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Thermogalvanic hydrogel-based e-skin for self-powered on-body dual-modal temperature and strain sensing

Microsystems & Nanoengineering 2024年第2期10卷 293-302页

作者： Zhaosu Wang Ning Li Xinru Yang Zhiyi Zhang Hulin Zhang Xiaojing Cui college of Electronic information and Optical engineering Taiyuan University of TechnologyTaiyuan030024China college of Materials Science and engineering Taiyuan University of TechnologyTaiyuan030024China School of Physics and information engineering Shanxi Normal UniversityTaiyuan030031China

Sensing of both temperature and strain is crucial for various diagnostic and therapeutic purposes.Here,we present a novel hydrogel-based electronic skin(e-skin)capable of dual-mode sensing of temperature and strain.The thermocouple ion selected for this study is the iodine/triiodide(I−/I3−)redox couple,which is a common component in everyday disinfectants.By leveraging the thermoelectric conversion in conjunction with the inherent piezoresistive effect of a gel electrolyte,self-powered sensing is achieved by utilizing the temperature difference between the human body and the external environment.The composite hydrogels synthesized from polyvinyl alcohol(PVA)monomers using a simple freeze‒thaw method exhibit remarkable flexibility,extensibility,and adaptability to human tissue.The incorporation of zwitterions further augments the resistance of the hydrogel to dehydration and low temperatures,allowing maintenance of more than 90%of its weight after 48 h in the air.Given its robust thermal current response,the hydrogel was encapsulated and then integrated onto various areas of the human body,including the cheeks,fingers,and elbows.Furthermore,the detection of the head-down state and the monitoring of foot movements demonstrate the promising application of the hydrogel in supervising the neck posture of sedentary office workers and the activity status.The successful demonstration of self-powered on-body temperature and strain sensing opens up new possibilities for wearable intelligent electronics and robotics.

关键词： hydrogel modal utilizing

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Enhancing the Goos-Hänchen shift based on quasi-bound states in the continuum through material asymmetric dielectric compound gratings

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Chinese Physics B 2024年第3期33卷 393-400页

作者：江孝伟方彬占春连 college of Optical and Electronic technology China Jiliang UniversityHangzhou 310018China college of information engineering Quzhou College of TechnologyQuzhou 324000China

Quasi-bound state in the continuum(QBIC)resonance is gradually attracting attention and being applied in Goos-Hänchen(GH)shift enhancement due to its high quality(Q)factor and superior optical confinement.Currently,symmetry-protected QBIC resonance is often achieved by breaking the geometric symmetry,but few cases are achieved by breaking the material symmetry.This paper proposes a dielectric compound grating to achieve a high Q factor and high-reflection symmetry-protectede QBIC resonance based on material asymmetry.Theoretical calculations show that the symmetry-protected QBIC resonance achieved by material asymmetry can significantly increase the GH shift up to-980 times the resonance wavelength,and the maximum GH shift is located at the reflection peak with unity reflectance.This paper provides a theoretical basis for designing and fabricating high-performance GH shift tunable metasurfaces/dielectric gratings in the future.

关键词： bound state in the continuum Goos-Hänchen shift dielectric compound grating material asymmetry

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Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

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Computers, Materials & Continua 2024年第2期78卷 2309-2335页

作者： Huixiang Xu college of information engineering Zhengzhou University of TechnologyZhengzhou450044China

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning p

关键词： Internet of Things(IoT) edge computing traffic data self-learning fuzzy-learning

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Constructing the Impossible Differential of Type-II GFN with Boolean Function and Its Application to WARP

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Chinese Journal of Electronics 2024年第1期33卷 80-89页

作者： Jiali SHI Guoqiang LIU Chao LI college of Liberal Arts and Sciences National University of Defense Technology Hunan engineering Research Center of Commercial Cryptography Theory and technology Innovation State Key Laboratory of information Security Institute of Information Engineering

Type-Ⅱ generalized Feist el network(GFN) has attracted a lot of attention for its simplicity and high parallelism.Impossible differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like ciphers.We deduce the impossible differential of Type-Ⅱ GFN by analyzing the Boolean function in the middle round.The main idea is to investigate the expression with the variable representing the plaintext(ciphertext) difference words for the internal state words.By adopting the miss-in-the-middle approach,we can construct the impossible differential of Type-Ⅱ GFN.As an illustration,we apply this approach to WARP,a lightweight 128-bit block cipher with a 128-bit key which was presented by Banik et al.at SAC 2020.The structure of WARP is a 32-branch Type-Ⅱ GFN.Therefore,we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on WARP.For the 32-round key recovery attack on WARP,some observations are used to mount an effective attack.Taking the advantage of the early abort technique,the data,time,and memory complexities are 2125.69chosen plaintexts,2126.6832-round encryptions,and 2100-bit,repectively.To the best of our knowledge,this is the best attack on WARP in the single-key scenario.

