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Self-Error Correcting Method for Magnetic-Array-Type Current Sensors in Multi-Core Cable Applications
Authors:
Xiaohu Liu,
Keyu Hou,
Kang Ma,
Jian Liu,
Angang Zheng,
Zhengwei Qu,
Wei Zhao,
Lisha Peng,
Songling Huang,
Shisong Li
Abstract:
Data-driven methods enable online assessment of error states in magnetic-array-type current sensors, and long-term measurement stability can be enhanced through further self-error correction. However, when the magnetic-array-type current sensors are applied to multi-conductor systems such as multi-core cables, the time-varying correlations among conductor currents may degrade the performance of mu…
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Data-driven methods enable online assessment of error states in magnetic-array-type current sensors, and long-term measurement stability can be enhanced through further self-error correction. However, when the magnetic-array-type current sensors are applied to multi-conductor systems such as multi-core cables, the time-varying correlations among conductor currents may degrade the performance of multi-latent-variable data-driven models for error evaluation. To address this issue, this paper proposes a robust self-error correcting method for magnetic-array-type current sensors even under significant variations in phase current correlations (e.g., large fluctuations in three-phase current imbalance). By incorporating phase current decoupling and principal component analysis (PCA), the correlation analysis of multi-latent variables (i.e., multi-conductor currents) is transformed into a single-latent-variable (corresponding to single phase current) modeling problem. Experimental results demonstrate that the proposed method effectively detects error drifts of magnetic field sensors as low as $2\times10^{-3}$ in relative error and $2\times10^{-3}$ rad in phase error. Accurate evaluation and correction of each magnetic field sensor's error drifts substantially eliminates the overall error drift in the magnetic-array-type current sensor, validating the feasibility and effectiveness of the proposed self-error correcting method.
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Submitted 8 December, 2025;
originally announced December 2025.
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Electric Field Switching of Magnon Spin Current in a Compensated Ferrimagnet
Authors:
Kaili Li,
Lei Wang,
Yu Wang,
Yuanjun Guo,
Shuping Lv,
Yuewei He,
Weiwei Lin,
Tai Min,
Shaojie Hu,
Sen Yang,
Dezhen Xue,
Aqun Zheng,
Shuming Yang,
Xiangdong Ding
Abstract:
Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic memory and logic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation caused by Joule heating. Electric-fie…
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Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic memory and logic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation caused by Joule heating. Electric-field switching of magnon spin current without charge current is highly desired but very challenging to realize. By integrating magnonic and piezoelectric materials, we demonstrate manipulation of the magnon spin current generated by the spin Seebeck effect in the ferrimagnetic insulator Gd3Fe5O12 (GdIG) film on a piezoelectric substrate. We observe reversible electric-field switching of magnon polarization without applied charge current. Through strain-mediated magnetoelectric coupling, the electric field induces the magnetic compensation transition between two magnetic states of the GdIG, resulting in its magnetization reversal and the simultaneous switching of magnon spin current. Our work establishes a prototype material platform that pave the way for developing magnon logic devices characterized by all electric field reading and writing and reveals the underlying physics principles of their functions.
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Submitted 25 November, 2023;
originally announced November 2023.
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Quantum Non-Demolition Measurement on the Spin Precession of Laser-Trapped $^{171}$Yb Atoms
Authors:
Y. A. Yang,
T. A. Zheng,
S. -Z. Wang,
W. -K. Hu,
Chang-Ling Zou,
T. Xia,
Z. -T. Lu
Abstract:
Quantum non-demolition (QND) measurement enhances the detection efficiency and measurement fidelity, and is highly desired for its applications in precision measurements and quantum information processing. We propose and demonstrate a QND measurement scheme for the spin states of laser-trapped atoms. On $^{171}$Yb atoms held in an optical dipole trap, a transition that is simultaneously cycling, s…
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Quantum non-demolition (QND) measurement enhances the detection efficiency and measurement fidelity, and is highly desired for its applications in precision measurements and quantum information processing. We propose and demonstrate a QND measurement scheme for the spin states of laser-trapped atoms. On $^{171}$Yb atoms held in an optical dipole trap, a transition that is simultaneously cycling, spin-state selective, and spin-state preserving is created by introducing a circularly polarized beam of control laser to optically dress the spin states in the excited level, while leaving the spin states in the ground level unperturbed. We measure the phase of spin precession of $5\times10^{4}$ atoms in a bias magnetic field of 20 mG. This QND approach reduces the optical absorption detection noise by $\sim$19 dB, to a level of 2.3 dB below the atomic quantum projection noise. In addition to providing a general approach for efficient spin-state readout, this all-optical technique allows quick switching and real-time programming for quantum sensing and quantum information processing.
