Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time.     

MCMCINLA Estimation of Missing Data and Its Application to Public Health Development in China in the Post-Epidemic Era

Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016–2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era.     

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer.To compensate for external disturbances,a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information.With the filtered regressor method,the algorithm has the advantage of eliminating the need for acceleration information,thus reducing the sensor requirements in applications.Using the information obtained from the filtered regressor,a batch of stored data is used to design an adaptive disturbance observer,ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values.The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior,including virtual leader tracking,inter-distance keeping,and collision avoidance.Numerical simulations verify the effectiveness of the algorithm proposed in the present study.     

On the Relation between Prediction and Imputation Accuracy under Missing Covariates

Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the use of modern Machine-Learning algorithms for imputation. This originates from their capability of showing favorable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine-Learning-based methods for both imputation and prediction are used. We see that even a slight decrease in imputation accuracy can seriously affect the prediction accuracy. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as the coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study.     

Interpolation algorithm for recovering the missing phase values in 3D measurement

In the three-dimensional (3D) phase measurement, some marks are usually adhered to the object in order to make the 3D registration process faster and easier. As covered with marks, local phase data are missing and have to be interpolated later. Considering the phase distribution nearby the marks, a gradient estimate (GE) interpolation algorithm is provided here. This algorithm recovers one pixel's missing phase value with the average of the estimated values which is calculated by gradients in eight directions nearby. Since this algorithm is a local processing, the missing phase values should be interpolated from the edge of the marks to the center. In the computer simulation and the practical experiment, compared with the same-size neighborhood mean (NM) algorithm and the Gerchberg-Saxton (GS) algorithm, this new algorithm achieves very good fit results with the least time. So it can be used as a practical tool for automatic missing phase interpolation.     

Robust scatter correction method for cone-beam CT using an interlacing-slit plate

Cone-beam computed tomography(CBCT) has been widely used in medical imaging and industrial nondestructive testing,but the presence of scattered radiation will cause significant reduction of image quality.In this article,a robust scatter correction method for CBCT using an interlacing-slit plate(ISP) is carried out for convenient practice.Firstly,a Gaussian filtering method is proposed to compensate the missing data of the inner scatter image,and simultaneously avoid too-large values of calculated inner scatter and smooth the inner scatter field.Secondly,an interlacing-slit scan without detector gain correction is carried out to enhance the practicality and convenience of the scatter correction method.Finally,a denoising step for scatter-corrected projection images is added in the process flow to control the noise amplification The experimental results show that the improved method can not only make the scatter correction more robust and convenient,but also achieve a good quality of scatter-corrected slice images.     

A time series analysis and a non-homogeneous Poisson model with multiple change-points applied to acoustic data

High levels of the so-called community noise may produce hazardous effect on the health of a population exposed to them for large periods of time. Hence, the study of the behaviour of those noise measurements is very important. In this work we analyse that in terms of the probability of exceeding a given threshold level a certain number of times in a time interval of interest. Since the datasets considered contain missing measurements, we use a time series model to estimate the missing values and complete the datasets. Once the data is complete, we use a non-homogeneous Poisson model with multiple change-points to estimate the probability of interest. Estimation of the parameters of the models are made using the usual time series methodology as well as the Bayesian point of view via Markov chain Monte Carlo algorithms. The models are applied to data obtained from two measuring sites in Messina, Italy.     

Multivariate Time Series Imputation: An Approach Based on Dictionary Learning

The problem addressed by dictionary learning (DL) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. Both the dictionary and the sparse representations are learned from the data. We show how DL can be employed in the imputation of multivariate time series. We use a structured dictionary, which is comprised of one block for each time series and a common block for all the time series. The size of each block and the sparsity level of the representation are selected by using information theoretic criteria. The objective function used in learning is designed to minimize either the sum of the squared errors or the sum of the magnitudes of the errors. We propose dimensionality reduction techniques for the case of high-dimensional time series. For demonstrating how the new algorithms can be used in practical applications, we conduct a large set of experiments on five real-life data sets. The missing data (MD) are simulated according to various scenarios where both the percentage of MD and the length of the sequences of MD are considered. This allows us to identify the situations in which the novel DL-based methods are superior to the existing methods.     

Heat conduction process on community networks as a recommendation model

Using heat conduction mechanism on a social network we develop a systematic method to predict missing values as recommendations. This method can treat very large matrices that are typical of internet communities. In particular, with an innovative, exact formulation that accommodates arbitrary boundary condition, our method is easy to use in real applications. The performance is assessed by comparing with traditional recommendation methods using real data.     

