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Full-Wave Decomposition and Mode Wave Analysis of Through-Tubing Monopole Acoustic Logging

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ji, Yunjia Huang, Jing Wang, Hua Bi, Qizhi Shi, Shaopeng Gu, Yiming Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Educ Guilin 541004 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

The integrity of cement used to bond steel casings and formation rock in cased wells is crucial for the hydraulic isolation of reservoir layers with shallow aquifers, production, and environmental safety. Geophysical acoustic logging is a highly effective tool for evaluating cement quality and has been widely used in single-cased wells. However, its performance diminishes in the presence of production tubings, which is common in plug and abandonment (P&A) issues. This reduced effectiveness primarily stems from the increased complexity of the recorded full-wave information, which becomes more challenging to interpret compared to cases without tubing. To address this challenge, this study investigates the characteristics of wave propagation excited by a point source in a through-tubing acoustic logging model. Mode waves in both good and bad bonding cases are fully identified by solving the associated characteristic equations. A full-wave decomposition method is proposed by evaluating the contribution of singularities in the complex-wavenumber plane using a complex function theory. A simulation example with a source frequency of 6 kHz and a receiver offset of 3 m is presented to illustrate the methodology. The correctness of the proposed method is validated through a comparison of full waveforms obtained by direct integration and component wave superposition methods. Research findings reveal that poor bonding quality introduces an additional Stoneley mode, which could potentially serve as an indicator of cement integrity. However, this mode is relatively weak, suggesting that advanced signal processing techniques may be required to enhance and detect such signals in practical applications.

关键词： Acoustics Cement industry Fluids Mathematical models Bonding Solids Propagation Analytical models Production Numerical models Cement integrity double strings downhole methods full-wave decomposition wave propagation

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Complementary performance analysis of general complex-valued diffusion LMS for noncircular signals

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SIGNAL PROCESSING 2019年 160卷 237-246页

作者： Shao, Xiaodan Chen, Feng Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing 400715 Peoples R China

A novel transient and steady-state complementary mean square analysis of the general diffusion complex least mean square (G-DCLMS) algorithm, including diffusion complex LMS (DCLMS) and the diffusion augmented complex LMS (DACLMS) algorithm is proposed. Considering the second order noncircular input and noise, we first propose the network complementary mean square error (NCMSE) and the complementary covariance matrix of weight error vector for G-DCLMS algorithm. Then, we quantify the real and imaginary components of the network MSE and network MSD of G-DCLMS independently using the proposed analysis approach, jointly with current network standard mean square error analysis method. Based on the similarity conjecture and the approximate uncorrelating transform (AUT), we develop a stability bound on the step-size to guarantee the stability of the proposed complementary covariance matrix of weight error vector, and also obtain the steady-state performance of the proposed NCMSE of G-DCLMS. The simulation results verify the effectiveness of the newly proposed analysis approach. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Distributed estimation Diffusion complex LMS Diffusion augmented complex LMS Second order noncircularity Network complementary mean square error

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Broken-motifs diffusion LMS algorithm for reducing communication load

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SIGNAL PROCESSING 2017年 133卷 213-218页

作者： Chen, Feng Shao, Xiaodan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing 400715 Peoples R China

This paper considers the diffusion LMS (DLMS) algorithm over wireless sensor network with motifs. Network motifs are local structural patterns which are quite common in wireless sensor network. In this paper, it is found that the estimation performance of DLMS algorithm has regular changes with the varying of motifs. This result supplies some guidelines on how to choose network with optimized estimation performance. Accordingly, a broken-motifs diffusion LMS algorithm (BM-DLMS) is proposed in which only a subset of edges are participated in communications per iteration. Simulations present that the BM-DLMS algorithm efficiently reduces the communication load with less performance degradation compared with other selection algorithms.

关键词： Wireless sensor network Diffusion LMS Network motifs Communication reduction

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Fast analysis of MEMS micromirror based on multi-physical electro-mechanical-damping coupling method

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JOURNAL OF MICROMECHANICS AND MICROENGINEERING 2025年第4期35卷 045005-045005页

作者： Chen, Shitao Su, Yu Yin, Qiupeng Zhou, Jie Li, Weichen Zhao, Yunong He, Kaixuan Guo, Xiaohui Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Luan Jianghuai Motor Co LTD Luan 237158 Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China 32nd Res Inst China Elect Technol Grp Corp Shanghai Peoples R China Anhui Univ Sch Integrated Circuits Hefei Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China East China Inst Photo Electron IC Bengbu Peoples R China

This paper presents an algorithm to improve the computational efficiency of electrostatically driven scanning micromirror with staggered vertical combs. Firstly, a single vertical comb is treated with an approximate periodic boundary. Through mechanical analysis, the mapping relationship between the deflection angle of the comb and the torque is obtained. It is found that when the deflection angle is small, the torque acting on the comb changes little and can be considered as a constant value. Based on the mapping relationship, the electrostatic torque of a single axis electrostatic scanning mirror with different voltages is extracted by considering only the electrostatic field action. In the subsequent deflection analysis of the micromirror, the extracted torque is added to the mechanical field, and the electrostatic field can be ignored. In order to further simplify the model and improve the computational efficiency, an equivalent model was established on the basis of the original single-axis scanning mirror. Immediately after, considering the effect of air damping, solid mechanics and thermal-viscous acoustic coupling calculations of the single-axis electrostatic driven mirror are carried out. Finally, the maximum mechanical deflection angle of the micromirror under different atmospheric pressure is obtained. The model proposed in this paper transforms the electro-mechanical coupling calculation into separate calculation of electrostatic field and mechanical field, which greatly improves the calculation efficiency.

