Mismatch Conditions Research Articles

Meta-Generalization for Domain-Invariant Speaker Verification

Automatic speaker verification (ASV) exhibits unsatisfactory performance under domain mismatch conditions owing to intrinsic and extrinsic factors, such as variations in speaking styles and recording devices encountered in real-world applications. To ensure robust performance under unseen conditions, domain generalization has been explored. However, an inherent contradiction exists between model discrimination and domain generalization, in which the discrimination ability may be reduced while learning to generalize. In this paper, to extract discriminative yet domain-invariant representations, we propose the meta-generalized speaker verification (MGSV) via meta-learning. Specifically, we propose a metric-based distribution optimization and a gradient-based meta-optimization to simultaneously supervise the spatial relationship between embeddings and improve the generalization ability of the model on unseen domains. In addition, we design multiple-single (MS) and simulated speaker verification (SSV) sampling strategies based on single-domain (SD) and single-single (SS) strategies to simulate the train/test domain mismatch more relevantly, thereby mining transferable speaker-related knowledge. SSV is chosen as the most effective method, as it substantially improves the domain generalization by ensuring that the model has learned to discriminate efficiently. Additionally, to intuitively reflect the model performance on the unseen domains, the proposed method is validated on cross-genre, cross-device, and cross-dataset tasks. The experimental results demonstrate that our proposed method achieves remarkable performance in handling domain mismatch issues in speaker verification.

Electrothermal Responses of Bonding Wire Arrays in GaN Power Amplifier

The device reliability problems of the bonding wire array in gallium nitride (GaN)-based high electron mobility transistor (HEMT) power amplifier become more and more serious due to high-voltage operation, impedance mismatching conditions, and so on. In this paper, both the experimental and theoretical studies on the electrothermal responses of bonding wire arrays in GaN power amplifier are performed. By using the finite element method to self-consistently solve the current transport and heat conduction equations, the temperature distributions of the GaN die surface and the bonding wire array under the continuous wave input signals of different power can be obtained, which are consistent with the experimental results captured by the IR microscopy of the measurement system.

A novel method to concurrently enhance heat conduction and natural convection inside PCM thermal buffer

A thermal buffer filled with phase change materials (PCM) can effectively prevent the intermittently working electronic devices from overheating and prolong the working time of the disposable electronic devices. This paper reported a novel composite PCM consisting of low-melting-temperature alloy bars embedded in pure paraffin that can concurrently improve thermal conductivity and natural convection during melting. Moreover, the effects of heat flux, filling height, and alloy mass fraction on the transient temperature of electronic devices and the effective protection time of thermal buffer were discussed experimentally. The results demonstrated that the heat conduction in stage one was enhanced by embedding alloy bars in pure paraffin, meanwhile the dripping of the alloy droplets in stage two not only enhanced the intensity of natural convection but also significantly delayed the increase of surface temperature. Moreover, correlations with a maximum error of 46∼126 s were obtained to predict the matching status between the thermal buffer and electronic devices, the effective protection time, etc., which was expected to guide the design of thermal buffers. In the future, some efforts should be made to alleviate the mismatching conditions, such as disturbing the boundary layers of natural convection by the elastic potential energy of spring or clockwork, etc.

Length matters: Functional flip of the short TatA transmembrane helix

The twin arginine translocase (Tat) exports folded proteins across bacterial membranes. The putative pore-forming or membrane-weakening component (TatAd in B. subtilis) is anchored to the lipid bilayer via an unusually short transmembrane α-helix (TMH), with less than 16 residues. Its tilt angle in different membranes was analyzed under hydrophobic mismatch conditions, using synchrotron radiation circular dichroism and solid-state NMR. Positive mismatch (introduced either by reconstitution in short-chain lipids or by extending the hydrophobic TMH length) increased the helix tilt of the TMH as expected. Negative mismatch (introduced either by reconstitution in long-chain lipids or by shortening the TMH), on the other hand, led to protein aggregation. These data suggest that the TMH of TatA is just about long enough for stable membrane insertion. At the same time, its short length is a crucial factor for successful translocation, as demonstrated here in native membrane vesicles using an in vitro translocation assay. Furthermore, when reconstituted in model membranes with negative spontaneous curvature, the TMH was found to be aligned parallel to the membrane surface. This intrinsic ability of TatA to flip out of the membrane core thus seems to play a key role in its membrane-destabilizing effect during Tat-dependent translocation.

