Optimal Time-Frequency Atom Search Based on Adaptive Genetic Algorithm

Adaptive signal decomposition is an important signal processing method.The chirp-based signal representation,for example,the Gaussian chirplet decomposition,has been an active research topic in the field of signal processing.A main challenge of the Gaussian chirplet decomposition is the numerical implementation of the matching pursuit,which is an adaptive signal decomposition scheme,and the challenge remains an open research topic.In this paper,a new optimal time-frequency atom search method based on the adaptive genetic algorithm is proposed,aiming to the low precision problem of the traditional methods.Firstly,a discrete formula of finite length time-frequency atom sequence is derived.Secondly,an algorithm based on the adaptive genetic algorithm is described in detail.Finally,a simulation is carried out,and the result displays its validity and stability.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Parametric adaptive time-frequency representation based on time-sheared Gabor atoms

A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.

Single-atom Pt on carbon nanotubes for selective electrocatalysis

Utilizing supported single atoms as catalysts presents an opportunity to reduce the usage of critical raw materials such as platinum,which are essential for electrochemical reactions such as hydrogen oxidation reaction(HOR).Herein,we describe the synthesis of a Pt single electrocatalyst inside single-walled carbon nanotubes(SWCNTs)via a redox reaction.Characterizations via electron microscopy,X-ray photoelectron microscopy,and X-ray absorption spectroscopy show the single-atom nature of the Pt.The electrochemical behavior of the sample to hydrogen and oxygen was investigated using the advanced floating electrode technique,which minimizes mass transport limitations and gives a thorough insight into the activity of the electrocatalyst.The single-atom samples showed higher HOR activity than state-of-the-art 30%Pt/C while almost no oxygen reduction reaction activity in the proton exchange membrane fuel cell operating range.The selective activity toward HOR arose as the main fingerprint of the catalyst confinement in the SWCNTs.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

A simple atomization approach enables monolayer dispersion of nano graphenes in cementitious composites with excellent strength gains

Carbon nano additives(CNAs)are critical to achieving the unique properties of functionalized composites,however,controlling the dispersion of CNAs in material matrix is always a challenging task.In this study,a simple atomization approach was successfully developed to promote the dispersion efficiency of graphene nanoplatelets(GNPs)in cement composites.This atomization approach can be integrated with the direct,indirect and combined ultrasonic stirrings in a homemade automatic stirring-atomization device.Mechanical and microstructure tests were performed on hardened cement pastes blended with GNPs in different stirring and mixing approaches.Results show that the direct ultrasonic stirrings enabled more homogeneous dispersions of GNP particles with a smaller size for a longer duration.The atomized droplets with the mean size of~100μm largely mitigated GNPs’agglomerations.Monolayer GNPs were observed in the cement matrix with the strength gain by up to 54%,and the total porosity decrease by 21%in 0.3 wt%GNPs dosage.The greatly enhanced dispersion efficiency of GNPs in cement also raised the cement hydration.This work provides an effective and manpower saving technique toward dispersing CNAs in engineering materials with great industrialization prospects.

Atomic layer deposition in advanced display technologies:from photoluminescence to encapsulation

Driven by the growing demand for next-generation displays,the development of advanced luminescent materials with exceptional photoelectric properties is rapidly accelerating,with such materials including quantum dots and phosphors,etc.Nevertheless,the primary challenge preventing the practical application of these luminescent materials lies in meeting the required durability standards.Atomic layer deposition(ALD)has,therefore,been employed to stabilize luminescent materials,and as a result,flexible display devices have been fabricated through material modification,surface and interface engineering,encapsulation,cross-scale manufacturing,and simulations.In addition,the appropriate equipment has been developed for both spatial ALD and fluidized ALD to satisfy the low-cost,high-efficiency,and high-reliability manufacturing requirements.This strategic approach establishes the groundwork for the development of ultra-stable luminescent materials,highly efficient light-emitting diodes(LEDs),and thin-film packaging.Ultimately,this significantly enhances their potential applicability in LED illumination and backlighted displays,marking a notable advancement in the display industry.

