Power Measurements Research Articles

Does a higher hashrate strengthen Bitcoin network security?

In the blockchain world, proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger. The hashrate, a measure of the computational power directed toward securing a blockchain through proof-of-work consensus, is a fundamental measure of preventing various attacks. This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain. We use vector error correction modeling to analyze the endogenous relationships between the hashrate, Bitcoin price, and transaction fee, revealing the need for an additional variable to achieve our aim. Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes. This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Detection and Correction of PMU Angle Deviation for Double-Circuit Line Using Reactive Power Measurements

Detection and Correction of PMU Angle Deviation for Double-Circuit Line Using Reactive Power Measurements

Tempering of Dual Phase steels: Microstructural evolutions and mechanical properties

Modelling the microstructural evolutions and mechanical properties during the tempering of Dual Phase steels is a key objective for industrials as this phenomenon has a strong impact on the final properties. After having determined the tempering kinetics of fully martensitic steels between 100°C and 550°C using thermoelectric Power and hardness measurements, time–temperature equivalences were applied to determine the activation energies of the mechanisms controlling martensite tempering. Two tempering stages were clearly identified and thanks to the use of TEM, SEM and tomography techniques, they could be attributed to cementite precipitation, its spheroidization and recovery phenomena. The second stage was found to be retarded with increasing the manganese content of the steel contrary to the first stage. From these studies, a JMAK model was developed to predict the microstructurals evolutions during tempering. Then, an extension of the Hybrid-mean Field Composite model developed in a previous paper to predict the tensile curves of fresh martensite was proposed to take into account the microstructural evolutions of martensite during tempering. The model was tested for a wide range of physical parameters of the microstructure (phase fraction and chemical composition) and for different tempering heat treatments and gave good agreement with experimental tensile curves.

Design and construction of an affordable optical power meter: micro- to milli-Watt in the 400–800 nm range

This study introduces the design, construction, and evaluation of an affordable optical power meter prototype, AYR (Affordable Yet Reliable) version 1.0, which operates effectively within the 400–800 nm range, using a silicon photodiode. Aimed at bridging the gap in accessibility to precise and reliable photonics instrumentation, especially in resource-constrained settings, AYR 1.0 leverages advancements in photodiode technology, additive manufacturing, and do-it-yourself electronics. The device incorporates a custom-built electronic circuit that facilitates accurate optical power measurement by converting light into electrical current. Through rigorous testing against a reliable commercial optical power meter, AYR 1.0 demonstrated exceptional accuracy and reliability. Sensitivity values ranged from ∼13 μA mW−1 at 405 nm to ∼796 μA mW−1 at 805 nm. The operational power range spanned from 0.003 mW to 242.0 mW, with linearity (R 2) values consistently above 0.9981, indicating high fidelity in measurement. Repeatability percentages varied between 99.4% and 99.9%, and response times ranged up to 55 μs, showcasing the prototype’s rapid and reliable response to changes in optical power. The key components include a low-cost silicon photodiode (2DU10), a differential trans-impedance amplifier circuit for signal processing, and a 3D-printed housing for the sensor head and console, contributing to its cost-effectiveness and robustness. The prototype’s total cost was 116 US dollars, highlighting its affordability and potential for widespread adoption.

Autoregulation Does Not Provide Additional Benefits to a Mixed Session Periodized Resistance Training Program in Trained Men.

