Composite rubber electret for electromechanical load detection

Non-intrusive Load Identification play an important role in daily life. It can monitor and predict grid load while statistics and analysis of user electricity information. Aiming at the problems of low non-intrusive load decomposition ability and low precision when two electrical appliances are started and stopped at the same time, a new type of clustering and decomposition algorithm is proposed. The algorithm first analyses the measured power and use DBSCAN to filter out the noise of the collected data. Secondly, the remaining power points are clustered using the Adaptive Gaussian Mixture Model (AGMM) to obtain the cluster centres of the electrical appliances, and finally correlate the corresponding current waveform to establish a load characteristic database. In terms of load decomposition, a mathematical model was established for the magnitude of the changing power and current. The Grasshopper optimization algorithm (GOA) is optimized by introducing simulated annealing (SA) to identify and decompose electrical appliances that start and stop at the same time. The result of the decomposition is checked by the current similarity test to determine whether the result of the decomposition is correct, thereby improving the recognition accuracy. Experimental data shows that the combination of DBSCAN and GMM can can identify similar power characteristics. The introduction of SA makes up for the weakness of GOA and gives full play to the advantages of GOA's high identification efficiency. Finally, the test is carried out through the load detection data of the simultaneous start and stop of the two equipment. The test results show that the proposed method can effectively identify the simultaneous start and stop of two loads and can solve the problem of low recognition rate caused by the similar load power, which lays the foundation for the development of non-intrusive load identification in the future.

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A New Load Detection Method and Circuit Analysis for Quasi Resonant Inverter

10.1109/icrera52334.2021.9598568 ◽

2021 ◽

Author(s):

Fatih Zungor ◽

Burhaneddin Emre ◽

Baris Oz ◽

Metin Ozturk

Keyword(s):

Detection Method ◽

Circuit Analysis ◽

Load Detection ◽

Resonant Inverter

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Inspiratory resistive load detection in conscious dogs

Journal of Applied Physiology ◽

10.1152/jappl.1991.70.3.1284 ◽

1991 ◽

Vol 70 (3) ◽

pp. 1284-1289 ◽

Cited By ~ 10

Author(s):

P. W. Davenport ◽

D. J. Dalziel ◽

B. Webb ◽

J. R. Bellah ◽

C. J. Vierck

Keyword(s):

Training Stimulus ◽

Detection Threshold ◽

Resistive Load ◽

Initial Training ◽

Breathing Patterns ◽

Physiological Mechanisms ◽

Mechanical Loads ◽

Load Detection ◽

Resistive Loads ◽

Tracheal Stoma

The physiological mechanisms mediating the detection of mechanical loads are unknown. This is, in part, due to the lack of an animal model of load detection that could be used to investigate specific sensory systems. We used American Foxhounds with tracheal stomata to behaviorally condition the detection of inspiratory occlusion and graded resistive loads. The resistive loads were presented with a loading manifold connected to the inspiratory port of a non-rebreathing valve. The dogs signaled detection of the load by lifting their front paw off a lever. Inspiratory occlusion was used as the initial training stimulus, and the dogs could reliably respond within the first or second inspiratory effort to 100% of the occlusion presentations after 13 trials. Graded resistances that spanned the 50% detection threshold were then presented. The detection threshold resistances (delta R50) were 0.96 and 1.70 cmH2O.l-1.s. Ratios of delta R50 to background resistance were 0.15 and 0.30. The near-threshold resistive loads did not significantly change expired PCO2 or breathing patterns. These results demonstrate that dogs can be conditioned to reliably and specifically signal the detection of graded inspiratory mechanical loads. Inspiration through the tracheal stoma excludes afferents in the upper extrathoracic trachea, larynx, pharynx, nasal passages, and mouth from mediating load detection in these dogs. It is unknown which remaining afferents (vagal or respiratory muscle) are responsible for load detection.

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Development of a standardized real time PCR for Torque teno viruses (TTV) viral load detection and quantification: A new tool for immune monitoring

Journal of Clinical Virology ◽

10.1016/j.jcv.2018.06.010 ◽

2018 ◽

Vol 105 ◽

pp. 118-127 ◽

Cited By ~ 15

Author(s):

Dorian Kulifaj ◽

Bénédicte Durgueil-Lariviere ◽

Faustine Meynier ◽

Eliza Munteanu ◽

Nicolas Pichon ◽

...

Keyword(s):

Viral Load ◽

Real Time ◽

Real Time Pcr ◽

Immune Monitoring ◽

Load Detection ◽

Detection And Quantification

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Uniaxial Compressive Stress Dependence of the High-Field Dielectric and Piezoelectric Performance of Soft PZT Piezoceramics

Journal of Materials Research ◽

10.1557/jmr.2004.19.3.834 ◽

2004 ◽

Vol 19 (3) ◽

pp. 834-842 ◽

Cited By ~ 58

Author(s):

Dayu Zhou ◽

Marc Kamlah ◽

Dietrich Munz

Keyword(s):

Electric Field ◽

Lead Zirconate Titanate ◽

Domain Switching ◽

Lead Zirconate ◽

Transverse Strain ◽

Dissipation Energy ◽

Depolarization Effect ◽

Uniaxial Compressive Stress ◽

Piezoelectric Performance ◽

Electromechanical Load

The influence of uniaxial prestress on dielectric and piezoelectric performance was studied for soft lead zirconate titanate piezoceramics. High electric field induced polarization and longitudinal/transverse strain were measured at different compression preload levels of up to −400 MPa. The parameters evaluated included polarization/strain outputs, dielectric permittivity, piezoelectric constants, and dissipation energy as a function of the mechanical preload and electric-field strength. The results indicate a significant enhancement of the dielectric and piezoelectric performance within a certain prestress loading range. At much higher stress levels, the predominant mechanical depolarization effect makes the material exhibit hardly any piezoeffect. However, the enhanced performance achieved by a small stress preload is accompanied by an unfavorable increased hysteresis, and consequently, increased energy loss, which is attributed to a larger extrinsic contribution due to more non-180° domain switching induced by the combined electromechanical load.

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