Data-Driven Iterative Learning Predictive Control for Power Converters

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

Study on fracture behavior of pipeline girth weld based on CTOD-Am method and Gurson ductile damage model

The mechanical properties of the materials in different regions of the pipeline girth weld are sometimes quite different, which will produce a more significant material constraint effect and complicate the fracture behavior of the girth weld. Therefore, this work adopts the finite element method combined with the material damage model to study the ductile tearing process of the weld. The results of the single edge notched bending (SENB) specimens with the weld center crack show that for the welds with different mismatch conditions, with the increase of the weld mismatch coefficient, the crack tip constraint decreases gradually, and the fracture toughness increases gradually. The smaller weld width will make this effect more significant. When the ratio of the weld width to the specimen width is larger than or equal to 0.43, the BM will basically no longer affect the crack tip constraint and the fracture toughness of the weld. The results of the SENB specimens with the interface crack show that for the overmatched weld, with the increase of the weld mismatch coefficient, the crack first grows along the interface and then deflects into the heat affected zone (base metal). When the weld mismatch coefficients are 1.44 and 1.86, the corresponding deflection angles are 14.84° and 24.61°. For undermatched weld, with the decrease of the weld mismatch coefficient, the crack growth will deflect into the weld metal. When the weld mismatch coefficients are 0.69 and 0.6, the corresponding crack growth deflection angles are 14.04° and 25.36°. The larger the degree of the weld mismatch, the more significant the deflection of the distribution of the equivalent plastic strain and the stress triaxiality ahead of the crack tip. The effect of the specimen thickness on the deflection angle of the interface crack growth is small. The verification shows that the numerical analysis results of this study are valid.

Power generation maximization of distributed photovoltaic systems using dynamic topology reconfiguration

The ‘mismatch losses’ problem is commonly encountered in distributed photovoltaic (PV) power generation systems. It can directly reduce power generation. Hence, PV array reconfiguration techniques have become highly popular to minimize the mismatch losses. In this paper, a dynamical array reconfiguration method for Total-Cross-Ties (TCT) and Series–Parallel (SP) interconnected PV arrays is proposed. The method aims to improve the maximum power output generation of a distributed PV array in different mismatch conditions through a set of inverters and a switching matrix that is controlled by a dynamic and scalable reconfiguration optimization algorithm. The structures of the switching matrix for both TCT-based and SP-based PV arrays are designed to enable flexible alteration of the electrical connections between PV strings and inverters. Also, the proposed reconfiguration solution is scalable, because the size of the switching matrix deployed in the proposed solution is only determined by the numbers of the PV strings and the inverters, and is not related to the number of PV modules in a string. The performance of the proposed method is assessed for PV arrays with both TCT and SP interconnections in different mismatch conditions, including different partial shading and random PV module failure. The average optimization time for TCT and SP interconnected PV arrays is 0.02 and 3 s, respectively. The effectiveness of the proposed dynamical reconfiguration is confirmed, with the average maximum power generation improved by 8.56% for the TCT-based PV array and 6.43% for the SP-based PV array compared to a fixed topology scheme.

An improved normalized gain-based score normalization technique for spoof detection algorithm

