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An underwater vest containing an antioxidant MXene hydrogel for sensitive recognition of fish locomotion

Microsystems & Nanoengineering 2024年第2期10卷 115-131页

作者： Chengxiu Yang Jiafei Hu Lihui Liu Shaowei Wu Mengchun Pan Yan Liu Haomiao Wang Peisen Li Qi Zhang Weicheng Qiu Huihui Luo college of intelligence Science and Technology National University of Defense TechnologyChangsha410073China

The perception of fish locomotion is important for understanding their adaptive behaviors and ethological characteristics.However,the main strategy used for extracting fish attitudes involves the use of a vision-based monitoring system,which is limited in its range of observation and cannot perform tracking for long times.Here,we report the use of a wearable tagging electronic device,referred to as an underwater vest,to capture the surrounding flow field disturbances triggered by swimming or momentary postural changes.All of these goals were achieved by integrating a pair of pseudocapacitive pressure-sensing units and a flexible circuit board.Notably,additional conditions,such as variable hydraulic pressures and minimal changes in fish posture,require high stability and sensitivity of the sensing units.Thus,hybrid hydrogel electrodes were developed through cross-linking MXene with holey-reduced graphene oxide nanosheets and further modification with 1-ethyl-3-methylimidazolium dicyanamide ionic liquids,which increased the interfacial capacitance and long-term interfacial activity of the MXene.Consequently,the sensing unit exhibited ultrahigh sensitivity(S_(max)~136,207 kPa^(−1))in an aquatic environment for 60 days and superior high-pressure resolution(10 Pa)within a wide working range of 1 MPa.Ultimately,an underwater vest integrated with such sensing units clearly distinguished and recorded fish locomotion.We believe that the designed device may open avenues in flow field monitoring and ocean current detection and provide new insights into the development of sensitive underwater tagging.

关键词： underwater fish linking

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Bio-inspired Design and Inverse Kinematics Solution of an Omnidirectional Humanoid Robotic Arm with Geometric and Load Capacity Constraints

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Journal of Bionic Engineering 2024年第2期21卷 778-802页

作者： Zhichao Zhu Zirong Luo Yiming Zhu Tao Jiang Minghai Xia Shanjun Chen Boyu Jin college of intelligence Science and Technology National University of Defense TechnologyChangshaChina

Inspired by the driving muscles of the human arm,a 4-Degree of Freedom(DOF)concentrated driving humanoid robotic arm is proposed based on a spatial double parallel four-bar mechanism.The four-bar mechanism design reduces the inertia of the elbow-driving unit and the torque by 76.65%and 57.81%,respectively.Mimicking the human pose regulation strategy that the human arm picks up a heavy object by adjusting its posture naturally without complicated control,the robotic arm features an integrated position-level closed-form inverse solution method considering both geometric and load capacity limitations.This method consists of a geometric constraint model incorporating the arm angle(φ)and the Global Configuration(GC)to avoid joint limits and singularities,and a load capacity model to constrain the feasible domain of the arm angle.Further,trajectory tracking simulations and experiments are conducted to validate the feasibility of the proposed inverse solution method.The simulated maximum output torque,maximum output power and total energy consumption of the robotic arm are reduced by up to 2.0%,13.3%,and 33.3%,respectively.The experimental results demonstrate that the robotic arm can bear heavy loads in a human-like posture,effectively reducing the maximum output torque and energy consumption of the robotic arm by 1.83%and 5.03%,respectively,while avoiding joints beyond geometric and load capacity limitations.The proposed design provides a high payload–weight ratio and an efficient pose control solution for robotic arms,which can potentially broaden the application spectrum of humanoid robots.

关键词： Humanoid robotic arm Bio-inspired design Inverse kinematics solution Load capacity constraint Geometric constraint

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Six million Q factor micro fused silica shell resonator with teeth-like tines released by femtosecond laser-assisted chemical etching

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Science China(Technological Sciences) 2024年第6期67卷 1923-1932页

作者： ZHAO Tao SHI Yan LU Kun XI Xiang WU XueZhong XIAO DingBang college of intelligence Science and Engineering National University of Defense TechnologyChangsha410073China

The quality factor(Q factor)is a crucial performance parameter for resonators.In this paper,a novel release method for highquality micro fused silica shell resonators with teeth-like tines with good surface morphology is proposed.This method is based on femtosecond laser-assisted chemical etching.First,the optimal energy range of femtosecond laser modification is obtained through mechanism analysis.Second,the optimal parameters for a straight line and arc pattern are determined by optimizing the average output power,processing speed,and processing spacing.The results demonstrate why edge breakage in rounded corners is easy under different parameters.Finally,according to these conclusions,the processing is performed on a micro fused silica shell resonator with a Q factor exceeding 6 million.In addition,subsurface damage is rare throughout the fabrication process,and the surface roughness of the released cross section reaches the nanometer level.The improved Q factor helps suppress mechanical thermal noise and reduce zero bias and zero bias drift,constituting the primary method for enhancing the performance of the resonant gyroscope.

