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Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself.

Dynamic stability of quasi-vertical GaN trench MOSFETs featuring a thick bottom dielectric (TBD) is thoroughly investigated. Degradation in forward drain current was observed as applying gate or drain stressing voltage, and further studied by time-resolved measurements. The drain current of the device can be maintained at 79%, compared to 61% of a reference device without TBD. Meanwhile, repeated switching tests conducted within a short on-state time demonstrate that the current collapse is confined to 10% after 500 switching cycles. The current collapse is related to electron capture at the dielectric/GaN interface, and the introduction of TBD reduces the electric field within the dielectric layer and suppresses the capture process of traps. Positive gate bias-induced threshold instability of the device with and without TBD is investigated. For the device with TBD, a small positive threshold voltage shift of 1 V is obtained. In addition, the effect of drain stressing voltage on devices is also revealed. High-resolution drain current transient spectroscopy displays the drain current reduction, attributing the degradation to captured electrons in the n–GaN layer. A capture activation energy of 0.26 eV is revealed by deep level transient spectroscopy. These findings reveal the efficacy of TBD inclusion in improving gate stability of GaN MOSFETs and underscore the critical importance of high-quality epitaxial growth for ensuring the stability of vertical devices. The stability characterization serves as a valuable reference for the development of reliable quasi-vertical GaN MOSFET devices. IEEE

This article presents a novel method for accurate online extraction of semiconductor ON-state resistance in the presence of measurement noise. In this method, the ON-state resistance value is extracted from the measured ON-state voltage of the semiconductors and the measured load current. The extracted ON-state resistance can be used for online condition monitoring of semiconductors. The proposed method is based on the extraction of selective harmonic content. The estimated values are further enhanced through an integral action that increases the signal-to-noise ratio, making the proposed method suitable in the presence of noisy measurements. The efficacy of the proposed method is verified through simulations in the MATLAB/Simulink environment, and experimentally. The estimated ON-state resistance values from the online setup are compared to offline measurements from an industrial curve tracer, where an overall estimation error of less than 1% is observed. The proposed solution maintains its estimation accuracy under variable load conditions and for different temperatures of the device under test., QC 20240705

The study focuses on analysing the high-level carrier lifetime (tau(HL)) in 4H silicon carbide (4H-SiC) PiN diodes under varying temperatures and proton implantation doses. The objective is to identify an empirical law applicable in technology computer-aided design (TCAD) modelling for SiC devices, describing the dependence of carrier lifetime on temperature to gain insights into how irradiation dose may influence the tau(HL). We electrically characterize diodes of different diameters subjected to different proton irradiation doses and examine the variations in current-voltage (I-V) and ideality factor (n) curves under various irradiation conditions. The effects of proton irradiation on the epitaxial layer are analysed through capacitance-voltage (C-V) measurements. We correlate the observed effects on I-V, n, and C-V curves to the hypothesis of formation of acceptor-type defects related to carbon vacancies, specifically the Z(1/2) defects generated during the irradiation process. The impact of irradiation on carrier lifetime is investigated by measuring tau(HL) using the open circuit voltage decay (OCVD) technique at different temperatures on diodes exposed to various H+ irradiation doses with constant ion energy. This investigation reveals the presence of a proportional relationship between 1/ tau(HL) and the dose of irradiated protons: the proportionality coefficient, referred to as the damage coefficient (K-T), exhibits an Arrhenius-type dependence on temperature. OCVD-measured lifetime on the various diodes demonstrates a power-law dependence of lifetime on temperature. The exponent of this dependence varies with the irradiation dose, notably showing an increase in temperature dependence at the highest H+ ion dose. This suggests a threshold-like dependence on H+ irradiation dose in the tau(HL) -temperature relationship., QC 20240620

When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights or operating on frozen, wet, or dry surfaces. Such influences can be modeled as discrete context variables. In the existing literature, such contexts are, if considered, mostly assumed to be known. In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables. To achieve this, we derive frequentist guarantees for multiclass classification, allowing us to estimate the current context from measurements. Furthermore, we propose an approach for identifying contexts through experiments. We discuss under which conditions we can retain theoretical guarantees and demonstrate the applicability of our algorithm on a Furuta pendulum with camera measurements of different weights that serve as contexts.