A spoof detection algorithm supports the speaker verification system to examine the false claims by an imposter through careful analysis of input test speech. The scores are employed to categorize the genuine and spoofed samples effectively. Under the mismatch conditions, the false acceptance ratio increases and can be reduced by appropriate score normalization techniques. In this article, we are using the normalized Discounted Cumulative Gain (nDCG) norm derived from ranking the speaker’s log-likelihood scores. The proposed scoring technique smoothens the decaying process due to logarithm with an added advantage from the ranking. The baseline spoof detection system employs Constant Q-Cepstral Co-efficient (CQCC) as the base features with a Gaussian Mixture Model (GMM) based classifier. The scores are computed using the ASVspoof 2019 dataset for normalized and without normalization conditions. The baseline techniques including the Zero normalization (Z-norm) and Test normalization (T-norm) are also considered. The proposed technique is found to perform better in terms of improved Equal Error Rate (EER) of 0.35 as against 0.43 for baseline system (no normalization) wrt to synthetic attacks using development data. Similarly, improvements are seen in the case of replay attack with EER of 7.83 for nDCG-norm and 9.87 with no normalization (no-norm). Furthermore, the tandem-Detection Cost Function (t-DCF) scores for synthetic attack are 0.015 for no-norm and 0.010 for proposed normalization. Additionally, for the replay attack the t-DCF scores are 0.195 for no-norm and 0.17 proposed normalization. The system performance is satisfactory when evaluated using evaluation data with EER of 8.96 for nDCG-norm as against 9.57 with no-norm for synthetic attacks while the EER of 9.79 for nDCG-norm as against 11.04 with no-norm for replay attacks. Supporting the EER, the t-DCF for nDCG-norm is 0.1989 and for no-norm is 0.2636 for synthetic attacks; while in case of replay attacks, the t-DCF is 0.2284 for the nDCG-norm and 0.2454 for no-norm. The proposed scoring technique is found to increase spoof detection accuracy and overall accuracy of speaker verification system.

Predictive Control of Grid-Connected Modified-CHB With Reserve Batteries in Photovoltaic Application Under Asymmetric Operating Condition

The system stability and injection of maximum active power to the grid are among the most important technical issues in cascaded H-bridge (CHB) based photovoltaic (PV) systems, especially when the irradiation of PV arrays connected to CHB cells are not uniform. In other words, the unequal irradiation of PV arrays leads to the unbalanced output power of H-bridge cells, which causes overmodulation and increased grid current total harmonic distortion in the CHB inverter. Conventional control methods cannot maintain the stability of a grid-connected CHB inverter under asymmetric operating conditions. So, this article proposes a new CHB-based structure with reserve batteries and a novel modulation technique to stabilize the system in heavy mismatching conditions and avoid overmodulation. Furthermore, the number of switches and series batteries is reduced, and the number of output voltage levels is increased compared to the conventional PV-CHB system with energy storage. The validity of the proposed solution is verified by simulation in MATLAB/Simulink, and then the experimental results are presented to confirm the theoretical and simulation results.

Theoretical and behavioral analysis of power optimizers for grid-connected photovoltaic systems

During the generation of electricity from a grid-connected photovoltaic (PV) system, non-uniform conditions are common. Such conditions occur when cells and/or PV modules of a PV system operate at different irradiances and/or temperatures. In this case, there is a limitation of operating power by the modules in the worst conditions. Thus, several PV architectures seek to mitigate losses under these conditions, such as power optimizers for PV systems (POPS). On that occasion, the paper presents theoretical aspects and experimental setup, filling a gap in the literature. For this, firstly, the theoretical curve of different converters is shown, and the temperature influence on the number of POPS in a string. Subsequently, the behavior of POPS in simulation and experimental is analyzed. As a result, a high-power extraction gain is seen compared to the conventional PV system in mismatch conditions, reaching 26.76% in relation to conventional architecture. Furthermore, the behavior of POPS works by compensating for input and output variables. With this behavior and the gain provided, POPS can be considered for scenarios with non-uniform conditions, especially if it is certified viability in simulations of PV software.

Conflict Adaptation Effect on Numerical Inductive Reasoning: An ERP Study.

Conflict adaptation is of particular importance to human information processing, as it assists in efficient responding when confronted with inconsistent information. Past investigators have focused on the role and mechanisms of conflict adaptation effects in cognitive control tasks, but there have been few studies of conflict adaptation effects in numerical inductive reasoning. In this study we adopted identical, perceptual mismatch and rule violation conditions to investigate conflict adaptation in numerical inductive reasoning. Behaviorally, we found shorter response times on trials following our experimental condition, as compared to pre-trials. In our event-related potential (ERP) electroencephalogram (EEG) results, N2 reflected the improvement in processing efficiency of rule violations in numerical inductive reasoning. Thus, these data suggest the presence of a conflict adaptation effect in high-level processing.

