have not been much successful in applying for commercial buildings. However, recent research has indicated that the accuracy of existing NILM methods can be improved by associating with occupancy data. Therefore, in this paper a novel occupancy detection algorithm is proposed which can detect occupancy status of individuals using the connectivity of their information technology (IT) devices to the local area network of the building. The model is validated using data collected at a university building, with mean errors of 01:23 and 04:02 minutes for the detection of arrival and departure. The occupancy profiles developed by the proposed model can be used to disaggregate energy consumption in a commercial building to appliance and occupant level.

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Djordjevic, Srdjan, Marko Dimitrijevic, and Vanco Litovski. "A non-intrusive identification of home appliances using active power and harmonic current." Facta universitatis - series: Electronics and Energetics 30, no. 2 (2017): 199–208. http://dx.doi.org/10.2298/fuee1702199d.

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In recent years, research on non-intrusive load monitoring has become very popular since it allows customers to better manage their energy use and reduce electrical consumption. The traditional non-intrusive load monitoring method, which uses active and reactive power as signatures, has poor performance in detecting small non-linear loads. This drawback has become more prominent because the use of nonlinear appliances has increased continuously during the last decades. To address this problem, we propose a NILM method that utilizes harmonic current in combination with the changes of real power. The advantages of the proposed method with respect to the existing frequency analysis based NILM methods are lower computational complexity and the use of only one feature to characterize the harmonic content of the current.

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Keskin, Omer F., Kevin Matthe Caramancion, Irem Tatar, Owais Raza, and Unal Tatar. "Cyber Third-Party Risk Management: A Comparison of Non-Intrusive Risk Scoring Reports." Electronics 10, no. 10 (May 13, 2021): 1168. http://dx.doi.org/10.3390/electronics10101168.

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Cybersecurity is a concern for organizations in this era. However, strengthening the security of an organization’s internal network may not be sufficient since modern organizations depend on third parties, and these dependencies may open new attack paths to cybercriminals. Cyber Third-Party Risk Management (C-TPRM) is a relatively new concept in the business world. All vendors or partners possess a potential security vulnerability and threat. Even if an organization has the best cybersecurity practice, its data, customers, and reputation may be at risk because of a third party. Organizations seek effective and efficient methods to assess their partners’ cybersecurity risks. In addition to intrusive methods to assess an organization’s cybersecurity risks, such as penetration testing, non-intrusive methods are emerging to conduct C-TPRM more easily by synthesizing the publicly available information without requiring any involvement of the subject organization. In this study, the existing methods for C-TPRM built by different companies are presented and compared to discover the commonly used indicators and criteria for the assessments. Additionally, the results of different methods assessing the cybersecurity risks of a specific organization were compared to examine reliability and consistency. The results showed that even if there is a similarity among the results, the provided security scores do not entirely converge.

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Journal articles on the topic 'Non-Intrusive methods'

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