关键词： MEMS single-axis electrostatic rotating micromirrors electrostatic torque the maximum mechanical deflection angle multi-physics simulation

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Brain Activation Differences of Six Basic Emotions Between 2D Screen and Virtual Reality Modalities

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 700-709页

作者： Xie, Jialan Lan, Ping Wang, Shiyuan Luo, Yutong Liu, Guangyuan Southwest Univ Inst Affect Comp & Informat Proc Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Inst Affect Comp & Informat ProcMinist Educ Coll Elect & Informat EngnKey Lab Cognit & Person Fac PsycholChongqing Key Lab Nonlinear Circuits & Chongqing 400715 Peoples R China

To our knowledge, it has been widely studied in Screen-2D modality for the six basic emotions proposed by Professor Paul Ekman, but there are only studies on their positive and negative valence in VR-3D modality. In this study, we will investigate whether the six basic emotions have stronger brain activation states in VR-3D modality than in Screen-2D modality. We designed an emotion-inducing experiment with six basic emotions (happiness, surprise, sadness, fear, anger, and disgust) to record the electroencephalogram (EEG) signals during watching VR-3D and Screen-2D videos. The power spectral density (PSD) was calculated to compare the brain activation differences between VR-3D and Screen-2D modalities during the induction of the six basic emotions. The results of statistical analysis of the relative power differences between VR-3D and Screen-2D modalities for each emotion revealed that both happiness and surprise presented greater differences in the $\alpha $ and $\gamma $ frequency bands, while sad, fear, disgust and anger all presented greater differences in the $\alpha $ and $\theta $ frequency bands, which are mainly observed in the frontal and occipital regions. On the other hand, the six emotions all yielded satisfactory classification accuracy (above 85%) by classification from a subset of power feature of the brain activation states in the same emotion between the two modalities. Overall, there are significant differences in the induction of same discrete emotions in VR-3D and Screen-2D modalities, with greater brain activation in VR-3D modalities. These findings provide a better understanding about the neural activity of discrete emotional tasks assessed in VR environments.

关键词： Videos Electroencephalography Visualization Three-dimensional displays Band-pass filters Particle measurements Atmospheric measurements Six basic emotions brain activation EEG virtual reality

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Error-robust multi-view subspace clustering with nonconvex low-rank tensor approximation and hyper-Laplacian graph embedding

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartC期133卷

作者： Pan, Baicheng Li, Chuandong Che, Hangjun Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Tensor based subspace clustering, a method aimed at partitioning multi-view data into distinct clusters through the tensor low-rank representation, has gained widespread popularity. Despite the effectiveness of tensor singular value decomposition (t-SVD) based nuclear norm in extracting high-dimensional information from multiple views, certain limitations persist. Firstly, while the commonly used l(2,1) norm enhances clustering robustness, it remains susceptible to the impact of high-intensity noises. Secondly, the t-SVD based nuclear norm provides a biased estimation of tensor rank. Thirdly, existing methods either fail to preserve manifold structures from the original space or only extract binary relationships between data points. To address these challenges and enhance clustering performance in both high-intensity noisy and real-world conditions, an error-robust multi-view subspace clustering with nonconvex low-rank tensor approximation and hyperLaplacian graph embedding (EMSC-NLTHG) method is proposed. Specifically, Cauchy loss function which reduces sensitivity to larger noises and outliers is introduced. Based on the Cauchy loss function, we develop the Cauchy pseudo norm to represent the reconstruction error in clustering to enhance the robustness. Moreover, rather than uniformly weighting all singular values in the t-SVD nuclear norm, a nonconvex tensor nuclear norm is adopted to add weights adaptively and approximate the true tensor rank. Additionally, hypergraph is embedded in the representation matrices to preserve local manifold structures and extract the complex multivariate relationships between data points. Extensive experiments demonstrate that the proposed EMSCNLTHG method consistently outperforms state-of-the-art techniques by approximately 18% to 40% on datasets corrupted by high-intensity noises and around 1% to 27% on eight real-world datasets across six popular evaluation metrics.