Performance of a new hybrid approach for detection of islanding for inverter-based DGs

This paper proposes a new hybrid method for islanding detection and minimizes the Non-Detection Zone (NDZ), for an inverter-based grid-connected distributed generation (DG) system. The proposed hybrid method is an integration of the modified active Sandia Frequency Shift (SFS) method and the passive reactive power variation technique. The modified active SFS method is obtained by adaptive tuning of the gain parameter Ksfs, using the Adaptive Particle Swarm Optimization (APSO) technique. The proposed hybrid method produces a significant reduction of NDZ and is suitable for more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy. The effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations.

The Impact of Varying Food Availability on Gene Expression in the Liver: Testing the Match-Mismatch Hypothesis.

BackgroundDuring early phases of life, such as prenatal or early postnatal development and adolescence, an organism's phenotype can be shaped by the environmental conditions it experiences. According to the Match-Mismatch hypothesis (MMH), changes to this environment during later life stages can result in a mismatch between the individual's adaptations and the prevailing environmental conditions. Thus, negative consequences in welfare and health can occur. We aimed to test the MMH in the context of food availability, assuming adolescence as a sensitive period of adaptation.MethodsWe have previously reported a study of the physiological and behavioral effects of match and mismatch conditions of high (ad libitum) and low (90% of ad libitum intake) food availability from adolescence to early adulthood in female C57BL/6J mice (n = 62). Here, we performed RNA-sequencing of the livers of a subset of these animals (n = 16) to test the effects of match and mismatch feeding conditions on the liver transcriptome.ResultsIn general, we found no effect of the match-mismatch situations. Contrarily, the amount of food available during early adulthood (low vs. high) drove the differences we observed in final body weight and gene expression in the liver, regardless of the amount of food available to the animals during adolescence. Many of the differentially expressed genes and the corresponding biological processes found to be overrepresented overlapped, implicating common changes in various domains. These included metabolism, homeostasis, cellular responses to diverse stimuli, transport of bile acids and other molecules, cell differentiation, major urinary proteins, and immunity and inflammation.ConclusionsOur previous and present observations found no support for the MMH in the context of low vs high food availability from adolescence to early adulthood in female C57BL/6J mice. However, even small differences of approximately 10% in food availability during early adulthood resulted in physiological and molecular changes with potential beneficial implications for metabolic diseases.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model’s accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32%, when compared to the previous state of the art methods. The good results yielded by our method leads us to argue in favour for a more data-centric approach to improve model’s accuracy. Furthermore, the developed method can be used to measure data effectiveness for semi-supervised deep learning model training.

Robust adaptive beamforming based on covariance matrix reconstruction with RCB principle

In this paper, a novel robust adaptive beamforming (RAB) technique based on the covariance matrix reconstruction is proposed to solve the array model mismatch. The proposed technique provides an alternative method to reconstruct the interference-plus-noise covariance matrix (IPNCM) and estimate the steering vector (SV) of the desired signal. In particular, an unknown error exists in the SV of the considered array. The proposed RAB algorithm adopts the robust Capon beamformer (RCB) principle to roughly estimate the SVs of the desired and interference signals. Based on those preliminary estimated SVs, an improved Capon power spectrum is constructed. Then the interference covariance matrix is reconstructed by utilizing the modified Capon power spectrum integrated over the union of several disjoint angular sectors. Then, the reconstructed covariance matrix is further refined by exploring the low rank property. Meanwhile, the SV of the desired signal is estimated by solving a modified quadratically constrained quadratic programming (QCQP) problem. The simulation results show that the RCB principle provides an accurate IPNCM reconstruction, and the proposed RAB algorithm outperforms the existing RAB techniques over a wide range of input signal-to-noise ratio (SNR) region under various mismatch conditions.

