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

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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

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

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 622-637页

作者： Han Xu Jiayi Ma Yixuan Yuan Hao Zhang Xin Tian Xiaojie Guo the Electronic Information School Wuhan UniversityWuhan 430072China IEEE the Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China the college of intelligence and computing Tianjin UniversityTianjin 300350China

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://***/hanna-xu/SLIE.

关键词： Color correction low-light image enhancement self-supervised learning.

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Computational Experiments for Complex Social Systems:Integrated Design of Experiment System

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1175-1189页

作者： Xiao Xue Xiangning Yu Deyu Zhou Xiao Wang Chongke Bi Shufang Wang Fei-Yue Wang college of intelligence and computing Tianjin UniversityTianjin 300350China School of Software Shandong UniversityJinan 250101China School of Artificial intelligence Anhui UniversityHefei 230601China School of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China Institute of Automation Chinese Academy of Sciences Beijing 100190China

Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the operations of complex social systems.Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems.Against this backdrop,computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation(e.g.,ABM),complexity science,and domain knowledge.However,in the process of applying computational experiments,the construction of experiment system not only considers a large number of artificial society models,but also involves a large amount of data and knowledge.As a result,how to integrate various data,model and knowledge to achieve a running experiment system has become a key challenge.This paper proposes an integrated design framework of computational experiment system,which is composed of four parts:generation of digital subject,generation of digital object,design of operation engine,and construction of experiment system.Finally,this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.

关键词： artificial system operations

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

Enhancing IoT Security:Quantum-Level Resilience against Threats

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Computers, Materials & Continua 2024年第1期78卷 329-356页

作者： Hosam Alhakami college of computing Umm Al-Qura UniversityMakkahSaudi Arabia

The rapid growth of the Internet of Things(IoT)operations has necessitated the incorporation of quantum computing technologies tomeet its expanding needs.This integration ismotivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario.The combination of IoT and quantum computing creates new privacy and security problems.This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications.We investigate the incorporation of quantum computing approaches within IoT security frameworks,with a focus on developing effective security mechanisms.Our research,which uses quantum algorithms and cryptographic protocols,provides a unique solution to protecting sensitive information and assuring the integrity of IoT systems.We rigorously analyze critical quantum computing security properties,building a hierarchical framework for systematic examination.We offer concrete solutions flexible to diverse aswell as ambiguous opinions through using a unified computational model with analytical hierarchy process(AHP)multi-criteria decision-making(MCDM)as the technique for ordering preferences by similarity to ideal solutions(TOPSIS)in a fuzzy environment.This study adds practical benefit by supporting practitioners in recognizing,choosing,and prioritizing essential security factors from the standpoint of quantum computing.Our approach is a critical step towards improving quantum-level security in IoT systems,strengthening their resilience against future threats,and preserving the IoT ecosystem’s long-term prosperity.

关键词： Quantum security quantum computing Internet of Things fuzzy decision-making

Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

Fast Fourier transform convolution neural network accelerator based on overlap addition

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The Journal of China Universities of Posts and Telecommunications 2024年

作者： You Chen Li Dejian Feng Xi Shen Chongfei Wei Jizeng Liu Yu School of Microelectronics Tianjin University college of intelligence and computing Tianjin University Beijing Smart-Chip Microelectronics Technology Company Limited

In convolutional neural networks（CNNs）， the floating point computation in the traditional convolution layer is enormous， and the execution speed of the network is limited by intensive computing， which makes it challenging to meet the real-time response requirements of complex applications. This work is based on the principle that the time domain convolution result equals the frequency domain dot multiplication result to reduce the amount of floating-point calculations for convolution. The input feature map and the convolution kernel are converted to the frequency domain by the fast Fourier transform（FFT）， and the corresponding dot multiplication is performed. Then the frequency domain result is converted back to the time domain， and the output result of the convolution is obtained. In the shared CNN， the input feature map is much larger than the convolution kernel， resulting in many invalid operations. The overlap addition method is proposed to reduce invalid calculations and speed up network execution better. This work designs a hardware accelerator for frequency domain convolution and verifies its efficiency on the Xilinx ZYNQ UltraScale+MPSoC ZCU102 board: comparing the calculation time of Visual Geometry Group16(VGG16) under the ImageNet dataset， compared to traditional convolution accelerators， the hardware acceleration of frequency-domain convolution has improved performance by 8.5 times.

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