考虑设备故障和电量管理的自动导引车调度方法

Intermediate charging and sudden failure of automatic guided vehicles(AGVs)interrupt and severely affect the stability and efficiency of scheduling.Therefore,an AGV scheduling approach considering equipment failure and power management is proposed for outfitting warehouses.First,a power consumption model is established for AGVs performing transportation tasks.The powers for departure and task consumption are used to calculate the AGV charging and return times.Second,an optimization model for AGV scheduling is established to minimize the total transportation time.Different conditions are defined for the overhaul and minor repair of AGVs,and a scheduling strategy for responding to sudden failure is proposed.Finally,an algorithm is developed to solve the optimization model for a case study.The method can be used to plan the charging time and perform rescheduling under sudden failure to improve the robustness and dynamic response capability of AGVs.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

Simultaneous Prediction of Retention Times and Peak Shapes of Sulfonamides in Reversed-Phase High-Performance Liquid Chromatography

In this paper, we propose a novelmethod based on the plate theory to simultaneously predict retention times and peak shapes under gradient elutions and different flow rates by reversed-phase high-performance liquid chromatography. The proposed method yielded excellent retention prediction results in experiments with 16 common sulfonamides under 18 gradient conditions and four different flow rates, including 0.7, 1.0, 1.3, and 1.5 mL/min. The mean absolute deviation was 0.70%, which indicates accurate prediction. Moreover, the proposed method predicts the change wellin peak shapes caused by the expansion or compression ofpeaks under different gradient conditions.

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice.We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection(ATAAD)patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use(ulinastatin).Patient characteristics at admission in the Chinese multicenter 5A study database(2016-2022)were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome(MODS).

Preparation of porous hollow silica spheres via a layer-by- layer process and the chromatographic performance

Hollow silica spheres possessing excellent mechanical properties were successfully prepared through a layer-by-layer process using uniform polystyrene （PS） latex fabricated by dispersion polymerization as template. The formation of hollow SiO2 micro-spheres, structures and properties were observed in detail by zeta potential, SEM, TEM, FTIR, TGA and nitrogen sorption porosimetry. The results indicated that the hollow spheres were uniform with particle diameter of 1.6 μm and shell thickness of 150 nm. The surface area was 511 m2/g and the pore diameter was 8.36 nm. A new stationary phase for HPLC was obtained by using C18-derivatized hollow SiO2 micro-spheres as packing materials and the chromatographic properties were evaluated for the separation of some regular small molecules. The packed column showed low column pressure, high values of efficiency （up to about 43 000 plates/m） and appropriate asymmetry factors.

Sufficient dimension reduction in the presence of controlling variables

We are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables.We suggest a profile least squares approach to perform partial dimension reduction for a general class of semi-parametric models.The asymptotic properties of the resulting estimates for the central partial mean subspace and the mean function are provided.In addition,a Wald-type test is proposed to evaluate a linear hypothesis of the central partial mean subspace,and a generalized likelihood ratio test is constructed to check whether the nonparametric mean function has a specific parametric form.These tests can be used to evaluate whether there exist interactions between the covariates and the controlling variables,and if any,in what form.A Bayesian information criterion(BIC)-type criterion is applied to determine the structural dimension of the central partial mean subspace.Its consistency is also established.Numerical studies through simulations and real data examples are conducted to demonstrate the power and utility of the proposed semi-parametric approaches.

The abstract of doctoral dissertation‘nonlinear wavelet density estimation and hazard rate estimation with data missing at random’

In this thesis,we establish non-linear wavelet density estimators and studying the asymptotic properties of the estimators with data missing at random when covariates are present.The outstanding advantage of non-linear wavelet method is estimating the unsoothed functions,however,the classical kernel estimation cannot do this work.At the same time,we study the larger sample properties of the ISE for hazard rate estimator.