Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.     

基于均匀设计抽样的改进遗传算法在回归模型中的应用

通过对佳点集遗传算法优缺点进行分析,利用均匀设计抽样（UDS）的理论和方法,对遗传算法中的交叉操作进行重新设计,提出一种改进的遗传算法。新算法将变量选择和变换选择并行实施,并结合统计信息准则处理回归模型选择问题。仿真实验表明新算法在求解精度、解的稳定性等方面有较大的提高。     

Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments

Considering that demand for healthcare services is constantly increasing, outpatient services must improve their performance. Being able to satisfy the demand with a limited outpatient service capacity is an important operational challenge. The objective of our research consists in studying the relationships and interactions between patient flows, resource capacity (number of consulting rooms and number of nurses) and appointment scheduling rules in order to improve an outpatient orthopaedic clinic performance. Discrete event simulation is used to model outpatient flows. An experimental design was developed to test how to assign consulting rooms and nurses to each orthopedist considering four appointment scheduling rules and three patient flow types of varied complexity. Analysis of variance and the Tukey test are used to evaluate the simulation results. Our conclusion is that in order to improve the outpatient orthopaedic clinic performance, resources (consulting rooms, nurses) and appointment scheduling rules must be adapted to the different patient flows.     

Multilevel design of experiments for statistical moment and probability calculation

A procedure for statistical moment estimation and reliability analysis using design of experiment (DOE) is proposed. A numerical method of finding the optimal levels and weights of DOE for statistical moment estimation is established and applied to three- and five-level cases. The four statistical moments of the system response function are then calculated from the full-factorial DOE, and the probability distribution of the system response function is obtained using the empirical distribution systems such as the Pearson system. The proposed method is tested through several examples and compared with other analysis methods, including the previous developments of a three-level full-factorial design. The results show that it relieves much of the difficulties met in the previous method and provides good accuracy compared to other methods for various input distributions.     

An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

In the application of on-line, dynamic process optimisation, adaptive estimation of the system states and parameters is usually needed to minimise the unavoidable model-process mismatch. This work presents an integrated approach to optimal model adaptation and dynamic optimisation, with specific focus on batch processes. An active approach is proposed whereby the input variables are designed so as to maximise the information content of the data for optimal model adaptation. Then, this active adaptation method is combined with the objective of process performance to form a multi-objective optimisation problem. This integrative approach is in contrast to the traditional adaptation method, where only the process performance is considered and adaptation is passively carried out by using the data as is. Two strategies for solving the multi-objective problem are investigated: weighted average and constrained optimisation, and the latter is recommended for the ease in determining the balance between these two objectives. The proposed methodology is demonstrated on a simulated semi-batch fermentation process.     

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Current research on design knowledge capture and reuse has predominantly focused on either the codification view of knowledge or the personalisation view of knowledge, resulting in a failure to address designers’ knowledge needs caused by a lack of context of information and insufficient computational support. Precisely motivated by this gap, this work aims to address the integration of these two views into a complete, contextual and trustworthy knowledge management scheme enabled by the emerging collaborative technologies. Specifically, a knowledge model is developed to represent an integrated knowledge space, which can combine geometric model, knowledge-based analysis codes and problem-solving strategies and processes. On this basis, a smart collaborative system is also designed and developed to streamline the design process as well as to facilitate knowledge capture, retrieval and reuse as users with different roles are working on various tasks within this process. An engineering case study is undertaken to demonstrate the idea of collaborative knowledge creation and sharing and evaluate the effectiveness of the knowledge representation model and the collaborative technologies employed. As evidenced in the development and evaluation, the methods proposed are effective for capturing an integrated knowledge space and the collaborative knowledge management system not only facilitates problem-solving using knowledge-based analysis but also supplies in-context tacit knowledge captured from the communications between users throughout the design process.     