关键词： micro hemispherical resonator fused silica laser-assisted chemical etching quality factor

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A deep Koopman operator-based modelling approach for long-term prediction of dynamics with pixel-level measurements

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CAAI Transactions on intelligence Technology 2024年第1期9卷 178-196页

作者： Yongqian Xiao Zixin Tang Xin Xu Xinglong Zhang Yifei Shi college of intelligence Science and Technology National University of Defense TechnologyChangshaChina

Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term *** adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.

关键词： deep neural networks image motion analysis image sequences sequential estimation

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Small tracking error correction for moving targets of intelligent electro-optical detection systems

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Frontiers of Mechanical Engineering 2024年第2期19卷 29-44页

作者： Cheng SHEN Zhijie WEN Wenliang ZHU Dapeng FAN Mingyuan LING college of intelligence Science and Technology National University of Defense TechnologyChangsha 410073China

Small tracking error correction for electro-optical systems is essential to improve the tracking precision of future mechanical and defense technology.Aerial threats,such as“low,slow,and small(LSS)”moving targets,pose increasing challenges to society.The core goal of this work is to address the issues,such as small tracking error correction and aiming control,of electro-optical detection systems by using mechatronics drive modeling,composite velocity–image stability control,and improved interpolation filter design.A tracking controller delay prediction method for moving targets is proposed based on the Euler transformation model of a two-axis,two-gimbal cantilever beam coaxial configuration.Small tracking error formation is analyzed in detail to reveal the scientific mechanism of composite control between the tracking controller’s feedback and the motor’s velocity–stability loop.An improved segmental interpolation filtering algorithm is established by combining line of sight(LOS)position correction and multivariable typical tracking fault diagnosis.Then,a platform with 2 degrees of freedom is used to test the system.An LSS moving target shooting object with a tracking distance of S=100 m,target board area of A=1 m^(2),and target linear velocity of v=5 m/s is simulated.Results show that the optimal method’s distribution probability of the tracking error in a circle with a radius of 1 mrad is 66.7%,and that of the traditional method is 41.6%.Compared with the LOS shooting accuracy of the traditional method,the LOS shooting accuracy of the optimized method is improved by 37.6%.

关键词： electro-optical detection system small tracking error moving target visual servo aiming contro

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Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1398-1409页

作者： Lihong Feng Bonan Huang Jiayue Sun Qiuye Sun Xiangpeng Xie college of Information Science and Engineering Northeastern UniversityShenyang 110819China college of Automation and college of Artificial intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China IEEE

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

关键词： Adaptive control event-triggered mechanisms for-mation containment(FC) heterogeneous multiagent systems time-varying group formation.

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RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

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Computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang college of Computer Science and Artificial intelligence Wenzhou UniversityWenzhou325035China Key Laboratory of intelligence Image Processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

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Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

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Chinese Physics B 2024年第4期33卷 245-252页

作者：吴亚勇王欣伟蒋国平 college of Automation and college of Artificial intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Engineering Laboratory for IOT Intelligent Robots Nanjing 210023China

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

关键词： multi-layer complex dynamical network nonlinear node dynamics target state estimation functional state observer

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Short-time maritime target detection based on polarization scattering characteristics

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Journal of Systems Engineering and Electronics 2024年第1期35卷 55-64页

作者： CHEN Shichao LUO Feng TIAN Min LYU Wanghan college of Computer and Information Engineering(college of Artificial intelligence) Nanjing Tech UniversityNanjing 211816China Hangzhou Institute of Technology Xidian UniversityHangzhou 311200China

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

关键词： sea clutter small target radar detection Cameron decomposition characteristics analysis

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A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting XIA Chunxu ZHANG Yan ZHANG Xueyan LIU Bo YANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University college of Artificial intelligence Jilin University college of Computer Science and Technology Jilin University college of Communication Engineering Jilin University

Graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named Graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification

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