A predictive model for regional zenith tropospheric delay correction

The conventional zenith tropospheric delay(ZTD)model(known as the Saastamoinen model)does not consider seasonal variations affecting the delay,giving it low accuracy and stability.This may be improved with adjustments to account for annual and semi-annual variations.This method uses ZTD data provided by the Global Geodetic Observing System to analyze seasonal variations in the bias of the Saastamoinen model in Asia,and then constructs a model with seasonal variation corrections,denoted as SSA.To overcome the dependence of the model on in-situ meteorological parameters,the SSA+GPT3 model is formed by combining the SSA and GPT3(global pressure-temperature)models.The results show that the introduction of annual and semi-annual variations can substantially improve the Saastamoinen model,yielding small and time-stable variations in bias and root mean square(RMS).In summer and autumn,the bias and RMS are noticeably smaller than those from the Saastamoinen model.In addition,the SSA model performs better in low-latitude and low-altitude areas,and bias and RMS decease with the increase of latitude or altitude.The prediction accuracy of the SSA model is also evaluated for external consistency.The results show that the accuracy of the SSA model(bias:-0.38 cm,RMS:4.43 cm)is better than that of the Saastamoinen model(bias:1.45 cm,RMS:5.16 cm).The proposed method has strong applicability and can therefore be used for predictive ZTD correction across Asia.

Electromagnetic scattering of the carbon nanotubes excited by an electric line source

An analytical solution is presented for the electromagnetic scattering from an infinite-length metallic carbon nanotube and a carbon nanotube bundle. The scattering field and scattering cross section are predicted using a modal technique based on a Bessel and Hankel function for the electric line source and a quantum conductance function for the carbon nanotube. For the particular case of an isolated armchair （10, 10） carbon nanotube, the scattered field predicted from this technique is in excellent agreement with the measured result. Furthermore, the analysis indicates that the scattering pattern of an isolated carbon nanotube differs from that of the carbon nanotube bundle of identical index （m, n） metallic carbon nanotubes.

Compact pulsed thulium‐doped fiber laser for topographical patterning of hydrogels

We report the generation of high energy 2μm picosecond pulses from a thulium-doped fiber master oscillator power amplifier system.The all-fiber configuration was realized by a flexible large-mode area photonic crystal fiber(LMA-PCF).The amplifier output is a linearly-polarized 1.5 ns,100 kHz pulse train with a pulse energy of up to 250μJ.Pulse compression was achieved with(2+2)-pass chirped volume Bragg grating(CVBG)to obtain a 2.8 ps pulse width with a total pulse energy of 46μJ.The overall system compactness was enabled by the all-fiber amplifier design and the multi-pass CVBG-based compressor.The laser output was then used to demonstrate high-speed direct-writing capability on a temperature-sensitive biomaterial to change its topography(i.e.fabricate microchannels,foams and pores).The topographical modifications of biomaterials are known to influence cell behavior and fate which is potentially useful in many cell and tissue engineering applications.

Influence of Annealing Temperature on the Properties of TiO_2 Films Annealed by ex situ and in situ TEM

TiO2 thin films were deposited on quartz substrates by DC reactive magnetron sputtering of a pure Ti target in Ar/O2 plasma at room temperature. The TiO2 films were annealed at different temperatures ranging from 300 to 800 ℃ in a tube furnace under flowing oxygen gas for half an hour each. The effect of annealing temperatures on the structure, optical properties, and morphologies were presented and discussed by using X-ray diffraction, optical absorption spectrura, and atomic force microscope. The films show the presence of diffraction peaks from the （101）, （004）, （200） and （105） lattice planes of the anatase TiO2 lattice. The direct band gap of the annealed films decreases with the increase of annealing temperature. While, the roughness of the films increases with the increases of annealing temperature, and some significant roughness changes of the TiO2 film surfaces were observed after the annealing temperature reached 800 ℃. Moreover, the influences of annealing on the microstructures of the TiO2 film were investigated also by in situ observation in transmission electron microscope.

