Imaging a moving point source from multi-frequency data measured at one and sparse observation points (part II): near-field case in 3D

In this paper, we introduce a frequency-domain approach to extract information on the trajectory of a moving point source. The method hinges on the analysis of multi-frequency near-field data recorded at one and sparse observation points in three dimensions. The radiating period of the moving point source is supposed to be supported on the real axis and a priori known. In contrast to inverse stationary source problems, one needs to classify observable and non-observable measurement positions. The analogue of these concepts in the far-field regime were firstly proposed in the authors' previous paper (SIAM J. Imag. Sci., 16 (2023): 1535-1571). In this paper we shall derive the observable and non-observable measurement positions for straight and circular motions in $\R^3$. In the near-field case, we verify that the smallest annular region centered at an observable position that contains the trajectory can be imaged for an admissible class of orbit functions. Using the data from sparse observable positions, it is possible to reconstruct the $\Theta$-convex domain of the trajectory. Intensive 3D numerical tests with synthetic data are performed to show effectiveness and feasibility of this new algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:2212.1423

Direct sampling method to inverse wave-number-dependent source problems (part I): determination of the support of a stationary source

This paper is concerned with a direct sampling method for imaging the support of a frequency-dependent source term embedded in a homogeneous and isotropic medium. The source term is given by the Fourier transform of a time-dependent source whose radiating period in the time domain is known. The time-dependent source is supposed to be stationary in the sense that its compact support does not vary along the time variable. Via a multi-frequency direct sampling method, we show that the smallest strip containing the source support and perpendicular to the observation direction can be recovered from far-field patterns at a fixed observation angle. With multiple but sparse observation directions, the shape of the convex hull of the source support can be recovered. The frequency-domain analysis performed here can be used to handle inverse time-dependent source problems. Our algorithm has low computational overhead and is robust against noise. Numerical experiments in both two and three dimensions have proved our theoretical findings

Dynamics of mass-spring-belt friction self-excited vibration system

In order to deeply study the non-smooth dynamic mechanism of self-excited vibration, the friction self-excited vibration system model containing the Stribeck friction model is established, which is a nonlinear dynamical mass-spring-belt model. For the established model, the critical instability speed is solved by the first approximate stability criterion of Lyapunov theory, and the stability of limit cycle is determined on the basis of curvature coefficient. Secondly, the bifurcation characteristics and system behaviors under different parameters are analyzed by using numerical simulation method. The results show that the theoretical analysis is feasible. Feed speed, damping coefficient and ratio of dynamic-static friction coefficient are the main factors that affect the system motion state. Thirdly, the Washout filter method is designed to control the bifurcation characteristics. By comparing the pre and post phase diagrams, results show that the amplitude of controlled system is reduced and the topology is improved after introducing the Washout filter. All the researches above prove that adding Washout filter into the system to control the bifurcation phenomenon is a more effective method

Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin

Digital twin is a key enabler to facilitate the development and implementation of new technologies in 5G and beyond networks. However, the complex structure and diverse functions of the current 5G core network, especially the control plane, lead to difficulties in building the core network of the digital twin. In this paper, we propose two novel data-driven architectures for modeling the 5G control plane and implement corresponding deep learning models, namely 5GC-Seq2Seq and 5GC-former, based on the Vanilla Seq2Seq model and Transformer decoder respectively. To train and test models, we also present a solution that allows the signaling messages to be interconverted with vectors, which can be utilized in dataset construction. The experiments are based on 5G core network signaling data collected by the Spirent C50 network tester, including various procedures related to registration, handover, PDU sessions, etc. Our results show that 5GC-Seq2Seq achieves over 99.98% F1-score (A metric to measure the accuracy of positive samples) with a relatively simple structure, while 5GC-former attains higher than 99.998% F1-score by establishing a more complex and highly parallel model, indicating that the method proposed in this paper reproduces the major functions of the core network control plane in 5G digital twin with high accuracy

