Improvements of nonlinear dynamic modeling of hot-film MAF sensor

The operating procedure of engine requires that the mass air flow (MAF) sensor can measure the pulsation air flow accurately. Therefore the MAF sensor must possess fast response speed. It is necessary to study the dynamic characteristic of the MAF sensor. Both the experimental equipments and method used by authors previously are improved to obtain the sensor response data accurately in this paper. The static and dynamic separable modeling method is adopted to build the uniform nonlinear dynamic model of the hot-film MAF sensor in the Hammerstein and Wiener forms, which can describe the large/small flow-rate and positive/negative step responses. The performance indexes are calculated by the actual and model responses.     

Temperature compensation of fluorescence intensity-based fiber-optic oxygen sensors using modified Stern–Volmer model

In practical sensing applications, temperature effects are of particular concern, and hence it is necessary to develop the means to correct the fluorescence intensity measurement in accordance with the working temperature. Accordingly, this study develops a modified Stern–Volmer model to compensate for the temperature drift of oxygen concentration measurements obtained using fiber-optic sensors. The oxygen sensors considered in this study are based on teraethylorthosilane (TEOS)/n-octyltriethoxysilane (Octyl-triEOS) or n-propyltrimethoxysilane (n-propyl-TriMOS)/3,3,3-trifluoropropyltrimethoxysilane (TFP-TriMOS) composite xerogels doped with platinum meso-tetrakis(pentafluorophenyl)porphine (PtTFPP).

The experimental results are fitted to the modified Stern–Volmer model in order to compute suitable values for a temperature compensation coefficient at different working temperatures. It is found that the proposed temperature compensation method reduces the difference in the oxygen concentration measurement for working temperatures in the range of 25–70 °C as compared to data without compensation. The linearity and sensitivity of PtTFPP-doped n-propyl-TriMOS/TFP-TriMOS sensor are better than PtTFPP-doped TEOS/Octyl-triEOS sensor for working temperatures in the range of 25–70 °C.

The proposed approach could provide a straightforward and effective means of improving the accuracy of fiber-optic oxygen sensors if a variable attenuator is designed according to the temperature compensation coefficient. Thus, the fiber-optic oxygen sensor with a variable attenuator could work in a broad temperature range without using a temperature sensor.     

Mixtures of robust probabilistic principal component analyzers

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to extract the local principal orientations in the data. An important issue with this generative model is its sensitivity to data lying off the low-dimensional manifold. In order to address this problem, the mixtures of robust probabilistic principal component analyzers are introduced. They take care of atypical points by means of a long tail distribution, the Student-t. It is shown that the resulting mixture model is an extension of the mixture of Gaussians, suitable for both robust clustering and dimensionality reduction. Finally, we briefly discuss how to construct a robust version of the closely related mixture of factor analyzers.     

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.     

A survey on the history of transaction management: from flat to grid transactions

Transactions have been around since the Seventies to provide reliable information processing in automated information systems. Originally developed for simple ‘debit-credit’ style database operations in centralized systems, they have moved into much more complex application domains including aspects like distribution, process-orientation and loose coupling. The amount of published research work on transactions is huge and a number of overview papers and books already exist. A concise historic analysis providing an overview of the various phases of development of transaction models and mechanisms in the context of growing complexity of application domains is still missing, however. To fill this gap, this paper presents a historic overview of transaction models organized in several ‘transaction management eras’, thereby investigating numerous transaction models ranging from the classical flat transactions, via advanced and workflow transactions to the Web Services and Grid transaction models. The key concepts and techniques with respect to transaction management are investigated. Placing well-known research efforts in historical perspective reveals specific trends and developments in the area of transaction management. As such, this paper provides a comprehensive, structured overview of developments in the area.     

网格工作流在军事信息网格中的应用研究

首先介绍了工作流和工作流管理系统的定义,阐述了网格工作流和军事信息网格的研究现状.其次研究了网格工作流的调度模型并分析了网格工作流调度过程,通过将工作流的思想应用到军事信息网格系统中,给出了基于网格工作流的军事信息网格系统总体设计架构,并对架构中的各个组成模块进行了研究.     

Realization of robust nonlinear model predictive control by offline optimisation

This paper describes a new method for increasing the computational efficiency of nonlinear robust model-based predictive control. It is based on the application of neuro-fuzzy networks and improves the computation efficiency by arranging the online optimisation to be done offline. The offline optimisation is realized by offline training a neuro-fuzzy network, consisting of zero-order T–S fuzzy rules, which is designed to approximate the input–output relationship of a robust model-based predictive controller. The design and the training of the neuro-fuzzy network are described, and the corresponding control algorithm is developed. Experiment results performed on the temperature control loop of an experimental air-handling unit (AHU) demonstrate the effectiveness of this approach.     

An atomistic-continuum inhomogeneous material model for the elastic bending of metal nanocantilevers

The equivalent elastic modulus of nanocantilever can be obtained using atomistic simulation. However, the use of this modulus to predict the bending of nanocantilever results in significant error compared with direct atomistic simulation. The error originates from the surface effect. In our current work, the nanocantilever is considered as an inhomogeneous continuum material. The distributions of materials parameters at the cross-section, such as atomistic elastic constants, are calculated from atomistic simulations. These atomistic-information-based materials parameters are used as inputs to continuum model. A numerical example case validates the presented model and methodology. To correctly predict the structure-property relations of elemental nano-structures are very important for the design of nano-devices. Our continuum model includes nano-effects and provides another way to study nanomechanics.     

无离群数据影响的水污染离子在线监测研究

采用离子选择性电极和电位恒定的参比电极作为敏感头,与高输入阻抗、高精度的电位计组成传感器,以LabVIEW为开发平台的虚拟仪器,通过数据采集卡,实时在线监测水体中多种离子含量,并对数据进行整理,以消除离群数据的影响,为水质污染的在线监测提供了一种具有理论指导意义和实用价值的新方法.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.