Non-intrusive Method Research Articles

A Nonintrusive Digital Current Sensing Method for DC–DC Converters With Wide Load Range

Current estimators for dc-dc converters reduce the requirement of current transducers (e.g. sense resistor, Hall sensor) which may be either expensive, invasive, and (or) require additional signal conditioning circuits. High-frequency inductor currents within dc-dc converters can be estimated instantaneously using voltage measurements taken at its terminals. The voltage measurement at the switching terminal of the inductor is assisted by a comparator, whose output state is related to the corresponding voltage magnitude. This paper presents a novel unified current estimation method in which a single comparator is required to detect the switch node status both in continuous and discontinuous modes of operation. The other measurements required for this method include sensing dc voltage magnitudes, which are essential measurements for a regulated dc-dc converter system; hence the proposed methodology can be easily integrated into existing digital control schemes. The proposed approach has been applied to first-order dc-dc converters and has been validated using a laboratory prototype of a digitally controlled boost converter system using an FPGA.

Design rules for Background Oriented Schlieren experiments with least-squares based displacement calculation

Background Oriented Schlieren (BOS) is a non-intrusive optical method used to visualize density variations in fluids. A camera is used to measure the distortion of a background image displaying a random pattern due to the refraction of the light induced by the local gradients in the refractive index of the under study. In contrast to conventional schlieren, for which direct observation is possible, the images obtained with the BOS technique have to be processed. In this contribution, a computationally affordable approach based on linear least-squares minimization and adapted from optical flow techniques is presented in detail and tested in the context of BOS with the Matlab GUI application comBOS. A series of synthetic images are first analyzed to devise design rules for the definition of an optimal random Gaussian dot background pattern. Tests are performed for comBOS, the commercial BOS module included in DaVis 10, and the free PIV tool PIVlab. It is found that the diameter of the dot images on the camera sensor should be at least 3 pixels, but can be larger without affecting the accuracy of the measurement with comBOS and DaVis 10, while PIVlab severely degrades the quality of the results for dots larger than 5 pixels. The contrast and the density of dots must also be sufficient such that a minimum of 75 % of the surface of the background is shaded. If these conditions are met, the maximum systematic error in the measured distortions is 0.006 pixel for comBOS and 0.001 pixel with DaVis 10 when local gradients are small. Similar errors are found with PIVlab for dots smaller than 5 pixels in diameter, but they increase rapidly for larger dots. The random error is typically smaller than 0.004 pixel in optimal conditions, but it increases significantly for images affected by a random noise or local gradients of the distortion. Finally, it is concluded with a realistic test case that both comBOS and DaVis 10 are well suited to capture the main features of a complex compressible flow with Schlieren imaging. Thus, the main advantage of comBOS is its affordability and robustness for researchers and educators willing to implement BOS, for example in educational contexts.

Influence of the water content on the complex conductivity of bentonite

Compacted bentonite is considered as a potential buffer material for deep geological disposals of high-level nuclear wastes. Methodologies to non-intrusively monitor the water content of such sealing materials are important in the context of the safety of these storage facilities and for various engineering applications as well. Induced polarization is a non-intrusive geophysical method sensitive to the water content of porous media. We investigated the complex conductivity spectra of 69 samples made of 2 distinct MX80 bentonites, one in the form of powder from crushed pellets (Type I) and the other in the form of a granulated bentonite mixture (GBM, type II). The samples are prepared at different compaction states and saturations. The pore water conductivity of the porous samples is estimated to be ∼2.5 S m−1 (25 °C) by two different methods. The complex conductivity spectra were obtained at room temperature (25 ± 2 °C) in the frequency range 1 Hz-45 kHz. In-phase and quadrature conductivities reflect conduction (electromigration) and polarization processes, respectively. At a given frequency, both the in-phase and quadrature conductivities increase with the water content along a trend that is independent of the compaction state. An induced polarization model based on the dynamic Stern layer model is used to explain these results. The first Archie's exponent m is inferred from the formation factor F using the in-phase conductivity data versus the pore water conductivity data at different salinities (NaCl, 25 °C). The dynamic Stern layer model applied to polarization correctly predicts the dependence of the in-phase conductivity, quadrature conductivity, and normalized chargeability with the water content and the Cation Exchange Capacity (CEC). This petrophysical work can easily be applied to time-domain induced polarization data collected in field conditions to monitor hydraulic barriers.

