论高等工程教育的“非工程化”倾向

论高等工程教育的“非工程化”倾向杨东平当前我国高等工程教育的改革，受到多重挑战和来自不同方向的压力。对一些用人单位的调查显示，国营大中型企业、科研院所等比较缺乏具有突出的学术水平、能够解决复杂问题和具有复合型知识能力结构的学科带头人、项目负责人、科...

纳米珍珠层人工骨构建骨诱导型组织工程化骨治疗犬股骨头坏死

背景:将生长因子和少量自体骨髓与支架材料复合构成诱导型组织工程化骨,省略了分离及体外培养细胞的步骤和费用,防止了相关的污染和细胞性状改变等,是一种现阶段较为可行的诱导骨组织再生的方法。目的:探讨纳米珍珠层人工骨作为骨形态发生蛋白和自体骨髓载体的可行性,以及其构建的诱导型组织工程化骨治疗股骨头坏死的疗效。方法:健康成年杂种犬建立股骨头坏死模型,分别给予髓芯减压结合植入纳米珍珠层人工骨治疗,单纯髓芯减压治疗。术后4、12周分批处死动物行影像学、生物力学及组织学检查。结果与结论:纳米珍珠层人工骨组的抗压强度高于髓芯减压组和坏死组(P=0.000),纳米珍珠层人工骨组新骨形成明显,表现为软骨内化骨;髓芯减压组未见新骨形成,仅纤维组织填充减压区。结果提示纳米珍珠层人工骨可以作为骨形态发生蛋白和自体骨髓的载体,其构建的诱导型组织工程化骨可改善股骨头抗压强度,促进新骨形成和进行坏死修复。

论包装工程专业“大工程观”

在介绍包装学科体系的基础上,提出了包装工程专业"大工程观",同时分析了其必要性。指出我国包装工程教育存在非工程化的倾向,提出了包装工程专业工程化的实施策略。

A New Method to Solve Robust Data Reconciliation in Nonlinear Process

Data reconciliation is an effective technique for providing accurate and consistent value for chemical process. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less-biased results. In this article, a new method is proposed to solve the robust data reconciliation problem of nonlinear chemical process. By using several technologies including linearization method, penalty function, virtual observation equation, and equivalent weights method, the robust data reconciliation problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results in a nonlinear chemical process demonstrate the efficiency of the proposed method.

Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

Tracking control of chaotic dynamical systems with feedback linearization

A new method was proposed for tracking the desired output of chaotic dy- namical system using the feedback linearization and nonlinear extended statement ob- server method. The feedback linearization was used to convert the nonlinear chaotic system into linear system. The extended Luenberger-like statements observer was de- signed to reconstructing and observing the unmeasured statements when the tracking controller was designed. By this way, the chaotic system could be forced to track vari- able desired output, which could be a time variant function or an equilibrium points. Taken the Lorenz chaotic system as example, the simulation results show the validity of the conclusion and effectiveness of the algorithm.

Integration optimization of novel electric power steering system based on quality engineering theory

The dynamic model of a novel electric power steering （EPS） system integrated with active front steering function （the novel EPS system） is built. The concepts and quantitative expressions of the steering road feel, steering sensibility, and steering operation stability are introduced. Based on quality engineering theory, the optimization algorithm is proposed by integrating the Monte Carlo descriptive sampling, elitist non-dominated sorting genetic algorithm （NSGA-II） and 6-sigma design method. With the steering road feel and the steering portability as optimization targets, the system parameters are optimized by the proposed optimization algorithm. The simulation results show that the system optimized based on quality engineering theory can improve the steering road feel, guarantee steering stability and steering portability and thus provide a theoretical basis for the design and optimization of the novel electric power steering system.

A Computer Model for Selecting Equipment in Earth-Fill Dam Projects

Selecting right equipment has been playing an important role in the success of construction projects. This paper presents a computer model, ESCMODEL, for equipment selection in Earth-fill dam projects. The proposed model is capable of assisting the users in making decisions to determine the size, number, type and schedule of presence of dozers, loaders, graders, excavators, trucks, sheepsfoot rollers and smooth wheel rollers. ESCMODEL can contribute to resolve this selection process through the application of an optimization technique, based on nonlinear programming. Three actual case studies of earth-fill dam projects are presented in order to illustrate the effectiveness and performance of the model.

Measurement Analysis in Electrochemical Discharge Machining （ECDM） Process： A Literature Review

Electrochemical discharge machining is considered to be a hybrid machining process that combines with EDM and ECM （electro chemical machining）, called ECDM. The material removal is based on two phenomena： electrochemical dissolution of the material and thermal erosion of electrical discharges that occur between the cathode ＆ anode electrodes. This process is better used for machining of non conducting materials efficiently. In this research paper shows that a brief literature review study of various measuring instruments used for analysis of various parameters of the electrochemical discharge machining process on various types of materials, tool material, input ＆ output parameters such as surface roughness, surface texture, material removal, tool wear etc..

Construction of Soliton-Cnoidal Wave Interaction Solution for the (2+1)-Dimensional Breaking Soliton Equation

In this paper, the truncated Painleve analysis and the consistent tanh expansion （CTE） method are developed for the （2＋1）-dimensional breaking soliton equation. As a result, the soliton-cnoidal wave interaction solution of the equation is explicitly given, which is dimcult to be found by other traditional methods. When the value of the Jacobi elliptic function modulus rn = 1, the soliton-cnoidal wave interaction solution reduces back to the two-soliton solution. The method can also be extended to other types of nonlinear evolution equations in mathematical physics.

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained mul- ti-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.