Process monitoring method based on correlation variable classification and vine copula

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.     

变厚度板中水平剪切波的传播特性分析

为研究超声导波在储罐壁板等变壁厚结构中的传播特性，建立了SH波在空间自由边界中的传播模型，基于各向同性弹性介质Navier-Stokes方程，推导了变厚度板中SH波的频散方程，分析了变厚度板中水平剪切波走时t与倾斜角θ间的相互作用关系；通过简谐点源的远场响应并引入板厚与传播位移的非线性关系，得出了变厚度板中SH波的远场响应方程。在此基础上，仿真分析和实验验证了水平剪切波在不同倾斜角变厚度板中的传播特性，结果表明：在变厚度板的薄端激励水平剪切波并沿结构表面传播时，无频散现象，且无波包分离现象；但随着变厚度板倾斜角θ增大，水平剪切波的走时和接收信号幅值均逐渐递减，且呈非线性关系。     

Input variable scaling for statistical modeling

Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection.     

通风量对菇渣好氧发酵温度时空特性的影响

采用不同通风量进行菇渣与猪粪高温共发酵,研究通风对发酵过程中堆体温度的时空变化的影响。结果表明,通风使堆体温度呈现出明显的梯度效应,不同处理各发酵时期的温度分布均由剖面中部向四周不断降低。高温区域的位置和面积随发酵时间呈动态变化规律,升温期温度在发酵罐中上部积聚,高温分布区类似椭圆形,高温期椭圆面积逐渐向堆体中部扩散和扩大,随后高温面积不断递减并向中下部迁移,降温期堆体各区域温度趋于一致。由等值线的疏密程度可知,温度的空间变异性大体呈现出高温期>第2次高温期>升温期>降温期的规律。结合试验各阶段的温度变化并考虑节能降耗,得出发酵升温期和高温期分别以0.05,0.067m3/(d·m3)进行通风,二次高温期与降温期以0.033 m3/(d·m3)进行通风条件最佳。     

An efficient approach for the S‐shaped penalty function

Interval goal programming (IGP) with a marginal penalty function (PF) was first proposed by Charnes and Collomb in 1972, and further improved by Kvanli and other researchers. Recently, Lu and Chen proposed an efficient logarithmic method to formulate IGP with an S‐shaped PF. However, their method requires adding many binary variables when the problem size becomes large, which increases the computational burden in the solution process. This study proposes an efficient approach for the S‐shaped PF. The arbitrary PF frequently appears in the fields of business and industry. However, none of the previous approaches have addressed arbitrary PFs without adding binary variables. The proposed approach can be easily extended to formulate an arbitrary PF in which binary variables are no longer required, regardless of the number of break points. The proposed method can improve the efficiency of IGP for solving large size management and decision problems in considering PFs. In order to demonstrate the correctness, usefulness of the proposed model, illustrative examples are provided.     

变邻域量子烟花算法求解CVRP

针对带容量约束的车辆路径问题，提出一种融合量子进化算法和变邻域优化策略的变邻域量子烟花算法。该算法采用等分随机键与最大位置法结合的实数编码方式，通过量子旋转门和非门变异提高算法全局搜索能力，同时运用结合2-Opt的变邻域优化策略加强局部搜索能力。选取17个基准算例进行参数实验和对比实验，实验结果表明，相对于对比算法，所提出的算法具有较好的寻优能力和收敛速度。     

粒子群属性聚类的位置隐私保护

针对基于位置服务中连续查询情况下，用户自身属性信息很容易被攻击者获取，并通过关联获得用户位置隐私的情况，提出了一种利用粒子群聚类加速相似属性用户寻找，并由相似属性匿名实现用户位置泛化的隐私保护方法。该方法利用位置隐私保护中常用的可信中心服务器，通过对发送到中心服务器中的查询信息进行粒子群属性聚类，在聚类的过程中加速相似属性用户的寻找过程，由相似属性用户完成位置泛化，以此实现位置隐私保护。实验结果证明，这种基于粒子群属性聚类的隐私保护方法具有高于同类算法的隐私保护能力，以及更快的计算处理速度。     

An efficient numerical scheme for solving fractional infinite-horizon optimal control problems

This paper presents an efficient numerical method to solve fractional infinite-horizon optimal control problems, where the dynamic control system depends on Caputo fractional derivatives. First, by a suitable change of variable, we transform the fractional infinite-horizon optimal control problem to a finite-horizon one. Then, with the help of an approximation, we replace the Caputo derivative to integer order derivative. According to the Pontryagin minimum principle (PMP) for optimal control problems and by constructing an error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for state, costate and control functions where these trial solutions are constructed by using two-layered perceptron neural network. Some numerical results are introduced to explain our main results.     

温拌LDPE改性沥青混合料性能研究

选择Sasobit和Evotherm 3G两种温拌剂对温拌LDPE沥青混合料制备工艺和性能进行研究。首先,比较变温等体积法和等黏温度法两种方法,并确定两种温拌混合料的拌和及击实温度。在此基础上,分别采用车辙试验、低温弯曲试验、中点加载弯曲试验以及冻融劈裂试验评价70#、LDPE、Sasobit-LDPE以及3G-LDPE四种沥青混合料的高温性能、低温性能、疲劳性能和水稳定性。结果表明,两种温拌技术均是可行的,Sasobit和3G温拌技术可使施工温度降低20℃和15℃。与热拌LDPE沥青混合料相比,Sasobit温拌沥青混合料具有更好的高温性能和水稳定性,在高应力比条件下抗疲劳性能更好,低温性能虽变差,但是优于70#沥青混合料;3G温拌沥青混合料的高温性能,低温性能以及水稳定性则与热拌LDPE沥青混合料各项性能较为接近,抗疲劳性能变差,但是优于70#沥青混合料。     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.