输入层自构造神经网络用于红外光谱多元校正

为了解决多组分红光谱定量分析中的特征的取和校正建模问题，本文提出了一种输入层自构造神经网络。在应用这种网络之前的预处理过程首先对训练数据进行分析，获得关于问题的某些先验知识。在训练阶段，神经网络根据先验知识自动选择输入层神经元的个数，同时确定网络参数。这种网络模型将特征提取和参数学习过程融为一体，有利于提高建模效率。利用仿真红外光谱的定量分析实验表明，这种网络模型不仅能够对光谱数据实现高效率的波长选择，并具有抑制随机噪声和非线性干扰的能力。     

Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets

Wavelet transform based techniques are used for signal-to-noise ratio (SNR) enhancement in ultrasonic non-destructive testing and evaluation of strong sound scattering materials. The overall denoising performance of a wavelet signal processor is conditioned by several processing parameters, including the type of wavelet, thresholding method, and threshold selection rules. Different thresholding procedures and threshold selection rules are analysed in this paper using the discrete wavelet transform and decomposition level dependent thresholds. Global performance is evaluated by means of the SNR enhancement using synthetic grain noise registers with an incrusted flaw signal, with different values of the input SNR, and experimental ultrasonic traces acquired from a carbon fibre reinforced plastic composite block.     

点源透过率测试系统精度标定与分析

点源透过率(PST)测试系统是评价光学系统杂光抑制水平高低的关键设备, 其系统精度的标定是研制难点, 针对此设计了一套用于点源透过率杂散光测试系统精度标定的校准镜头. 利用Tracepro建模分析了校准镜头在不同离轴角下的PST值, 并用此系统对校准镜头不同离轴角下的PST值进行了实测, 与其理论分析值进行比对完成设备精度的标定, 同时通过实测数据分析了测试误差, 给出了系统测试精度和测试极限水平. 结果表明, 在双柱罐内洁净度为ISO 7级的环境水平下, 系统的可见光PST测试极限水平为10^-8, 测试精度对数值优于0.5, 测量重复性为7.9%, 根据对探测系统探测能力的评估, 系统的PST极限测试水平为10^-10.     

A simple and fast technique for on-line fMRI data analysis

In the present work a simple technique for fMRI data analysis is presented. Artifacts due to random and stimulus-correlated motions are corrected without image registration procedures. The first step of our procedure is the calculation of the raw activation map by correlation analysis. The task related motion artifacts arise at the tissue interfaces, including vessels: when image intensity gradient is calculated the high values correspond to interface regions. To eliminate stimulus-correlated motion artifacts the intensity gradient image, obtained from the fMRI data set, is compared to the raw activation map. Since small random motions decrease the value of the correlation coefficient (R) of the external pixels of the activation areas, in the last step of our analysis procedures the clusters are extended to connected pixels having R values smaller than the defined threshold. Each cluster is expanded until the R value of the cluster average intensity is kept constant. The procedure has been tested with both GRE and EPI studies. The presented approach is a fast and robust technique useful for preliminary or on-line analysis of fMRI data.     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

Acoustic properties of organic powders as ultrasonic contrast agents

The results of experiments on measuring attenuation and the effective acoustic nonlinear parameter of the second order are given for a suspension of cocoa-powder in water at different concentrations of the suspension. In the process of evaluating the value of the nonlinear parameter the attenuation in the suspension and generation of the second harmonic not only in the suspension but also in water are taken into account. The obtained results are evidence of the possibility of using a suspension of cocoa-powder in water as a technical substitute for ultrasonic contrast agents. The values of attenuation (up to 60 m⁻¹ at the concentration of 1 g of the powder per 1 l of water) and the nonlinear parameter (up to 120 m⁻¹ at the same concentration) mean that the suspension of cocoa-powder in water has smaller attenuation and the nonlinear parameter than ultrasonic contrast agents at the same concentration. However, these values for the suspension differ considerably from corresponding values for water or blood and, therefore, a suspension of cocoa-powder in water is a promising “substitute” for ultrasonic contrast agents in the case of technical testing of systems for nonlinear tomography of a blood flow, but cannot replace them in medical studies.     