DIFER: Differentiable Automated Feature Engineering

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to treating AutoFE as a coarse-grained black-box optimization problem over a discrete space. In this work, we propose an efficient gradient-based method called DIFER to perform differentiable automated feature engineering in a continuous vector space. DIFER selects potential features based on evolutionary algorithm and leverages an encoder-predictor-decoder controller to optimize existing features. We map features into the continuous vector space via the encoder, optimize the embedding along the gradient direction induced by the predicted score, and recover better features from the optimized embedding by the decoder. Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.Comment: 8 pages, 5 figure

A time domain sampling method for inverse acoustic scattering problems

This work concerns the inverse scattering problems of imaging unknown/inaccessible scatterers by transient acoustic near-field measurements. Based on the analysis of the migration method, we propose efficient and effective sampling schemes for imaging small and extended scatterers from knowledge of time-dependent scattered data due to incident impulsive point sources. Though the inverse scattering problems are known to be nonlinear and ill-posed, the proposed imaging algorithms are totally ``direct'' involving only integral calculations on the measurement surface. Theoretical justifications are presented and numerical experiments are conducted to demonstrate the effectiveness and robustness of our methods. In particular, the proposed static imaging functionals enhance the performance of the total focusing method (TFM) and the dynamic imaging functionals show analogous behavior to the time reversal inversion but without solving time-dependent wave equations

Effect of Panax notoginsenoside Rg1 on bidirectional regulation of blood glucose level in mice

230-235Panax notoginseng saponin (PNS) is one of the key bioactive components of dry root and rhizome of Panax notoginseng (Burk.), a well known as Tianqi in Traditional Chinese Medicine (TCM). Although PNS has been shown to possess various pharmacological activities, such as being antithrombotic, neuroprotective, anti-inflammatory and hypolipidemic, etc.,  its effects on blood glucose levels have not been well documented but for some preliminary reports. It deserves a detailed in vivo investigation in animal model. Thus, to investigate the bi-directional regulation of ginsenoside Rgl (Rg1) on blood glucose in mice, Rg1 with high purity was prepared from panax notoginseng saponins by normal phase silica gel column chromatography and reverse phase C18 preparative chromatography. Normal 4-week-old mice were randomly divided into normal control group, normal control group with Rg1, glucose gavage control group, glucose gavage control group with Rg1, insulin treated control group and overnight fasting control group with or without Rg1 (n = 10). The mice in the control group were intragastrically administered with PBS solution, and the mice in the Rg1 groups were intragastrically administered with Rg1 once a day at the doses of 0.5, 1.0 and 1.5 mg/kg for consecutive 7 days. After the last drug, blood glucose (BG) levels were measured at 0.5 (30 min) and 1 h after administration using a simultaneous automatic biochemical analyzer to observe the effect of Rg1 on BG levels. Compared with the model group, Rg1 significantly decreased the BG levels of hyperglycemic mice induced by glucose gavage (P P <0.05), and had no significant effects on the normal group of mice. Panax notoginseng saponin Rgl has a significant bidirectional regulatory effect on glucose levels in mice. When the blood glucose of mice increases, intragastric administration of Rg1 can effectively reduce the blood glucose; conversely, when the blood glucose of mice is low, Rg1 can effectively increase the blood glucose value

Clustering-Based Energy-Efficient Broadcast Tree in Wireless Networks

The characteristics of wireless networks present formidable challenges to the study of broadcasting problem. A crucial issue in wireless networks is the energy consumption, because of the nonlinear attenuation properties of radio signals. Another crucial issue is the trade-off between reaching more nodes in a single hop by using higher power versus reaching fewer nodes in that single hop by using lower power. Given a wireless network with a specified source node that broadcasts messages to all other nodes in the network, the minimum energy broadcast (MEB) problem is NP-hard. In this paper, we propose a hybrid approach CBEEB(clustering-based energy-efficient broadcast) for the MEB problem based on clustering. Theoretical analysis indicates the efficiency and effectiveness of CBEEB. Simulation results show that CBEEB has better performance compared with the existing heuristic approaches