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%.

Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data

Missing values occur in bio-signal processing for various reasons,including technical problems or biological char-acteristics.These missing values are then either simply excluded or substituted with estimated values for further processing.When the missing signal values are estimated for electroencephalography (EEG) signals,an example where electrical signals arrive quickly and successively,rapid processing of high-speed data is required for immediate decision making.In this study,we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set.The proposed method solves the problem of a biased model,which inevitably results from simply removing incomplete data rather than estimating them,and thus reduces the loss of information by incorporating missing values in real time.By using an incremental approach,the proposed method alsominimizes memory usage and processing time of continuously arriving data.Experimental results show that the proposed method assigns more accurate missing values than previous methods.

Studies on unfolding energy spectra of neutrons using maximumlikelihood expectation–maximization method

Energy spectra of neutrons are important for identification of unknown neutron sources and for determination of the equivalent dose. Although standard energy spectra of neutrons are available in some situations, e.g., for some radiotherapy treatment machines, they are unknown in other cases, e.g., for photoneutrons created in radiotherapy rooms and neutrons generated in nuclear reactors. In situations where neutron energy spectra need to be determined, unfolding the required neutron energy spectra using the Bonner sphere spectrometer (BSS) and nested neutron spectrometer (NNS) has been found promising. However, without any prior knowledge on the spectra, the unfolding process has remained a tedious task. In this work, a standalone numerical tool named ‘‘NRUunfold’’ was developed which could satisfactorily unfold neutron spectra for BSS or NNS, or any other systems using similar detection methodology. A generic and versatile algorithm based on maximum-likelihood expectation– maximization method was developed and benchmarked against the widely used STAY’SL algorithm which was based on the least squares method. The present method could output decent results in the absence of precisely calculated initial guess, although it was also remarked that employment of exceptionally bizarre initial spectra could lead to some unreasonable output spectra. The neutron count rates computed using the manufacturer’s response functions were used for sensitivity studies. The present NRUunfold code could be useful for neutron energy spectrum unfolding for BSS or NNS applications in the absence of a precisely calculated initial guess.

Study on the Development and Implementation of Different Big Data Clustering Methods

Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.

基于非对称拉普拉斯分布的混合分位数回归参数估计

利用非对称拉普拉斯分布提出一种新的混合分位数回归模型.传统模型仅考虑位置参数,而所提出模型同时考虑了位置参数和尺度参数,并利用期望最大化(expectation maximization,EM)算法对模型参数进行估计.数值分析结果表明,参数估计的精度在各个分位数上均较为理想,并且估计精度随着样本量的增加而提高.最后运用所提出模型及其算法对城市房价数据进行分析.

A multi-target tracking algorithm based on Gaussian mixture model

Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.

Software sensor for slab reheating furnace

It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is proposed to make more effective use of those measurements that are already available, which has great importance both to slab quality and energy saving. The proposed method is based on the mixtures of Gaussian processes (GP) with the expectation maximization (EM) algorithm employed for parameter esti- mation of the mixture of models. The mixture model can alleviate the computational complexity of GP and also accords with the changes of operating condition in practical processes. It is demonstrated by on-line estimation of the furnace gas temperature in 1580 reheating furnace in Baosteel Corporation (Group).

Improved H-infinity channel estimator based on EM for MIMO-OFDM systems

H-infinity estimator is generally implemented in timevariant state-space models, but it leads to high complexity when the model is used for multiple input multiple output with orthogo- hal frequency division multiplexing （MIMO-OFDM） systems. Thus, an H-infinity estimator over time-invariant system models is pro- posed, which modifies the Krein space accordingly. In order to avoid the large matrix inversion and multiplication required in each OFDM symbol from different transmit antennas, expectation maximization （EM） is developed to reduce the high computational load. Joint estimation over multiple OFDM symbols is used to resist the high pilot overhead generated by the increasing number of transmit antennas. Finally, the performance of the proposed estimator is enhanced via an angle-domain process. Through performance analysis and simulation experiments, it is indicated that the pro- posed algorithm has a better mean square error （MSE） and bit error rate （BER） performance than the optimal least square （LS） estimator. Joint estimation over multiple OFDM symbols can not only reduce the pilot overhead but also promote the channel performance. What is more, an obvious improvement can be obtained by using the angle-domain filter.

Speech Enhancement Based on Approximate Message Passing

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).

Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

To cope with the arbitrariness of the network delays,a novel method,referred to as the composite particle filter approach based on variational Bayesian(VB-CPF),is proposed herein to estimate the clock skew and clock offset in wireless sensor networks.VB-CPF is an improvement of the Gaussian mixture kalman particle filter(GMKPF)algorithm.In GMKPF,Expectation-Maximization(EM)algorithm needs to determine the number of mixture components in advance,and it is easy to generate overfitting and underfitting.Variational Bayesian EM(VB-EM)algorithm is introduced in this paper to determine the number of mixture components adaptively according to the observations.Moreover,to solve the problem of data packet loss caused by unreliable links,we propose a robust time synchronization(RTS)method in this paper.RTS establishes an autoregressive model for clock skew,and calculates the clock parameters based on the established autoregressive model in case of packet loss.The final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian distribution.Moreover,RTS shows good robustness to the continuous and random dropout of time messages.

Nonlinear System Identification with Unknown Piecewise Time-Varying Delay

Identification of nonlinear systems with unknown piecewise time-varying delay is concerned in this paper.Multiple auto regressive exogenous(ARX) models are identified at different process operating points,and the complete dynamics of the nonlinear system is represented by using a combination of a normalized exponential function as the probability density function with each of the local models.The parameters of the local ARX models and the exponential functions as well as the unknown piecewise time-varying delays are estimated simultaneously under the framework of the expectation maximization(EM) algorithm.A simulation example is applied to demonstrating the proposed identification method.

时变条件下MIMO-OFDM系统中的信道估计算法

本文设计了时变多径衰落条件下MIMO-OFDM系统中一种新的信道估计算法。该算法结合递归EM算法和Kalman预测对时变信道进行跟踪。借助软球形译码器(List Sphere Decoder,LSD)产生的搜索列表,递归EM算法序贯遍历搜索列表中可能的符号组合来估计各个子载波上的信道频率响应;基于获得的信道频率响应估计,Kalman预测器利用衰落信道的时域二阶统计特性进一步跟踪信道时变。仿真结果表明:本文设计的算法可以有效跟踪信道时变,性能优于传统的软输入Kalman滤波算法。

Some Pathological Knowledge Discovered in Large Database of Type 2 Diabetes

Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes.

Failure prognostic of systems with hidden degradation process

Systems with a hidden degradation process are perva- sive in the real world. Degrading critical components will under- mine system performance and pose potential failures in the future. Prognostic aims at predicting potential failures before it evolves into faults. A prognostic procedure based on expectation maxi- mization and unscented Kalman filter is proposed. System state, sensor measurement and hidden degradation process are viewed as data （incomplete or missing） in the expectation maximization method. System state and hidden degradation process are esti- mated by a unscented Kalman filter upon sensor measurements. Component-specific parameters in a degradation process are iden- tified on the estimation of the degradation process. Residual life is measured by the median of estimated residual life distribution. The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance. Residual life estimation consists conservatively with the trend and is evalu- ated in terms of relative errors. Simulation results are reasonable. The proposed prognostic method expects applications in practice.

Enhanced EM-based channel estimation for MIMO-OFDM in highly mobile channels

An enhanced expectation maximization （ with channel time variation is proposed for mobile EM） based iterative channel estimator for coping multiple input multi output orthogonal frequency division multiplexing （MIMO OFDM） systems. In the proposed scheme, the recursive least squares （RLS） algorithm is applied to track the time varying channel impulse response （CIR） within several symbols. By using the tracked time varying CIR, the ICI are constructed and then cancelled from the received signal, thus reducing their impactions on the channel estimation. Moreover, based on an o ver sampled complex exponential basis expansion model （ OCE BEM）, an improved channel predic tor is derived in order to improve the initial channel estimates accuracy of the iterative estimator. Simulation results show that ying scenarios with a smaller the proposed scheme outperforms the classic counterpart in time var cost of complexity.

Inheritance of the Male Sterility in a New Photo/Thermo-Sensitive Genie Male Sterile Line B06S of Rice

The major male sterile genes in a new photo/thermo-sensitive genie male sterile (PTGMS) line B06S of rice were analyzed by the manipulation of mixture distribution theory. The results indicated that a pair of major male sterile nuclear genes with large effects were responsible for controlling the male sterility of B06S.

A Complex Algorithm for Solving a Kind of Stochastic Programming

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.

A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge concepts.Most existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning scenarios.In this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis results.Specifically,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness factors.Furthermore,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model parameters.Our UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.