基于ikPCA-FABAS-KELM的短期风电功率预测

为了增强在短期风电功率预测领域中传统数据驱动机器学习模型的精度,提出基于ikPCA-FABAS-KELM的短期风电功率预测模型.首先,对主成分分析进行改进,提出可逆核主成分分析(ikPCA),在保证数据特征的同时,降低输入数据的复杂度,以提升模型运行速度;其次,引入萤火虫个体吸引策略对天牛须算法(BAS)进行改进,提出FABAS算法;最后,利用FABAS算法对核极限学习机(KELM)的正则化参数C和核参数γ进行寻优,降低人为因素对模型盲目训练的影响,提高模型预测精度.仿真结果显示,提出的预测模型有效提高了传统模型的预测精度.

Improved statistical fluctuation analysis for two decoy-states phase-matching quantum key distribution

Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant system performance to deteriorate when data size is below 1010.In this work,an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution,offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security.Moreover,we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff-Hoeffding boundary methods on system performance.The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff-Hoeffding approach,and approach the results obtained when the Gaussian approximation is employed.At the same time,the proposed scheme retains the same security level as the Chernoff-Hoeffding method,and is even more secure than the Gaussian approximation.

基于FA-ISSA-PPR模型的旋风分离器分离效率预测

旋风分离器是气田开发中常用的气固分离设备,准确预测旋风分离器的分离效率对于指导其结构设计和方法优化具有重要意义。在对数据集进行相关性分析的基础上,采用因子分析(factor analysis, FA)简化变量,降低预测模型的复杂程度,利用改进的樽海鞘群算法(improved salp swarm algorithm, ISSA)对投影寻踪(projection pursuit regression, PPR)的模型参数进行优化,形成FA-ISSA-PPR组合模型。结果表明,利用FA模型,原数据集的10个变量可以简化合并为4个公因子,分别代表尺寸参数、颗粒沉降特性、粒子运行轨迹和等效分割粒径对分离效率的影响;与半经验模型和其余机器学习模型相比,组合模型在预测精度和训练时间上具有一定的优越性,在测试样本上的平均绝对误差(MAE)为0.005 91,R^(2)可达0.995,证明了其在小样本、非线性数据分析上的准确性、鲁棒性和泛化性。

基于文本Embedding和相似度计算的FA范围准确性校核方法研究

在现代电力系统核心运营中,馈线自动化扮演着提高供电可靠性和服务质量的核心角色。针对FA系统故障定位的精确性挑战,提出了一种基于文本向量化和相似度技术的创新校核方法。与传统手动校核流程相比,该方法显著提升了故障诊断的准确度和操作效率。此项研究为电力系统故障响应与维护流程提供了一种高效、可靠的技术解决方案,对确保电网运行的稳定性和安全性具有极为重要的意义。

New seismic attribute:Fractal scaling exponent based on gray detrended fluctuation analysis

Seismic attributes have been widely used in oil and gas exploration and development. However, owing to the complexity of seismic wave propagation in subsurface media, the limitations of the seismic data acquisition system, and noise interference, seismic attributes for seismic data interpretation have uncertainties. Especially, the antinoise ability of seismic attributes directly affects the reliability of seismic interpretations. Gray system theory is used in time series to minimize data randomness and increase data regularity. Detrended fluctuation analysis （DFA） can effectively reduce extrinsic data tendencies. In this study, by combining gray system theory and DFA, we propose a new method called gray detrended fluctuation analysis （GDFA） for calculating the fractal scaling exponent. We consider nonlinear time series generated by the Weierstrass function and add random noise to actual seismic data. Moreover, we discuss the antinoise ability of the fractal scaling exponent based on GDFA. The results suggest that the fractal scaling exponent calculated using the proposed method has good antinoise ability. We apply the proposed method to 3D poststack migration seismic data from southern China and compare fractal scaling exponents calculated using DFA and GDFA. The results suggest that the use of the GDFA-calculated fractal scaling exponent as a seismic attribute can match the known distribution of sedimentary facies.

Analysis and Pollution Assessment on the Dissolved Organic Matter in the Sediment of Hydro-fluctuation Belt(Yunyang Part)

By using the ultraviolet absorption spectrum,the fluorescence spectrum and three dimensional fluorescence spectra,the composition of dissolved organic matter(DOM) and the humification degree,etc.in the sediment of hydro-fluctuation belt(Yunyang part) were analyzed.The relationship between the test parameters and the pollution degree in the region was discussed.The research results of UV spectrum data E3/E4 showed that in 4 sampling sites which included Huangshi Town,Gaoyang Town,Shuangjiang Town and Quma Town,the humification degree in the sediment of hydro-fluctuation belt in Shuangjiang Town sampling site was lower,and the aromaticity was smaller.Moreover,the effect of human factor was comparatively smaller.The fluorescence index value which represented the source of humus in DOM was 1.62-1.88.It showed that the biological and terrestrial source both existed.Three dimensional spectra results showed that the pollution degrees in 4 sampling sites were all smaller,and some prevention measures should be taken early.