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Submitted 16 September, 2022;
originally announced September 2022.
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Measurement of the Electric Dipole Moment of $^{171}$Yb Atoms in an Optical Dipole Trap
Authors:
T. A. Zheng,
Y. A. Yang,
S. -Z. Wang,
J. T. Singh,
Z. -X. Xiong,
T. Xia,
Z. -T. Lu
Abstract:
The permanent electric dipole moment (EDM) of the $^{171}$Yb $(I=1/2)$ atom is measured with atoms held in an optical dipole trap (ODT). By enabling a cycling transition that is simultaneously spin-selective and spin-preserving, a quantum non-demolition measurement with a spin-detection efficiency of 50$\%$ is realized. A systematic effect due to parity mixing induced by a static E field is observ…
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The permanent electric dipole moment (EDM) of the $^{171}$Yb $(I=1/2)$ atom is measured with atoms held in an optical dipole trap (ODT). By enabling a cycling transition that is simultaneously spin-selective and spin-preserving, a quantum non-demolition measurement with a spin-detection efficiency of 50$\%$ is realized. A systematic effect due to parity mixing induced by a static E field is observed, and is suppressed by averaging between measurements with ODTs in opposite directions. The coherent spin precession time is found to be much longer than 300 s. The EDM is determined to be $d({\rm^{171}Yb})={\color{black}(-6.8\pm5.1_{\rm stat}\pm1.2_{\rm syst})\times10^{-27}\ e\ \rm cm}$, leading to an upper limit of $|d({\rm^{171}Yb})|<{\color{black}1.5\times10^{-26}\ e\ \rm cm}$ ($95\%$ C.L.). These measurement techniques can be adapted to search for the EDM of $^{225}$Ra.
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Submitted 17 July, 2022;
originally announced July 2022.
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Magic Wavelengths of the Yb $6s^2{}\,^1S_0-6s6p\,{}^3P_1$ Intercombination Transition
Authors:
T. A. Zheng,
Y. A. Yang,
M. S. Safronova,
U. I. Safronova,
Zhuan-Xian Xiong,
T. Xia,
Z. -T. Lu
Abstract:
We calculate and measure the magic wavelengths for the $6s^2{}\,^1S_0-6s6p\,{}^3P_1$ intercombination transition of the neutral ytterbium atom. The calculation is performed with the \textit{ab initio} configuration interaction (CI) + all-order method. The measurement is done with laser spectroscopy on cold atoms in an optical dipole trap. The magic wavelengths are determined to be 1035.68(4) nm fo…
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We calculate and measure the magic wavelengths for the $6s^2{}\,^1S_0-6s6p\,{}^3P_1$ intercombination transition of the neutral ytterbium atom. The calculation is performed with the \textit{ab initio} configuration interaction (CI) + all-order method. The measurement is done with laser spectroscopy on cold atoms in an optical dipole trap. The magic wavelengths are determined to be 1035.68(4) nm for the $π$ transition ($Δm = 0$) and 1036.12(3) nm for the $σ$ transitions ($|Δm| = 1$) in agreement with the calculated values. Laser cooling on the narrow intercombination transition could achieve better results for atoms in an optical dipole trap when the trap wavelength is tuned to near the magic wavelength.
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Submitted 12 November, 2020;
originally announced November 2020.