Metal-organic framework-based single-atom electro-/ photocatalysts: Synthesis, energy applications, and opportunities

Single-atom catalysts(SACs)have gained substantial attention because of their exceptional catalytic properties.However,the high surface energy limits their synthesis,thus creating significant challenges for further development.In the last few years,metal–organic frameworks(MOFs)have received significant consideration as ideal candidates for synthesizing SACs due to their tailorable chemistry,tunable morphologies,high porosity,and chemical/thermal stability.From this perspective,this review thoroughly summarizes the previously reported methods and possible future approaches for constructing MOF-based(MOF-derived-supported and MOF-supported)SACs.Then,MOF-based SAC's identification techniques are briefly assessed to understand their coordination environments,local electronic structures,spatial distributions,and catalytic/electrochemical reaction mechanisms.This review systematically highlights several photocatalytic and electrocatalytic applications of MOF-based SACs for energy conversion and storage,including hydrogen evolution reactions,oxygen evolution reactions,O_(2)/CO_(2)/N_(2) reduction reactions,fuel cells,and rechargeable batteries.Some light is also shed on the future development of this highly exciting field by highlighting the advantages and limitations of MOF-based SACs.

Single-atom photo-catalysts:Synthesis,characterization,and applications

Single-atom catalysts(SACs)are gaining popularity in catalytic reactions due to their nearly 100%atomic utilization and defined active sites,which provide great convenience for studying the catalytic mechanism of catalysts.However,SACs still present challenges such as complex formation processes,low loading and easy agglomeration of catalysts.Herein,we systematically discuss the synthesis methods for SACs,including coprecipitation,impregnation,atomic layer deposition,pyrolysis and Anti-Ostwald ripening etc.Various techniques for characterizing single-atom catalysts(SACs)are described in detail.The utilization of individual atoms in various photocatalytic reactions and their mechanisms of action in different reactions are explained.The purpose of this review is to introduce single-atom synthesis methods,characterization techniques,specific catalytic action and their applications in the direction of photocatalysis,and to provide a reference for the industrialization of photocatalytic single-atoms,which is currently impossible,in the hope of promoting further development of photocatalytic single-atoms.

Efficient loading of cesium atoms in a magnetic levitated dimple trap

We report a detailed study of magnetically levitated loading of ultracold ^(133)Cs atoms in a dimple trap.The atomic sample was produced in a combined red-detuned optical dipole trap and dimple trap formed by two small waist beams crossing a horizontal plane.The magnetic levitation for the ^(133)Cs atoms forms an effective potential for a large number of atoms in a high spatial density.Dependence of the number of atoms loaded and trapped in the dimple trap on the magnetic field gradient and bias field is in good agreement with the theoretical analysis.This method has been widely used to obtain the Bose–Einstein condensation atoms for many atomic species.

FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.

Cooperation between single atom catalyst and support to promote nitrogen electroreduction to ammonia:A theoretical insight

The co-catalysis between single atom catalyst(SAC)and its support has recently emerged as a promising strategy to synergistically boost the catalytic activity of some complex electrochemical reactions,encompassing multiple intermediates and pathways.Herein,we utilized defective BC_(3)monolayer-supported SACs as a prototype to investigate the cooperative effects of SACs and their support on the catalytic performance of the nitrogen reduction reaction(NRR)for ammonia(NH_(3))production.The results showed that these SACs can be firmly stabilized on these defective BC_(3)supports with high stability against aggregation.Furthermore,co-activation of the inert N_(2)reactant was observed in certain embedded SACs and their neighboring B atoms on certain BC3 sheets due to the noticeable charge transfer and significant N–N bond elongation.Our high-throughput screening revealed that the Mo/DV_(CC)and W/DV_(CC)exhibit superior NRR catalytic performance,characterized by a low limiting potential of−0.33 and−0.43 V,respectively,which can be further increased under acid conditions based on the constant potential method.Moreover,varying NRR catalytic activities can be attributed to the differences in the valence state of active sites.Remarkably,further microkinetic modeling analysis displayed that the turnover frequency of N_(2)–to–NH_(3)conversion on Mo/DV_(CC)is as large as 1.20×10^(−3)s^(−1)site−1 at 700 K and 100 bar,thus guaranteeing its ultra-fast reaction rate.Our results not only suggest promising advanced electrocatalysts for NRR but also offer an effective avenue to regulate the electrocatalytic performance via the co-catalytic metal–support interactions.

