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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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Adaptively Enhancing Facial Expression Crucial Regions via a Local Non-local Joint Network

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Machine intelligence Research 2024年第2期21卷 331-348页

作者： Guanghui Shi Shasha Mao Shuiping Gou Dandan Yan Licheng Jiao Lin Xiong School of artificial intelligence Xidian UniversityXi'an 710000China Applied Research Laboratory Sense TimeXi'an 710000China

Facial expression recognition(FER)is still challenging due to the small interclass discrepancy in facial expression data.In view of the significance of facial crucial regions for FER,many existing studies utilize the prior information from some annotated crucial points to improve the performance of FER.However,it is complicated and time-consuming to manually annotate facial crucial points,especially for vast wild expression images.Based on this,a local non-local joint network is proposed to adaptively enhance the facial crucial regions in feature learning of FER in this paper.In the proposed method,two parts are constructed based on facial local and non-local information,where an ensemble of multiple local networks is proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region.In particular,the attention weights obtained by the non-local network are fed into the local part to achieve interactive feedback between the facial global and local information.Interestingly,the non-local weights corresponding to local regions are gradually updated and higher weights are given to more crucial regions.Moreover,U-Net is employed to extract the integrated features of deep semantic information and low hierarchical detail information of expression images.Finally,experimental results illustrate that the proposed method achieves more competitive performance than several state-of-the-art methods on five benchmark datasets.

关键词： Facial expression recognition deep neural network multiple network ensemble attention network facial crucial regions

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Deciphering the Shared and Specific Drug Resistance Mechanisms of Anaplastic Lymphoma Kinase via Binding Free Energy Computation

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Research 2024年第1期2023卷 663-674页

作者： Yang Yu Zhe Wang Lingling Wang Qinghua Wang Rongfan Tang Sutong Xiang Qirui Deng Tingjun Hou Huiyong Sun Department of Medicinal Chemistry China Pharmaceutical UniversityNanjing 210009JiangsuP.R.China Innovation Institute for artificial intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058ZhejiangP.R.China

Anaplastic lymphoma kinase(ALK),a tyrosine receptor kinase,has been proven to be associated with the occurrence of numerous malignancies.Although there have been already at least 3 generations of ALK inhibitors approved by FDA or in clinical trials,the occurrence of various mutations seriously attenuates the effectiveness of the drugs.Unfortunately,most of the drug resistance mechanisms still remain obscure.Therefore,it is necessary to reveal the bottom reasons of the drug resistance mechanisms caused by the mutations.In this work,on the basis of verifying the accuracy of 2 main kinds of binding free energy calculation methodologies[end-point method of Molecular Mechanics with Poisson-Boltzmann/Generalized Born and Surface Area(MM/PB(GB)SA)and alchemical method of Thermodynamic Integration(TI)],we performed a systematic analysis on the ALK systems to explore the underlying shared and specific drug resistance mechanisms,covering the one-drug-multiple-mutation and multiple-drug-onemutation cases.

关键词： drugs Anaplastic clinical

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Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science China(Information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei ZHAO Xin LIU Yun LI State Key Laboratory of Reliability and intelligence of Electrical Equipment Hebei University of Technology School of artificial intelligence Hebei University of Technology School of Economics and Management Hebei University of Technology Industrial artificial intelligence Centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution

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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of artificial intelligence and Computer Science Jiangnan UniversityWuxi214122China The Center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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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 Technology

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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RecBERT:Semantic recommendation engine with large language model enhanced query segmentation for k-nearest neighbors ranking retrieval

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Intelligent and Converged Networks 2024年第1期5卷 42-52页

作者： Richard Wu the Dublin Unified School District and the SF artificial intelligence Club DublinCA 94568USA

The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments.Most modern recommendation systems rely on manual tagging,relying on administrators to label the features of a class,or story,which a user comment corresponds to.Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity,then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors.However,neither approach is able to fully utilize this user-generated unstructured natural language data,reducing the scope of these recommendation systems.This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed,and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class.In order to match a query containing content from multiple user comments belonging to the same class,the construction of a subquery channel for computing class-level similarity is proposed.This channel uses query segmentation of the aggregate query into subqueries,performing k-nearest neighbors(KNN)search on each individual subquery.RecBERT achieves state-of-the-art performance,outperforming other state-of-the-art models in accuracy,precision,recall,and F1 score for classifying comments between four and eight classes,respectively.RecBERT outperforms the most precise state-of-the-art model(distilRoBERTa)in precision by 6.97%for matching comments between eight classes.

关键词： sentence transformer simple contrastive learning large language models query segmentation k-nearest neighbors

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Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 708-722页

作者： Hongmin Liu Qi Zhang Yufan Hu Hui Zeng Bin Fan the School of intelligence Science and Technology and the Institute of artificial intelligence University of Science and Technology BeijingBeijing 100083China the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China IEEE

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

关键词： Multi-expert learning underwater image enhancement unsupervised learning

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Equilibrium Strategy of the Pursuit-Evasion Game in Three-Dimensional Space

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 446-458页

作者： Nuo Chen Linjing Li Wenji Mao School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049 Institute of Automation Chinese Academy of SciencesBeijing 100190China

The pursuit-evasion game models the strategic interaction among players, attracting attention in many realistic scenarios, such as missile guidance, unmanned aerial vehicles, and target defense. Existing studies mainly concentrate on the cooperative pursuit of multiple players in two-dimensional pursuit-evasion games. However, these approaches can hardly be applied to practical situations where players usually move in three-dimensional space with a three-degree-of-freedom control. In this paper,we make the first attempt to investigate the equilibrium strategy of the realistic pursuit-evasion game, in which the pursuer follows a three-degree-of-freedom control, and the evader moves freely. First, we describe the pursuer's three-degree-of-freedom control and the evader's relative coordinate. We then rigorously derive the equilibrium strategy by solving the retrogressive path equation according to the Hamilton-Jacobi-Bellman-Isaacs(HJBI) method, which divides the pursuit-evasion process into the navigation and acceleration phases. Besides, we analyze the maximum allowable speed for the pursuer to capture the evader successfully and provide the strategy with which the evader can escape when the pursuer's speed exceeds the threshold. We further conduct comparison tests with various unilateral deviations to verify that the proposed strategy forms a Nash equilibrium.

关键词： Differential game equilibrium strategy pursuit-evasion game threedegree-of-freedom control

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Nested Saturated Control of Uncertain Complex Cascade Systems Using Mixed Saturation Levels

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1163-1174页

作者： Meng Li Zhigang Zeng Wuhan National Laboratory for Optoelectronics Wuhan 430074 School of artificial intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of artificial intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

This study addresses the problem of global asymptotic stability for uncertain complex cascade systems composed of multiple integrator systems and non-strict feedforward nonlinear systems. To tackle the complexity inherent in such structures, a novel nested saturated control design is proposed that incorporates both constant saturation levels and state-dependent saturation levels. Specifically, a modified differentiable saturation function is proposed to facilitate the saturation reduction analysis of the uncertain complex cascade systems under the presence of mixed saturation levels. In addition, the design of modified differentiable saturation function will help to construct a hierarchical global convergence strategy to improve the robustness of control design scheme. Through calculation of relevant inequalities, time derivative of boundary surface and simple Lyapunov function,saturation reduction analysis and convergence analysis are carried out, and then a set of explicit parameter conditions are provided to ensure global asymptotic stability in the closed-loop systems. Finally, a simplified system of the mechanical model is presented to validate the effectiveness of the proposed method.

关键词： method saturation differentiable

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