Improved Control in Single Phase Inverter Grid-Tied PV System Using Modified PQ Theory

Grid-connected reactive-load compensation and harmonic control are becoming a central topic as photovoltaic(PV)grid-connected systems diversified.This research aims to produce a high-performance inverter with a fast dynamic response for accurate reference tracking and a low total har-monic distortion(THD)even under nonlinear load applications by improving its control scheme.The proposed system is expected to operate in both stand-alone mode and grid-connected mode.In stand-alone mode,the proposed controller supplies power to critical loads,alternatively during grid-connected mode provide excess energy to the utility.A modified variable step incremental conductance(VS-InCond)algorithm is designed to extract maximum power from PV.Whereas the proposed inverter controller is achieved by using a modified PQ theory with double-band hysteresis current controller(PQ-DBHCC)to produce a reference current based on a decomposition of a single-phase load current.The nonlinear rectifier loads often create significant distortion in the output voltage of single-phase inverters,due to excessive current harmonics in the grid.Therefore,the proposed method generates a close-loop reference current for the switching scheme,hence,minimizing the inverter voltage distortion caused by the excessive grid current harmonics.The simulation findings suggest the proposed control technique can effectively yield more than 97%of power conversion efficiency while suppressing the grid current THD by less than 2%and maintaining the unity power factor at the grid side.The efficacy of the proposed controller is simulated using MATLAB/Simulink.

Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading.Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units.Combining the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup.However,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by shades.To this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and recognition.Our image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image variations.For training and evaluation,the image dataset is annotated to produce the ground truth of all the images.Consequently,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the digits.It proves to be robust against the mentioned image variations compared with the traditional handcrafted features.Our proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

Leveraging on few-shot learning for tire pattern classification in forensics

This paper presents a novel approach for tire-pattern classification,aimed at conducting forensic analysis on tire marks discovered at crime scenes.The classification model proposed in this study accounts for the intricate and dynamic nature of tire prints found in real-world scenarios,including accident sites.To address this complexity,the classifier model was developed to harness the meta-learning capabilities of few-shot learning algorithms(learning-to-learn).The model is meticulously designed and optimized to effectively classify both tire patterns exhibited on wheels and tire-indentation marks visible on surfaces due to friction.This is achieved by employing a semantic segmentation model to extract the tire pattern marks within the image.These marks are subsequently used as a mask channel,combined with the original image,and fed into the classifier to perform classification.Overall,The proposed model follows a three-step process:(i)the Bilateral Segmentation Network is employed to derive the semantic segmentation of the tire pattern within a given image.(ii)utilizing the semantic image in conjunction with the original image,the model learns and clusters groups to generate vectors that define the relative position of the image in the test set.(iii)the model performs predictions based on these learned features.Empirical verification demonstrates usage of semantic model to extract the tire patterns before performing classification increases the overall accuracy of classification by∼4%.

Mitigation of Deane and Hamill phenomenon in gallium nitride high-voltage power supply for electric propulsion system application

The step-up resonant converters are widely adopted to provide high voltage in kV-level for electric propulsion system due to their high efficiency,low mass,modularisation,and high-power density.The bipolar Cockcroft-Walton voltage multiplier(BiCWVM)is a major circuit that steps up the voltage in the resonant converter.However,the diode nonlinearity in BiCWVM can introduce self-sustained quasi-periodic oscillations in the voltage and current waveforms,which is commonly known as the Deane and Hamill(DH)phenomenon.The oscillation can lead to higher magnetic loss and control failure,and it is more likely to present in the gallium nitride-based converter due to the highfrequency operation.The authors aim to investigate and mitigate the DH phenomenon systematically so that proper mitigation can be implemented.To facilitate the investigation,the circuit before the BiCWVM in the converter is derived and modelled as a voltage source v_(m)and a series inductor L_(sy).Also,the reverse recovery process of the diode in the BiCWVM can be represented by a piecewise-linear(PWL)model,with the simplified circuit and PWL model,the relationship between voltage and current under different operating conditions can be determined with ease.The relationship allows to understand the mechanism of diode reverse recovery in BiCWVM that leads to DH phenomenon.Finally,a hybrid-/full-silicon carbide(SiC)design is proposed to mitigate the DH phenomenon,which is verified experimentally for a 300-kHz,5-W,20-V/1.5-kV GaN-based step-up resonant converter.