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A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors
Authors:
Ao Zheng,
Hongkai Yang,
Li Zhang,
Yuxiang Xing
Abstract:
With the growing technology of photon-counting detectors (PCD), spectral CT is a widely concerned topic which has the potential of material differentiation. However, due to some non-ideal factors such as cross talk and pulse pile-up of the detectors, direct reconstruction from detected spectrum without any corrections will get a wrong result. Conventional methods try to model these factors using c…
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With the growing technology of photon-counting detectors (PCD), spectral CT is a widely concerned topic which has the potential of material differentiation. However, due to some non-ideal factors such as cross talk and pulse pile-up of the detectors, direct reconstruction from detected spectrum without any corrections will get a wrong result. Conventional methods try to model these factors using calibration and make corrections accordingly, but depend on the preciseness of the model. To solve this problem, in this paper, we proposed a novel deep learning-based monochromatic image synthesis method working in sinogram domain. Different from previous deep learning-based methods aimed at this problem, we designed a novel network architecture according to the physical model of cross talk, and it can solve this problem better in an ingenious way. Our method was tested on a cone-beam CT (CBCT) system equipped with a PCD. After using FDK algorithm on the corrected projection, we got quite more accurate results with less noise, which showed the feasibility of monochromatic image synthesis by our method.
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Submitted 19 July, 2020;
originally announced July 2020.
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A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT
Authors:
Ao Zheng,
Hewei Gao,
Li Zhang,
Yuxiang Xing
Abstract:
Helical CT has been widely used in clinical diagnosis. Sparsely spaced multidetector in z direction can increase the coverage of the detector provided limited detector rows. It can speed up volumetric CT scan, lower the radiation dose and reduce motion artifacts. However, it leads to insufficient data for reconstruction. That means reconstructions from general analytical methods will have severe a…
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Helical CT has been widely used in clinical diagnosis. Sparsely spaced multidetector in z direction can increase the coverage of the detector provided limited detector rows. It can speed up volumetric CT scan, lower the radiation dose and reduce motion artifacts. However, it leads to insufficient data for reconstruction. That means reconstructions from general analytical methods will have severe artifacts. Iterative reconstruction methods might be able to deal with this situation but with the cost of huge computational load. In this work, we propose a cascaded dual-domain deep learning method that completes both data transformation in projection domain and error reduction in image domain. First, a convolutional neural network (CNN) in projection domain is constructed to estimate missing helical projection data and converting helical projection data to 2D fan-beam projection data. This step is to suppress helical artifacts and reduce the following computational cost. Then, an analytical linear operator is followed to transfer the data from projection domain to image domain. Finally, an image domain CNN is added to improve image quality further. These three steps work as an entirety and can be trained end to end. The overall network is trained using a simulated lung CT dataset with Poisson noise from 25 patients. We evaluate the trained network on another three patients and obtain very encouraging results with both visual examination and quantitative comparison. The resulting RRMSE is 6.56% and the SSIM is 99.60%. In addition, we test the trained network on the lung CT dataset with different noise level and a new dental CT dataset to demonstrate the generalization and robustness of our method.
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Submitted 23 October, 2019; v1 submitted 8 October, 2019;
originally announced October 2019.
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Nonreciprocity light propagation in coupled microcavities system beyond weak-excitation approximation
Authors:
A. S. Zheng,
G. Zhang,
H. Chen,
T. Mei,
J. Liu
Abstract:
We propose an alternative scheme for nonreciprocal light propagation in two coupled cavities system, in which a two-level quantum emitter is coupled to one of the optical microcavities. For the case of parity-time (\textrm{PT}) system (i.e., active-passive coupled cavities system), the cavity gain can significantly enhance the optical nonlinearity induced by the interaction between a quantum emitt…
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We propose an alternative scheme for nonreciprocal light propagation in two coupled cavities system, in which a two-level quantum emitter is coupled to one of the optical microcavities. For the case of parity-time (\textrm{PT}) system (i.e., active-passive coupled cavities system), the cavity gain can significantly enhance the optical nonlinearity induced by the interaction between a quantum emitter and cavity field beyond weak-excitation approximation. The giant optical nonlinearity results in the non-lossy nonreciprocal light propagation with high isolation ratio in proper parameters range. In addition, our calculations show that nonreciprocal light propagation will not be affected by the unstable output field intensity caused by optical bistability and we can even switch directions of nonreciprocal light propagation by appropriately adjusting the system parameters.
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Submitted 6 May, 2016; v1 submitted 3 May, 2016;
originally announced May 2016.