Tailoring local structures of atomically dispersed copper sites for highly selective CO_(2) electroreduction

Atomically‐dispersed copper sites coordinated with nitrogen‐doped carbon(Cu–N–C)can provide novel possibilities to enable highly selective and active electrochemical CO_(2) reduction reactions.However,the construction of optimal local electronic structures for nitrogen‐coordinated Cu sites(Cu–N_(4))on carbon remains challenging.Here,we synthesized the Cu–N–C catalysts with atomically‐dispersed edge‐hosted Cu–N_(4) sites(Cu–N_(4)C_(8))located in a micropore between two graphitic sheets via a facile method to control the concentration of metal precursor.Edge‐hosted Cu–N_(4)C_(8) catalysts outperformed the previously reported M–N–C catalysts for CO_(2)‐to‐CO conversion,achieving a maximum CO Faradaic efficiency(FECO)of 96%,a CO current density of–8.97 mA cm^(–2) at–0.8 V versus reversible hydrogen electrode(RHE),and over FECO of 90%from–0.6 to–1.0 V versus RHE.Computational studies revealed that the micropore of the graphitic layer in edge‐hosted Cu–N_(4)C_(8) sites causes the d‐orbital energy level of the Cu atom to shift upward,which in return decreases the occupancy of antibonding states in the*COOH binding.This research suggests new insights into tailoring the locally coordinated structure of the electrocatalyst at the atomic scale to achieve highly selective electrocatalytic reactions.

Preparation of single atom catalysts for high sensitive gas sensing

Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.

Physics package based on intracavity laser cooling ^(87)Rb atoms for space cold atom microwave clock

This article proposes a new physics package to enhance the frequency stability of the space cold atom clock with the advantages of a microgravity environment. Clock working processes, including atom cooling, atomic state preparation,microwave interrogation, and transition probability detection, are integrated into the cylindrical microwave cavity to achieve a high-performance and compact physics package for the space cold atom clock. We present the detailed design and ground-test results of the cold atom clock physics package in this article, which demonstrates a frequency stability of 1.2×10^(-12) τ^(-1/2) with a Ramsey linewidth of 12.5 Hz, and a better performance is predicted with a 1 Hz or a narrower Ramsey linewidth in microgravity environment. The miniaturized cold atom clock based on intracavity cooling has great potential for achieving space high-precision time-frequency reference in the future.

Atom substitution of the solid-state electrolyte Li_(10)GeP_(2)S_(12)for stabilized all-solid-state lithium metal batteries

Solid-state electrolyte Li_(10)GeP_(2)S_(12)(LGPS)has a high lithium ion conductivity of 12 mS cm^(-1)at room temperature,but its inferior chemical stability against lithium metal anode impedes its practical application.Among all solutions,Ge atom substitution of the solid-state electrolyte LGPS stands out as the most promising solution to this interface problem.A systematic screening framework for Ge atom substitution including ionic conductivity,thermodynamic stability,electronic and mechanical properties is utilized to solve it.For fast screening,an enhanced model Dop Net FC using chemical formulas for the dataset is adopted to predict ionic conductivity.Finally,Li_(10)SrP_(2)S_(12)(LSrPS)is screened out,which has high lithium ion conductivity(12.58 mS cm^(-1)).In addition,an enhanced migration of lithium ion across the LSr PS/Li interface is found.Meanwhile,compared to the LGPS/Li interface,LSrPS/Li interface exhibits a larger Schottky barrier(0.134 eV),smaller electron transfer region(3.103?),and enhanced ability to block additional electrons,all of which contribute to the stabilized interface.The applied theoretical atom substitution screening framework with the aid of machine learning can be extended to rapid determination of modified specific material schemes.

Atomic transport dynamics in crossed optical dipole trap

We study the dynamical evolution of cold atoms in crossed optical dipole trap theoretically and experimentally. The atomic transport process is accompanied by two competitive kinds of physical mechanics, atomic loading and atomic loss.The loading process normally is negligible in the evaporative cooling experiment on the ground, while it is significant in preparation of ultra-cold atoms in the space station. Normally, the atomic loading process is much weaker than the atomic loss process, and the atomic number in the central region of the trap decreases monotonically, as reported in previous research. However, when the atomic loading process is comparable to the atomic loss process, the atomic number in the central region of the trap will initially increase to a maximum value and then slowly decrease, and we have observed the phenomenon first. The increase of atomic number in the central region of the trap shows the presence of the loading process, and this will be significant especially under microgravity conditions. We build a theoretical model to analyze the competitive relationship, which coincides with the experimental results well. Furthermore, we have also given the predicted evolutionary behaviors under different conditions. This research provides a solid foundation for further understanding of the atomic transport process in traps. The analysis of loading process is of significant importance for preparation of ultra-cold atoms in a crossed optical dipole trap under microgravity conditions.

Unveiling the early stage evolution of local atomic structures in the crystallization process of a metallic glass

The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders,and this leads to an observation of a novel symmetry convergence phenomenon,which can be understood as an atomic structure manifestation of the ergodicity.Furthermore,in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass is not the total amount of icosahedral clusters,but the degree of global connectivity among them.