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize reliable analysis using traditional signal processing methods such as statistical analysis or spectral analysis, which is done in Fourier domain. Information in different frequency band can be extracted by using wavelet analysis. On the evidence of the composition of the pressure fluctuation signals, energy of low frequency (ELF) is proposed to show the transition of fluidized regimes from bubbling fluidization to turbulent fluidization. Plots are presented to describe the fluidized bed's evolution to help identify the state of different flow regimes and provide a characteristic curve to identify the fluidized status effectively and reliably.

Relationships of exponents in multifractal detrended fluctuation analysis and conventional multifractal analysis

Multifractal detrended fluctuation analysis （MF-DFA） is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis （DFA）, which was developed for detecting the long-range correlation and the fractal properties in stationary and non-stationary time series. Although MF-DFA has become a widely used method, some relationships among the exponents established in the original paper seem to be incorrect under the general situation. In this paper, we theoretically and experimentally demonstrate the invalidity of the expression r（q） = qh（q） - 1 stipulating the relationship between the multifractal exponent T（q） and the generalized Hurst exponent h（q）. As a replacement, a general relationship is established on the basis of the universal multifractal formalism for the stationary series as .t-（q） = qh（q） - qH - 1, where H is the nonconservation parameter in the universal multifractal formalism. The singular spectra, a and f（a）, are also derived according to this new relationship.

Detrended Fluctuation Analysis on Correlations of Complex Networks Under Attack and Repair Strategy

We analyze the correlation properties of the Erd6s-Rdnyi random graph （RG） and the Barabdsi-Albert scale-free network （SF） under the attack and repair strategy with detrended fluctuation analysis （DFA）. The maximum degree kmax, representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree 〈k〉, revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.

Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.

Analysis and evaluation on pressure fluctuations in air dense medium fluidized bed

Pressure fluctuations contribute to the instability of separation process in air dense medium fluidized bed, which provides a high motivation for further study of underlying mechanisms. Reasons for generation and propagation of pressure fluctuations in the air dense medium fluidized bed have been discussed.Drift rate and collision rate of particles were employed to deduce the correlation between voidage and pressure fluctuations. Simultaneously, a dynamic pressure fluctuation measuring and analysis system was established. Based on frequency domain analysis and wavelet analysis, collected signals were disassembled and analyzed. Results show gradually intensive motion of particles increases magnitudes of signal components with lower frequencies. As a result of violent particle motion, the magnitude of real pressure signal's frequency experienced an increase as air velocity increased moderately. Wavelet analysis keeps edge features of the real signal and eliminates the noise efficaciously. The frequency of denoised signal is closed to that of pressure signal identified in frequency domain analysis.

Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

Fatty acids(FAs) provide energy and also can be used to trace trophic relationships among organisms.Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months.We examined fatty acid profiles in aestivated and non-aestivated A.japonicus using multivariate analyses(PERMANOVA,MDS,ANOSIM,and SIMPER).The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly.The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers.Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation,and long chain FAs such as eicosapentaenoic(EPA) and docosahexaenoic acid(DHA) that produced from intestinal degradation,are digested during deep aestivation.Specific changes in the fatty acid composition of A.japonicus during aestivation needs more detailed study in the future.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

基于FA-PSO-RBF神经网络的富氧底吹铜锍品位预测模型

铜锍品位是富氧底吹铜熔炼过程中的一个关键工艺参数,针对铜锍品位实时检测困难、检测结果滞后时间长、指导生产工艺参数优化滞后等问题,基于生产数据深入挖掘及处理,提出了一种基于FA-PSO-RBF神经网络的铜锍品位预测模型。首先为了降低模型的预测误差,利用FA分析方法对原始生产数据进行降维处理,确定主要因子数量为6个,并计算因子得分,然后针对RBF神经网络模型对关键参数依赖性较大的不足,利用改进PSO算法对网络结构中的关键参数进行寻优,最后,以因子得分为输入,铜锍品位值为输出,通过实际生产数据验证模型的准确性,并与RBF、标准PSO-RBF预测模型进行对比,结果表明,本文构建的铜锍品位预测模型预测精度更高,与标准PSO-RBF预测模型相比,RMSE和MAE的值分别降低了17.2%和21.2%,该预测模型对富氧底吹铜熔炼生产过程参数优化控制提供了一种方法借鉴。

Source Apportionment of Ambient PM_(10) in the Urban Area of Longyan City,China:a Comparative Study Based on Chemical Mass Balance Model and Factor Analysis Method

