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Science China Mathematics 2024年第2期67卷 237-252页

作者： Hong-Lin Liao tao tang tao Zhou School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China SUSTech International Center for Mathematics Shenzhen 518055China NCMIS&LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

The positive definiteness of real quadratic forms with convolution structures plays an important rolein stability analysis for time-stepping schemes for nonlocal operators. In this work, we present a novel analysistool to handle discrete convolution kernels resulting from variable-step approximations for convolution operators.More precisely, for a class of discrete convolution kernels relevant to variable-step L1-type time discretizations, weshow that the associated quadratic form is positive definite under some easy-to-check algebraic conditions. Ourproof is based on an elementary constructing strategy by using the properties of discrete orthogonal convolutionkernels and discrete complementary convolution kernels. To our knowledge, this is the first general result onsimple algebraic conditions for the positive definiteness of variable-step discrete convolution kernels. Using theunified theory, we obtain the stability for some simple nonuniform time-stepping schemes straightforwardly.

关键词： discrete convolution kernels positive definiteness variable time-stepping orthogonal convolution kernels complementary convolution kernels

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The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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Chinese Journal of Electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying GAO Yuan CAO tao tang School of Electronics and Information Engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Multi-classification support vector machine Adaptive network-based fuzzy inference system Train air braking system Fault-tolerant voting Multi-sensors

Enhancing single-cell encapsulation in droplet microfluidics with fine-tunable on-chip sample enrichment

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Microsystems & Nanoengineering 2024年第1期10卷 149-160页

作者： tao tang Hao Zhao Shaofei Shen Like Yang Chwee Teck Lim Department of Biomedical Engineering National University of Singapore117583 SingaporeSingapore Integrative Sciences and Engineering Programme NUS Graduate SchoolNational University of Singapore119077 SingaporeSingapore Shanxi Key Lab for Modernization of TCVM College of Life ScienceShanxi Agricultural UniversityTaiguShanxi 030801China Institute for Health Innovation&Technology National University of Singapore117599 SingaporeSingapore Mechanobiology Institute National University of Singapore117411 SingaporeSingapore Institute for Digital Molecular Analytics and Science Nanyang Technological University636921 SingaporeSingapore

Single-cell encapsulation in droplet microfluidics is commonly hindered by the tradeoff between cell suspension density and on-chip focusing performance.In this study,we introduce a novel droplet microfluidic chip to overcome this challenge.The chip comprises a double spiral focusing unit,a flow resistance-based sample enrichment module with fine-tunable outlets,and a crossflow droplet generation unit.Utilizing a low-density cell/bead suspension(2×10^(6) objects/mL),cells/beads are focused into a near-equidistant linear arrangement within the double spiral microchannel.The excess water phase is diverted while cells/beads remain focused and sequentially encapsulated in individual droplets.Focusing performance was assessed through numerical simulations and experiments at three flow rates(40,60,80μL/min),demonstrating successful focusing at 40 and 80μL/min for beads and cells,respectively.In addition,both simulation and experimental results revealed that the flow resistance at the sample enrichment module is adjustable by punching different outlets,allowing over 50%of the aqueous phase to be removed.YOLOv8n-based droplet detection algorithms realized the counting of cells/beads in droplets,statistically demonstrating single-cell and bead encapsulation rates of 72.2%and 79.2%,respectively.All the results indicate that this on-chip sample enrichment approach can be further developed and employed as a critical component in single-cell encapsulation in water-in-oil droplets.

关键词： beads outlet sample

ResoNet:Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks

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Advances in Atmospheric Sciences 2024年第7期41卷 1289-1298页

作者： Pumeng LYU tao tang Fenghua LING Jing-Jia LUO Niklas BOERS Wanli OUYANG Lei BAI Shanghai Artificial Intelligence Laboratory Shanghai 200072China Zhejiang Meteorological Observatory Hangzhou 310002China Institute for Climate and Application Research(ICAR) Nanjing University of Information Science and TechnologyNanjing 210044China Technical University of Munich Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.

关键词： deep learning ENSO CNN transformer

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A Residual Frequency Offset Estimation Method Based on Range Migration Fitting

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Journal of Beijing Institute of Technology 2024年第2期33卷 103-110页

作者： Liting Zhang Hao Huan Ran tao Xiaogang tang School of Information and Electronics Beijing Institute of Technology School of Aerospace Information Space Engineering University

Recently, researchers have proposed an emitter localization method based on passive synthetic aperture. However, the unknown residual frequency offset(RFO) between the transmitter and the receiver causes the received Doppler signal to shift, which affects the localization accuracy. To solve this issue, this paper proposes a RFO estimation method based on range migration fitting. Due to the high frequency modulation slope of the linear frequency modulation(LFM)-modulation radar signal, it is not affected by RFO in range compression. Therefore, the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then, the matched filters are designed under different RFOs. When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time, the given RFO is the real RFO between the transceivers. The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s, the pulse repetition frequency(PRF) of the transmitter radar signal is not less than 1 k Hz, the range detection is not larger than 1 000 km, and the signal noise ratio(SNR) is not less than –5 d B.

