以任意高维纠缠态为量子信道的受控隐形传态

目的是利用高维量子纠缠态为量子信道，讨论未知单粒子态的受控隐形传输问题。以三维量子纠缠态为信道，提出一个二维任意单粒子态的受控隐形传输协议。提出了以任意[d]-维量子纠缠态为量子信道，[t]-维任意单粒子态的隐形传输协议[(t相似文献   

Reduced basis method for managed pressure drilling based on a model with local nonlinearities

To circumvent restrictions of conventional drilling methods, such as slow control actions and inability to drill depleted reservoirs, a drilling method called managed pressure drilling (MPD) has been developed. In MPD, single-phase flow processes can be modeled as a feedback interconnection of a high-order linear system and a low-order nonlinear system. These nonlinearities appear locally both inside and at the boundaries of the computational domain. To obtain a fast simulation platform for real-time purposes (eg, online model-based controller implementation), model order reduction is required for MPD. However, the local nonlinearities render applying model order reduction techniques challenging. In this study, a new approach is proposed to deal with such nonlinearities within the reduced basis (RB) context and it is successfully tested on a model for MPD. Contrary to the classical RB technique, the proposed approach not only does not generate nonphysical spikes at the locations of these local nonlinearities but also yields high speedup factors. The obtained reduced-order model can be used for efficient online simulation and controller design for drilling systems with MPD.     

Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV

In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial octorotor helicopter control is proposed in presence of actuator faults. Radial Base Function Neural Network (RBFNN), Fuzzy Logic Control approach (FLC) and Sliding Mode Control (SMC) technique are used to design a controller, named Fault Tolerant Control (FTC), for each subsystem of the octorotor helicopter. The proposed FTC scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the proposed FTC can greatly alleviate the chattering effect, good tracking in presence of actuator faults.     

A RBF based finite difference method for option pricing under regime-switching jump-diffusion model

Abstract

In this article, we presented a method for option pricing problem under regime-switching jump-diffusion models. We have proposed a numerical method for solving a partial integro-differential equation (PIDE) for pricing European option and for solving linear complementarity problem (LCP), to evaluate the price of American options. We use implicit explicit method for time semi discretization, followed by radial basis function based finite difference (RBF-FD) method for spatial discretization to solve PIDE. The proposed method is further extended to solve the LCP by coupling it with operator splitting method. Numerical simulation is done for European and American option to demonstrate efficiency and accuracy of the proposed method.     

对于厚度70mm〜140mm焊缝超声检测的讨论

超声检测作为一种面积型缺陷的有效检测方法,被广泛应用在气化炉、除氧器、汽包等的焊缝检测上。随着近年来电厂、化工厂对质量的要求提升,确保焊缝质量就成了生产制造厂的重点。对于这些重型容器,焊缝厚度普遍在70mm~140mm,在这个厚度区间,采取的多道焊接,工艺控制不严很容易产生缺陷。无损检测标准的制定只是简单的对此厚度划分两个区间,并未给出划定此区间的依据。本文通过实际检测气化炉、除氧器、汽包等焊缝来讨论超声检测在厚度70mm?140mm区间的划定依据。     

Comparison of approaches to the numerical modelling of pure metals solidification using the control volume method

The thermal processes proceeding in the casting-mould system are analysed. Solidification process can be described in the different ways. One of them consists in the application of the Fourier–Kirchhoff equation in which the source function controlling the phase change is introduced. In this paper, two approaches to the modelling of pure metals solidification, i.e. the solidification proceeding at the constant temperature and in the artificially introduced interval of temperature are considered. Numerical model used at the stage of computations is constructed using the Control Volume Method in which the principle of conservation of energy (taking also into account the release of latent heat of solidification) is applied to each control volume ensuring the correctness of the model. The use of Voronoi/Thiessen tessellation to the discretization of the casting-mould system can accurately reproduce the shape of each sub-domain. In the final part of the paper, the examples of computations are shown.     

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Abstract

The expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene–Butadiene modified asphalt is more consistent compared to the Styrene–Butadiene–Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt.     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.