Research on High Speed Communication Method of ELF-EM While Drilling Based on Adaptive Combined Filtering Algorithm

In terms of the electromagnetic wave measurement while drilling,the extremely low frequency electromagnetic(ELF-EM)signal below 20Hz is usually chosen as the downhole data carrier.To improve the transmission rate of ELF-EM signal and the signal to noise ratio(SNR)at the receiving end,the DQPSK modulation was proposed as the modulation method for the communication of electromagnetic wave system.Different from the traditional IQ orthogonal modulation and coherent demodulation methods,the proposed phase selection modulation and correlation algorithm demodulation are easier to implement and more practical.With regard to the communication synchronization,a fast algorithm,which based on the normalized cross-relation number,was used for waveform matching,and the maximum point of the correlation coefficient was used as the starting point of communication synchronization.The communication simulation results show that the proposed DQPSK modulation signal based on the adaptive combined filtering algorithm has better terminal error rate and transmission rate than the traditional modulation method.Under the same carrier frequency and code width,the transmission rate of DQPSK modulation is 4 to 5 times and 2 times that of PPM modulation and 2DPSK modulation respectively.The communication modulation and demodulation modes as well as the decoding algorithm with combined adaptive filter proposed in this paper can effectively solve practical engineering problems.

Dual Extended Kalman Filter for Combined Estimation of Vehicle State and Road Friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.

Combined Adaptive Multiple Subtraction Based on Optimized Event Tracing and Extended Wiener Filtering

The surface-related multiple elimination(SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

Frequency-Domain GTLS Identification Combined with Time-Frequency Filtering for Flight Flutter Modal Parameter Identification

The aim of this paper is to present a new method for flight flutter modal parameter identification in noisy environment. This method employs a time-frequency （TF） filter to reduce the noise before identification, which depends on the localization property of sweep excitation in TF domain. Then, a generalized total least square （GTLS） identification algorithm based on stochastic framework is applied to the enhanced data. System identification with noisy data is transformed into a generalized total least square problem, and the solution is carried out by the generalized singular value decomposition （GSVD） to avoid the intensive nonlinear optimization computation. A nearly maximum likelihood property can be achieved by ＇optimally＇ weighted generalized total least square. Finally, the efficiency of the method is illustrated by means of flight test data.

VLC-Based Indoor Positioning Algorithm Combined with OFDM and Particle Filter

Visible light communications(VLC) have recently attracted a growing interest and can be a potential solution to realize indoor positioning,however,the performance of existing indoor positioning system is limited by multipath distortion inside a room.In order to combat the effect of multipath distortion,this paper proposes an LED-based indoor positioning algorithm combined with hybrid OFDM(HOFDM),in which asymmetrically clipped optical OFDM(ACOOFDM) is transmitted on the odd subcarriers while using pulse amplitude modulated discrete multitone(PAM-DMT) to modulate the imaginary part of each even subcarrier.In this scheme,we take a combined approach where a received-signal-strength(RSS) technique is employed to determine the location of the receiver and realize the 3-D positioning by Trust-region-based positioning.Moreover,a particle filter is used to further improve the positioning accuracy.Results confirm that this proposed positioning algorithm can achieve high accuracy even with multipath distortion,and the algorithm has better performance when combined with particle filter.

Fuzzy Controller Based 3Phase 4Wire Shunt Active Filter for Mitigation of Current Harmonics with Combined p-q and Id-Iq Control Strategies

As more and more variable frequency drives (VFDs), electronic ballasts, battery chargers, and static Var compensators are installed in facilities, the problems related to harmonics are expected to get worse. As a result Active power filter (APF) gains much more attention due to excellent harmonic compensation. But still the performance of the active filter seems to be in contradictions with different control strategies. This paper presents detailed analysis to compare and elevate the performance of two control strategies for ex-tracting reference currents of shunt active filters under balanced, un-balanced and non-sinusoidal conditions by using Fuzzy controller. The well known methods, instantaneous real active and reactive power method (p-q) and active and reactive current method (id-iq) are two control methods which are extensively used in active filters. Extensive Simulations are carried out with fuzzy controller for both p-q and Id-Iq methods for different voltage conditions and adequate results were presented. Simulation results validate the superior per-formance of active and reactive current control strategy (id-iq) with fuzzy controller over active and reactive power control strategy (p-q) with fuzzy controller.

DESIGN OF MAXIMALLY FLAT FIR FILTERS BASED ON EXPLICIT FORMULAS COMBINED WITH OPTIMIZATION

A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-word-length FIR filter design problem was converted into optimization of the filter coefficients, An optimization method combined with local discrete random search and simulated annealing was proposed, with the result of optimum solution in the sense of Chebyshev approximation. The proposed method can simplify the design process of FIR filter and reduce the calculation burden. The simulation result indicates that the proposed method is superior to the traditional round off method and can reduce the value of the objective function to 41%～74%.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Speech Enhancement Using Cross-Correlation Compensated Multi-Band Wiener Filter Combined with Harmonic Regeneration

The speech signal in general is corrupted by noise and the noise signal does not affect the speech signal uniformly over the entire spectrum. An improved Wiener filtering method is proposed in this paper for reducing background noise from speech signal in colored noise environments. In view of nonlinear variation of human ear sensibility in frequency spectrum, nonlinear multi-band Bark scale frequency spacing approach is used. The cross-correlation between the speech and noise signal is considered in the proposed method to reduce colored noise. To overcome harmonic distortion introduced in enhanced speech, in the proposed method regenerate the suppressed harmonics are regenerated. Objective and subjective tests were carried out to demonstrate improvement in the perceptual quality of speeches by the proposed technique.

