Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

激光熔覆SiO2/La2O3梯度生物陶瓷涂层生物活性研究

目的研究SiO_2含量对钛合金表面激光熔覆梯度生物陶瓷涂层生物活性的影响。方法利用激光熔覆技术,采用梯度成分设计思想,固定涂层中稀土氧化物La_2O_3的添加量,在钛合金TC4表面制备了掺杂不同含量SiO_2的梯度生物陶瓷涂层。采用金相显微镜(OM)、X射线衍射仪(XRD)、扫描电子显微镜(SEM)、噻唑蓝(MTT)及荧光素双醋酸酯(FDA)染色等测试手段,研究了SiO_2含量对激光熔覆制备梯度涂层的组织结构和生物活性的影响。结果 SiO_2在激光熔覆过程中可以降低梯度生物陶瓷涂层的开裂敏感性,并起到细化晶粒的作用。当SiO_2掺杂量为2.5%时,激光熔覆过程中诱导合成的HA+CaTiO_3数量最大;当SiO_2掺杂量为7.5%时,模拟体液(SBF)实验表明,涂层的矿化沉积能力最强。MTT测试表明,SiO_2掺杂量为7.5%的涂层细胞增殖数量的OD值最大,细胞能够紧贴涂层表面生长。FDA染色分析表明,SiO_2掺杂量为7.5%的涂层上细胞数量最多,且分布均匀。结论 SiO_2掺杂量深刻影响着生物活性陶瓷相HA和Ca_2SiO_4数量,进而影响生物陶瓷涂层的生物活性。SiO_2掺杂量为7.5%的涂层具有最佳的生物相容性及生物活性。     

A novel flow field with controllable pressure gradient to enhance mass transport and water removal of PEM fuel cells

An easily machined novel flow field with controllable pressure gradient across adjacent channels was designed and a two dimensional, across-the-channel, two-phase model was developed to study the gas transport and water removal of the novel configuration. The effect of channel-rib width ratio, GDL thickness and pressure gradient on the profiles of oxygen concentration and water saturation within the GDL were investigated. Special attention was paid to the mechanisms of the promoted mass transport and water removal rates under a pressure gradient. The model was validated by experiments with various channel-rib ratios and GDL thicknesses at different operating pressure. The results revealed that, oxygen concentration was increased, and the water saturation was reduced under the rib with a pressure gradient generated across the adjacent channels. The optimal pressure gradient is between 0.1 to 0.2 atm for the studied channel geometry and configuration. The mechanisms of the improved cell performance were elucidated.     

In-line characterization of dispersion uniformity evolution during a twin-screw blending extrusion based on near-infrared spectroscopy

Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near-infrared (NIR) spectroscopy to in-line characterize dispersion uniformity evolution during a twin-screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.     

Design and Fabrication of Concentration‐Gradient Generators with Two and Three Inlets in Microfluidic Chips

A simple and low‐cost method for designing and fabricating concentration‐gradient generators with two and three inlets is proposed which can generate different concentration gradients at varying flow velocities. The microchannel structure was designed in S‐shape and left‐right symmetry. The concentration‐gradient generator was simulated based on the finite element method. The microchannels were processed on a computer numerical control (CNC) engraving and milling machine on poly(methylmethacrylate) substrate, and then two concentration‐gradient generators were fabricated by hot bonding technology. The results of experiment and simulation were compared to prove the feasibility of the method. Flow velocity was an important factor for generating different concentration gradients. The concentration‐gradient profiles of the generators with two and three inlets present approximately linear and quadratic curves.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Stiffness improvement of swirl gripper based on gap height and force estimation

The swirl gripper is an electrically activated noncontact handling device that uses swirling airflow to generate a lifting force. This force can be used to pick up a workpiece placed underneath the swirl gripper without any contact. It is applicable, for example, on a semiconductor wafer production line, where contact must be avoided during the handling and moving of a workpiece to minimize damage. When a workpiece levitates underneath a swirl gripper, the gap height between them is critical for safe handling. Therefore, in this study, we develop a theoretical model of the swirl gripper, based on which a method to estimate the levitation gap height by detecting pressure at two points is proposed. Experiments indicate that the estimated gap height can track changes in actual gap height accurately in real time, when the gap height is relatively small and the inertia of airflow in the gap is negligible. In addition, the force between the gripper and workpiece can also be estimated using the detected pressure. As a result, a desired relationship between the force and gap height can be achieved by adjusting the rotating speed of the fan according to the real-time estimated force and gap height using a microcontroller. The control system was experimentally verified using a desired linear relationship. Because the stiffness of the force decreases with increasing gap height for a constant gripper fan rotating speed, the linear relationship between the force and gap height, which means a constant stiffness, is expected to enhance handling stability of workpieces.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Generalized Marshall Olkin Inverse Lindley Distribution with Applications

In this article, a new generalization of the inverse Lindley distribution is introduced based on Marshall-Olkin family of distributions. We call the new distribution, the generalized Marshall-Olkin inverse Lindley distribution which offers more flexibility for modeling lifetime data. The new distribution includes the inverse Lindley and the Marshall-Olkin inverse Lindley as special distributions. Essential properties of the generalized Marshall-Olkin inverse Lindley distribution are discussed and investigated including, quantile function, ordinary moments, incomplete moments, moments of residual and stochastic ordering. Maximum likelihood method of estimation is considered under complete, Type-I censoring and Type-II censoring. Maximum likelihood estimators as well as approximate confidence intervals of the population parameters are discussed. A comprehensive simulation study is done to assess the performance of estimates based on their biases and mean square errors. The notability of the generalized Marshall-Olkin inverse Lindley model is clarified by means of two real data sets. The results showed the fact that the generalized Marshall-Olkin inverse Lindley model can produce better fits than power Lindley, extended Lindley, alpha power transmuted Lindley, alpha power extended exponential and Lindley distributions.     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.