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Combine deep learning and Bayesian analysis to separate overlapping gravitational wave signals

Science China(Physics,Mechanics & Astronomy) 2025年第5期68卷 205-219页

作者： Cunliang Ma Weiguang Zhou Zhoujian Cao Mingzhen Jia Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and TechnologyGanzhou 341000China Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCASHangzhou 310024China

Future gravitational wave(GW)observatories,such as the Einstein Telescope,are anticipated to encounter overlapping GW signals,presenting considerable obstacles to GW data processing techniques,including signal identification and parameter *** this letter,we propose a scheme of combining deep learning and Bayesian analysis to disentangle overlapping GW *** deep learning part takes a data-driven approach that employs an encoder-separation-decoder framework which is powerful enough to extract the shape of the signal even when the GW signals completely *** Bayesian analysis part takes the matched filtering technique to extract the amplitude of the GW *** scheme can facilitate the utilization of existing GW detection and parameter estimation methods for future instances of overlapping *** methodology effectively reduces biases in parameter estimation when handling multiple intertwined ***,this marks the first known instance where deep learning has been successfully utilized to disentangle overlapping GW signals.

关键词： gravitational wave separation machine learning Einstein Telescope parameter estimation

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Prediction of transmission spectrum from photonic energy band via autoencoder network

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IEEE Photonics technology Letters 2025年第13期37卷 737-740页

作者： Mo, Wenqin Zhu, Yaling Yin, Yuanxing Dong, Kaifeng Jin, Fang Song, Junlei China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Optical topological insulators, as an emerging type of photonic material, present substantial benefits for optical communication. The advanced pattern recognition capabilities of deep learning have propelled the inverse design of topological photonic structures into a hot area of research. However, existing methods encounter difficulties with the analysis of intricate band structures. Therefore, we construct a deep learning model capable of handling two-dimensional data of photonic energy band. Utilizing an autoencoder network, our model indirectly trains the photonic energy band and transmission spectrum, by converting them into feature coding. It integrates a transmission spectrum autoencoder and a photonic energy band autoencoder, processing energy band data to predict the corresponding transmission spectrum. The results indicate that compared to convolutional neural network (CNN), our model can accurately predict transmission spectrum, and perform better on both evaluation metrics of RMSE and R2. This work enhances prediction accuracy by leveraging energy band diagrams to constrain optical properties. © 1989-2012 IEEE.

关键词： Topological insulators

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A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and systems 2024年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation,capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application,the entire SNN is quantized and deployed on a real memristive hardware platform,achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： intelligent robots

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Simulation and optimization of inchworm-like pneumatic crawling actuator based on ABAQUS software 7

Simulation and optimization of inchworm-like pneumatic crawl...

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7th International Conference on Robotics, Control and Automation Engineering, RCAE 2024

作者： Mu, Junqi Zong, Xiaofeng Wei, Yiyang Hou, Xuping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

ISBN: (纸本)9798350355642

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators. © 2024 IEEE.

关键词： Aspect ratio

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Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion

Automatic Depression Detection Network Based on Facial Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Min Xu, Lingxiang Liu, Lei Wang, Xiaohua Li, Hongbo Yang, Jiaoyun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China Hefei University of Technology National Smart Eldercare International Science and Technology Cooperation Base Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350386226

Artificial intelligence methods offer objectivity and convenience in automatic depression detection, however, current research often neglects the critical role of facial landmarks. This oversight results in insufficient spatial structure information and a lack of detailed local representation, which fails to capture the nuanced semantic information crucial for identifying depression-related clues. To address these issues, we introduce a novel dual-branch network model comprising the Landmark-Image-Landmark Net (LIL Net) and the Global Context Vision Transformer Net (GCVit Net). Through a dual-stream, multiscale, and cross-fusion strategy, LIL Net is designed to extract original facial image features alongside landmark features, prioritizing the detailed semantic information of potential depression clues. LIL Net employs an innovative LIL Attention approach to jointly learn multiscale features from facial landmarks and images, thereby enhancing the model's ability to capture fine-grained depression-related cues. Furthermore, the Multi-scale Feature Fusion (MSFF) module fuses the obtained multiscale features, augmenting the semantic expression of potential depression clues within facial landmarks via attention mechanisms. Meanwhile, the GCVit Net branch network supplements global information by extracting global facial features. Finally, the features from both branches are concatenated to enhance the accuracy of depression degree predictions. Experimental results demonstrate that our model has superior performance in detecting depression compared to existing methods. We release our code at https://***/xlx777/LIL-Net. © 2024 IEEE.

关键词： Artificial intelligence

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A Deep GRU-Autoencoder for Dimentionality Reduction in Hand Skeleton Data Sequences classification 1

A Deep GRU-Autoencoder for Dimentionality Reduction in Hand ...