Klimatutmaningar, lagändringar och ett ökat miljötänk har tvingat transportsektorn att ställa om till eldrift. Batterier och elmotorer har utvecklats kraftigt och är nu ett alternativ även för tunga fordon. Ett vanligt förekommande problem med elmotorer i fordon är lagerströmmar, här kallat gnista eller blixthändelser. Dessa uppstår på grund av oönskade urladdningar i motorn och förorsakar skador på lagren i motorn. Syftet med motorlager är att avlasta och minska friktionen kring motoraxeln. För att förebygga problemet och se förbättringar eller försämringar av olika åtgärder som görs krävs att lagerströmmar kan identifieras utifrån mätdata. Detta examensarbete analyserar relevant forskning inom området för att sedan introducera en metod och en algoritm för att identifiera lagerströmmar i samarbete med Scania CV. Algoritmen består av tre olika parametrar som påverkar identifieringen av de oönskade strömmarna på olika sätt. Verktyget lyckades identifierade lagerströmmar i olika mätdata, och hittade skillnader i antalet blixthändelser mellan olika körningar av provobjektet. Dock krävs vidare utveckling av verktyget och möjligheten att bearbeta annan typ av data som exempelvis spänningar i motorn för atthitta bättre samband., A changing climate, changing laws and an increased environmental consciousnesshas forced the transport sector to transition to electric power. Batteries and electric motors have seen a quick and powerful development which means that they are now an alternative even for heavy vehicles. A common problem with electric motors forvehicles is bearing currents. The bearing currents occur as a result of electrical discharges in the motor and can damage the bearings inside the motor. The purpose of motor bearings is to offload and reduce friction for the motor shaft. To prevent the issue and to see improvements or deteriorations from different preventativemeasures it is critical to be able to identify bearing currents from data. This thesis analyzes relevant research in the area before introducing a method and an algorithm for detecting bearing currents in cooperation with Scania CV. The algorithm is composed of three different parameters which affects the identification in different ways. The tool was able to identify bearing currents from various data and found differences between the number of bearing currents between different test runs of the motor. However, more development of the tool and the possibility to process different kinds of data like voltages inside the motor is needed to be able to find better patterns in the data.

Klimatutmaningar, lagändringar och ett ökat miljötänk har tvingat transportsektorn att ställa om till eldrift. Batterier och elmotorer har utvecklats kraftigt och är nu ett alternativ även för tunga fordon. Ett vanligt förekommande problem med elmotorer i fordon är lagerströmmar, här kallat gnista eller blixthändelser. Dessa uppstår på grund av oönskade urladdningar i motorn och förorsakar skador på lagren i motorn. Syftet med motorlager är att avlasta och minska friktionen kring motoraxeln. För att förebygga problemet och se förbättringar eller försämringar av olika åtgärder som görs krävs att lagerströmmar kan identifieras utifrån mätdata. Detta examensarbete analyserar relevant forskning inom området för att sedan introducera en metod och en algoritm för att identifiera lagerströmmar i samarbete med Scania CV. Algoritmen består av tre olika parametrar som påverkar identifieringen av de oönskade strömmarna på olika sätt. Verktyget lyckades identifierade lagerströmmar i olika mätdata, och hittade skillnader i antalet blixthändelser mellan olika körningar av provobjektet. Dock krävs vidare utveckling av verktyget och möjligheten att bearbeta annan typ av data som exempelvis spänningar i motorn för atthitta bättre samband., A changing climate, changing laws and an increased environmental consciousnesshas forced the transport sector to transition to electric power. Batteries and electric motors have seen a quick and powerful development which means that they are now an alternative even for heavy vehicles. A common problem with electric motors forvehicles is bearing currents. The bearing currents occur as a result of electrical discharges in the motor and can damage the bearings inside the motor. The purpose of motor bearings is to offload and reduce friction for the motor shaft. To prevent the issue and to see improvements or deteriorations from different preventativemeasures it is critical to be able to identify bearing currents from data. This thesis analyzes relevant research in the area before introducing a method and an algorithm for detecting bearing currents in cooperation with Scania CV. The algorithm is composed of three different parameters which affects the identification in different ways. The tool was able to identify bearing currents from various data and found differences between the number of bearing currents between different test runs of the motor. However, more development of the tool and the possibility to process different kinds of data like voltages inside the motor is needed to be able to find better patterns in the data.

The topology of a device will determine the impact said device has on the grid and how immune that device is for disturbances in the grid. LED lamps are very commonly used devices, with different topologies available in the market, each topology showing different behavior when connected to a grid. For power quality studies, it is important to classify LED lamps, without breaking them to know the topology. Several classification methods are found in the literature with this purpose. In this paper, four methods from different papers for classifying LED lamps have been applied to a group of 21 LED lamps with active power consumption below 25 W. It has been observed that the applicability of the methods may lead to a gap of knowledge needed for classification, leaving space for personal criteria when classifying, that can be afforded using unsupervised Machine Learning. Two unsupervised Machine Learning methods were applied using the electrical parameters and statistics proposed in literature., Validerad;2023;Nivå 2;2023-07-12 (sofila)

The topology of a device will determine the impact said device has on the grid and how immune that device is for disturbances in the grid. LED lamps are very commonly used devices, with different topologies available in the market, each topology showing different behavior when connected to a grid. For power quality studies, it is important to classify LED lamps, without breaking them to know the topology. Several classification methods are found in the literature with this purpose. In this paper, four methods from different papers for classifying LED lamps have been applied to a group of 21 LED lamps with active power consumption below 25 W. It has been observed that the applicability of the methods may lead to a gap of knowledge needed for classification, leaving space for personal criteria when classifying, that can be afforded using unsupervised Machine Learning. Two unsupervised Machine Learning methods were applied using the electrical parameters and statistics proposed in literature., Validerad;2023;Nivå 2;2023-07-12 (sofila)

