Defect depth is an essential indicator in magnetic flux leakage (MFL) detection and estimation. The quantification errors for defect depth are closely related to length and width errors, and this feature has always been used to support the operator's judgment in defect identification. However, the existing defect quantification algorithms based on shallow and deep neural networks only employed simple general network structures inspired by the field of artificial intelligence; consequently, these network structures lack the support of physical concepts and result in large quantification errors regarding defect size, especially depth. In this article, to describe and integrate the above theory into a deep neural network, we propose a physics-informed doubly fed cross-residual network (DfedResNet) suitable for MFL defect detection based on deep learning. Physics-based MFL defect quantification theory is studied and integrated into loss functions during the neural network training. DfedResNet quantifies defects in MFL data and automatically extracts deep features of defects. The experimental results show that it effectively achieves high-precision quantification of defect length, width, and depth simultaneously, especially defect depth. Moreover, it considers data from all three dimensions during network training, and use the originally measured magnetic signal data in place of recognized images to avoid defect information loss and further improve the quantification accuracy. The deep DfedResNet model proposed in this article reduces defect length and width quantification errors to within 0.3 mm and defect depth quantification errors to within 0.4% t . In addition, compared with other network structures and traditional algorithms, DfedResNet improves defect quantification accuracy by 1–2 orders of magnitude and thus achieves a high quantification performance.

Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is essential for pipeline safety assessments. In recent years, deep-learning technologies have been applied gradually to the data analysis of pipeline MFL testing, and remarkable results have been achieved. To the best of our knowledge, this review is a pioneering effort on comprehensively summarizing deep learning for MFL detection and evaluation of oil and gas pipelines. The majority of the publications surveyed are from the last five years. In this work, the applications of deep learning for pipeline MFL inspection are reviewed in detail from three aspects: pipeline anomaly recognition, defect quantification, and MFL data augmentation. The traditional analysis method is compared with the deep-learning method. Moreover, several open research challenges and future directions are discussed. To better apply deep learning to MFL testing and data analysis of oil and gas pipelines, it is noted that suitable interpretable deep-learning models and data-augmentation methods are important directions for future research.

A precise instrument, called a watt balance, compares mechanical power measured in terms of the meter, the second, and the kilogram to electrical power measured in terms of the volt and the ohm. A direct link between mechanical action and the Planck constant is established by the practical realization of the electrical units derived from the Josephson and the quantum Hall effects. We describe in this paper the fourth-generation watt balance at the National Institute of Standards and Technology (NIST), and report our initial determination of the Planck constant obtained from data taken in late 2015 and the beginning of 2016. A comprehensive analysis of the data and the associated uncertainties led to the SI value of the Planck constant, h = 6.626 069 83(22) × 10⁻³⁴ J s. The relative standard uncertainty associated with this result is 34 × 10⁻⁹.

The magnet system is one of the key elements of a watt balance. For the new watt balance currently under construction at the National Institute of Standards and Technology, a permanent magnet system was chosen. We describe the detailed construction of the magnet system, first measurements of the field profile, and shimming techniques that were used to achieve a flat field profile. The relative change of the radial magnetic flux density is <; 10^-4 over a range of 5 cm. We further characterize the most important aspects of the magnet and give order of magnitude estimates for several systematic effects that originate from the magnet system.

The National Institute of Metrology (NIM, China) proposed a joule balance method to measure the Planck constant in 2006, and built the first prototype NIM-1 to verify its principle with a relative uncertainty of 8.9 × 10 ^–6 by 2013. Since 2013, a new joule balance NIM-2 has been designed, with a series of improvements to reduce the measurement uncertainty. By April 2017, NIM-2 has been constructed and can be employed to measure the Planck constant in vacuum. A first measurement on NIM-2 yields a determination of the Planck constant is 6.626 069 2(16) × 10 ^–34 Js with a relative uncertainty of 2.4 × 10 ^–7 . The determination differs in relative terms by −1.27 × 10 ^–7 from the CODATA 2014 value. Further improvement of NIM-2 is still in progress towards 10 ^–8 level uncertainty in the future.

As the complexity increases in modern power systems, power quality (PQ) analysis considering interharmonics has become a challenging and important task. This article proposes a novel decomposition and estimation method for instantaneous PQ indices (PQIs) monitoring in single- and three-phase systems with interharmonics and transient disturbances. To separate the interharmonic components, a set of new scaling filter and wavelet filter with narrow transition bands is designed for the undecimated wavelet packet transform (UWPT). Furthermore, a two-stage decomposition method for multitone voltage and current signals is proposed. The Hilbert transform (HT) is applied to calculate the instantaneous amplitude and phase of each frequency component, which accordingly allows the monitoring of different PQI parameters. Numerical tests are conducted to check the performance of the proposed method. The test results show that, compared to other conventional approaches, instantaneous PQIs estimated by the proposed method present significant advances for tracking transitory changes in power systems and could be considered as a helpful tool for high-accuracy PQ detections.