关键词： passive localization range migration fitting (RMF) residual frequency offset (RFO)

Machine Learning-Aided Data Analysis in Single-Protein Conductance Measurement with Electron Tunneling Probes

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Chinese Journal of Chemistry 2024年第1期42卷 67-72页

作者： Yuxin Yang tao Jiang Ye Tian Biaofeng Zeng Longhua tang State Key Laboratoryof Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang University HangzhouZhejiang 310027China Nanhu Brain-computer Interface Institute HangzhouZhejiang 311100China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical EngineeringZhejiang University HangzhouZhejiang 310027China

The electrical tunneling sensors have excellent potential in the next generation of single-molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities.Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information.Despite the extraordinarily large amount of data that has been gathered,it is still difficult to correlate signal transformations with molecular processes,which creates great obstacles for signal analysis.Machine learning is an effective tool for data analysis that is currently gaining more significance.It has demonstrated promising results when used to analyze data from single-molecule electrical measurements.In order to extract meaningful information from raw measurement data,we have combined intelligent machine learning with tunneling electrical signals.For the purpose of analyzing tunneling electrical signals,we investigated the clustering approach,which is a classic algorithm in machine learning.A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering.Additionally,customized statistical algorithms were designed.It has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

关键词： Tunneling sensor Single-molecule measurement Machine learning Single-protein conductance Molecular electrochemistry Nanotechnology Molecular electronics

Spatial distribution of supra-permafrost groundwater in the Qinghai-Tibet Engineering Corridor using inversion models

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Advances in Climate Change Research 2024年第1期15卷 31-41页

作者： Yu GAO Ming-tang CHAI Wei MA Yu-tao GAO State Key Laboratory of Frozen Soils Engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 73000China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China School of Civil and Hydraulic Engineering Ningxia UniversityYinchuan 750021China

Supra-permafrost groundwater(SPG)is a key factor that causes damage to highways and railways in the Qinghai-Tibet Engineering Corridor(QTEC).It is difficult to monitor SPG in the field due to their complex formation mechanisms and movement characteristics.Traditional single-site field monitoring studies limit the spatial and temporal precision of SPG spatial distribution.To determine the moisture content of shallow soils and the SPG distribution along the QTEC,this work employed the temperature vegetation dryness index and remote sensing models for groundwater table distribution models.The accuracies of the models were validated using mea-surements obtained from different sites in the corridor.In the permafrost zones of the QTEC,72%,22%and 6%of the SPG were located at depths of 0.5-1,1 m,respectively.Meanwhile,79.4%of the area along the Qinghai-Tibet Highway(QTH)(Xidatan-tanggula)section contained SPG.In these sections with SPG,37.9%have an SPG table at depths of 0.5-0.8 m.This study preliminarily explored the SPG distribution in the QTEC with a 30 m resolution.The findings can help improve the spatial scale of SPG research,provide a basis for the analysis of the hydrothermal mechanisms,and serve as a guide in the assessment of operational risks and road structure designs.

关键词： Dryness index Landsat Soil moisture Groundwater level Qinghai-Tibet Engineering Corridor

A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

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Computers, Materials & Continua 2024年第4期79卷 1335-1351页

作者： Xue Zhao Shaojun tao Hongying tang Jiang Wang Baoqing Li Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai200050China

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

关键词： Node scheduling pre-selection target tracking WSN

An intermittent lithium deposition model based on CuMnbimetallic MOF derivatives for composite lithium anode with ultrahigh areal capacity and current densities

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Nano Research 2024年第4期17卷 2763-2769页

作者： tao Wei Yanyan Zhou Cheng Sun Xingtong Guo Shoudong Xu Daifen Chen Yongfu tang School of Energy and Power Jiangsu University of Science and TechnologyZhenjiang 212003China State Key Laboratory of Metastable Materials Science and Technology(MMST) Hebei Key Laboratory of Applied ChemistryYanshan UniversityQinhuangdao 066004China College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China

Recently,three-dimensional(3D)conductive frameworks have been chosen as the host for composite lithium(Li)metal anode because of their exceptional electrical conductivity and remarkable thermal and electrochemical stability.However,Li tends to accumulate on the top of the 3D frameworks with homogenous lithiophilicity and Li dendrite still growth.This work firstly designed a bimetallic metal-organic framework(MOF)(CuMn-MOF)derived Cu_(2)O and Mn_(3)O_(4) nanoparticles decorated carbon cloth(CC)substrates(CC@Cu_(2)O/Mn_(3)O_(4))to fabricate a composite Li anode.Thanks to the synergistic effects of lithiophilic Cu_(2)O and Mn_(3)O_(4),the CC@Cu_(2)O/Mn_(3)O_(4)@Li symmetrical cell can afford a prolonged cycling lifespan(1400 h)under an ultrahigh current density and areal capacity(6 mA·cm^(-2)/6 mAh·cm^(-2)).When coupled with the LiFePO_(4)(LFP)cathode,the LFP||CC@Cu_(2)O/Mn_(3)O_(4)@Li full cell demonstrated a superior performance of 89.7 mAh·g^(-1) even at an extremely high current density(10 C).Furthermore,it can also be matched well with LiNi_(0.5)Co^(0).2Mn_(0.3)O_(2)(NCM523)cathode.Importantly,to explain the excellent performances of the CC@Cu_(2)O/Mn_(3)O_(4)@Li composite anode,an intermittent model was also proposed.This study offers a novel model that can enhance our comprehension of the Li deposition behavior and pave the way to attain stable and safe Li metal anodes by employing bimetallic MOF-derived materials to construct 3D frameworks.

关键词： lithium metal anode(LMA) three-dimensional(3D)frameworks intermittent lithiophilic model high areal capacity bimetallic metal-organic frameworks(MOFs)

Emerging memristors and applications in reservoir computing

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Frontiers of physics 2024年第1期19卷 219-239页

作者： Hao Chen Xin-Gui tang Zhihao Shen Wen-tao Guo Qi-Jun Sun Zhenhua tang Yan-Ping Jiang School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou Higher Education Mega CenterGuangzhou 510006China

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

关键词： reservoir computing memristor resistive switching artificial synapse neuromorphic computing