Yumin Liu

A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.

Due to the complexity and uncertainty of third-party logistics (3PL) provider selection circumstances, the research on the hybrid multi-criteria decision-making (HMCDM) method with fuzzy hesitation information is becoming more and more important. Based on symmetry principles, both the objectivity of the decision information and the subjectivity of decision makers’ (DMs) preferences should be considered in the HMCDM method. In this paper, a novel interactive decision-making method to deal with the 3PL provider selection problem of hesitant fuzzy sets, intuitionistic fuzzy sets and real numbers is developed. We first investigate the positive and negative ideal solutions of the alternative and the satisfaction degree of the DMs under hybrid multi-criteria circumstances. Then, the interactive HMCDM models based on satisfaction degrees are established, which can use objective decision information to rank alternatives and, symmetrically, the preference information of the DMs is also taken into account. DMs can modify their preference information using the models and thus make the most reasonable selection of 3PL provider. Finally, the case analysis and sensitivity analysis show that the change of parameter and the setting of the satisfaction lower limit will not affect the optimal rank of alternatives, and the feasibility of the proposed method is confirmed.

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

We investigate the tail dependence between sovereign debt distress and bank non-performing loans (NPLs) using a large sample of developed and emerging countries in recent decades. Considering the feedback loop of sovereign debt and bank loan distress, we use three copula models to analyze the asymmetry of tail dependence structure between sovereign debt exposure and bank NPLs. We use the Gaussian copula marginal regression to control the concurrent impact of other macroeconomic variables. We provide evidence that sovereign debt indicates an important determinant of NPLs. We also find that there is tail dependence between sovereign debt distress and bank NPLs, whereas the tail dependence coefficients vary across countries. Our findings shed light on the influence of fiscal distress on bank loan distress and provide immediate implications for the design of macro prudential and financial policy.

Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural network (CNN) model. Firstly, motivated by the pasting case study, the spatial-temporal data are transformed into process images for capturing spatial and temporal interrelationship. Then, the CNN recognition model is presented for identifying different types of these process images, leading to the identification of abnormal process with spatial-temporal data. The specific architecture parameters of CNN are determined step by step. According to the performance comparison with alternative methods, the proposed method is able to accurately identify the abnormal process with spatial-temporal data.

This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. Linear and functional principal component analysis (FPCA) and biclustering are used to show the clustering pattern of NPLs and government debt for 25 EU and BRICS countries (Brazil, Russia, India, China and South Africa) during the period of 2006 to 2017 through high-dimensional visualizations. The results demonstrate that the government debt markets of EU countries experienced a similar trend in terms of NPLs, with a similar size of NPLs across debt markets. Through visualization, we find that the government debt and NPLs of EU and BRICS countries increased drastically after the crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis is lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. We also find that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.

The green supplier selection (GSS) problem is one of the most pressing issues that can directly affect manufacturer performance. GSS has been studied in previous literature, which is considered to be a typical multiple criteria group decision making (MCGDM) problem. The ordered weighted hesitant fuzzy MCGDM method can present the importance of each possible value, and the priority relationship among criteria has rarely been studied. In this study, we first extend the prioritized average (PA) operator to the ordered weighted hesitant fuzzy set (OWHFS) for solving the both problems. The generalized ordered weighted hesitant fuzzy prioritized weighted average operator (GOWHFPWA) is recommended, and some desirable properties are discussed. Based on this operator, a novel MCGDM method for GSS is developed. A numerical example of GSS is then given to prove the robustness of the proposed approach, and a sensitivity analysis is used to identify the robustness of the proposed method. Finally, a comparative analysis based on the MCGDM approach with the hesitant fuzzy prioritized weighted average (HFPWA) operator is illustrated to indicate the validity and advantages of the proposed approach.

Geometric hashing (GH) is a general model-based recognition scheme. GH is widely used in the industrial products assembly and inspection tasks. The aim of this study is to speed up the geometric hashing pattern recognition method for the purpose of real-time object detection applications. In our method, a pattern is decomposed into some sub-patterns to reduce the data number in hash table bins. In addition, the sub-patterns are recorded in a plurality of hash tables. Finally we improve the recognition performance by combining with image pyramid and edge direction information. To confirm the validity of our proposed method, we make a complexity analysis, and apply our method to some images. Both complexity analysis and experiment evaluations have demonstrated the efficiency of this technique.