The NUBASE2016 evaluation of nuclear properties

This paper presents the NUBASE2016 evaluation that contains the recommended values for nuclear and decay properties of 3437 nuclides in their ground and excited isomeric(T_(1/2)≥100 ns) states.All nuclides for which any experimental information is known were considered.NUBASE2016 covers all data published by October 2016 in primary(journal articles) and secondary(mainly laboratory reports and conference proceedings) references,together with the corresponding bibliographical information.During the development of NUBASE2016,the data available in the "Evaluated Nuclear Structure Data File"(ENSDF) database were consulted and critically assessed for their validity and completeness.Furthermore,a large amount of new data and some older experimental results that were missing from ENSDF were compiled,evaluated and included in NUBASE2016.The atomic mass values were taken from the "Atomic Mass Evaluation"(AME2016,second and third parts of the present issue).In cases where no experimental data were available for a particular nuclide,trends in the behavior of specific properties in neighboring nuclides(TNN) were examined.This approach allowed to estimate values for a range of properties that are labeled in NUBASE2016 as "non-experimental"(flagged "#").Evaluation procedures and policies used during the development of this database are presented,together with a detailed table of recommended values and their uncertainties.     

Camera-based model to predict the total difference between effect coatings under directional illumination

A camera-based model is established to predict the total difference for samples of metallic panels with effect coatings under directional illumination,and the testing results indicate that the model can precisely predict the total difference between samples with metallic coatings with satisfactory consistency to the visual data. Due to the limited amount of testing samples,the model performance should be further developed by increasing the training and testing samples.     

Bayesian estimation of a dynamic structure's response

This study focuses on the estimation of uncertainty associated with the stress/strain prediction procedures from dynamic test data of structural systems. An accurate prediction of the maximum response levels for physical components during in-field operating conditions is essential for evaluating their performance and life characteristics, as well as for investigating their behavior in light of system design and reliability assessment. Stress/strain inference for a dynamic system is based on the combination of experimental data and results from the analytical/numerical model of the component under consideration. Both modeling challenges and testing limitations contribute to the introduction of various sources of uncertainty within the given estimation procedure with consequent reduced confidence in the predicted response.The objective of this work is to quantify the uncertainties present in the current response estimation process by means of a Bayesian-network representation of the modeling process which allows for a rigorous synthesis of modeling assumptions and information from experimental data, as it takes into account the multi-directional nature of uncertainty propagation. More specifically, the focus is on the residual uncertainty associated with the system's inferred response, and its dependence upon the amount of test data being included in the estimation analysis.Both discrete and linear Gaussian networks were investigated with a focus on their training accuracy and performance in the presence of nonlinear relationships among the physical quantities, weak cause-effect nodal links, as well as different sensitivity levels with respect to infused evidence.     

A study of the transition to instability in a Rijke tube with axial temperature gradient

A horizontal Rijke tube with an electric heat source is a system convenient for studying the fundamental principles of thermoacoustic instabilities both experimentally and theoretically. Given the long history of the device, there is a surprising lack of accurate data defining its behavior. In this work, the main system parameters are varied in a quasi-steady fashion in order to find stability boundaries accurately. The chief purposes of this study are to obtain precise values of the system parameters at the transition to instability with specified uncertainties and to determine how well the experimental results can be explained with existing theory. Measurement errors are reported, and the influence of experimental procedures on the results is discussed. A form of hysteresis effect at stability boundaries has been observed. Mathematical modelling is based on a thermal analysis determining the temperature of the heater and the temperature field in the air inside the tube, which, consequently, affects acoustical mode shapes. Solutions of the linearized wave equation for a non-uniform medium, including losses and a heat source term, determine the stability properties of the eigen modes. Calculated results are compared with experimental data and with results of the modelling based on the common assumption of a constant temperature in the tube. The mathematical model developed here can be applied to designing thermal devices with low Mach number flows, where thermoacoustic issue is a concern.     

Uncovering missing links with cold ends

To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links with low-degree nodes, namely links in the probe set are of lower degree products than a random sampling. Experimental analysis on ten local similarity indices and four disparate real networks reveals a surprising result that the Leicht–Holme–Newman index [E.A. Leicht, P. Holme, M.E.J. Newman, Vertex similarity in networks, Phys. Rev. E 73 (2006) 026120] performs the best, although it was known to be one of the worst indices if the probe set is a random sampling of all links. We further propose an parameter-dependent index, which considerably improves the prediction accuracy. Finally, we show the relevance of the proposed index to three real sampling methods: acquaintance sampling, random-walk sampling and path-based sampling.