Probing Sulfur Vacancies in CVD-Grown Monolayer MoS2 on SiO2/Si in the Temperature Range 750–900°C

This work reports morphologically alike, high-quality monolayer MoS2 flakes with a similar strain at various growth temperatures (750–900°C) achieved by adjusting sulfur temperature. The growth dynamics of MoS2 are correlated with changes in the photoluminescence (PL) and Raman peak positions. Monolayer MoS2 crystals are synthesized at different growth temperatures from 750°C to 900°C using chemical vapor deposition (CVD). We examined the structural quality and aimed to extract the recombination mechanisms in MoS2 using low-temperature, variable, and low-laser-intensity PL measurements. Our studies of the defect-associated bound exciton emission are well correlated with the blueshift in the A1g mode of Raman spectra, blueshift in PL spectra, and x-ray photoelectron spectroscopy results for crystal grown at 900°C. Our research findings not only shed light on a thorough, non-intrusive method for modifying growth parameters to enhance optical performance, but they also suggest a way to modify the optical characteristics of MoS2 while maintaining the morphology.

Dynamic response analysis of a shaft-disk-drum rotor system with interval uncertainties

In this work, the dynamic responses of an uncertain flexible shaft-disk-drum rotor under elastic support and connection are investigated, where uncertain parameters are considered as unknown but bounded interval variables. The energy method is used to derive deterministic analytical model for the flexible shaft-disk-drum rotor, which is validated by modal tests. A non-intrusive interval method based on Chebyshev polynomial approximation is introduced to evaluate the uncertain dynamic response of the flexible shaft-disk-drum rotor system. Comparative studies of Monte Carlo simulation, scanning method and the interval method are conducted to illustrate the validity and accuracy of the approach proposed here.

Non-intrusive residential electricity load decomposition via low-resource model transferring

Non-intrusive load decomposition (NILD) technology has a broad application prospect because it can deeply excavate the internal electricity consumption data of customers and obtain electricity consumption information. A non-intrusive load decomposition method based on model transfer is proposed to address the problem of insufficient accumulation of historical electricity consumption data of some household customers. Firstly, Time2vec, an embedding layer with time vectors, is introduced as an encoder to convert the time series into a continuous vector space, which improves the computational efficiency of the model for power time series information. Then, by introducing the Channel-wise attention mechanism, favorable residential electricity load characteristics can be effectively obtained. The model transfer method is used to fine-tune the pre-trained model based on complete data to overcome the problem of insufficient accumulation of historical electricity consumption data of some households. The experimental results show that the proposed method is a significant improvement over the existing state-of-the-art methods. The proposed method significantly improves the accuracy of load decomposition while reducing the model parameters. The model parameters are reduced by 62.4%, and the average absolute error is reduced by 13.8%–59.3%. Even with a small number of data training source domains, the proposed method is still able to accurately obtain the power consumption information of each appliance, proving that the proposed method overcomes the problem of insufficient accumulation of electricity consumption data of household users.

A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning

The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model’s lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data.

FNIRS Hyperscanning of Right-brain Synchronization between Therapist-client in Sandplay Therapy

This study investigated inter-right brain synchrony between therapist and client in Sandplay therapy, using hyperscanning technique based on functional near-infrared spectroscopy (fNIRS). fNIRS is a non-intrusive method that measures changes in oxyhemoglobin the cerebral blood. A total of seven therapist-client pairs-i.e., 14 participants-wore fNIRS devices on their heads and engaged in two sessions of Sandplay therapy, with each session lasting for 30 minutes. The study observed synchronization in the right and left prefrontal cortices of both therapists and clients in all seven pairs, during every session. Interestingly enough, synchronization occurred not only while the pairs engaged in verbal communication about the completed sandpicture but also during the non-verbal process of clients' creating sandpictures. The outcome of the study hence suggests neurobiological fundamentals for the therapeutic relationship between therapist and client, which is also called the therapeutic resonance, relational mutual regression, therapeutic alliance, and mother-child unity.