Objective: Double-loop H-field probes are often used to measure current on loop antennae for magnetic resonance imaging (MRI). Loop crosstalk limits the dynamic range of direct measurements with such probes. The crosstalk can be removed by simple calibration. This work analyses the quantitative relation of a probe's calibrated S21 with the RF coil current. Method: The analytical relation between RF coil current and calibrated S21 measurements of a probe is established with the multi-port network theory, and verified by full-wave simulation and benchtop measurements. The effect of calibration is demonstrated by measuring the 1H trap frequency, the active detuning, and the preamplifier decoupling. Results: The calibration removes the effect of crosstalk in a probe and improves the lower bound of |S21|. The calibrated S21 is proportional to coil current. In the lower frequency range, the ratio of calibrated S21 to coil current changes almost linearly with frequency. Impact: The calibration method improves the sensitivity of probe measurements and facilitates fine-tuning current-suppressing circuits like active detuning circuits, traps, preamplifier decoupling. The linear frequency dependency between S21 measurements and coil current allows easy, fair comparison of coil current up to 128 MHz, and in some cases 298 MHz, helping build multi-nucleus coils.

An accurate energy yield estimation for building-integrated photovoltaic (PV) applications with colored glass under field conditions is a key factor for architects, building planners, and potential investors. The power output of these installations depends not only on the angle of incidence but also on the incident solar spectrum. The aim of this work is to simulate the short-circuit current of PV single-cell laminates with colored solar glass under field conditions. Therefore, we use angular-dependent spectral responsivity measurement data from a physical setup. Test samples with gold-, blue-, blue-green-, and gray-colored solar glass and a reference with standard solar glass, oriented on a tilted surface, are simulated. The results for one month of field validation indicate a good agreement with a relative root mean square error between 1.9% and 2.5% for the test samples. A model comparison reveals larger errors for the blue, blue-green, and gold test samples when the spectral responsivity at normal incidence and the incident angle modifier for broadband direct irradiation are used instead. It is concluded that short-circuit current simulation using indoor characterization from spectrally resolved measurement setups provides improved accuracy that is particularly suitable for bankable energy yield estimates of colored building-integrated PV modules.

This paper proposes a novel attack identification mechanism for internet-of-things-based (IoT-based) converter-composed DC grids, where each agent collects its own and neighbours’ measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free or average-model-based attack detection theories, this mechanism is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution or agent instigation attacks, through comparing estimated signals with received ones for signal source authentication, both self-sensors and neighbours will be inspected. Eventually, not only can such attacks be detected, but also will respective attack sources be identified. A simulation case of 4-agent 800V IoT-based DC grid on Simulink and a hardware case of 3-agent 90V IoT-based DC grid on dSpace testing platform were investigated. Experimental results revealed that the estimation ratio error kept lower than 3.9% and all attacks were successfully identified, verifying the effectiveness of the proposed mechanism.

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The study of currents flowing through the fuselage of an airplane is of vital importance from the standpoint of electromagnetic compatibility due to their potential for causing severe failures on embedded systems. Direct lightning strikes can produce extremely high currents propagating through the fuselage panels. Such high currents are critical when flowing through mechanical joints, a point of complex analysis due to the existing electrical discontinuities. Consequently, understanding the propagation of these currents is vital for protecting avionics through improved joints not only from a structural standpoint but also from an electromagnetic perspective. In this paper, we propose designing and validating a Rogowski coil sensor to measure the current flux through the mechanical joints that attach different fuselage panels. This sensor is theoretically analyzed, and a circuit model is provided. Then, a PCB Rogowski coil is designed and manufactured for measuring high-intensity pulsed currents with a bandwidth of 10 MHz. Subsequently, the probe is characterized through its S-parameters. Next, its equivalent lumped-element circuit is extracted and simulated for validating the sensor’s transient response. Finally, the Rogowski coil sensor is experimentally evaluated in the measurement of a surge current pulse that emulates a real scenario., This work was supported in part by the Spanish “Ministerio de Ciencia e Innovacion” under ´ project PID2019-106120RBC31/AEI/10.13039/501100011033., Peer Reviewed, Postprint (author's final draft)

In this article, gain errors in current sensing for highly dynamic three-phase drives with three current sensor measurements are compensated without the aid of the machine plant model. As three current sensors are used, redundant information from the neutral current measurement is extracted by constructing its mathematical relation with the measured time-varying dq current. The proposed method compensates for non-symmetrical current sensor gain errors even in the presence of offset errors. A Recursive Sliding Window Least Square method is proposed to minimize the computation effort such that the method is suitable to be applied online. It does not incorporate a low-pass filter or averaging method based on electrical speed, making it suitable for highly dynamic applications. It is able to be applied in any three-phase AC machine, as the machine plant is not considered. Analysis, simulation and experimental results obtained from the hardware prototype by mimicking the actual application of high-precision mechatronics demonstrate the validity of the proposed method.