Bartolomei, S, Francesco, L, Latini, D, and Hoffman, JR. Autoregulation does not provide additional benefits to a mixed session periodized resistance training program in trained men. J Strength Cond Res XX(X): 000-000, 2024-The aim of this investigation was to study how autoregulation impacted training volume, performance, and muscle size on a 10-week mixed session periodized (MSP) resistance training program, characterized by the inclusion of different training foci in each session. Twenty-four resistance trained men were assigned to an autoregulated mixed session periodized (AMSP group; n = 13; age = 26.2 ± 4.9 y; body mass = 82.0 ± 8.7 kg; height = 176.8 ± 6.0 cm) or into an MSP (n = 11; age = 24.0 ± 2.6; body mass = 81.3 ± 10.5 kg; height = 174.0 ± 5.4 cm) group. Subjects in both groups trained 5 days per week for 10 weeks and performed the same exercises. The difference between the groups consisted in the use of a perceived recovery-based scale to adjust the individual training volume in the AMSP program. Maximal strength (bench press and squat 1 repetition maximum), power (bench press throw and countermovement jump), and muscle architecture (muscle thickness [MT] of biceps brachii, trapezius, vastus lateralis and vastus medialis) were collected before and after the 10-week training period. In addition, training volume and session load were calculated for each training session. A higher total training volume (p < 0.001) was seen in AMSP program compared with MSP program, but no differences (p > 0.05) were noted in the average session load. No significant differences between the groups were detected for MT of both upper-body and lower-body muscles (p's > 0.05) and lean body mass (p = 0.681). No significant differences between the groups were detected for any strength or power measurements. Results of this study indicate that a perceived recovery-based AMSP training program was not more effective than an MSP training program for increasing muscle size and performance in resistance trained men.

Interlaboratory Comparison of Power Measurements at Millimetre- and Sub-Millimetre-Wave Frequencies

The aim of this paper is to compare the power measurement capabilities in millimetre- and sub-millimetre-wave frequency bands of several national metrology institutes and one research institute. The first comparison, in WR-6.5 waveguide (110 GHz to 170 GHz), involved NPL, TUBITAK UME and PTB. The second comparison, in WR-1.5 waveguide (500 GHz to 750 GHz), involved NPL, METAS, TUBITAK UME, LNE, WAT, GUM and VDI. Two types of travelling standards were used for these comparisons: a thermoelectric power sensor in the WR-6.5 band and a calorimetric power sensor in the WR-6.5 and WR-1.5 bands. The thermoelectric power sensor was characterised by the participants against their own standards and a generalised effective efficiency was calculated. The calorimetric power sensor operating in the WR-6.5 band was measured to observe its behaviour during the comparison and was also measured in the WR-1.5 band after being fitted with a suitable waveguide taper and used in conjunction with a frequency multiplier. The participants measured the output of the calorimetric power sensor and their own power sensor standard. A normalised power ratio method was used as a comparison parameter for the WR-1.5 band measurements. In addition, a pyroelectric power standard was used by METAS to measure absolute power, and a frequency of 650 GHz was used as a link between the absolute power and the power ratios. Finally, all but two of the measurement points compared between the participants achieved agreement in terms of En scores less than 1. For the first time, an interlaboratory comparison of power measurements at sub-millimetre frequencies has been performed and, overall, good agreement was achieved between the different laboratories.

Wi‐Fi fingerprint‐based indoor location: Multiple fingerprints and multiple classifiers with two‐layer fusion weights

SummaryIndoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM.

Design and implementation of RF power levels measurement system from indoor to outdoor in Isparta province

This paper presents a novel, low-cost, and portable RF power measurement system, operating in GSM 900 and GSM 1800 frequency bands. This measurement model consists of four basic circuits; microstrip folded dipole antenna (MFDA) that received the ambient RF signals, a power meter that generates DC output voltage according to received RF signal power level, a microprocessor that converts the data's analog to digital, and a computer to record and screen the time-varying data. Seventeen measurements, each for six hours, were performed in nine locations in Isparta for the indoor-to-outdoor scenario. The range between the minimum and maximum power values of the average RF power levels obtained in time-dependent measurements was calculated as approximately 15 dB, 18 dB, and 33 dB for indoor, semi-indoor, and outdoor ambients, respectively. The indoor/semi-indoor/outdoor ambient RF power levels were respectively varied between −55.1 dBm to −4.13 dBm, −43.92 dBm to −6.12 dBm, −44.89 dBm to 0.1 dBm in all measurements.