A Mismatch Correction Method for Electrode Offset in Electrical Impedance Tomography

Electrical impedance tomography (EIT) has attracted research interests in imaging of brain function, detection of breast cancer, monitoring of lung function and other medical fields. For EIT, its image reconstruction is largely affected by variation of electrode placement arranged around the detected object. Note that electrode offset is unavoidable in some medical applications, leading to low-quality reconstructed images or even failure of reconstruction. In this paper, a novel method is proposed to improve reconstruction quality in case of electrode offset. By identifying which electrode moves, mismatched boundary voltage caused by movement of electrode is corrected. After mismatch correction, image reconstruction is conducted with total generalized variation regularization method. To demonstrate performance of the proposed method in suppressing impact of electrode offset on reconstruction, extensive work is carried out. Mismatch corrections for four models under a series of counterclockwise or clockwise movements of a certain electrode are studied. The reconstruction results show that the proposed method can effectively improve the image quality under electrode offset. Even in presence of larger electrode offset and noise, reconstructed images have been greatly improved. Quantitative evaluation is also conducted by calculating relative blur radius (RBR). It is shown that RBR values are much closer to 1 compared with results under mismatch conditions. Image reconstruction when two electrodes move is also investigated which further validate the performance of the proposed method. This method is potential for mismatch correction in some medical applications.

Novel Deadbeat Predictive Current Control for PMSM With Parameter Updating Scheme

Deadbeat predictive current control (DPCC) has been employed for permanent magnet synchronous machines (PMSMs) due to its satisfactory steady-state and dynamic performances. However, dependence on the accuracy of parameters is a major barrier for its widespread application. To address this problem, a novel control method that can update related items with internal parameters online, in which winding resistance, flux linkage, and stator inductance are considered simultaneously, is proposed in this article. First, the current prediction errors in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -axis and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -axis are analyzed and stored. It has to be mentioned that the extended Kalman filter (EKF) is applied to filter the measured current and optimize the estimated current. Second, through the error of two consecutive control periods, the related items can update in real time. Finally, an updating mechanism based on DPCC is established to control the drive system. Since only measured current is used in the control system, the proposed algorithm is easy to implement. The simulation and experimental results indicate the effectiveness of the proposed method under complex parameters mismatch conditions.

An Improved Model-Free Predictive Current Control Scheme for Open-Winding PMSM with Common DC Bus

To improve the parameter robustness of the open-winding permanent magnet synchronous motor (OW-PMSM), this paper proposes an improved model-free predictive current control (MFPCC) method. First, the first-order ultra-local model of OW-PMSM is established, which does not contain any motor parameter. Secondly, a sliding mode observer (SMO) is introduced into the model to estimate the current in d-q-0 axis. Then, to suppress the torque ripple generated by the zero-sequence current (ZSC), the central hexagonal modulation strategy and the q-axis current injection method are combined, and the compensation current that needs to be injected into the q-axis is calculated according to the estimated value of ZSC. Finally, after considering the one-step delay, the voltage reference at the next instant is calculated based on the estimated values of d-q axis current and compensation current. Simulation and experimental results indicate that the proposed method can improve the parameter robustness of OW-PMSM, and the performance of current and torque has been improved under various parameter mismatch conditions.

Modeling and Energy Generation Evaluations of Large-Scale Photovoltaic Plants Equipped With Panel-Level DC Optimizers

The distributed maximum power point tracking (DMPPT) technology, based on a DC optimizer (DCO, a DC/DC micro-converter) for each single photovoltaic (PV) panel, is one of the most popular solutions to mitigating the waste of solar energy when suffering mismatch conditions. However, the trade-off between the additional costs of deploying the panel-level power electronic equipment and the improved generation benefits of a large-scale PV plant (LPP) remains to be further studied. This study presents a static modeling method for the DCO-based distributed LPPs to study the long-term energy generation characteristics based on historical hourly weather data and then evaluate the economic benefits. The operational characteristics of the PV strings equipped with series-connected DCOs for three different topologies (Boost, Buck, and Buck-boost) are investigated, and then the control strategies for the PV-DCO generation units are proposed to maximize the energy generation of LPPs under frequent mismatch conditions. Different mismatch scenarios caused by the panel aging, geographical location settings, and the partial shading in PV arrays are simulated in the model. Six typical centralized or distributed PV plant configurations are carried out for comparison in case studies, to explore the generation characteristics and the advantages of energy production for the DCO-based distributed LPPs. Besides, the Levelized cost of energy (LCOE) which considers both the energy generation benefits and investment costs is introduced to the economic evaluation of different structures of LPPs.