The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

Particle filter for nonlinear systems with multi-sensor asynchronous random delays

This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.

Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation

In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.

On The Hardening of The Spectrum of High-Energy Particles Formed in Heavy-Ion Collisions Considered within The Framework of The Hydrodynamic Approach

The emission of high-energy particles in 16O + 197Au collisions at energy 20 MeV / nucleon is considered within the framework of the time evolution of a hot spot taking into account the hydrodynamic compression and expansion stages. In addition, the evaporation of the particles that are formed in the early (hot) stage of the evolution of the hot spot is included in the calculation of the spectrum. This leads to a hardening of the particle spectrum in its high-energy part, which is in agreement with experimental data.

Aerosol Spectra and New Particle Formation Observed in Various Seasons in Nanjing

The aerosol number spectrum and gas pollutants were measured and the new particle formation （NPF） events were discussed in Nanjing. The results showed that the size distributions of aerosol number concen- trations exhibited distinct seasonal variations, implying the relations of particle sizes and their sources and sinks. The number concentrations of particles in the nuclei mode （10-30 nm）, Aitken mode （30-100 nm）, accumulation mode （100 -1000 nm） and coarse mode （〉1μm） varied in the order of summer 〉 spring 〉 autumn, summer 〉 autumn 〉 spring, autumn 〉 summer 〉 spring, and spring 〉 autumn 〉summer, re- spectively. The diurnal variation of total aerosol number concentrations showed three peaks in all observed periods, which corresponded to two rush hours and the photochemistry period at noon. In general, the NPF in summer occurred under the conditions of east winds and dominant air masses originating from marine areas with high relative humidity （50%-70%） and strong solar radiations （400 -700 W m-2）. In spring, the NPF were generally accompanied by low relative humidity （14%-30%） and strong solar radiations （400-600 W m-2）. The new particle growth rates （GR） were higher in the summertime in the range of 10- 16 nm h-1. In spring, the GR were 6.8-8.3 nm h-1. Under polluted air conditions, NPF events were seldom captured in autumn in Nanjing. During NPF periods, positive correlations between 10- 30 nm particles and 03 were detected, particularly in spring, indicating that NPF can be attributed to photochemical reactions.

Measurement of Attenuation of Ultrasonic Propagating through the Thin Layer Media with Time Delay Spectrum

The ultrasonic attenuation coefficient is one of the most important acoustic parameters to character the performance of a thin layer media, but it can not be measured due to mutual superposition of multiple reflected waves at the same interface in ultrasonic testing. Ultrasonic pulse echo and lamb wave to evaluate the thin layer media can not obtain attenuation coefficient at present. In this paper, analytical method was used to study the acoustics characteristic of thin layer media with the ultrasonic echo testing. Meanwhile, the process of ultrasonic attenuation measurement was presented. Simulation and experimental investigation is focused on a thin layer of rubber. Attenuation coefficient was introduced and evaluation mathematics model was established by the two echoes cross-correlation with and without the thin layer media based on the time delay spectrum. It involved the parameters related to the acoustic properties of the thin layer media. Through calculating the sound velocity and acoustic impedance with the evaluation model, it can deduce the relation between the attenuation coefficient and the frequency. Through analyzing the simulation results, it indicated that the attenuation coefficients were invariable with the varying of the frequency. However, the attenuation coefficients increased with the frequency increasing by ultrasonic testing the thin layer of rubber. The reason was that the attenuation factor was not taken into account during the simulation. This method overcomes shortcomings that the traditional ultrasonic testing can not evaluate the thin layer media whose thickness is less than motivation wavelength. It is a new solution to study the attenuation characteristic and on-line nondestructive evaluation in the thin layer media.

Studies on the Lanthanum-Modified Lead Titanate Ultrafine Particles by Raman Spectroscopy

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Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network(RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative(PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization(PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.

A numerical method for spherical particle motion in turbulent flow with large Reynolds number

A new mathematical model, fluctuation spectrum random trajectory model (FSRTM) for the particle motion in environmental fluid was developed using Lagrangian method, in which the time mean velocity of the fluid was calculated by a time mean velocity formula for two dimensional homogeneous shear turbulent flows in open channel, the velocity fluctuation of the fluid was determined by Fourier expansion and fluctuation spectrum, and the particle motion equation was solved using Ronge Kutta method. For comparison, the spherical cation exchange resins with a density of 1 44 g/cm\+3 and diameters ranging from 0 50—0 60 mm, 0 60—0 70 mm and 0 80—0 90 mm were selected as the experimental solid particles, and their moving velocities and trajectories in shear turbulent flows with the flow Reynolds number of 4710, 10240, 11900 and 20760 were investigated. The comparing analyses of the modeled results with the measured results have shown that the model developed in this paper can describe the motions of the particles in shear turbulent flow.