Two-dimensional Keitz method for the radiant power measurement of planar ultraviolet excilamps

Planar XeBr∗ and XeCl∗ excilamps emitting noncoherent narrowband UVB light (280–315 nm) are now widely used to cure psoriasis and vitiligo as well as to improve vitamin D synthesis. The two-dimensional integral formula has been deducted in this study, which is a good method and has great practical significance to calculate the total radiant power and assess the energy efficiency of a planar UV lamp. The measured radiant power of planar white LED lamps through a two-dimensional Keitz formula has been compared to that of gonio-photometer, verifying the applicability of the formula. The optimum measurement distance is dependent on the lamp length (1.5L≤D≤3.5L) for which the derivation from the two methods can be controlled within 10%. The planar XeBr∗ excilamps have been measured and compared to coaxial excilamps, which show similar patterns of change for the radiant characteristics. Since the planar radiant power formula only needs to measure normal illuminance at a certain distance from the symmetric center of the lamp, it is more convenient to use and is a low-cost method to promote the development of large-sized planar ultraviolet lamps.

Examining performance calibration in smart power system electricity metering based on environmental perception attention LSTM-network

The operating environment greatly influences the accuracy of power metering devices, resulting in variations and inconsistencies in measurement results across different working situations. A calibration model for power metering devices is proposed in this study, considering a range of environmental circumstances. The first step involves investigating the environmental conditions that impact the accuracy of power metering devices. The mutual information approach is utilized to identify environmental disturbances affecting device accuracy. A machine learning-driven symmetry attention Long Short-Term Memory (LSTM) network addresses measurement errors, capitalizing on the network’s symmetry data knowledge. Ultimately, the efficacy of the suggested approach is substantiated through the utilization of performance indicators, namely, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the proposed method can effectively reduce the errors of the power measurement device in all quarters, and the error reduction effect is over 10% in the spring, which is better than other models, demonstrating exemplary performance in correcting the calibration errors of the power measurement device.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Abstract Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE=0.00571) and the highest R-squared value (R^2=0.93877). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Critical Considerations in Power Measurements for the Precise Estimation of Energy Costs in Plasma NOx Synthesis

Critical Considerations in Power Measurements for the Precise Estimation of Energy Costs in Plasma NOx Synthesis

Electroencephalographic Correlates of Clinical Severity in the Natural history study of RTT and Related Disorders.

This study was undertaken to characterize quantitative electroencephalographic (EEG) features in participants from the Natural history study of RTT and Related Disorders and to assess the potential for these features to act as objective measures of cortical function for Rett syndrome (RTT). EEG amplitude and power features were derived from the resting EEG of 60 females with RTT (median age = 10.7 years) and 26 neurotypical females (median age = 10.6 years). Analyses focus on group differences and within the RTT group, associations between the EEG parameters and clinical severity. For a subset of participants (n = 20), follow-up data were available for assessing the reproducibility of the results and the stability in the parameters over 1 year. Compared to neurotypical participants, participants with RTT had greater amplitude variability and greater low-frequency activity as reflected by greater delta power, more negative 1/f slope, and lower theta/delta, alpha/delta, beta/delta, alpha/theta, and beta/theta ratios. Greater delta power, more negative 1/f slope, and lower power ratios were associated with greater severity. Analyses of year 1 data replicated the associations between 1/f slope and power ratios and clinical severity and demonstrated good within-subject consistency in these measures. Overall, group comparisons reflected a greater predominance of lower versus higher frequency activity in participants with RTT, which is consistent with prior clinical interpretations of resting EEG in this population. The observed associations between the EEG power measures and clinical assessments and the repeatability of these measures underscore the potential for EEG to provide an objective measure of cortical function and clinical severity for RTT. ANN NEUROL 2024;96:175-186.