关键词： WARP Feistel cipher Impossible differential attack

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Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

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Computer Modeling in engineering & Sciences 2024年第5期139卷 1239-1261页

作者： Yujie Duan Jianguo Liang Jianglin Liu Haifeng Gao Yinhui Li Jinzhu Zhang Xinyu Wen college of Mechanical and Vehicle engineering Taiyuan University of TechnologyTaiyuan030024China college of Electronic information and Optical engineering Taiyuan University of TechnologyTaiyuan030024China School of Electronic information engineering Taiyuan University of Science and TechnologyTaiyuan030000China

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

关键词： Constant tension control anti-fluctuation strategy tension fluctuation observer time-varying parameters fractional-order PID controller feedforward compensate

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Two simple memristive maps with adaptive energy regulation ano digital signal process verification

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第5期25卷 382-394页

作者： Feifei YANG Lujie REN Jun MA Zhigang ZHU college of Electrical and information engineering Lanzhou University of TechnologyLanzhou 730050China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of information Science and engineering Dalian Polytechnic UniversityDalian 116034China

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms,while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy flow.The involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive systems.In this study,two kinds of memristors are used to couple a capacitor or an inductor,along with a nonlinear resistor,to build different neural circuits.The corresponding circuit equations are derived to develop two different types of memristive oscillators,which are further converted into two kinds of memristive maps after linear transformation.The Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive circuits.The Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and variables.The two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation,and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps.A digital signal process(DSP)platform is used to verify these results.Our scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.

关键词： Hamilton energy Discrete memristor Self-adaptive regulation Digital signal process(DSP)implementation

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A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

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Science China Technological Sciences 2024年第7期67卷 2074-2086页

作者： WANG Rui LI Ming-Jia WANG YiBo SUN QiuYe ZHANG PinJia college of information Science and engineering Northeastern University School of Mechanical engineering Beijing Institute of Technology Department of Electrical engineering Tsinghua University

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

关键词： regional integrated electric-thermal energy systems real-time power balance virtual asynchronous machine adaptive inertia small-signal stability

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Effects of vacancy and external electric field on the electronic properties of the MoSi_(2)N_(4)/graphene heterostructure

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Chinese Physics B 2024年第3期33卷 542-550页

作者：梁前罗祥燕钱国林王远帆梁永超谢泉 college of Big Data and information engineering Institute of New Optoelectronic Materials and TechnologyGuizhou UniversityGuiyang 550025China

Recently,the newly synthesized septuple-atomic layer two-dimensional(2D)material MoSi_(2)N_(4)(MSN)has attracted attention worldwide.Our work delves into the effect of vacancies and external electric fields on the electronic properties of the MSN/graphene(Gr)heterostructure using first-principles calculation.We find that four types of defective structures,N-in,N-out,Si and Mo vacancy defects of monolayer MSN and MSN/Gr heterostructure are stable in air.Moreover,vacancy defects can effectively modulate the charge transfer at the interface of the MSN/Gr heterostructure as well as the work function of the pristine monolayer MSN and MSN/Gr heterostructure.Finally,the application of an external electric field enables the dynamic switching between n-type and p-type Schottky contacts.Our work may offer the possibility of exceeding the capabilities of conventional Schottky diodes based on MSN/Gr heterostructures.

关键词： MoSi_(2)N_(4) vacancy defects external electric field Schottky contacts

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A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

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Global Energy Interconnection 2024年第3期7卷 293-312页

作者： Qiaoling Yang Kai Chen Jianzhang Man Jiaheng Duan Zuoqi Jin college of Electrical and information engineering Lanzhou University of TechnologyLanzhou 730050P.R.China

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

关键词： Wind turbine power curve Abnormal data cleaning Community detection Louvain algorithm Mathematical morphology operation

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the college of Computer Science and engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of information Science and engineering Ocean University of ChinaQingdao 266000China the college of Computer Science and engineering Shandong University of Science and TechnologyQingdao 266590China the college of Computer Science and engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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