Control Chart for Monitoring Autocorrelated Process with Multiple Exogenous Inputs

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes.     

Multiplicative consistency analysis for interval fuzzy preference relations: A comparative study

To examine the multiplicative consistency of interval fuzzy preference relations (IFPRs), this paper first analyzes the limitations associated with the previous consistency concepts. Accordingly, a new consistency concept is defined that is an extension of the crisp case and overcomes limitations in the previous concepts. Next, a linear programming model to judge the consistency of IFPRs is constructed, and an approach to derive multiplicative consistent IFPRs is introduced. Furthermore, goal-programming models to determine missing values in an incomplete IFPR are constructed that have the highest consistent level with respect to known values. Moreover, a multiplicative consistency and consensus based method for group decision making with IFPRs is developed that can address incomplete and inconsistent cases. Finally, two practical decision-making problems are offered to demonstrate the feasibility and efficiency of the new method, and an analysis of a numerical and theoretical comparison with several related methods is performed.     

研发过程中知识产权风险的控制

企业知识产权包括商标、专利和著作权等，它是企业重要的无形资产。目前，国内大多数企业还不够重视企业的知识产权保护，尤其是专利保护。在研发过程中的专利申请及保护不及时、不到位，导致企业从项目研发到市场占有的整个过程蒙受巨大风险。从知识产权保护角度，探讨了企业研发过程中有关风险的防范措施。     

Simulating random variables using moment-generating functions and the saddlepoint approximation

When we are given only a transform such as the moment-generating function of a distribution, it is rare that we can efficiently simulate random variables. Possible approaches such as the inverse transform using numerical inversion of the transform are computationally very expensive. However, the saddlepoint approximation is known to be exact for the Normal, Gamma, and inverse Gaussian distribution and remarkably accurate for a large number of others. We explore the efficient use of the saddlepoint approximation for simulating distributions and provide three examples of the accuracy of these simulations.     

论“被现代”图景在现代中国文学中的展现

"现代"是由各种向不同方向扯动的诸多主题共同建构的一种历史凝缩的结果。"现代"是背靠启蒙运动和工业革命的社会进程。它的发展将整个世界卷入其中。它同时拥有积极阳光的一面和腐烂阴暗的一面。后者集中体现在那些没有两大背景的"被现代"的第三世界国家。现代中国就是不幸处于现代时空中的第三世界国家。在现代中国文学中,我们能够看到"被现代"所带来的无奈与衰颓。老舍的《骆驼祥子》就是展现这幅"被现代"图景的典型作品之一。     

近代城市空间改造的时空进程

近代城市人口的增长、城市职能的变化、市政机构的出现带来了城市空间的转变。２０世纪初，中国开始了大范围的城市基础设施改造与新建。其中最有代表性的是拆城、填濠、修筑环城马路的工程，此后全国大部分的城市改造都遵循了相似的模式。本文通过对近１２００座治所城市城墙变动信息的研究发现，２０世纪大规模的城市空间改造最初主要从东南沿海省份开始，３０年代末至４０年代的战争打破了这一过程，但随着新中国成立后城市建设的恢复，空间改造的进程继续向西部省份推进，总体上呈现由东向西，由中心向边缘发展的特点。     

Finding Bhaskar in all the wrong places? Causation,process, and structure in Bhaskar and Deleuze

This article examines the reception of Roy Bhaskar amongst some contemporary Deleuzians. It proceeds by rejecting the all too often predilection of opposing realism to ‘postmodernism’ or ‘post‐structuralism’ arguing instead for the need to bring one into dialogue with the other. To this end, the paper explores the resonances and points of departure between the work of Gilles Deleuze and Roy Bhaskar. In particular, it examines the language of causation, object‐oriented versus process‐oriented ontologies, as well as the charge by Deleuzians that Bhaskar is an essentialist. Through this engagement it attempts to develop and rethink explanation and causation in terms of a more chaotic ontology of machines, centered around the concept of structure, process, and production in an open, heterogeneous, and dynamic world. The end result is a more chaotic concept of realism.     

Missing value imputation method for disaster decision-making using K nearest neighbor

Due to destructiveness of natural disasters, restriction of disaster scenarios and some human causes, missing data usually occur in disaster decision-making problems. In order to estimate missing values of alternatives, this paper focuses on imputing heterogeneous attribute values of disaster based on an improved K nearest neighbor imputation (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers (TFNs) are introduced and three types of attributes (i.e. linguistic term sets, intervals and real numbers) are converted to TFNs. Then the correlated degree model is utilized to extract related attributes to form instances that will be used in K nearest neighbor algorithm, and a novel KNNI method merging with correlated degree model is presented. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and effectiveness.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.