Score Vector Research Articles

Bolt defect classification algorithm based on knowledge graph and feature fusion

At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm.

Extremal dependence measure for functional data

Principal component analysis is one of the most fundamental tools of functional data analysis. It leads to an efficient representation of infinitely dimensional objects, like curves, by means of multivariate vectors of scores. We study the dependence between extremal values of the scores using the extremal dependence measure (EDM). The EDM has been proposed and studied for positive bivariate observations. After extending it to multivariate observations, we focus on its application to the vectors of scores of functional data. Estimated scores form a triangular array of dependent random variables. We derive condition guaranteeing that a suitable estimator of the EDM based on these scores converges to the population EDM and is asymptotically normal. These conditions are completely different from those encountered in the second-order theory of functional data. They are formulated within the framework of functional regular variation. Large sample theory is complemented by an application to intraday return curves for certain stocks and by a simulation study.

Research on Interval-Valued Intuitionistic Fuzzy Multi-Attribute Decision Making Based on Projection Model

Background: Regarding the multi-attribute decision making where the decision information is the interval-valued intuitionistic fuzzy number and the attribute weight information is not completely determined. Methods: Intuitionistic fuzzy set theory introduces non-membership function, as an extension of the fuzzy set theory, it has certain advantages in solving complex decision-making problems. A projection model based interval-valued intuitionistic fuzzy multi-attribute decision-making scheme was proposed in this study. The objective weight of the attribute was obtained using improved intervalvalued intuitionistic fuzzy entropy, and thus the comprehensive weight of the attribute was obtained according to the preference information. Results: In the aspect of the decision-making matrix processing, the concept of interval-valued intuitionistic fuzzy ideal point and its related concepts were defined, the score vector of each scheme was calculated, the projection model was constructed to measure the similarity between each scheme and the interval-valued intuitionistic fuzzy ideal point, and the scheme was sorted according to the projection value. Conclusion: The efficiency and usability of the proposed approach are considered in the case study.

Computational Options for Standard Errors and Test Statistics with Incomplete Normal and Nonnormal Data in SEM

ABSTRACT This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or expected, whether the observed information matrix is estimated numerically or using an analytic asymptotic approximation, and whether the information matrix and the outer product matrix of the score vector are evaluated at the saturated or at the structured estimates. A variety of different standard errors and robust test statistics become possible by varying these options. We review the asymptotic properties of these computational variations, and we show how to obtain them using lavaan in R. We hope that this article will encourage methodologists to study the impact of the available computational options on the performance of standard errors and test statistics in SEM.

Correction to: The Weibull Fréchet distribution and its applications

Afify et al. [The Weibull Fréchet distribution and its applications, J. Appl. Stat., 43 (2016), pp. 2608–2626] defined and studied a new four-parameter lifetime model called the Weibull Fréchet distribution. They made some mistakes in presenting the log-likelihood function and the components of score vector. In this note, we will correct them.

Ensemble and calibration multiply robust estimation for quantile treatment effect

ABSTRACT Quantile treatment effects can be important causal estimands in the evaluation of biomedical treatments or interventions for health outcomes such as birthweight and medical cost. However, the existing estimators require either a propensity score model or a conditional density vector model is correctly specified, which is difficult to verify in practice. In this paper, we allow multiple models for propensity score and conditional density vector, then construct a class of calibration estimators based on multiple imputation and inverse probability weighting approaches via empirical likelihood. The resulting estimators multiply robust in the sense that they are consistent if any one of these models is correctly specified. Moreover, we propose another class of ensemble estimators to reduce computational burden while ensuring multiple robustness. Simulations are performed to evaluate the finite sample performance of the proposed estimators. Two applications to the birthweight of infants born in the United States and AIDS Clinical Trials Group Protocol 175 data are also presented.

Structural change detection in ordinal time series.

Change-point detection in health care data has recently obtained considerable attention due to the increased availability of complex data in real-time. In many applications, the observed data is an ordinal time series. Two kinds of test statistics are proposed to detect the structural change of cumulative logistic regression model, which is often used in applications for the analysis of ordinal time series. One is the standardized efficient score vector, the other one is the quadratic form of the efficient score vector with a weight function. Under the null hypothesis, we derive the asymptotic distribution of the two test statistics, and prove the consistency under the alternative hypothesis. We also study the consistency of the change-point estimator, and a binary segmentation procedure is suggested for estimating the locations of possible multiple change-points. Simulation results show that the former statistic performs better when the change-point occurs at the centre of the data, but the latter is preferable when the change-point occurs at the beginning or end of the data. Furthermore, the former statistic could find the reason for rejecting the null hypothesis. Finally, we apply the two test statistics to a group of sleep data, the results show that there exists a structural change in the data.