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The MIL-STD-461 CE101 test is one of the essential low frequency emission tests in the range of 30 Hz – 10 kHz for military and aerospace equipment, however, its application remains challenging. The primary concern is the effect of the power source or grid impedance on test results because the LISNs are not functional in such a low frequency range. Therefore, the emission levels depend on the source impedance and, consequently, the reproducibility of test results is generally poor. In this paper, we thoroughly analyse this problem and propose a solution based on the live impedance measurement of the CE101 test circuit. For the live impedance measurement, we used the CS101 military low frequency immunity test system along with the addition of FFT and current measurement capabilities., Peer Reviewed, Postprint (author's final draft)

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a reliable health assessment strategy for battery pack is important but challenging when the joint requirements of computational burden, modelling cost, estimation accuracy, and battery equalisation are considered. This paper proposes a balancing current ratio (BCR)-based solution to realise satisfactory state-of-health (SoH) estimation performance of all series-connected cells within a pack, while the overall reliance on cell-level battery models is also reduced. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy for effectively estimating SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance (OCV-R) model is adopted, the typical estimation error of our proposed solution can still be bounded within only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also improved. IEEE

Measurement of physiological reactions to persuasive messages can improve our understanding of psychological processes of persuasion, and potentially further enhance and personalize current persuasion interventions. However, little is known about the relationship between psychophysiology and persuasive processes. This study focused on four persuasion principles: scarcity, commitment, consensus, and authority, and people's susceptibility to them. Physiological measures included the cardiovascular, respiratory, and electrodermal system, as well as facial motor systems. Psychological measures consisted of self-reported attitude towards oral care and susceptibility to persuasion (STPS). We performed a randomized within-subject experiment in which fifty-six participants viewed persuasive messages deploying the aforementioned persuasion principles to improve their oral care. Results indicated different physiological patterns during persuasion versus rest. We found no different physiological patterns in exposure to distinct persuasion principles, nor a clear correlation with susceptibility to individual persuasion principles. However, mixed model analysis illustrated that overall STPS scores help explain variance in reactivity of skin conductance level and skin conductance response, and reactivity in the zygomaticus major: lower susceptibility relates to higher reactivity. Summarizing, we have found no conclusive support for distinct psychophysiological patterns associated with different persuasion principles, although overall susceptibility seems to be reflected in physiology to some extent.

Geomagnetically induced currents (GICs) are referred to the quasi-DC current flows in power networks, driven by complex space weather-related phenomena. Such currents are a potential threat to the power delivery capability of electrical grids. To mitigate the detrimental impacts of GICs on critical infrastructures, the GICs should be monitored in power systems. Being inherently DC from the power frequency point of view, the components of GICs are, however, challenging and costly to monitor in AC power grids. This paper puts forward a novel methodology for the real-time estimation of GICs in power transformers. Such aim is attained by means of an extended Kalman filter (EKF)-based approach, mounted on the nonlinear state-space model of the transformer, whose parameters can be derived from standard tests. The proposed EKF-based algorithm employs the available measurements for the transformer differential protection. The proposed approach, relying on the differential current, can properly deal with the external sources of interference like harmonic excitation and loading. The EKF-based estimator presented is validated by simulation and experimental data. The results verify the ability of the proposed approach to robustly estimate the GIC level during various operating conditions., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Electrical Power Grids

Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system’s dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squares-based estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator’s accuracy is improved by using a new PV-scaling-aided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications., Funding: Swedens innovation agency, Vinnova, through project iQDeep [2018-02700]

A current meter has been designed, intended for surveillance of low-power battery driven devices with a large dynamic range in their current consumption. Among such devices may, for example, be nodes in a wireless sensor network (WSN). The large dynamic range originates in the nodes’ states from an active mode for data collection and communication to a sleep mode for preserving very limited energy and prolonging the lifetime of the device. The active mode draws current up to hundreds of milliamperes whilst the sleep mode’s current may be as low as 22 nA. This makes the dynamic range larger than 6 orders of magnitude. To perform measurements that prevail over noise sources and resolution of involved components, an increase in the SNR must be made. If the current is measured with a shunt resistor, this may be done by varying its resistance as required. The resistance should be as high as possible to maximize the SNR, but low enough such that the burden voltage of the current meter is kept within tolerances of the device. A literature study was done, and with the gained knowledge a circuit was designed and simulated in LTSpice. Then a printed circuit board (PCB) of the circuit was designed and implemented using Altium Designer (an ECAD program), manufactured and hand-soldered. The final PCB was tested but shown to have some design flaws that deteriorated the performance, for example too frequent switching of the current range.

Composite insulation systems usually exhibit nonlinear phenomena, such as voltage-dependent dielectric response and high-order current harmonics. In this article, a high-voltage system capable of performing both dielectric frequency response (DFR) and polarization/depolarization current (PDC) measurements up to 20 kV is devised. The nonlinear properties of the motor stator and power transformer insulation systems are studied. Experimental results show that the voltage-dependent effect becomes significant as the voltage magnitude approaches the nominal and frequency decreases. The third current harmonic component is found to be dominant over the other components and has a strong correlation with the decrease in the dissipation factor. By taking both the fundamental and third-harmonic components into account, more consistent voltage-dependent characteristics are revealed. These features are important indications of insulation defects that are often neglected in field diagnostic testings. The investigation of the nonlinear properties helps to enhance the condition diagnosis reliability., QC 20220407

The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness., QC 20211105

LQXFA/B production series cryogenic assemblies are being built for the LHC upgrade by the HL-LHC Accelerator Upgrade Project (AUP). These contain a pair of MQXFA quadrupole magnets combined as a cold mass within a vacuum vessel, and are to be installed in the IR regions of the LHC. The LQXFA/B are being tested at 1.9 K to assess alignment and magnetic performance at Fermilab's horizontal test facility. The ∼10 m long assembly must meet stringent specifications for quadrupole strength and harmonic field integrals determination, magnetic axis location, and for variations in axis position and local field profiles. A multi-probe, PCB-based rotating coil and a Single Stretched Wire system are employed for these measurements. To accurately determine rotating coil location and angles within the cold mass, a laser tracker is utilized to record multiple targets at one end of the probe. This paper describes the measurements, probes/equipment, and techniques used to perform the necessary characterization of the cold mass.