Obtaining vehicle parameters from bridge dynamic response:a combined semi-analytical and particle filtering approach

Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.

Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

一种基于Filter Faster R-CNN的数字PCR液滴检测技术

目的研究液滴数字聚合酶链式反应(ddPCR)液滴检测技术,去除图像中灰尘、气泡、芯片表面的划痕以及微小凹陷等因素产生的异常点对结果的影响,实现高通量、稳定和准确的ddPCR液滴的自动检测。方法提出Filter Faster R-CNN ddPCR液滴检测模型。使用Faster R-CNN生成液滴预测框,之后使用异常点过滤模块(Filter)去除阳性液滴预测框中的异常点。以诺如病毒片段的质粒为模板进行ddPCR实验,建立一个ddPCR数据集,用于模型的训练(2462例,约占78.56%)和测试(672例,约占21.44%)。对异常点过滤模块的3个过滤支路在验证集上进行消融实验,通过与其他ddPCR液滴检测模型进行比较的对比实验以及进行ddPCR的绝对定量实验。结果在少尘和多尘的环境中,Filter Faster R-CNN阳性液滴准确率为98.23%和88.35%,综合指标F1分数分别达到了99.15%和99.14%,高于其他相比较的模型。独立样本T检验的结果证明,相比未添加过滤模块的网络,添加过滤模块后能够显著提示模型在多尘环境中的阳性准确率。在ddPCR绝对定量实验中,将商业化流式检测设备的结果作为标准浓度,绘制了回归线。结果显示,回归线斜率为1.0005,截距为-0.025,决定系数达到了0.9997,二者结果高度一致。结论本文提出了一种基于Filter Faster R-CNN的ddPCR液滴检测技术,为在多种环境条件下的ddPCR实验提供了鲁棒的液滴检测方法。

Grain Yield,Biomass Accumulation,and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress

Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in saline lands,primarily due to insufficient water resources and inadequate irrigation facilities.Consequently,combined salinity-drought stress poses a major threat to rice production.In this study,two salinity levels(NS,non-salinity;HS,high salinity)along with three drought treatments(CC,control condition;DJ,drought stress imposed at jointing;DH,drought stress imposed at heading)were performed to investigate their combined influences on leaf photosynthetic characteristics,biomass accumulation,and rice yield formation.Salinity,drought,and their combination led to a shortened growth period from heading to maturity,resulting in a reduced overall growth duration.Grain yield was reduced under both salinity and drought stress,with a more substantial reduction under the combined salinity-drought stress.The combined stress imposed at heading caused greater yield losses in rice compared with the stress imposed at jointing.Additionally,the combined salinity-drought stress induced greater decreases in shoot biomass accumulation from heading to maturity,as well as in shoot biomass and nonstructural carbohydrate(NSC)content in the stem at heading and maturity.However,it increased the harvest index and NSC remobilization reserve.Salinity and drought reduced the leaf area index and SPAD value of flag leaves and weakened the leaf photosynthetic characteristics as indicated by lower photosynthetic rates,transpiration rates,and stomatal conductance.These reductions were more pronounced under the combined stress.Salinity,drought,and especially their combination,decreased the activities of ascorbate peroxidase,catalase,and superoxide dismutase,while increasing the contents of malondialdehyde,hydrogen peroxide,and superoxide radical.Our results indicated a more significant yield loss in rice when subjected to combined salinity-drought stress.The individual and combined stresses of salinity and drought diminished antioxidant enzyme activities,inhibited leaf photosynthetic functions,accelerated leaf senescence,and subsequently lowered assimilate accumulation and grain yield.

Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

A NOVEL SWEPT LASER SOURCE BASED ON COMBINED TUNABLE FILTERS FOR OCT

A novel broad tunable bandwidth and narrow instantaneous line-width linear swept laser source using combined tunable filters working at 1,300 nm center wavelength is proposed.The combined filters consist of a fiber Fabry
Perot tunable filter and a tunable filter based on diffractive grating with scanning polygon mirror.In contrast to traditional method using single tunable filter,the trade-off between bandwidth and instantaneous line-width is alleviated.Parallel implementation of two semiconductor optical amplifiers with different wavelength range is adopted in the laser resonator for broadband light amplification.The Fourier domain mode locking swept laser source with combined tunable filters offers broadband tunable range with narrow instantaneous line-width,which is especially benefiting for high-quality optical frequency domain imaging.The proposed Fourier domain mode locking swept laser source provides a tuning range of 160 nm with instantaneous line-width of about 0.01nm at sweeping rate of 15 kHz,a finesse of 16,000 is thus achieved.