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking.

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.

Statistical Identification of Important Nodes in Biological Systems.

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

Estimating the Covariance Matrix of the Maximum Likelihood Estimator Under Linear Cluster-Weighted Models

In recent years, the research into cluster-weighted models has been intense. However, estimating the covariance matrix of the maximum likelihood estimator under a cluster-weighted model is still an open issue. Here, an approach is developed in which information-based estimators of such a covariance matrix are obtained from the incomplete data log-likelihood of the multivariate Gaussian linear cluster-weighted model. To this end, analytical expressions for the score vector and Hessian matrix are provided. Three estimators of the asymptotic covariance matrix of the maximum likelihood estimator, based on the score vector and Hessian matrix, are introduced. The performances of these estimators are numerically evaluated using simulated datasets in comparison with a bootstrap-based estimator; their usefulness is illustrated through a study aiming at evaluating the link between tourism flows and attendance at museums and monuments in two Italian regions.

Assessment of Item and Test parameters: Cosine Similarity Approach

The paper proposes new measures of difficulty and discriminating values of binary items and test consisting of such items and find their relationships including estimation of test error variance and thereby the test reliability, as per definition using cosine similarities. The measures use entire data. Difficulty value of test and item is defined as function of cosine of the angle between the observed score vector and the maximum possible score vector. Discriminating value of test and an item are taken as coefficient of variation (CV) of test score and item score respectively. Each ranges between 0 and 1 like difficulty value of test and an item. With increase in number of correct answer to an item, item difficulty curve increases and item discriminating curve decreases. The point of intersection of the two curves can be used for item deletion along with other criteria. Cronbach alpha was expressed and computed in terms of discriminating value of test and item. Relationship derived between test discriminating value and test reliability as per theoretical definition. Empirical verifications of proposed measures were undertaken. Future studies suggested.re to enter text.

PLS for classification

Partial Least Squares regression (PLS) is a multivariate technique developed to perform regression in the case of multivariate responses when multicollinearity, redundancy and noise affect the predictors. In spite of several efforts have been made to extend PLS to classification problems, this is still a current field of research. In the present study, a new technique called PLS for classification is introduced to solve the general G-class problem. It is developed within a self-consistent framework based on linear algebra and on the theory of compositional data. After the introduction of the notion of probability-data vector, the space of the predictors and that of the conditional probabilities are linked, and a well-defined least squares problem, whose solution specifies the relationship between probabilities and predictors, is solved by a suitable reformulation of PLS2. The method estimates directly the conditional probability of the class membership given the predictors. The score vectors are introduced only in a second step to improve model interpretation. The main properties of PLS for classification and its relationships with PLS-DA are discussed. One simulated and one real data sets are investigated to show how the method works in practice.

Quantized Cooperative Spectrum Sensing in Bandwidth-Constrained Cognitive V2X Based on Deep Learning

The output of the network in a deep learning (DL) based single-user signal detector, which is a normalized 2 × 1 class score vector, needs to be transmitted to the fusion center (FC) by occupying a large amount of the communication channel (CCH) bandwidth in the cooperative spectrum sensing (CSS). Obviously, in cognitive radio for vehicle to everything (CR-V2X), it is particularly important to propose a method that makes full use of the bandwidth-constrained CCH to obtain the optimal detection performance. In this paper, we firstly propose a novel single-user spectrum sensing method based on modified-ResNeXt in CR-V2X. The simulation results show that our proposed method performs better than two advanced DL based spectrum sensing methods with shorter inference time. We then introduce a quantization-based cooperative spectrum sensing (QBCSS) algorithm based on DL in CR-V2X, and the impact of the number of reported bits on the sensing results is also discussed. Through the experimental results, we conclude that the QBCSS algorithm reaches the optimal detection performance when the number of bits for quantizing local sensing data is 4. Finally, according to the conclusion, a bandwidth-constrained QBCSS scheme based on DL is proposed to make full use of the CCH with limited capacity to achieve the optimal detection performance.

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.

Successive Halving Top-k Operator

We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible. The need to perform softmax iteratively on the entire vector of scores is avoided using a tournament-style selection. As a result, a much better approximation of top-k and lower computational cost is achieved compared to the previous approach.

