Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

W-doped In_(2)O_(3) nanofiber optoelectronic neuromorphic transistors with synergistic synaptic plasticity

Neuromorphic devices that mimic the information processing function of biological synapses and neurons have attracted considerable attention due to their potential applications in brain-like perception and computing. In this paper,neuromorphic transistors with W-doped In_(2)O_(3)nanofibers as the channel layers are fabricated and optoelectronic synergistic synaptic plasticity is also investigated. Such nanofiber transistors can be used to emulate some biological synaptic functions, including excitatory postsynaptic current(EPSC), long-term potentiation(LTP), and depression(LTD). Moreover, the synaptic plasticity of the nanofiber transistor can be synergistically modulated by light pulse and electrical pulse.At last, pulsed light learning and pulsed electrical forgetting behaviors were emulated in 5×5 nanofiber device array.Our results provide new insights into the development of nanofiber optoelectronic neuromorphic devices with synergistic synaptic plasticity.

Intrinsic Electronic Properties of BN-Encapsulated,van der Waals Contacted MoSe_(2)Field-Effect Transistors

Two-dimensional(2D)semiconductors have attracted considerable interest for their unique physical properties.Here,we report the intrinsic cryogenic electronic transport properties in few-layer MoSe_(2)field-effect transistors(FETs)that are fully encapsulated in ultraclean hexagonal boron nitride dielectrics and are simultaneously van der Waals contacted with gold electrodes.The FETs exhibit electronically favorable channel/dielectric interfaces with low densities of interfacial traps(<1010cm^(-2)),which lead to outstanding device characteristics at room temperature,including near-Boltzmann-limit subthreshold swings(65 mV/dec),high carrier mobilities(53–68 cm^(2)·V-1·s^(-1)),and negligible scanning hystereses(<15 mV).The dependence of various contact-related parameters with temperature and carrier density is also systematically characterized to understand the van der Waals contacts between gold and MoSe_(2).The results provide insightful information about the device physics in van der Waals contacted and encapsulated 2D FETs.

Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

Current-perpendicular-to-plane transport properties of 2D ferromagnetic material CrTe_(2)

Heterostructures of van der Waals(vdW)ferromagnetic materials have become a focal point in research of lowdimensional spintronic devices.The current direction in spin valves is commonly perpendicular to the plane(CPP).However,the transport properties of the CPP mode remain largely unexplored.In this work,current-in-plane(CIP)mode and CPP mode for CrTe_(2) thin films are carefully studied.The temperature-dependent longitudinal resistance transitions from metallic(CIP)to semiconductor behavior(CPP),with the electrical resistivity of CPP increased by five orders of magnitude.More importantly,the transport properties of the CPP can be categorized into a single-gap tunneling-through model with the activation energy(Ea)of1.34 meV/gap at 300–150 K,the variable range hopping model with a linear negative magnetoresistance at 150–20 K,and weak localization region with a nonlinear magnetic resistance below 20 K.This study explores the vertical transport in CrTe_(2) materials for the first time,contributing to understand its unique properties and to pave the way for its potential in spin valve devices.

A Probing Model of Secret Key Generation Based on Channel Autocorrelation Function

Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.

A 1-bit electronically reconfigurable beam steerable metasurface reflectarray with multiple polarization manipulations

A 1-bit electronically controlled metasurface reflectarray is presented to achieve beam steering with multiple polarization manipulations. A metsurface unit cell loaded by two PIN diodes is designed. By switching the two PIN diodes between ON and OFF states, the isotropic and anisotropic reflections can be flexibly achieved. For either the isotropic reflection or the anisotropic reflection, the two operation states achieve the reflection coefficients with approximately equal magnitude and 180°out of phase, thus giving rise to the isotropic/anisotropic 1-bit metasurface unit cells. With the 1-bit unit cells, a 12-by-12 metasurface reflectarray is optimally designed and fabricated. Under either y-or x-polarized incident wave illumination, the reflectarray can achieve the co-polarized and cross-polarized beam scanning, respectively, with the peak gains of 20.08 d Bi and 17.26 d Bi within the scan range of about ±50°. With the right-handed circular polarization(RHCP) excitation, the left-handed circular polarization(LHCP) radiation with the peak gain of 16.98 d Bic can be achieved within the scan range of ±50°. Good agreement between the experimental results and the simulation results are observed for 2D beam steering and polarization manipulation capabilities.

Overview of radar detection methods for low altitude targets in marine environments

In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.

Fabrication of YAG:Ce^(3+) and YAG:Ce^(3+),Sc^(3+) Phosphors by Spark Plasma Sintering Technique

In this study,a single-doped phosphors yttrium aluminum garnet(Y_(3)Al_(5)O_(12),YAG):Ce^(3+),single-doped YAG:Sc^(3+),and double-doped phosphors YAG:Ce^(3+),Sc^(3+) were prepared by spark plasma sintering(SPS)(lower than 1 200℃).The characteristics of synthesized phosphors were determined using scanning electron microscopy(SEM),X-ray diffraction(XRD),and fluorescence spectroscopy.During SPS,the lattice structure of YAG was maintained by the added Ce^(3+) and Sc^(3+).The emission wavelength of YAG:Ce^(3+) prepared from SPS(425-700 nm) was wider compared to that of YAG:Ce^(3+) prepared from high-temperature solid-state reaction(HSSR)(500-700 nm).The incorporation of low-dose Sc^(3+) in YAG:Ce^(3+) moved the emission peak towards the short wavelength.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm

Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.

Wigner function of optical cumulant operator and its dissipation in thermo-entangled state representation

To conveniently calculate the Wigner function of the optical cumulant operator and its dissipation evolution in a thermal environment, in this paper, the thermo-entangled state representation is introduced to derive the general evolution formula of the Wigner function, and its relation to Weyl correspondence is also discussed. The method of integration within the ordered product of operators is essential to our discussion.

Revealing the role of calcium ion intercalation of hydrated vanadium oxides for aqueous zinc-ion batteries

Exploring suitable high-capacity V_(2)O_(5)-based cathode materials is essential for the rapid advancement of aqueous zinc ion batteries(ZIBs).However,the typical problem of slow Zn^(2+)diffusion kinetics has severely limited the feasibility of such materials.In this work,unique hydrated vanadates(CaVO,BaVO)were obtained by intercalation of Ca^(2+)or Ba^(2+)into hydrated vanadium pentoxide.In the CaVO//Zn and BaVO//Zn batteries systems,the former delivered up to a 489.8 mAh g^(-1)discharge specific capacity at 0.1 A g^(-1).Moreover,the remarkable energy density of 370.07 Wh kg^(-1)and favorable cycling stability yard outperform BaVO,pure V_(2)O_(5),and many reported cathodes of similar ionic intercalation compounds.In addition,pseudocapacitance analysis,galvanostatic intermittent titration(GITT)tests,and Trasatti analysis revealed the high capacitance contribution and Zn^(2+)diffusion coefficient of CaVO,while an in-depth investigation based on EIS elucidated the reasons for the better electrochemical performance of CaVO.Notably,ex-situ XRD,XPS,and TEM tests further demonstrated the Zn^(2+)insertion/extraction and Zn-storage mechanism that occurred during the cycle in the CaVO//Zn battery system.This work provides new insights into the intercalation of similar divalent cations in vanadium oxides and offers new solutions for designing cathodes for high-capacity aqueous ZIBs.

Coarse-fine joint target parameter estimation method based on AN-RSC in OFDM passive radar

In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.