In recent years the vehicle industries have been making much efforts to reduce the environmentalimpact by switching from the traditional combustion engine to electrification with battery driven electricvehicles. Producing electric trucks requires more batteries and operates at a higher voltage and currentthan commercial electric cars. Therefore, detection and protection from short-circuits are crucial issuesin the development of these vehicles to ensure safety. This master thesis project aims to investigatedifferent methods for current measurement, which are commercially available and used in the batteryjunction box in an electric vehicle for current monitoring and detection of overcurrent and short-circuits. A practical experimental validation is then performed to test the desaturation detection method as ashort-circuit detection method with a solid state breaker. The experimental results showed that theshort-circuit could be detected within just 7µs and the total time for the solid state breaker to detectand cut the circuit could be done within around 15µs. The discussion around the current measurementmethod came to the conclusion that the Hall sensor or the flux gate current transducer could be apossible replacement of the shunt that is currently being used in order to reduce losses as the systemoperates at higher current levels. Comparison of this method with different existing current measurement sensors is suggested to do inthe future work.

A current meter has been designed, intended for surveillance of low-power battery driven devices with a large dynamic range in their current consumption. Among such devices may, for example, be nodes in a wireless sensor network (WSN). The large dynamic range originates in the nodes’ states from an active mode for data collection and communication to a sleep mode for preserving very limited energy and prolonging the lifetime of the device. The active mode draws current up to hundreds of milliamperes whilst the sleep mode’s current may be as low as 22 nA. This makes the dynamic range larger than 6 orders of magnitude. To perform measurements that prevail over noise sources and resolution of involved components, an increase in the SNR must be made. If the current is measured with a shunt resistor, this may be done by varying its resistance as required. The resistance should be as high as possible to maximize the SNR, but low enough such that the burden voltage of the current meter is kept within tolerances of the device. A literature study was done, and with the gained knowledge a circuit was designed and simulated in LTSpice. Then a printed circuit board (PCB) of the circuit was designed and implemented using Altium Designer (an ECAD program), manufactured and hand-soldered. The final PCB was tested but shown to have some design flaws that deteriorated the performance, for example too frequent switching of the current range.

In recent years the vehicle industries have been making much efforts to reduce the environmentalimpact by switching from the traditional combustion engine to electrification with battery driven electricvehicles. Producing electric trucks requires more batteries and operates at a higher voltage and currentthan commercial electric cars. Therefore, detection and protection from short-circuits are crucial issuesin the development of these vehicles to ensure safety. This master thesis project aims to investigatedifferent methods for current measurement, which are commercially available and used in the batteryjunction box in an electric vehicle for current monitoring and detection of overcurrent and short-circuits. A practical experimental validation is then performed to test the desaturation detection method as ashort-circuit detection method with a solid state breaker. The experimental results showed that theshort-circuit could be detected within just 7µs and the total time for the solid state breaker to detectand cut the circuit could be done within around 15µs. The discussion around the current measurementmethod came to the conclusion that the Hall sensor or the flux gate current transducer could be apossible replacement of the shunt that is currently being used in order to reduce losses as the systemoperates at higher current levels. Comparison of this method with different existing current measurement sensors is suggested to do inthe future work.

Condition monitoring of the insulation health of electric machines is usually performed with offline tests. In recent years, online condition monitoring methods for the groundwall insulation have been introduced. The majority of these works are based on leakage current measurements, requiring additional equipment compared to the standard one in converters. The condition monitoring method in this work instead relies on the high-frequency ringing of stator currents occurring after a converter switching transition. This work evaluates the method experimentally in a randomly-wound stator winding of an induction machine prototype equipped with taps. The insulation condition change is forced by inserting external capacitors. Through the analysis of different metrics for the health monitoring and a detailed discussion of the hardware and software requirements for an electric drive, a viable way forward for the practical implementation of the approach is proposed., QC 20230117

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids., QC 20221026

This work establishes and validates a Grid Graph Signal Processing (G-GSP) framework for estimating the state vector of a radial distribution feeder. One of the key insights from GSP is the generalization of Shannon's sampling theorem for signals defined over the irregular support of a graph, such as the power grid. Using a GSP interpretation of Ohm's law, we show that the system state can be well approximated with relatively few components that correspond to low-pass Graph Fourier Transform (GFT) frequencies. The target application of this theory is the formulation of a three-phase unbalanced Distribution System State Estimation (DSSE) formulation that recovers the GFT approximation of the system state vector from sparse Advanced Metering Infrastructure (AMI) measurements. To ensure convergence of G-GSP for DSSE, the proposed solution relies on a convex relaxation technique. Furthermore, we propose an optimal sensor placement algorithm for AMI measurements. Numerical results demonstrate the efficacy of the proposed method., QC 20220909