In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples and various pollution sources which were collected in January 2010 in Longyan with inductivity coupled plasma-mass spectrometry（ICP-MS）.Then chemical mass balance（CMB） model and factor analysis（FA） method were applied to comparatively study the inorganic components in the sources and receptor samples.The results of factor analysis show that the major sources were road dust,waste incineration and mixed sources which contained automobile exhaust,soil dust/secondary dust and coal dust during the daytime in Longyan City,China.There are two major sources of pollution which are soil dust and mixture sources of automobile exhaust and secondary dust during the night in Longyan.The results of CMB show that the major sources are secondary dust,automobile exhaust and road dust during the daytime in Longyan.The major sources are secondary dust,soil dust and automobile exhaust during the night in Longyan.The results of the two methods are similar to each other and the results will guide us to plan to control the PM10 pollution sources in Longyan.

基于改进DFA和LDA的永磁同步电机机械故障检测

为提高故障检测的精度,研究了变转速工况下永磁同步电机的机械故障检测方法.首先,分析了电机轴承、转子偏心及其复合故障的振动特性;其次,采用Vold-Kalman算法对故障特征分量进行跟踪提取,并通过信号重构消除转速变化对故障特征分量的影响;提出一种基于改进去趋势波动分析和线性判别式分析的机械故障检测方法,实现对重构信号的故障特征提取和故障检测;最后,对所提出故障检测方法的有效性进行实验验证.实验结果表明文中所提出方法的故障检测精度为88%.

Time-series analysis of the characteristic pressure fluctuations in a conical fluidized bed with negative pressure

The negative pressure conical fluidized bed is widely used in the pharmaceutical industry.In this study,experiments based on the negative pressure conical fluidized bed are carried out by changing the material mass and particle size.The pressure fluctuation signals are analyzed by the time and the frequency domain methods.A method for absolutely characterizing the degree of the energy concentration at the main frequency is proposed,where the calculation is to divide the original power spectrum by the average signal power.A phenomenon where the gas velocity curve temporarily stops growing is observed when the material mass is light,and the particle size is small.The standard deviation and kurtosis both rapidly change at the minimum fluidization velocity and thus can be used to determine the flow regime,and the variation rule of the kurtosis is independent of both the material mass and particle size.In the initial fluidization stage,the dominant pressure signal comes from the material movement;with the increase in the gas velocity,the power of a 2.5 Hz signal continues to increase.A method of dividing the main frequency by the average cycle frequency can conveniently determine the fluidized state,and a novel concept called stable fluidized zone proposed in this paper can be obtained.Controlling the gas velocity within the stable fluidized zone ensures that the fluidized bed consistently remains in a stable fluidized state.

Multi-scale and multi-fractal analysis of pressure fluctuation in slurry bubble column bed reactor

The Daubechies second order wavelet was applied to decompose pressure fluctuation signals with the gas flux varying from 0.18 to 0.90 m3/h and the solid mass fraction from 0 to 20% and scales 1?9 detail signals and the 9th scale approximation signals. The pressure signals were studied by multi-scale and R/S analysis method. Hurst analysis method was applied to analyze multi-fractal characteristics of different scale signals. The results show that the characteristics of mono-fractal under scale 1 and scale 2, and bi-fractal under scale 3?9 are effective in deducing the hydrodynamics in slurry bubbling flow system. The measured pressure signals are decomposed to micro-scale signals, meso-scale signals and macro-scale signals. Micro-scale and macro-scale signals are of mono-fractal characteristics, and meso-scale signals are of bi-fractal characteristics. By analyzing energy distribution of different scale signals,it is shown that pressure fluctuations mainly reflects meso-scale interaction between the particles and the bubble.

Pore pressure fluctuations of overlying aquifer during residual coal mining and water-soil stress coupling analysis

Three test models and a simulation model were constructed based on the prevailing conditions of the Taiping coalmine in order to analyze pore pressure fluctuations of an overlying aquifer during residual coal mining. As well, the relation between pore pressure and soil stress was evaluated. The model tests show the vibrations of pore pressure and soil stress as a result of mining activities. The simulation model tells of the response characteristics of pore pressure after mining and its distribution in the sand aquifer. The comparative analysis reveals that pore pressure and soil stress vibration are activated by unexpected events occurring in mines, such as collapsing roofs. An increased pore pressure zone always lies above the wall in front or behind the working face of a mine. Both pore pressure and vertical stress result in increasing and decreasing processes during movements of the working face of a mine. The vibration of pore pressure always precedes soil stress in the same area and ends with a sharp decline. Changes in pore pressure of sand aquifer are limited to the area of stress changes. Obvious changes are largely located in a very small frame over the mining face.

Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.