An improved generalized predictive control algorithm based on the difference equation CARIMA model for the SISO system with known strong interference

ABSTRACT

The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC.     

Kinetic modeling of liquid-phase esterification of acetic acid with n-butanol using heterogeneous poly(o-methylene p-toluene sulfonic acid) as catalyst

Liquid-phase esterification of acetic acid with n-butanol to n-butyl acetate is studied in the presence of a polymeric catalyst, that is, poly(o-methylene p-toluene sulfonic acid). The performance of the proposed catalyst is compared with the other commercially available homogeneous and heterogeneous catalysts in terms of its activity. Experiments are conducted in an isothermal stirred batch reactor to study the effects of speed of agitation, temperature, and catalyst loading on the rate of reaction. A concentration-based pseudo-homogeneous (PH) kinetic model and activity-based kinetic models such as PH, Eley-Rideal (ER), and Langmuir-Hinselwood-Hougen-Watson (LHHW) models are developed. All the models considered in this study resulted in similar percentage deviation close to 4%. Further, kinetic models are validated through additional experiments, and it is observed that the simple concentration-based PH model is able to predict experimental data with least deviation compared to activity-based PH, ER, and LHHW models. The developed kinetic models are also tested using the Fisher-Snedecor test (F-test) and are found to be acceptable. By incorporating both modeling data and validation data, the overall absolute average deviations of different models are found to be concentration-based PH model 4.354%, activity-based PH model 5.006%, ER I model 5.189%, ER II model 5.403%, ER III model 5.437%, and LHHW model 6.104%, illustrating the superiority of the simple concentration-based PH model.     

Exact solution of the two-mode quantum Rabi model

An analytical method is developed to study the two-mode quantum Rabi model. For certain specific parameter conditions, especially for the resonant conditions, we obtain an infinite number of the exact solutions of the eigenfunctions and associated energies. It is shown that there exist new types of the exact energies which do not correspond to the level-crossings. Our analytical method may find applications in some related models.     

基于DID模型的ITS对北京交通拥堵改善的研究:评价期的视角

采用双重差分模型,基于智能交通系统(ITS)效用发挥滞后时间为3年,选择2010年和2014年作为评价时点对ITS实施效果和北京交通拥堵之间的相关性进行分析.DID模型计量分析的最终结果显示,ITS效果的发挥明显改善了北京交通拥堵问题.为了验证评价结果的可信度,通过改变评价时点这一参数对评价结果进行稳健性检验,结果仍显示ITS效应发挥对北京交通拥堵改善有很大的促进作用.     

Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk. Supplementary material for this article is available online.     

Influence of metal ion on chelate–aryl stacking interactions

CCSD(T)/CBS and DFT methods are employed to study the stacking interactions of acetylacetonate‐type (acac‐type) chelates of nickel, palladium, and platinum with benzene. The strongest chelate–aryl stacking interactions are formed by nickel and palladium chelate, with interaction energies of −5.75 kcal mol⁻¹ and −5.73 kcal mol⁻¹, while the interaction of platinum chelate is weaker, with interaction energy of −5.36 kcal mol⁻¹. These interaction energies are significantly stronger than stacking of two benzenes, −2.73 kcal mol⁻¹. The strongest nickel and palladium chelate–aryl interactions are with benzene center above the metal area, while the strongest platinum chelate–aryl interaction is with the benzene center above the C2 atom of the acac‐type chelate ring. These preferences arise from very different electrostatic potentials above the metal ions, ranging from very positive above nickel to slightly negative above platinum. While the differences in electrostatic potentials above metal atoms cause different geometries with the most stable interaction among the three metals, the dispersion (correlation energy) component is the largest contribution to the total interaction energy for all three metals.     

Numerical analysis of stress distribution in the upper arm tissues under an inflatable cuff: Implications for noninvasive blood pressure measurement

An inflatable cuff wrapped around the upper arm is widely used in noninvasive blood pressure measurement. However, the mechanical interaction between cuff and arm tissues, a factor that potentially affects the accuracy of noninvasive blood pressure measurement, remains rarely addressed. In the present study, finite element (FE) mod-els were constructed to quantify intra-arm stresses generated by cuff compression, aiming to provide some theoretical evi-dence for identifying factors of importance for blood pressure measurement or explaining clinical observations. Obtained results showed that the simulated tissue stresses were highly sensitive to the distribution of cuff pressure on the arm sur-face and the contact condition between muscle and bone. In contrast, the magnitude of cuff pressure and small variations in elastic properties of arm soft tissues had little influence on the efficiency of pressure transmission in arm tissues. In par-ticular, it was found that a thickened subcutaneous fat layer in obese subjects significantly reduced the effective pres-sure transmitted to the brachial artery, which may explain why blood pressure overestimation occurs more frequently in obese subjects in noninvasive blood pressure measurement.     

甲型H1N1流感的死亡率高吗——Bayes计量分析框架下的建模与估算

在全球甲型H1N1流感大流行背景下,本文在充分考虑各国甲流感死亡率可能存在个体混合效应、独立效应、相关效应及空间相关效应基础上,运用Bayes计量分析框架下的模型选择标准确定描述各国甲流感死亡率的最优模型,并基于该模型对不同国家甲流感死亡率进行估算。结果显示:个体独立、空间相关效应模型能很好拟合各国甲流感疫情统计数据,利用该模型估算的全球甲流感平均死亡率为0.577%。     

A high accuracy flow segmentation method in crowded scenes based on streakline

Flow segmentation based on similar motion patterns in crowded scenes remains an open problem in computer vision due to inherent complexity and vast diversity found in such scenes. To solve this problem, the streakline framework based on Lagrangian fluid dynamics had been proposed recently. However, this framework computed optical flow field using conventional optical flow method (Lucas Kanade method) which has poor anti-interference performance, and serious deviation would be brought to the computation of optical flow field. Moreover, our experimental results show that using the formulation of streak flow similarity in this framework can result in incorrect flow segmentation. Therefore, we combine this framework with a high accurate variational model, and modify the corresponding formulation of streak flow similarity after analyzing the streakline framework in detail. Finally, an improved method is proposed to solve flow segmentation in crowded scenes. Experiments are done to compare these two methods and results verify the validity and accuracy of our method.