Condition monitoring of the insulation health of electric machines is usually performed with offline tests. In recent years, online condition monitoring methods for the groundwall insulation have been introduced. The majority of these works are based on leakage current measurements, requiring additional equipment compared to the standard one in converters. The condition monitoring method in this work instead relies on the high-frequency ringing of stator currents occurring after a converter switching transition. This work evaluates the method experimentally in a randomly-wound stator winding of an induction machine prototype equipped with taps. The insulation condition change is forced by inserting external capacitors. Through the analysis of different metrics for the health monitoring and a detailed discussion of the hardware and software requirements for an electric drive, a viable way forward for the practical implementation of the approach is proposed., QC 20230117

Dielectric response measurement is widely used to characterize oil/oil-impregnated pressboards in order to evaluate the condition of transformer insulation. Insulation systems with oil can exhibit voltage dependence at low frequencies. This is due to the limited number of mobile charges in the liquid, rather than to the dielectric relaxation processes that may, in some cases, be of more interest to study. This work investigates the voltage-dependent properties of oil/oil-impregnated pressboards under ac voltages with regard to the complex permittivities. Frequency-domain spectroscopy (FDS) measurements are made with a dc voltage added to the ac voltage stimulus. Considered influences are the voltage amplitudes and polarity as well as the dependence on temperature. Properties of the space-charge polarization in the oil and oil-impregnated pressboard are calculated and discussed based on the dc-biased measurements. It is seen that using the dc bias in the FDS measurements of oil/oil-impregnated pressboard can significantly decrease the voltage dependence of the results that are caused by ion drift and consequent depletion of ions from the bulk liquid. Based on the dc-biased measurements, the complex permittivity due to the linear and nonlinear polarization can be separated., QC 20220523

Current sensor fault diagnostic is critical to the safety of lithium-ion batteries (LIBs) to prevent over-charging and over-discharging. Motivated by this, this article proposes a novel residual statistics-based diagnostic method to detect two typical types of sensor faults, leveraging only the 50 current-voltage samples at the startup phase of the LIB system. In particular, the load current is estimated by using particle swarm optimization (PSO)-based model matching with measurable initial system states. The estimation residuals are analyzed statistically with Monte-Carlo simulation, from which an empirical residual threshold is generated and used for accurate current sensor fault diagnostic. The residual evaluation process is well proved with high robustness to the measurement noises and modeling uncertainties. The proposed method is validated experimentally to be effective in current sensor fault diagnosis with low miss alarm rate (MAR) and false alarm rate (FAR)., QC 20220502

Condition monitoring of the insulation health of electric machines is usually performed with offline tests. In recent years, online condition monitoring methods for the groundwall insulation have been introduced. The majority of these works are based on leakage current measurements, requiring additional equipment compared to the standard one in converters. The condition monitoring method in this work instead relies on the high-frequency ringing of stator currents occurring after a converter switching transition. This work evaluates the method experimentally in a randomly-wound stator winding of an induction machine prototype equipped with taps. The insulation condition change is forced by inserting external capacitors. Through the analysis of different metrics for the health monitoring and a detailed discussion of the hardware and software requirements for an electric drive, a viable way forward for the practical implementation of the approach is proposed., QC 20230117

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The study of electric currents flowing through the airframe is important from an EMC standpoint due to the potential to cause severe aircraft failures. Nevertheless, this is a challenging task because of the complex shape, materials, joints, and apertures found in most fuselages. In this paper, we present the design, simulation, and experimental validation of a test fixture intended to emulate the discontinuity between two fuselage panels mechanically attached. This fixture allows several configurations of the mechanical joint, as well as, the insertion of current probes. The main features of the fixture are analysed, such as bandwidth and frequency performance. Subsequently, simulation and S-parameters measurements are used to investigate the performance of several screwed joint configurations., Peer Reviewed, Postprint (author's final draft)

Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications., Funding: Swedens innovation agency, Vinnova, through project iQDeep [2018-02700]

Elevated thermal stress-related aging is significant on the oil-impregnated paper (OIP) used as insulation in high-pressure gas cables (HPGCs). The aim of this article is to develop a cheap alternative for lab dielectric measuring and characterizing temperature-dependent parameters for OIP. First, this article derives the operating thermal conditions of the grid-aged cable based on IEC standards after analyzing the loading data using machine learning techniques to determine the elevated temperature levels for the experiments. Second, a novel lab-fabricated inexpensive electronics circuit is developed for polarization and depolarization current (PDC) measurements which can be adapted for such measurements over expensive commercial devices. From the measured parameters, an extended three-branch Debye model is optimized using a developed error function approach based on the Akaike information criterion (AIC) and goodness of fit. The model indicated a reduction in the branch resistance with temperature elevation and aging, whereas the branch capacitance revealed an increasing trend. The resultant relaxation time (RC) showed a decrease overall. Last, a short-duration frequency domain spectrum was analyzed and extrapolated to obtain parameters for a wide range of frequencies and fit in a Cole-Cole model, derived for oil-paper insulation. The time constants obtained from this model also confirmed a reducing trend across the temperature and aging variations and the model parameter, the alpha coefficient showed a decreasing trend. Last, the effect of the measured dielectric parameters is reflected with breakdown values to investigate the effect of temperature on the electrical life of insulation., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Electrical Power Grids, DC systems, Energy conversion & Storage, High Voltage Technology Group