Stochastic analysis for in-plane dynamic responses of low-speed uniformity of tires due to geometric defects

Tire uniformity is defined as the dynamic mechanical characteristics of pneumatic tires due to the defects originating in tire components during manufacturing processes. These defects lead to deviations in the structural and material parameters of tires and, in turn, induce variations in the dynamic response. In this scenario, this paper proposes a probabilistic model of the low-speed uniformity analysis for predicting the impacts of the uncertainties in the geometric defects and the vertical load on the dynamic responses. The deterministic coupled rigid–flexible ring model, combined with the generalized polynomial chaos expansion theory, is developed to simulate the radial force variation due to radial run-out. In this approach, the non-uniform tire radius and the vertical load are selected as a set of random variables and are approximated by the generalized polynomial chaos expansions. The distributions of these input variables are identified from 200 measured tires by the Pearson model. A non-intrusive probabilistic collocation method is adopted to evaluate the coefficients of the generalized polynomial chaos expansions, and the selection of collocation points is also illustrated. Finally, the distributions of the radial force variation and its first harmonic due to the geometric defects and vertical loads are predicted in comparison with measurements. Moreover, the distributions of the dynamic response caused by each parameter and different variance levels are discussed and verified by the measurement data from 1130 tires.

Non-intrusive load decomposition algorithm based on temporal convolutional network and long short-term memory model combined with strongly correlated power sequences

The deep learning neural network method is used to complete the non-intrusive load decomposition task, which often has the problems of poor model performance and low decomposition accuracy. In order to solve the problem that the decomposition accuracy is reduced because the model cannot fully learn all the power characteristics of a specific load in deep learning, a non-intrusive method based on the combination of TCN-LSTM two-layer neural network and strongly correlated power sequence is proposed. Firstly, the load decomposition algorithm learns the time-attribute power information mapping relationship through TCN, obtains the load state pre-identification result and coupling it with the prior data, obtains the independent strongly correlated power sequence of each electrical device to replace the original aggregate power sequence to improve the proportion of the target signal in the mixed signal, and inputs it into the LSTM to capture advanced time-domain information. The measured power sequence is used as a label to implement the neural network model. After training, the decomposed power sequences of five experimental loads were obtained and compared with the actual power sequences. In the part of case analysis, the UK-DALE public data set is used to verify the algorithm in the paper, and by comparing the algorithm in the paper with two typical algorithms, the effectiveness of the model in the paper is verified, and the high-precision load decomposition task is completed.

Non-Intrusive Voltage-Inversion Measurement Method for Overhead Transmission Lines Based on Near-End Electric-Field Integration

Existing electric-field integral inversion methods have limited field application conditions, and they are difficult to arrange electric-field measurement points on high-span overhead lines. This paper proposes a non-intrusive voltage measurement method for overhead transmission lines based on the near-end electric-field integration method. First, the electric-field distribution under 10 kV lines is calculated by finite element simulation software. The electric-field distribution of the plumb line and the discrete integral node below the wire are analyzed. Then, based on traditional electric-field integration, a line-voltage-inversion measurement method based on near-end electric-field integration is proposed. In addition, a voltage-monitoring system based on near-end electric-field integration is constructed. Next, the numerical integration types, the number of integration nodes, and the scale coefficient of the near-end region of the inversion algorithm are optimized with the electric-field simulation data. Finally, to verify the voltage-inversion method proposed in this paper, a test platform for overhead-line voltage is constructed using a MEMS electric-field sensor. The results indicate that the voltage-inversion error is 5.75%. The research results will provide theoretical guidance for non-intrusive voltage-inversion measurement of overhead lines.

Body Mass Index (BMI) is a Popular Anthropometric Tool to Measure Obesity Among Adults

This paper tries to discuss aspects of Body Mass Index (BMI) that represents an index of body fat content in human, and is extensively used worldwide to measure different grades of obesity. BMI is simple, inexpensive and non-intrusive method of screening for weight categories. It is directly related to various morbidity and premature mortality in many nations irrespective of age, sex, social status, and ethnicity. This study also tries to show some drawbacks of body mass index that arise due to gender, age, social status, and ethnic differences; and also for confidence on self-reported values of weight and height for BMI measurement. At present underweight, overweight, obesity, physical inactivity, and unhealthy eating habits are all responsible for the various non-communicable diseases. The aim of the study is to discuss aspects of BMI in brief.

A Novel Non-Intrusive Imaging Technique to Quantify Shrinkage of Elephant Foot Yam During Convective Drying

AbstractThe current work discusses the design and development of a novel convective drying system which predicts both the drying and the shrinkage characteristics of any food material simultaneously at different drying air velocities and temperatures. In the present work, the shrinkage characteristics of a cylindrical-shaped Elephant Foot Yam (EFY) food sample are determined by a non-intrusive imaging method. The top- and side-view images of the EFY sample at drying air velocities of 2, 4, and 6 m/s and at air temperatures of 313, 323, and 333 K are captured and processed using an in-house image processing code. The shrinkage characteristics reveal the nature of the transient variation of the bulk volume and the bulk density of the EFY with its moisture content. Suitable correlations developed for the bulk volume of the EFY suggest that it varies quadratically with moisture content, whereas the bulk density varies exponentially with moisture content for all drying conditions. It was also found that the developed methodology can predict the transient volume and density of the drying EFY sample for different cases of air velocities and temperature with significant accuracies.

Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%.

Improved phase fraction measurement via non-intrusive optical sensing for vertical upward gas -liquid flow

This paper presents the design and application of a non-intrusive optical sensor for the accurate measurement of phase fractions under a vertical upward gas-liquid flow. The sensor was made of two pairs of emitter and photo receivers operated at a wavelength of 1480 nm. The measurement principle of the sensor is based on the disparity in refractive indexes of gas and liquid that provides a proportional relationship between the phase fraction and light intensity. A flow regime dependent calibration model was then developed which corrects for structural scattering derived from the calibrated sensor responses. The model was able to measure local and average phase fractions under varied flow regimes with errors of ± 1.25% and ± 2% relative to photography and swell level methods respectively with a combined uncertainty of ± 2.3%. Comparisons of the non-intrusive optical sensor with the homogenous, Drift flux and Armard phase fraction correlations showed reasonable agreements. The concept of slip as a dominant effect for flow regime in the slug, churn and annular flow regimes accounted for these disparities. The work demonstrates the efficacy of a low cost, non-intrusive method to determine phase fraction in two phase flow over a wide range of flow conditions.

Harmonic transform-based non-parametric density estimation method for forward uncertainty propagation and reliability analysis

Reconstructing the probability density function (PDF) of the limit state function (LSF) is regarded as a non-intrusive method for forward uncertainty propagation and reliability analysis. In this paper, a novel non-parametric density estimation method is proposed to derive the unknown distribution of the LSF for this purpose, where the efficiency and accuracy are ensured. The main innovation of the paper is to derive the PDF of the LSF via a novel concept, i.e., the harmonic transform, regardless of the complexity of the problem. First, the non-parametric method for PDF estimation based on complex fractional moments/Mellin transform is briefly revisited, where the limitations are also pointed out. Inspired from Mellin transform, the concept of harmonic transform is put forward, which overcomes the disadvantages of Mellin transform. Then, the unknown PDF is recovered via the inverse harmonic transform, where the analytical formula is derived accordingly. Numerical estimation method is also given for practical implementations, where some critical issues are also pointed out. Detailed step-by-step procedures of the proposed method are also outlined. The proposed method is a non-parametric one since it does not require a distribution model in advance. Some commonly-used distributions are first applied to validate the efficacy of the proposed method. Then, four numerical examples involving different types of problems are extensively investigated to validate the proposed method for forward uncertainty propagation and reliability analysis. The results demonstrate that the proposed method is effective for deriving the PDFs for both static and dynamic reliability analyses with nonlinear explicit or implicit LSFs.

Quantification of biodegradation rate of hydrocarbons in a contaminated aquifer by CO2 δ13C monitoring at ground surface

Ground surface analysis of CO2 emissions with δ13C determination is experimentally demonstrated to be a potential methodology to monitor, on line, the dynamics of petroleum-hydrocarbon biodegradation in soil aquifers, thanks to the improvement of the Isotopic Ratio Infra Red Spectroscopy technique. Biodegradation rate of remaining hydrocarbon substrates in groundwater can be quantified using basic application of the Rayleigh equations, by δ13CCO2 analysis released at ground surface above the pollution plume instead of usual approaches based on groundwater hydrocarbons δ13C analysis, when physical and chemical properties for the contaminated site meet appropriate conditions.The validation approach for that gasoline contaminated specific site is discussed and verified by comparison of first order attenuation rate constant determined from δ13CCO2 analysis emitted at ground surface and from δ13CTOLUENE analysis in ground water. A kinetic fractionation factor α of 0.9979 (or ε value of −2.1 ± 0.5‰) is estimated for the biodegradation of the most reactive hydrocarbon substrates (TEX). The treatment of this Rayleigh equations by linear regression of δ13CCO2 values along the predominant direction of groundwater flow leads to the following results and conclusions for that site: (i) first order biodegradation rate constants (and annual variation) are maximum after the activation of a Permeable Reactive Barrier (PRB) in May 2014: 0.92(+0.29–0.17) year−1, and during July and October: 0.46(+0.14–0.09) year−1 and minimum in mid-winter in February 2015: 0.17(+0.05–0.03) year−1, given by the estimation range for ε. These results are in the lower range with reported in literature for similar contaminated sites (1.6–18 year−1) considering natural attenuation under sulfate reducing conditions and (ii) the seasonal variation of the first order biodegradation rate constant is mainly correlated with the seasonal variation of the CO2 flux, where maximum values are in summers and minimum values in winters. Both seasonal variations are mainly due to the annual cycle of the natural biodegradation activity at the scale of the pollution plume, rather than the activation of the PRB.This work demonstrates that δ13CCO2 analysis released at ground surface from biodegradation of groundwater hydrocarbons could provide, under characterized and appropriate conditions, a non-intrusive (without soil samplings), fast, and low-cost online method to monitor and therefore to optimize soil remediation processes in real time. (Monitored Natural Attenuation or Enhanced Bioremediation).