基于粒子群优化的Wiener模型辨识与实例研究

针对一类工业过程中可描述成Wiener模型的非线性系统,其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题．利用粒子群优化(PSO)算法在整个参数空间内并行搜索获得极小值优化问题的最优解(Wiener模型的最优估计),通过对粒子的迭代轨迹进行分析,改进了PSO算法中惯性权重和学习因子的选择．通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性,并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究,取得了满意的辨识效果．     

Automated finite element model updating of full-scale structures with PARameter Identification System (PARIS)

This paper presents a software framework, PARIS (PARameter Identification System), developed for automated finite element model updating for structural health monitoring. With advances in Application Programming Interfaces (API) for modern computing, the traditional boundaries between different standalone software packages hardly exist. Now complex problems can be distributed between different software platforms with advanced and specialized capabilities. PARIS takes advantage of the advancements in the computing environment and interfacing capabilities provided by commercial software to systematically distribute the structural parameter estimation problem into an iterative optimization and finite element analysis problem across different computing platforms. Three validation examples using simulated nondestructive test data for updating full-scale structural models under typically encountered damage scenarios are included. The results of model updating process for realistic structural models and their systematic treatment provide enhanced understanding of the aforementioned parameter estimation process and an encouraging path towards its feasible field application for structural health monitoring and structural condition assessment.     

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.     

On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: Nonlinear regression case

Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-the-parameters. In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed. In this article, a Fisher Information Matrix (FIM) based OED method is proposed that considers local model and partition parameters. Due to the nonlinear model, the FIM depends on the model parameters that are subject of the subsequent identification. To resolve this paradoxical situation, at first a model-free space filling design (such as Latin Hypercube Sampling) is carried out. The collected data permits making design decisions such as determining the number of local models and identifying the parameters of an initial TS model. This initial TS model permits a FIM-based OED, such that data is collected which is optimal for a TS model. The estimates of this first stage will in general not be ideal. To become robust against parameter mismatch, a sequential optimal design is applied. In this work the focus is on D-optimal designs. The proposed method is demonstrated for three nonlinear regression problems: an industrial axial compressor and two test functions.     

改进滑模模型参考自适应的PMSM无传感控制

针对MRAS（模型参考自适应）观测器对PMSM（永磁同步电机）参数变化和外部干扰敏感的缺点,设计了一种用于PMSM无传感控制的软开关滑模模型参考自适应观测器。该观测器将滑模控制与MRAS相结合,并构造了边界层可变的正弦饱和函数,以抑制由于滑模控制引起的系统抖动,增强系统鲁棒性。同时引入sigmoid函数,以提高滑模软切换速度控制器的稳定性。实验仿真结果表明,软开关滑模模型参考自适应控制不仅可以实现边界层内的快速收敛、弱化抖动,而且跟踪能力强。     

Multi-agent collaborative 3D design with geometric model at different levels of detail

This paper presents a scheme for collaborative 3D design using product model at various levels of detail (LODs). Design features are selectively hidden at each level from certain participants, depending on their actual needs and individual accessibility in the collaboration. A tree data structure represents the feature hierarchy of CAD construction, the link between feature and LOD, and 2D mesh data for display control of each feature. An XML/XSLT-based approach is proposed to enable real-time visualization of different LOD models in distributed environment. A collaborative design system is implemented using multi-agent technologies, which focuses on function design of each agent, interactions among agents, the client–server structure, and generation of the LOD data using the XML/XSLT approach. A scenario of synchronous 3D mold assembly demonstrates that geometric categorization of product model provides an operational mechanism for assuring security of information sharing in engineering collaborations over the Internet. It also validates the effectiveness of the agent technologies for automating complex engineering activities.     

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.     