In standard clinical practice, cuff devices are widely used for non-invasive blood pressure measurements (NIBP). However, cuff-based NIBP is prone to large errors especially in cases of hypo- and hypertension. In addition to this, the cuff measurement principle allows for estimation of a number of other hemodynamic parameters (e. g. cardiac output, arterial stiffness, augmentation index) by means of analysis of the pulse waveform and/or pulse amplitude recorded in the cuff pressure. However, in standard practice, the cuff is still only used for the measurement of BP.A key reason for the observed measurement errors and the limited set of extracted parameters lies in our poor understanding of the cuff device as a transducer; the cuff pressure response to arm volume pulsations (the cuff transfer function TFcuff) depends on a large number of factors. It is not yet clear to what extent the cuff contributes to the NIBP error, or if the cuff in its current format is reliable for measurement of arterial pulse waveform/amplitude. In this study, we investigate the isolated cuff in order to gain a better understanding of the main sources of measurement errors. By using an experimental setup which measures the cuff response to mechanically simulated arm pulsations, we find that the cuff can explain part of the NIBP errors in cases of hypotension. Also, we find that pulse waveforms can be estimated using a standard cuff; however, measurement of the pulse volume amplitude is unfeasible. We demonstrate that the observed inaccuracies can be eliminated by usage of a calibrator device which obtains real-time information on TFcuff. These findings will enable further investigation of the effects related to the pulse travel along the compressed limb and the interaction between cuff, arm tissue, distal arm hemodynamics and arterial walls for the development of improved occlusion-based measurement strategie

This thesis describes the design of a CMOS magnetic field sensor system for measuring electronic currents. A hybrid system, using Hall plates and a pick-up loop is chosen to obtain improved performance compared to conventional Hall based magnetic field sensors. The pick up loop enables to reduce the current consumption and increases the bandwidth of the system. The design of the pick-up loop and readout amplifier are described in detail. Simulation results of the pick-up loop signal path offer a factor 10 increase in bandwidth using 24% less current compared to a system consisting of two Hall plate channels.

Condition monitoring of the insulation health of electric machines is usually performed with offline tests. In recent years, online condition monitoring methods for the groundwall insulation have been introduced. The majority of these works are based on leakage current measurements, requiring additional equipment compared to the standard one in converters. The condition monitoring method in this work instead relies on the high-frequency ringing of stator currents occurring after a converter switching transition. This work evaluates the method experimentally in a randomly-wound stator winding of an induction machine prototype equipped with taps. The insulation condition change is forced by inserting external capacitors. Through the analysis of different metrics for the health monitoring and a detailed discussion of the hardware and software requirements for an electric drive, a viable way forward for the practical implementation of the approach is proposed., QC 20230117

Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications., Funding: Swedens innovation agency, Vinnova, through project iQDeep [2018-02700]

Whereas interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension. This is in contrast with the simplex method that always admits an exponential bound. We introduce a new polynomial-time path-following interior point method where the number of iterations also admits a combinatorial upper bound O(2nn1.5 log n) for an n-variable linear program in standard form. This complements previous work by Allamigeon, Benchimol, Gaubert, and Joswig (SIAGA 2018) that exhibited a family of instances where any path-following method must take exponentially many iterations. The number of iterations of our algorithm is at most O(n1.5 log n) times the number of segments of any piecewise linear curve in the wide neighborhood of the central path. In particular, it matches the number of iterations of any path following interior point method up to this polynomial factor. The overall exponential upper bound derives from studying the max central path', a piecewise-linear curve with the number of pieces bounded by the total length of 2n shadow vertex simplex paths. From the existence of a line segment in the wide neighborhood we derive strong implications on the structure of the corresponding segment of the central path. Our algorithm is able to detect this structure from the local geometry at the current iterate, and constructs a step direction that descends along this segment. The bound O(n1.5 log n) that applies for arbitrarily long line segments is derived from a combinatorial progress measure. Our algorithm falls into the family of layered least squares interior point methods introduced by Vavasis and Ye (Math. Prog. 1996). In contrast to previous layered least squares methods that partition the kernel of the constraint matrix into coordinate subspaces, our method creates layers based on a general subspace providing more flexibility. Our result also imp

This article proposes a method for reducing the loading effects when a dc voltage or current is measured in a linear circuit with a digital multimeter (DMM). In the proposed method, the voltmeter is placed in series to estimate the current, and the ammeter is placed in parallel to estimate the voltage, which is the opposite of the conventional approach. Its application is particularly of interest when the equivalent resistance between the nodes of the dc voltage (current) under measurement is high (low). In comparison with the conventional method, the relative error is up to a factor of 10 4 lower if the equivalent resistance equals the shunt (input) resistance of the DMM when a dc current (voltage) is measured, Postprint (author's final draft)