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1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023

作者： Ameur, Safa Mohamedali, Mahjoub K Hali Fa, Anouar Ben Universite de Sousse Ecole Nationale d'Ingenieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Universite de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350394689

Hand Gesture Recognition (HGR) has gained significant attention in various fields, including sign language interpretation and human-computer interaction. The classification of hand skeleton data sequences poses a challenge due to the high dimensionality of the input data. In this work, we propound a novel approach for dimensionality reduction in hand skeleton data sequences classification using a Deep Gated Reccurent Unit Autoencoder model (DGRU-AE). Our primary goal in this study is to efficiently decrease the dimensionality of hand gesture data, all the while retaining both the spatial and temporal information to ensure precise classification. We accomplish this by leveraging a DGRU-AE architecture, combining the strengths of recurrent neural networks and autoencoders. Our methodology involves preprocessing the hand skeleton data sequences, including normalization and feature extraction, through training the DGRUAE using suitable loss functions and optimization algorithms. Experimental evaluation on a benchmark datasets demonstrates the effectiveness of our suggested approach. The DGRU-AE achieves significant dimensionality reduction while maintaining high classification accuracy, outperforming existing methods. Our approach not only reduces computational complexity but also facilitates a more compact and meaningful representation of hand gestures. This work contributes to the advancement of HGE techniques by addressing the challenge of dimensionality reduction in classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Physics and applications of Raman distributed optical fiber sensing

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Light(Science & Applications) 2022年第6期11卷 1059-1087页

作者： Jian Li Mingjiang Zhang College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuanShanxi 030024China Key Laboratory of Advanced Transducers and Intelligent Control Systems(Ministry of Education and Shanxi Province) Taiyuan University of TechnologyTaiyuan 030024China

Raman distributed optical fiber sensing has been demonstrated to be a mature and versatile scheme that presents great flexibility and effectivity for the distributed temperature measurement of a wide range of engineering applications over other established *** past decades have witnessed its rapid development and extensive applicability ranging from scientific researches to industrial ***,there are four theoretical or technical bottlenecks in traditional Raman distributed optical fiber sensing:(ⅰ)The difference in the Raman optical attenuation,a low signal-to-noise ratio(SNR)of the system and the fixed error of the Raman demodulation equation restrict the temperature measurement accuracy of the system.{ⅱ)The sensing distance and spatial resolution cannot be reconciled.(ⅲ)There is a contradiction between the SNR and measurement time of the system.(ⅳ)Raman distributed optical fiber sensing cannot perform dual-parameter *** on the above theoretical and technical bottlenecks,advances in performance enhancements and typical applications of Raman distributed optical fiber sensing are reviewed in this *** of this optical system technology with knowledge based,that is,demodulation technology *** further the performance and accuracy of these systems.

关键词： fiber optical contradiction

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Multi-UAV Coverage Path Planning Method Based on Improved Particle Swarm Optimization

Multi-UAV Coverage Path Planning Method Based on Improved Pa...

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2024 China Automation Congress, CAC 2024

作者： Zhou, Yan Xiong, Yonghua School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350368604

The widespread application of unmanned aerial vehicle (UAV) has made the coverage path planning problem for multiple UAV systems a research hotspot. In this paper, we propose a multi-UAV solution to achieve complete coverage of multi-region. Based on this solution, we present an improved particle swarm optimization (PSO) algorithm that combines PSO algorithm, greedy strategy, and back-and-forth (BAF) algorithm. Its purpose is to plan the optimal paths for multi-UAV while achieving the shortest total distance and the highest UAV cooperation efficiency with the minimum number of UAVs, thereby enhancing the multi-UAV coverage cooperation and path optimization capabilities. Simulation results demonstrate the feasibility and superiority of this algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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3D Geometry Design of the Human Thorax and Forward Calculation for Electrical Impedance Tomography 20

3D Geometry Design of the Human Thorax and Forward Calculati...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Alwaz, Youssef Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa University of Sousse National Engineering School of Sousse Sousse Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Technische Universität Chemnitz Measurement and Sensor Technology Chemnitz09111 Germany

ISBN: (纸本)9798350332568

Electrical Impedance Tomography (EIT) for lung monitoring reflects the internal conductivity distribution of human tissues based on the boundary voltages that are measured through given electrodes. The electrodes are placed around the human chest. A low-amplitude current is injected through driving electrodes, and the boundary voltages are measured through other electrodes. In this context, this work has elaborated a strategy for the design of a 3D geometry of the human thorax based on sequential Computed Tomography (CT) scans. This geometry will be used for the simulation and analysis of the EIT measurement. Image segmentation has been used to extract the contour within different layers of the CT scans to form the final 3D geometry of the chest containing the lungs and the thorax. The generated geometry was later meshed and repaired. Mesh properties were controlled through the Skewness quality metric. A multi-layered design has been elaborated to mimic different human chest layers such as body fluid, muscle, and skin resulting in three models. These models had given the accuracy of 62%, 63%, and 65% of skewed elements relative to the existing volume. Later on, the electrodes were placed on the thorax geometry for EIT forward simulation, and the effect of the multi-layered model has been investigated. © 2023 IEEE.

关键词： Finite element method

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