Development and monitoring of an integrated bioprocess for production of a potential malaria vaccine with Pichia pastoris

An engineered variant of the yeast Pichia pastoris was used to produce the potential malaria vaccine D1M1. This paper describes the implementation of an expanded bed adsorption chromatography step into a sequential cultivation process of the yeast in order to link protein expression, cell release, and product capture of the secreted protein in a fully automated plant. The process is operated with a semi-continuous strategy for an integrated production. Process improvements have been achieved using design of experiments. Resulting purities of product achieved up to 87% with recoveries of 51% in a single downstream operation. Furthermore, a multivariate analysis of historical batch data was used to develop a golden batch model for process monitoring.     

基于改进模型的气象无人机测风模型的硬件设计

基于改进型皮托-静压管测风模型分析了所需测量的物理量,选用适当传感器,设计了以C8051F120为主控制器、包含各测量模块的气象无人机机载测风系统。对系统的组成结构和硬件电路作了详细介绍,给出了相关软件流程。     

An improved robust model predictive control approach to systems with linear fractional transformation perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control (MPC) approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.     

Computation of the exact Cramer–Rao lower bound for the parameters of a nonsymmetric half-plane 2-D ARMA model

The Cramer–Rao lower bound (CRLB) that gives the minimal achievable variance/standard deviation for any unbiased estimator offers a useful tool for an assessment of the consistency of parameter estimation techniques. In this paper, a closed-form expression for the computation of the exact CRLB on unbiased estimates of the parameters of a two-dimensional (2-D) autoregressive moving average (ARMA) model with a nonsymmetric half-plane (NSHP) region of support is developed. The proposed formulation is mainly based on a matrix representation of 2-D real-valued discrete and homogeneous random field characterized by the NSHP ARMA model. Assuming that the random field is Gaussian, the covariance matrix of the NSHP ARMA random field is first expressed in terms of the model parameters. Then, using this matrix structure, a closed-form expression of the exact Fisher information matrix required for the CRLB computation of the NSHP ARMA model parameters is developed. Finally, the main formulas derived for the NSHP ARMA model are rearranged for its autoregressive and moving average counterparts, separately. Numerical simulations are included to demonstrate the behavior of the derived CRLB formulas.     

A strategic model for supply chain design with logical constraints: formulation and solution

This paper proposes a strategic production–distribution model for supply chain design with consideration of bills of materials (BOM). Logical constraints are used to represent BOM and the associated relationships among the main entities of a supply chain such as suppliers, producers, and distribution centers. We show how these relationships are formulated as logical constraints in a mixed integer programming (MIP) model, thus capturing the role of BOM in the selection of suppliers in the strategic design of a supply chain. A test problem is presented to illustrate the effectiveness of the formulation and solution strategy. The results and their managerial implications are discussed.Scope and purposeSupply chain design is to provide an optimal platform for efficient and effective supply chain management. The problem is often an important and strategic operations management problem in supply chain management. This paper shows how the mixed integer programming modeling techniques can be applied to supply chain design problem, where some complicated relations, such as bills of materials, are involved. We discuss how to solve such a complicated model efficiently.     

基于改进特征袋模型的奶牛识别算法

针对特征袋（BOF）模型中存在特征计算耗时、识别精度低的不足,提出一种新的改进BOF模型以提高其目标识别的精度和效率,并将其应用于奶牛个体识别。该算法首先引入优化方向梯度直方图（HOG）特征对图像进行特征提取和描述,然后利用空间金字塔匹配原理（SPM）生成图像基于视觉词典的直方图表示,最后自定义直方图交叉核作为分类器核函数。该算法在项目组自行拍摄的数据集（包含15类奶牛、共7500张奶牛头部图像）上的实验结果表明,使用基于SPM的BOF模型将算法的识别率平均提高2个百分点;使用直方图交叉核相比使用高斯核将算法的识别率平均提高2.5个百分点;使用优化HOG特征,相比使用传统HOG特征将算法识别率平均提高21.3个百分点,运算效率为其1.68倍;相比使用尺度不变特征变换（SIFT）特征,在保证平均识别精度达95.3%的基础上,运算效率为其7.10倍。分析结果可知,该算法在奶牛个体识别领域具有较好的鲁棒性和实用性。