Self-Organized Criticality Properties of the Turbulence-Induced Particle Flux at the Plasma Edge of the HT-6M Tokamak

Power spectrum and the probability distribution function(PDF)of the turbulence-induced particle fluxΓin the velocity shear layer of the HT-6M edge region have been measured and analysed.Three regions of frequency dependence(f0,f-1,f-4)have been observed in the spectrum of the flux.The PDF of the flux displays aΓ-1 scaling over one decade inΓ.Using the rescaled-range statistical technique,we find that the degree of the self-similarity(Hurst exponent)of the particle flux in the measured region ranges from 0.64 to 0.83.All of these results may mean that the plasma transport is in a state characterized by the self-organized criticality.

Throughput-efficient wireless system and blind detection via improved particle filtering

This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the extension to channel capacity are given.Importantly,a novel sequential es-timation and detection approach for this EBPSK system is proposed.The basic idea is to design a proba-bilistic approximation method for the computation of the maximum a posterior distribution via particle fil-tering method(PF).Subsequently,a new important function in PF is presented,so that the performanceof the detector has a great improvement.Finally,computer simulation illustrates that EBPSK system hasvery high transmission rate,and also the good performance of the proposed PF detector is demonstrated.

Multi-technique characterizations of main elements in the vehicle exhaust particles collected in a tunnel in Shnaghai

In this study,vehicle exhaust particles were collected three locations (the middle,entrance and outside) of the Dapu Road tunnel in downtown Shanghai,and the particle samples were characterized using SEM,XAFS,ICP-MS and M?ssbauer spectrometry,and the oxidative damage was assessed by plasmid DNA.Most iron-containing particles are found from vehicle exhaust,and iron oxide is the major species in all samples.Its concentration in the particles inside the tunnel is higher than that the outside particles.The iron particles inside the tunnel have higher proportion of water-soluble fraction in hydrous iron sulfate form or smaller size.ZnCl2 is the main soluble fraction in zinc-containing particles,while higher percentage of insoluble fraction existed in the particles outside of tunnel.Major species of lead-containing particles are PbSO4,Pb3(PO4)2 and PbCO3.In addition,the soluble fraction of other transition metallic elements as Ti and V is higher in the particles inside the tunnel than that outside the tunnel.The plasmid DNA assay results indicate that the particles from vehicle exhaust have a stronger oxidative damage and inflammation than that from outside of the tunnel.

Placenta Percreta with Bladder Invasion:Delayed Bladder Bleeding After Hysterectomy

Placenta percreta is a rare condition,which can lead to significant morbidity and potentially,mortality.This paper discusses two cases of complete placenta previa with placenta percreta invading the urinary bladder.Both patients underwent caesarean hysterectomy and experienced delayed bladder bleeding postoperatively.

A Particle Filter of Blind Equalization and Multiuser Detection in Asynchronous DS/CDMA Systems

The particle filter(PF)is proposed to be the asynchronous direct-sequence code-division multiple-access(DS/CDMA)multiuser detector without knowing the channel state information.The PF performs symbol detection according to the joint posterior density probability of simulated particles including relative delays,fading gains and symbols via sequential importance sample and resample.A simplified scheme is also proposed by separating the independent relative delays and fading with symbols.These parameters are modeled as the extended aggressive processes and estimated by the Kalman filter,so as to provide their arbitrary importance distribution for symbol detection.Simulation results show that the bit error rate of the PF is less than conventional detectors.Moreover,the complexity of PF is moderate comparable to other nonlinear suboptimal approaches.

基于多源数据的馈线谐波频谱估计方法

随着分布式电源和非线性负荷的投运规模不断增加,电网谐波污染愈加严重和复杂,有必要对各馈线谐波电流进行监测。但基于经济性考虑,对变电站所有馈线均进行谐波电流监测并不可行。针对上述问题,提出了基于多源数据的馈线谐波频谱估计方法。该方法基于电网现有电能质量监测系统、调度系统、营销业务应用系统等多源数据,只需对母线谐波电压和进线谐波电流开展监测,即可估计出变电站所有馈线的谐波电流情况。首先,根据馈线谐波电流的相量运算关系和功率关系构建馈线谐波频谱估计的目标函数。其次,基于多源数据利用改进粒子群算法求解目标函数,进而得到各馈线的各次谐波电流含有率及馈线谐波电流间的相角差,实现了各馈线谐波电流频谱的估计。最后,通过仿真算例和实测算例验证了方法的可行性。算例表明该方法仅需在变电站母线处安装一台电能质量监测终端,结合现有业务系统的基本电气数据等信息,可实现馈线谐波频谱的估计,有助于节约变电站监测装置的配置成本。