关键词： Multi-view clustering Low-rank tensor approximation Subspace clustering Error-robust clustering

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Novel Bi-doped ZnFe2O4 nanofibers based gas sensor for enhanced n-butanol sensing

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 157卷

作者： Wang, Bo Wang, Zhenxing Wan, Yu Wang, Yimu Gao, Shuang Chen, Yan Luo, Qingsong Feng, Changhao Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Background: Developing high-performance n-butanol gas sensors is of great significance for environmental protection and human health. The Bi-doped ZnFe2O4 nanofibers exhibit promising potential as a viable option for nbutanol sensors. Methods: In this study, a series of high response and fast response n-butanol gas sensors were successfully fabricated by Bi-doped ZnFe2O4 nanofibers with different atomic percentages (0 %, 1 %, 3 %, 5 %), which were synthesized by a facile electrospinning method. Significant findings: The nanofibers with a Bi/Zn molar ratio of 3 % showed a response value of 23.6 to 100 ppm nbutanol at 175 C-degrees, which was 2.4 times higher than that of the pure ZnFe2O4 nanofibers, and with a lower concentration detection limit (1.5 for 100 ppb n-butanol). In addition, the gas sensing materials had a good selectivity and long-term stability to n-butanol, and the mechanism of the good sensing performance of Bi doped ZnFe2O4 nanofibers for n-butanol is also analyzed in this paper.

关键词： Bi-doped znfe2o4 Electrospinning Nanofibers N -butanol sensors

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Exploring Regulation Effect of Jogging on Human Emotion Based on Pulse Signal 9

Exploring Regulation Effect of Jogging on Human Emotion Base...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yuan, Jianguang Liu, Guangyuan Southwest Univ Sch Elect & Informat Engn Key Lab Nonlinear Circuits & Intelligent Informat Chongqing Peoples R China

ISBN: (纸本)9781509035588

Until recently, claims for the psychological benefits of physical exercise have tended to precede supportive evidence. Actually, emotional effects of exercise remain confusing, both positive and negative effects being reported. Meanwhile, several studies have shown that the aerobic exercise has impact on human's mood through making subjective evaluation of the Profile of Mood States (POMS) before and after exercise. However, the conclusion via comparing with the Profile of Mood States (POMS) scores was lack of the objective basis. In order to explain the result that exercise has effects of adjustment to fear emotion from the objective point of view, therefore, this study researched exercise which can improve mood based on the subjects' physiological signal, and analyzed data using the affective computing. Issues involved in this study are discussed.

关键词： aerobic exercise mood physiological signal affective computing

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Reliability analysis of optical neural networks with non-ideal signal transmission

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OPTICAL FIBER TECHNOLOGY 2024年 87卷

作者： Su, Ye Fu, Pengju Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu Xie, Yiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing Peoples R China Key Lab Networks & Cloud Comp Secur Univ Chongqing Peoples R China

Machine learning plays a significant role in various fields. As a fundamental part of machine learning, matrix operations are one of the most important computational parts of neural networks. However, the advancement of Moore's law is hindered by limitations in the number and size of transistors on electronic chips. Therefore, implementing an optical neural network (ONN) using photonic devices to accomplish matrix-vector multiplication (MVM) has attracted significant attention. Compared with traditional electronic neural networks, ONN exhibits faster computational speed, higher parallel input capability and greater energy efficiency. Nevertheless, crosstalk and signal loss are inevitable characteristics in optical signal transmission, which can degrade the performance of ONN. In this paper, we present analysis models and simulations to assess the effects of crosstalk and loss on optical computing systems. We have observed that the accuracy of ONN degrades when crosstalk and loss are present. In severe cases, ONN is unable to complete the classification task. To mitigate the interference of crosstalk, we have designed a novel double-layer structure to reduce the number of optical devices required for signal transmission and optimize the impact of crosstalk on ONN performance. For the identical classification task, the ONN based on a double-layer structure exhibits better interference immunity than the generic ONN, with a 10% performance improvement. In particular, performance is improved by 50% when only crosstalk is considered.

关键词： Loss and crosstalk Optical neural networks Optical matrix computing

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Data-Driven Fault-Tolerant Reinforcement Learning Containment Control for nonlinear Multiagent Systems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第1期8卷 416-426页

作者： Wang, Xin Zhao, Chen Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400075 Peoples R China TM Univ Qatar Doha 23874 Qatar

This article concentrates on the data-driven containment problem for a class of nonlinear discrete-time multiagent systems via reinforcement learning. A novel two-layer control architecture is designed. In the first layer, a reference model is introduced with which all signals of the multiagent systems will reach synchronization. On account of the critic-actor neural network architecture, an adaptive neural network controller with a multigradient recursive reinforcement learning algorithm and less learning parameters method is designed to tackle the tracking issues and actuator faults. Then in the distributed control layer, the virtual containment control input is developed via policy iteration with critic-actor neural networks such that the containment error will converge to a small neighborhood of the origin. Note that the proposed method makes the solution of optimal containment control problem independent of system dynamics and takes energy costs into consideration. Besides, the semiglobally uniformly ultimately bounded property of signals in the closed-loop system and the policy iteration convergence are guaranteed. Finally, some numerical illustrations are attached to consolidate the effectiveness of our proposed mechanism.

关键词： Mutiagent systems containment control reinforcement learning data-driven actuator faults

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