Entanglement of purification in random tensor networks

The entanglement of purification EP(A∶B) is a powerful correlation measure, but it is notoriously difficult to compute because it involves an optimization over all possible purifications. In this paper, we prove a new inequality: EP(A∶B)≥12SR(2)(A∶B), where SR(n)(A∶B) is the Renyi reflected entropy. Using this, we compute EP(A∶B) for a large class of random tensor networks at large bond dimension and show that it is equal to the entanglement wedge cross section EW(A∶B), proving a previous conjecture motivated from AdS/CFT. Published by the American Physical Society 2024

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

Light-Field Optimization of Deep-Ultraviolet LED Modules for Efficient Microbial Inactivation

Public awareness of preventing pathogenic microorganisms has significantly increased. Among numerous microbial prevention methods, the deep-ultraviolet (DUV) disinfection technology has received wide attention by using the nitride-based light-emitting diode (LED). However, the light extraction efficiency of DUV LEDs and the utilization rate of emitted DUV light are relatively low at the current stage. In this study, a light distribution design (referred to as the reflective system) was explored to enhance the utilization of emitted DUV from LEDs, leading to successful and efficient surface and air disinfection. Optical power measurements and microbial inactivation tests demonstrated an approximately 79% improvement in average radiation power density achieved by the reflective system when measured at a 5 cm distance from the irradiation surface. Moreover, a statistically significant enhancement in local surface disinfection was observed with low electric power consumption. The reflective system was integrated into an air purifier and underwent air disinfection testing, effectively disinfecting a 3 m3 space within ten minutes. Additionally, a fluorine resin film at the nanolevel was developed to protect the light module from oxidation, validated through a 1200 h accelerated aging test under humid conditions. This research offers valuable guidance for efficient and energy-saving DUV disinfection applications.

Sub-photon accuracy noise reduction of a single shot coherent diffraction pattern with an atomic model trained autoencoder

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning.

Detecting clear‐sky periods from photovoltaic power measurements

AbstractA method for detecting clear‐sky periods from photovoltaic (PV) power measurements is presented and validated. It uses five tests dealing with parameters characterizing the connections between the measured PV power and the corresponding clear‐sky power. To estimate clear‐sky PV power, a PV model has been designed using as inputs downwelling shortwave irradiance and its direct and diffuse components received at ground level under clear‐sky conditions as well as reflectivity of the Earth's surface and extraterrestrial irradiance, altogether provided by the McClear service. In addition to McClear products, the PV model requires wind speed and temperature as inputs taken from ECMWF twentieth century reanalysis ERA5 products. The performance of the proposed method has been assessed and validated by visual inspection and compared to two well‐known algorithms identifying clear‐sky periods with broadband global and diffuse irradiance measurements on a horizontal surface. The assessment was carried out at two stations located in Finland offering collocated 1‐min PV power and broadband irradiance measurements. Overall, total agreement ranges between 84% and 97% (depending on the season) in discriminating clear‐sky and cloudy periods with respect to the two well‐known algorithms serving as reference. The disagreement fluctuating between 6% and 15%, depending on the season, primarily occurs while the PV module temperature is adequately high and/or when the sun is close to the horizon with many more interactions between the radiation, the atmosphere and the ground surface.

Fresnel lenses and auto tracking to increase solar panel output power

This solar panel, which is equipped with microcontroller-based auto tracking, is also equipped with a Fresnel lens to obtain more optimal power because the Fresnel lens has the property of bending or refracting light rays that pass through it so that it can focus sunlight. The Fresnel lens focuses sunlight radiation onto the solar panels. To determine the magnitude of the influence of Fresnel lenses on increasing the power produced by solar panels, two solar panels of the same size (11×11) cm were moved by auto tracking using the same axis. One solar panel is not fitted with a Fresnel lens, and the other is fitted with a Fresnel lens measuring 30×30 cm, where the distance between the Fresnel lens and the solar panel is 5 cm. Measurements of the output power of both solar panels were carried out simultaneously, namely from 08.00 to 18.00 WIB (West Indonesia time). The power output of both solar panels was measured in 30-minute intervals, resulting in 21 measurements. Solar panels using Fresnel lenses produce an output power that is 105.306% more significant than that of those without Fresnel lenses.