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

The development of Vehicular Ad-hoc Network(VANET)technology is helping Intelligent Transportation System(ITS)services to become a reality.Vehicles can use VANETs to communicate safety messages on the road(while driving)and can inform their location and share road condition information in real-time.However,intentional and unintentional(e.g.,packet/frame collision)wireless signal jamming can occur,which will degrade the quality of communication over the channel,preventing the reception of safety messages,and thereby posing a safety hazard to the vehicle’s passengers.In this paper,VANET jamming detection applying Support Vector Machine(SVM)machine learning technology is used to classify jamming and non-jamming situations.The analysis is based on two cases which include normal traffic and heavy traffic conditions,where the results show that the probability of packet dropping will increase when many vehicles are using the wireless channel simultaneously.When using SVM classification,the most appropriate feature set applied in determining a jamming situation shows an accuracy of 98%or higher.Furthermore,more advanced jamming attacks need to be considered for preparation of more reliable and safer autonomous ITS services.Such research can use vehicular communication transmission and reception data based on selected published datasets.In this paper,an additional adversarial defense algorithm using the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)method is proposed,which assumes that evolutionary attacks of the jammer will attempt to confuse the trained classifier.The simulation results show that applying DBSCAN can improve the accuracy by elimination of outliers before conducting classification testing.

A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks

As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

RSS-Based Indoor Localization System with Single Base Station

The paper proposes an Indoor Localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable antennas.The proposed algorithm measures Received Signal Strength(RSS)and maps it to the location in the room by estimating signal strength of a direct line of sight(LOS)signal and signal of the first order reflection from the wall.The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office,sampling 21 different locations in the room.It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80%confidence Cumulative Distribution Function(CDF)user level,demonstrating the ability to accurately estimate the receiver’s location within the room.The system is intended as a cost-efficient indoor localization technique,offering simplicity and easy integration with existing wireless communication systems.Unlike comparable single base station localization techniques,the proposed system does not require beam scanning,offering stable communication capacity while performing the localization process.

Optimal allocation method of energy storage for integrated renewable generation plants based on power market simulation

This study designs and proposes a method for evaluating the configuration of energy storage for integrated re-newable generation plants in the power spot market,which adopts a two-level optimization model of“system simulation+plant optimization”.The first step is“system simulation”which is using the power market simu-lation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant.The second step is“plant optimization”which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage.In the third step,“sys-tem simulation”is conducted again,and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage.In the case analysis of the provincial power spot market,an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted.The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large.In the case that the investment benefit of energy storage only considers the in-come of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services,the income of energy storage cannot fulfill the economic requirements of energy storage investment.

An LCC-HVDC Adaptive Emergency Power Support Strategy Based on Unbalanced Power On-Line Estimation

HVDC auxiliary power control can significantly improve the transient stability of AC/DC power grid.An HVDC adaptive emergency power support method based on unbalanced power on line estimation is proposed in this paper.By establishing the extended state equation of the system,the on line dynamic estimation of unbalanced power of the system was realized.On this basis,power support was realized based on the principle of the ladder increment.The optimal DC was selected by the power support factor,and the emergency power support controller was installed on the DC.This emergency power support method can realize dynamic optimal power support with minimized control cost.The three infeed HVDC system was built on PSCAD.The simulation results show the effectiveness of the proposed method.

Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

A single-channel electroencephalography(EEG)device,despite being widely accepted due to convenience,ease of deployment and suitability for use in complex environments,typically poses a great challenge for reactive brain-computer interface(BCI)applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles.In this study,a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal.The proposed decoder is constructed based on Gaussian Process model(GPM)which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions.To evaluate the effectiveness of the proposed method,the GPM is compared against other competitive techniques which include k-Nearest Neighbors,linear discriminant analysis,support vector machine,ensemble learning and neural network.Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96%and mean absolute error of no greater than 0.8 cm/s.In addition,the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks,the proposed GPM exhibits consistent performance across all stimuli considered,thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.

谱域OCT的印刷电路板三防漆厚度测量

三防漆是一种广泛应用在印刷电路板(PCB)的保护性涂层,可以有效保护PCB使其免受恶劣环境的损害。三防漆的厚度是评价三防漆涂层质量的关键指标,因此需要对三防漆涂层进行厚度检测。提出了将谱域光学相干断层扫描成像技术(spectral domain optical coherence tomography,SD-OCT)与图像分割算法相结合,实现对三防漆涂层厚度快速无损测量。为了提高测量精度,选用了宽带SLD光源(带宽:180 nm)来设计SD-OCT系统,系统轴向分辨率达到1.72μm。同时设计了基于边缘跟踪的涂层分割算法来实现三防漆涂层的快速准确分割。为了评估所设计方法的准确性,利用传统的金相切片方法进行了三防漆的厚度测量,将测量结果与该方法测量结果进行比较,分析了两种方法检测到的涂层上下边界吻合程度以及厚度差异。此外,还将所提出的涂层分割算法与我们组之前研究的基于图像梯度的边缘检测算法进行对比,分析了两种方法在测量结果的准确性和运行效率方面的差异,以此来评估该方法的优劣势。结果表明,所设计的三防漆厚度测量方法与传统的金相切片方法的测量结果具有很好的一致性,可以准确地实现三防漆的厚度测量;基于SD-OCT系统的三维成像能力可以直观地看到三防漆厚度地形图,克服了传统的金相切片方法无法进行区域性的三防漆厚度测量的缺陷;相比之前提出的基于图像梯度的边缘检测算法,此方法测量结果更加准确,效率显著提升,具备实时测量的潜力。