ARMA process for speckled data

Synthetic aperture radar (SAR) systems are efficient to deal with remote sensing issues. In contrast, SAR images are affected by speckle noise, due to the use of coherent illumination in their capturing. This noise imposes both a granular interference on such images (precluding their interpretability) and a multiplicative and non-Gaussian nature on their data. The multiplicative modelling is often used to surpass previous difficulties, mainly its particular case the distribution. In this paper, we introduce a new time series modelling for SAR imagery, called autoregressive-moving-average (ARMA) process. We derive some of its mathematical properties: score vector, Fisher information matrix, residual analysis and prediction equations. The maximum likelihood estimation procedure for -ARMA parameters is discussed and some asymptotic behaviours for its estimates are quantified by Monte Carlo experiments. Applications to Munich and Foulum SAR actual images are made. Results show the proposed model can outperform the Γ-ARMA model.

Defect detection in concrete plates with impulse-response test and statistical pattern recognition

Statistical and machine learning analysis of structural health monitoring data is a popular approach for efficient structural level defect detection. However, the application of these techniques to stress-wave methods such as impact-echo and impulse-response data for local level detection has been limited. In this research, statistical pattern recognition in conjunction with the impulse-response test is shown to provide an efficient means for the detection of defects in concrete plates. For this purpose, the Frequency Response Function (FRF) derived from the impulse-response test following ASTM C1740 protocols at specific points on the test plate to define the feature space matrix. Analytical results demonstrate that the variability of the FRF increases in the presence of defects, which forms the physical basis for the proposed pattern recognition algorithm. First, Principal Component Analysis (PCA) is performed on the covariance of the feature space matrix to identify the dominant features of the FRFs and to determine the number of statistically significant factors or principal components. Factor scores are next used to identify locations on the plate that are associated most closely to each pattern. The generalized Extreme Value Studentized (ESD) test and box and whisker plots are applied to the factor score vector of all the test points to objectively identify test points with defects and rank these based on their severity. Two experimental specimens are used to demonstrate the applicability of the proposed detection algorithm. The first specimen, a partially reinforced clamped concrete plate, is used to demonstrate the relationship between the shapes and variability of the FRFs and the severity of defects (delaminations), the efficacy of the factor score as damage sensitive feature, and identification and the ranking of the severity of defects by using outlier statistical tests. The second specimen is used to demonstrate the efficiency of the procedure for detecting both void and honeycomb defects in a larger reinforced concrete plate on elastic supports. The proposed procedure is shown to provide similar levels of detectability as the highly refined yet time consuming ultrasonic shear-wave tomography test. While the impulse-response test has been in use for the condition assessment of concrete elements other than drilled shaft piles since 1980′s, defect detection has been based primarily on empirical observations and correlations with limited features of the frequency response function. The use of pattern recognition techniques to the full range of the frequency response function is proposed and shown to greatly improve the detection and characterization of defects.

Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil

Models based on the Kumaraswamy law are used with variables that assume values in (0, 1). In some cases, however, the data contain zeros and/or ones, that is, there is data inflation. We introduce a class of regression models that can be used with such inflated data, namely: the class of inflated Kumaraswamy regression models. We consider inflation at zero, at one, and at both zero and one. We introduce the model and provide closed‐form expressions for its score vector and Fisher's information matrix. The proposed model is used to evaluate the impacts of different conditioning variables on the proportion of people who live in households with inadequate water supply and sewage in Brazilian municipalities. Our results reveal that policies directed to increasing the population share with college education in places where it is low are particularly effective in reducing the prevalence of people who live under inadequate sanitation conditions.

Research on Movie Recommendation Algorithm Based on Stack De-noising Auto-encoder

Focused on the problems that the randomness of the noise-adding operation in the de-noising auto-encoder (DAE), and the data matrix does not consider the impact of trusted users on the deep preferences of target users, this paper proposes a recommendation algorithm based on stack de-noising auto-encoder (SDAE) which integrates the preferences of trusted users. Firstly, the score vector is used as the input of the auto-encoder, and the mask vector is designed to train the potential preference of the target user. Secondly, the deep preference of the target user and the trusted user is obtained by the weighted fusion of the features of the two hidden layers of the auto-encoder. Thirdly, in order to reduce the impact of noise on the prediction accuracy, the cascaded auto-encoder model is constructed and trained according to the greedy training method layer by layer training. Finally, SDAE model is compared with other models on different data sets. The experimental results show that SDAE model has better recommendation performance.

Recent progress in parameter change test for integer-valued time series models

In this study, we review a recent progress regarding the change point test for integer-valued time series models, specifically concentrating on the CUSUM test for integer-valued autoregressive (INAR) and generalized autoregressive conditional heteroscedastic (INGARCH) models. Because time series often experience changes in underlying models, the change point test has been a fundamental issue in time series analysis during the past decades. We first introduce the CUSUM test in a general set-up and then construct estimate-, score vector- and residual-based CUSUM tests in INAR and INGARCH models and state their limiting null distributions. Finally, the residual-based CUSUM of squares test and the robust change point test based on the density power divergence are addressed.