混合颗粒系蒙特卡罗消光模型及反演方法

在单一颗粒系中,消光光谱法颗粒粒径测量模型通常基于Mie散射理论和Lambert-Beer(LB)定律。但多种颗粒物构成的混合颗粒系的消光特性更为复杂,颗粒粒径与混合比均会影响消光谱,需要发展新的理论模型。作者设计一种蒙特卡罗(Monte Carlo,MC)原理的混合颗粒系消光模型,将入射光束离散化成光子,通过追踪其从发射至接收/逃逸过程中所有事件,统计光子去向并研究混合颗粒系的消光特性。为了验证蒙特卡罗模型的准确性,分别对聚苯乙烯和玻璃微珠单一颗粒系中消光谱进行数值计算,结果与Lambert-Beer定律预测误差小于2%。进一步将模型扩展至由聚苯乙烯和玻璃微珠构成的混合颗粒系,其消光谱随玻璃微珠占比(混合比)增大而依次递增,直至混合比为1时退化为单一颗粒系,而波长减小时,消光随混合比变化趋势由线性向非线性转变,两种颗粒自身消光特性差异越大则非线性趋势越明显,说明混合系消光值由颗粒类型、混合比、颗粒粒径和光波长共同决定且相互耦合。根据预测消光谱,采用三种全局最优算法对混合颗粒粒径和混合比参数反演。结果表明:混合比单参数反演结果的误差最小,均在1.5%以内;对两种颗粒粒径进行双参数反演时,误差均在3%以内;对两种粒径和混合比进行三参数同步反演时,误差增大,但均在10%以内。综合分析三种反演算法,PSO算法耗费时间最长,是其他算法的数倍,IGA算法效果较好且更加稳定。初步验证了设计的蒙特卡罗模型可应用于混合颗粒系的消光谱预测,实现了两种颗粒粒径与颗粒系混合比的同步反演。

改性膨润土对污水中重金属离子吸附实验研究

这是一篇陶瓷及复合材料领域的论文。为了研究改性膨润土对污水中重金属离子的吸附效果,分析pH值、初始浓度、时间、固液比、温度和粒径对膨润土吸附效果的影响,并采用微观手段研究在吸附重金属离子前后膨润土内部矿物成分的变化以及红外光谱图的变化规律。结果表明:当改性膨润土掺量为0.2 g、吸附时间为1.5 h、温度设置为40℃,pH值设定为6,初始浓度均设定为200 mg/L和固液比设置为0.8 g/L时,改性膨润土的吸附效果达到较佳。改性膨润土经过吸附实验后,可以检测到明显的CuSO_(4)衍射峰,但是改性膨润土内部其他矿物成分不变,这说明改性膨润土可以有效地吸附污水中的铜离子。

山东半岛一次典型冷流暴雪的粒子谱特征分析

为深入研究山东半岛冷流暴雪微物理特征及变化,利用激光降水现象仪(OTT Parsivel)、多普勒天气雷达和常规气象观测资料,对2020年初冬山东半岛一次东北冷涡后强冷空气造成的暴雪过程的雪粒子谱特征进行了分析。结果表明:(1)本次暴雪过程中,渤海上空850 hPa温度为-16℃,大连到山东半岛为北风与偏西风的辐合,强降雪发生在对流不稳定的大气层结中。(2)降雪粒子谱呈多峰分布,强降雪过程的初始发展、稳定和消散阶段,及一个强降雪对流系统的生成发展、成熟和消散阶段,雪粒子谱均有不同。雪粒子Gamma谱三参数特征:形状因子μ在零线附近波动,斜率参数λ小于1.0 mm^(-1),lgN0为3 m-3·mm^(-1-μ)左右。(3)强降雪时,多个数浓度中心对应的雪粒子下落末速度均在1.0 m·s^(-1)以下,最大雪粒子数浓度的峰值下落末速度为0.55 m·s^(-1)。在直径1.0 mm左右时,浓度大的雪粒子的下落末速度存在峰值,其贴近未结霜雪粒子速度曲线,说明冷流降雪的多数雪粒子不易结霜,以干雪为主。雪粒子以密凇附的枝状和聚并的板状两种类型为主,两种类型雪粒子下落末速度V和粒子直径D的关系系数和指数分别为0.62和0.33及0.69和0.41,拟合关系分别为V=0.62D^(0.33)和V=0.69D^(0.41)。

祁连山冷锋过程中积层混合云微物理特征飞机观测研究

利用高性能空中国王飞机搭载的DMT(Droplet Measurement Technologies)探测系统,分析2020年6月15—16日祁连山地区两次冷锋过程前后积层混合云的宏微观特征。结果表明:冷锋前CIP(Cloud Imaging Probe)粒子平均浓度在10~1~10~2 L^(-1),PIP(Precipitation Imaging Probe)粒子浓度在10~2 L^(-1);CIP粒子数浓度随高度逐渐上升,降水粒子在7 300 m时浓度达到峰值后下降;平均粒子谱拓宽速率为3.5 mm·km^(-1),随着高度的下降而降低。冷锋前存在对流泡区域,对流泡内粒子浓度均高出对流泡外,粒子谱拓宽速率高出层云一倍。冷锋后大CIP粒子粒子浓度为10~2 L^(-1),降水粒子浓度为10~0~10~2 L^(-1),粒子谱拓宽速率为1.5 mm·km^(-1),较冷锋前粒子谱拓宽速率小1.7倍。锋前后粒子均呈现多峰型;锋前积层混合云云中对流泡内外谱型一致,云顶处对流泡内外峰值粒径不同。锋前云粒子形状为聚合状、枝状、柱状和不规则状,以聚合状为主;云内冰晶以凇附和聚合增长为主;对流泡内外在粒子形态上没有明显差异。锋后粒子形状以不规则粒子为主,云粒子增长形式以聚合增长为主,并伴有上层云向下层播撒大云粒子的现象。