Single Ended Fault Location Estimation for EHV Transmission Systems

A novel numerical algorithm for fault location estimation of single-phase-to-earth fault on EHV transmission lines is presented in this paper. The method is based on one-terminal voltage and current data and is used in a procedure that provides the automatic determination of faulted types and phases, rather than requires engineer to specify them. The loop and nodal equations comparing the faulted phase to non-fauhed phases of multi-parallel lines are introduced in the fauh location estimation models, in which source impedance of remote end is not involved. Precise algorithms of locating fault are derived. The effect of load flow and fauh resistance, on the location accuracy, are effectively eliminated. The algorithms are demonstrated by digital computer simulations.

Risk-based Two-stage Optimal Scheduling of Energy Storage System with Second-life Battery Units

With the growing adoption of Electrical Vehicles(EVs),it is expected that a large number of on-board Li-ion batteries will be retired from EVs in the near future.Retired batteries will typically retain 80%of their initial capacities and can be recycled as second life batteries(SLBs).Although the capital costs of SLBs are much cheaper,their operational reliability is an important concern since used batteries may suffer from a higher failure rate.This paper aggregates brand new batteries and SLBs together to improve power system’s operating performance with renewable energy resources.In the context of a day-ahead and intra-day dispatch framework,a two-stage coordinated optimal scheduling method is proposed.Specifically,the energy cost of brand-new batteries and SLBs is calculated based on detailed battery degradation model,and the reliability of batteries is modeled based on the Weibull distribution.Moreover,Conditional value at risk(CVaR)criterion is applied to evaluate the risk induced by intermittent renewable power output,load demand variation and SLBs failure probability.Simulation tests demonstrate the effectiveness of the proposed method.

Three-dimensional macroscopic absorbents: From synergistic effects to advanced multifunctionalities

The accelerated arriving of 5G era has brought a new round of intelligent transformation which will completely emancipate smart terminal devices.While the subsequent deleterious effect of electromagnetic wave on electronic devices is increasingly serious,driving the growth of next-generation electromagnetic wave absorbents.As a tactful combination of components and structures,three-dimensional(3D)macroscopic absorbents with fascinating synergy afford exceptional electromagnetic wave absorption,and tremendous efforts have been devoted to this investigation.However,in terms of macroscopic absorbents and their synergistic effect,few reviews are proposed to comb the latest achievements and detailed synergy.This review article focuses on the synergistic effect of macro-architectured absorbents mainly including structure-induced synergy,structure-components synergy,and multiple-components induced synergy.And then the potential construction principles and strategies of macroscopic absorbents are combed.Significantly,the key information for structures and components manipulation including nano-micro design and components regulation is further dissected by critically selected cutting-edge 3D macroscopic absorbents.Moreover,a brief summary of multifunctional electromagnetic wave absorbents(EWAs)-based macroscopic structures is presented.Finally,the development prospects and challenges of these materials are discussed.

Improved Proximal Policy Optimization Algorithm for Sequential Security-constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer

In recent years,reinforcement learning(RL)has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods.It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities.However,existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration,which poses a risk of issuing instructions that threaten the safe operation of power system.Therefore,we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF)based on expert knowledge and safety layer to determine active power dispatch strategy,voltage optimization scheme of the units,and charging/discharging dispatch of energy storage systems.The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy.Additionally,to avoid line overload,we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem.Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

基于摩擦伏特效应的超滑表征技术

Friction occurs in our daily lives, and statistics have shown that it consumes approximately 1/3 of the primary energy. In addition,mechanical interface friction significantly impacts the efficiency,noise, accuracy, corrosion, reliability, and operational lifespans of equipment. Several studies have revealed various methods for reducing the coefficient of friction (COF) to the superlubricity state(COF <0.01) through efficient lubrication technology.