Condition monitoring of the insulation health of electric machines is usually performed with offline tests. In recent years, online condition monitoring methods for the groundwall insulation have been introduced. The majority of these works are based on leakage current measurements, requiring additional equipment compared to the standard one in converters. The condition monitoring method in this work instead relies on the high-frequency ringing of stator currents occurring after a converter switching transition. This work evaluates the method experimentally in a randomly-wound stator winding of an induction machine prototype equipped with taps. The insulation condition change is forced by inserting external capacitors. Through the analysis of different metrics for the health monitoring and a detailed discussion of the hardware and software requirements for an electric drive, a viable way forward for the practical implementation of the approach is proposed., QC 20230117

Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications., Funding: Swedens innovation agency, Vinnova, through project iQDeep [2018-02700]

Neural Networks (NNS) can solve very hard classification and estimation tasks but are less well suited to solve complex sensor fusion challenges, such as end-to-end control of autonomous vehicles. Nevertheless, NN can still be a powerful tool for particular sub-problems in sensor fusion. This would require a reliable and quantifiable measure of the stochastic uncertainty in the predictions that can be compared to classical sensor measurements. However, current NN'S output some figure of merit, that is only a relative model fit and not a stochastic uncertainty. We propose to embed the NN'S in a proper stochastic system identification framework. In the training phase, the stochastic uncertainty of the parameters in the (last layers of the) NN is quantified. We show that this can be done recursively with very few extra computations. In the classification phase, Monte-Carlo (MC) samples are used to generate a set of classifier outputs. From this set, a distribution of the classifier output is obtained, which represents a proper description of the stochastic uncertainty of the predictions. We also show how to use the calculated uncertainty for outlier detection by including an artificial outlier class. In this way, the NN fits a sensor fusion framework much better. We evaluate the approach on images of handwritten digits. The proposed method is shown to be on par with MC dropout, while having lower computational complexity, and the outlier detection almost completely eliminates false classifications., Funding: Swedens innovation agency, Vinnova, through project iQDeep [2018-02700]

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. In contrast, graphs that have a single type of nodes and edges, are often called homogeneous graphs. Measuring similarities among objects is an important task in data mining applications, such as web search, link prediction, and clustering. Currently, several similarity measures are defined for HINs. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of similarity scores. This makes using existing similarity measures in real applications difficult. To address this problem, we extend SimRank, a well-known similarity measure on homogeneous graphs, to HINs, by introducing the concept of the decay graph. The newly proposed similarity measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, and the efficiency of our proposed algorithms for computing HowSim scores, are demonstrated by extensive experiments. IEEE

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a reliable health assessment strategy for battery pack is important but challenging when the joint requirements of computational burden, modelling cost, estimation accuracy, and battery equalisation are considered. This paper proposes a balancing current ratio (BCR)-based solution to realise satisfactory state-of-health (SoH) estimation performance of all series-connected cells within a pack, while the overall reliance on cell-level battery models is also reduced. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy for effectively estimating SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance (OCV-R) model is adopted, the typical estimation error of our proposed solution can still be bounded within only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also improved.

This article proposes a compensation method for gain and offset errors in the current sensing of three-phase AC machine drives with three current sensor measurements during exact or close to standstill conditions. Errors are treated sequentially based on the elliptical trajectory method, a mathematical manipulation by demodulating faulted time-varying d, q currents using the machine position. As three current sensors are used, a correction factor through measured neutral current is proposed such that the impact of machine resistance deviation and inverter non-idealities are minimized. The method does not incorporate any additional hardware, making it low-cost and straightforward in its implementation. It is proposed as a tool for initial commissioning before the machine runs. It is robust to rotor position perturbations and does not need a mechanical lock for compensation in standstill conditions. The effectiveness of the suggested approach is shown by analysis, modelling, and experimental findings acquired from a hardware prototype by mimicking the actual use of high-precision mechatronics.

Measurement of physiological reactions to persuasive messages can improve our understanding of psychological processes of persuasion, and potentially further enhance and personalize current persuasion interventions. However, little is known about the relationship between psychophysiology and persuasive processes. This study focused on four persuasion principles: scarcity, commitment, consensus, and authority, and people's susceptibility to them. Physiological measures included the cardiovascular, respiratory, and electrodermal system, as well as facial motor systems. Psychological measures consisted of self-reported attitude towards oral care and susceptibility to persuasion (STPS). We performed a randomized within-subject experiment in which fifty-six participants viewed persuasive messages deploying the aforementioned persuasion principles to improve their oral care. Results indicated different physiological patterns during persuasion versus rest. We found no different physiological patterns in exposure to distinct persuasion principles, nor a clear correlation with susceptibility to individual persuasion principles. However, mixed model analysis illustrated that overall STPS scores help explain variance in reactivity of skin conductance level and skin conductance response, and reactivity in the zygomaticus major: lower susceptibility relates to higher reactivity. Summarizing, we have found no conclusive support for distinct psychophysiological patterns associated with different persuasion principles, although overall susceptibility seems to be reflected in physiology to some extent.