Enhanced heat transfer in Poiseuille–Rayleigh–Bénard flows based on dielectric-barrier-discharge plasma actuation

Poiseuille–Rayleigh–Bénard (PRB) flow has been observed in nature as well as many industrial applications. Enhancing the rate of heat transfer of PRB flow has long been a subject of interest in the relevant research. This study proposed a novelty non-intrusive method to control PRB flow through numerical simulations by using jets generated by nine groups of alternating-current dielectric-barrier-discharge (AC-DBD) plasma actuators arranged in the spanwise direction. We considered PRB flows (Pr = 2/3) in air in channels with an aspect ratio equal to length/height = 20, with Reynolds numbers in the range of 10 ≤ Re ≤ 100 and a Rayleigh number of Ra = 10 000. The effect of plasma control on PRB flow was qualitatively and quantitatively analyzed. The results showed that at a low Reynolds number (Re = 10, 20, 30), the jet generated by the plasma actuators promoted the plume on the wall to form stable transversal rolls and enhance mixed convection. At a high Reynolds number (Re = 50, 100), the jet suppressed Poiseuille flow, promoted the rise in the flow of heat at the bottom wall, and enhanced the vertical temperature gradient. Moreover, steady DBD plasma actuation-based control significantly improved the coefficient of heat transfer of the flow, at times providing up to a tripling of transport compared to the unactuated case. The results here are useful for technological and industrial applications.

A non-intrusive load decomposition method of resident by multi-scale attention mechanism

Non-intrusive load monitoring (NILM) is one of the important technologies in home energy management and power demand response scenario. However, the presence of multi-mode appliances and appliances with close power values have affected in diminishing the accuracy of identification based NILM algorithms. To tackle these challenges, the work proposes a resident load decomposition method combining multi-scale attention mechanism and convolutional neural network. At the first stage, the attention scores of the normal load data at the previous few moments of the attention model are smoothed dynamically against the abnormal scores at the current moment. The load identification attention model is optimized by constraint factors. Then, on this basis, convolution filters of different sizes are used to model the mixed load data of different electrical equipment, to mine more abundant characteristic information. Finally, to illustrate the proposed processes and validate its effectiveness, taking the PLAID data set as an example, the method proposed in the article is compared with respect to the existing NILM techniques. The experimental results show that the method based on the multi-scale attention mechanism in this paper can greatly improve the effect of load decomposition. Moreover, it reduces the confusion problem of electrical appliance identification with similar load characteristics.

DRA-net: A new deep learning framwork for non-intrusive load disaggregation

The non-intrusive load decomposition method helps users understand the current situation of electricity consumption and reduce energy consumption. Traditional methods based on deep learning are difficult to identify low usage appliances, and are prone to model degradation leading to insufficient classification capacity. To solve this problem, this paper proposes a dilated residual aggregation network to achieve non-intrusive load decomposition. First, the original power data is processed by difference to enhance the data expression ability. Secondly, the residual structure and dilated convolution are combined to realize the cross layer transmission of load characteristic information, and capture more long sequence content. Then, the feature enhancement module is proposed to recalibrate the local feature mapping, so as to enhance the learning ability of its own network for subtle features. Compared to traditional network models, the null-residual aggregated convolutional network model has the advantages of strong learning capability for fine load features and good generalisation performance, improving the accuracy of load decomposition. The experimental results on several datasets show that the network model has good generalization performance and improves the recognition accuracy of low usage appliances.