On-line identification of interacting two-tank system

In order to demonstrate the effectiveness of the process identification algorithm, on-line parameter estimator is evaluated experimentally by using two-tank system with interaction. On-line parameter estimator used in this paper is based on a recursive parameter estimation algorithm. MIMO linear, bilinear and quadratic models based on ARMA model are used to identify two-tank system. A quadratic model for two-tank system with interaction is developed to confirm the propriety of MIMO quadratic model used in identification of two-tank system. The results of on-line identification experiments on the two-tank system show that the estimated parameters of each model converge and the output tracking errors are bounded by disturbance bound. But, the quadratic model showed the best convergence.     

State estimation for high-dimensional chemical processes

The application of conventional observer designs for high-dimensional systems may not always be practical due to high computational requirements or the resulting observers being too sensitive to measurement noise. In order to address these issues, this paper presents two observer design techniques for state estimation of high-dimensional chemical processes. One technique is used for systems with inputs, whereas the other one is specifically geared towards systems that are not excited from the outside. Both of these observers are applicable to linear and with a modification to non-linear systems.The main idea behind the presented observer designs is that a reduced-order observer is implemented instead of a conventional state estimator. The motivation is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and only reconstruct the parts of the system where accurate state estimation is possible. The observer designs are illustrated on a 30-tray distillation column model. Additionally, it has been shown that the location of process measurements has a major effect on the performance of the presented reduced-order observers.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

A data porting tool for coupling models with different discretization needs

The presented work is part of a larger research program dealing with developing tools for coupling biogeochemical models in contaminated landscapes. The specific objective of this article is to provide researchers with a data porting tool to build hexagonal raster using information from a rectangular raster data (e.g. GIS format). This tool involves a computational algorithm and an open source software (written in C). The method of extending the reticulated functions defined on 2D networks is an essential key of this algorithm and can also be used for other purposes than data porting. The algorithm allows one to build the hexagonal raster with a cell size independent from the geometry of the rectangular raster. The extended function is a bi-cubic spline which can exactly reconstruct polynomials up to degree three in each variable. We validate the method by analyzing errors in some theoretical case studies followed by other studies with real terrain elevation data. We also introduce and briefly present an iterative water routing method and use it for validation on a case with concrete terrain data.     

A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices.     

A model driven architecture for the development of smart card software

Smart cards are portable integrated devices that store and process data. Speed, security and portability properties enable smart cards to have a widespread usage in various fields including telecommunication, transportation and the credit card industry. However, the development of smart card applications is a difficult task due to hardware and software constraints. The necessity of the knowledge of both a very low-level communication protocol and a specific hardware causes smart card software development to be a big challenge for the developers. Written codes tend to be error-prone and hard to debug because of the limited memory resources. Hence, in this study, we introduce a model driven architecture which aims to facilitate smart card software development by both providing an easy design of smart card systems and automatic generation of the required smart card software from the system models. Differentiating from the previous work, the study in here contributes to the field by both providing various smart card metamodels in different abstraction layers and defines model-to-model transformations between the instances of these metamodels in order to support the realization of the same system on different smart card platforms. Applicability of the proposed methodology is shown for rapid and efficient application development in two major smart card frameworks: Java Card and ZeitControl Basic Card. Lessons learned during the industrial usage of the architecture are also reported in the paper. Finally, we discuss how the components of the architecture can be integrated in order to provide a domain-specific language for smart card software.     

数据挖掘预测模型在脑损伤患者认知功能康复中的应用

为了更好地预测后天性脑损伤ABI(Acquired Brain Injury)患者认知功能康复的影响因素,提出基于决策树(DT)、多层感知器(MLP)和广义回归神经网络(GRNN)的三种预测模型。借助于10折交叉验证测试算法,通过专一性、灵敏度和精度分析以及混淆矩阵分析对模型的性能进行测试,从而获得新的知识以评估和改善认知功能康复过程中的有效性。实验结果表明,基于DT的模型的模拟结果明显比其他模型更为优越,预测平均精度可高达90.38%。     

Integral approximation—an approach to reduced models for particulate processes

In this contribution, a model reduction technique for population balance systems describing particulate processes is presented. This technique is based on integral approximation and allows the derivation of highly accurate moment models. In contrast to other model reduction methods which can be found in literature, this integral approximation technique can be applied for arbitrarily complex phenomena specifications. The applicability of the presented method will be demonstrated for different example processes by comparing the dynamic behavior of the original population balance models with those of the derived reduced models of moments.     

基于K-L散度的恶意代码模型聚类检测方法

在云计算应用环境下,由于服务系统越来越复杂,网络安全漏洞和被攻击情况急剧增加,传统的恶意代码检测技术和防护模式已无法适应云存储环境的需求。为此,通过引入高斯混合模型,建立恶意代码的分层检测机制,使用信息增益和文档频率等方法分析和提取样本数据特征值,结合K-L散度特性,提出基于K-L散度的恶意代码模型聚类检测方法。采用KDDCUP99数据集,使用Weka开源软件完成数据预处理和聚类分析。实验结果表明,在结合信息增益和文档频率进行特征分析的前提下,与贝叶斯算法相比,该方法在虚拟环境中恶意代码的平均检测时间降低16.6%,恶意代码的平均检测率提高1.05%。     

Information systems use as strategy practice: A multi-dimensional view of strategic information system implementation and use

Information systems (IS) are strategic in so far as they are used to realize strategic intent. Yet, while much has been said about aligning IS functionality with the strategic intent and how to organizationally implement strategically aligned systems, less is known of how to successfully implement strategic change associated with system use – a truly critical challenge within strategic IS implementation. Drawing on a strategy-as-practice perspective we address this gap by developing a multi-dimensional view of IS strategy, conceptualizing three key challenges in the IS strategy process, to explain how and why a paper mill, despite successfully implementing a strategic production management system, failed to produce intended strategic change. We call this outcome strategy blindness: organizational incapability to realize the strategic intent of implemented, available system capabilities. Using a longitudinal case study we investigate how cognitive rigidity of key actors and fixed, interrelated practices shaped the implementation of the new production system. We also identify core components and dynamics that constitute a richer multi-dimensional view of the IS strategy implementation (alignment) process. In particular, we identify three salient factors that contribute to strategy blindness – mistranslation of intent, flexibility of the IT artifact and cognitive entrenchment – and discuss how they affect strategic implementation processes. We conclude by discussing implications of our findings for IS strategy theory and practice, especially the contribution of strategy-as-practice to this stream of research.