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Discovery of homogeneously dispersed pentacoordinated Al(V) species on the surface of amorphous silica-alumina
Authors:
Zichun Wang,
Yijiao Jiang,
Xianfeng Yi,
Cuifeng Zhou,
Aditya Rawal,
James Hook,
Zongwen Liu,
Feng Deng,
Anmin Zheng,
Alfons Baiker,
Jun Huang
Abstract:
The dispersion and coordination of aluminium species on the surface of silica-alumina based materials are essential for controlling their catalytic activity and selectivity. Al(IV) and Al(VI) are two common coordinations of Al species in the silica network and alumina phase, respectively. Al(V) is rare in nature and was found hitherto only in the alumina phase or interfaces containing alumina, a b…
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The dispersion and coordination of aluminium species on the surface of silica-alumina based materials are essential for controlling their catalytic activity and selectivity. Al(IV) and Al(VI) are two common coordinations of Al species in the silica network and alumina phase, respectively. Al(V) is rare in nature and was found hitherto only in the alumina phase or interfaces containing alumina, a behavior which negatively affects the dispersion, population, and accessibility of Al(V) species on the silica-alumina surface. This constraint has limited the development of silica-alumina based catalysts, particularly because Al(V) had been confirmed to act as a highly active center for acid reactions and single-atom catalysts. Here, we report the direct observation of high population of homogenously dispersed Al(V) species in amorphous silica-alumina in the absence of any bulk alumina phase, by high resolution TEM/EDX and high magnetic-field MAS NMR. Solid-state 27Al multi-quantum MAS NMR experiments prove unambiguously that most of the Al(V) species formed independently from the alumina phase and are accessible on the surface for guest molecules. These species are mainly transferred to Al(VI) species with partial formation of Al(IV) species after adsorption of water. The NMR chemical shifts and their coordination transformation with and without water adsorption are matching that obtained in DFT calculations of the predicted clusters. The discovery presented in this study not only provides fundamental knowledge of the nature of aluminum coordination, but also paves the way for developing highly efficient catalysts.
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Submitted 17 April, 2016;
originally announced April 2016.
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EIT intensity correlation power broadening in a buffer gas
Authors:
Aojie Zheng,
Alaina Green,
Michael Crescimanno,
Shannon O'Leary
Abstract:
EIT noise correlation spectroscopy holds promise as a simple, robust method for performing high resolution spectroscopy used in optical magnetometry and clocks. Of relevance to these applications, we report here on the role of buffer gas pressure and magnetic field gradients on power broadening of Zeeman EIT noise correlation resonances.
EIT noise correlation spectroscopy holds promise as a simple, robust method for performing high resolution spectroscopy used in optical magnetometry and clocks. Of relevance to these applications, we report here on the role of buffer gas pressure and magnetic field gradients on power broadening of Zeeman EIT noise correlation resonances.
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Submitted 25 March, 2016; v1 submitted 30 December, 2014;
originally announced January 2015.
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Photonic analog computing with integrated silicon waveguides
Authors:
Jianji Dong,
Ting Yang,
Aoleng Zheng,
Xinliang Zhang
Abstract:
The spectra of silicon integrated waveguides are tailored to process analog computing (i.e.,differential and integral) in optical domain with huge bandwidth.With the theory of signal and system, we design some silicon integrated devices to implement photonic differentiator and optical differential equation solver. The basic principle is to tailor the spectra of silicon integrated waveguides to mee…
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The spectra of silicon integrated waveguides are tailored to process analog computing (i.e.,differential and integral) in optical domain with huge bandwidth.With the theory of signal and system, we design some silicon integrated devices to implement photonic differentiator and optical differential equation solver. The basic principle is to tailor the spectra of silicon integrated waveguides to meet the requirements of analog computing circuits. These analog photonic integrated circuits are very promising in future computing systems with high speed, low cost, and compact size. We also plan to employ these basic computing units in more complex computing modules.
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Submitted 9 September, 2014;
originally announced September 2014.
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A survey of statistical network models
Authors:
Anna Goldenberg,
Alice X Zheng,
Stephen E Fienberg,
Edoardo M Airoldi
Abstract:
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociolog…
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Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.
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Submitted 29 December, 2009;
originally announced December 2009.