Multiobjective constrained MPC with simultaneous closed‐loop identification

Model predictive control (MPC) methodologies are commonly used techniques for constrained control problems. In this paper, the principle of prioritized multiobjective optimization is incorporated in an adaptive MPC framework in order to improve the closed‐loop performance in the case of time‐varying systems. Instead of weighting the different control goals, the proposed methodology creates a hierarchy according to the importance of each objective and optimizes each one separately. In each optimization step a constraint is added, so that previous in rank objective functions maintain their optimal values. Adaptive capabilities are introduced in the proposed MPC formulation, by considering the persistent excitation requirement as a top priority objective, which is optimized first. The efficiency of the proposed MPC configuration is evaluated through three dynamic processes and the expected advantages are confirmed. Copyright © 2006 John Wiley & Sons, Ltd.     

Security Attack Testing (SAT)—testing the security of information systems at design time

For the last few years a considerable number of efforts have been devoted into integrating security issues into information systems development practices. This has led to a number of languages, methods, methodologies and techniques for considering security issues during the developmental stages of an information system. However, these approaches mainly focus on security requirements elicitation, analysis and design issues and neglect testing. This paper presents the Security Attack Testing (SAT) approach, a novel scenario-based approach that tests the security of an information system at the design time. The approach is illustrated with the aid of a real-life case study involving the development of a health and social care information system.     

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.     

Hot Spots Generated in Conducting Spheres by Electromagnetic Waves and Biological Implications

The distribution of the heating potential generated by an incident electromagnetic plane wave on a conducting sphere simulating the human head was investigated. It was found that for a sphere of 10-cm radius having the same electrical characteristics as those of biological tissues, no hot spots are generated inside. While at lower frequencies the heating is relatively uniform with some polarization effects, for frequencies above 1000 MHz only skin heating takes place. For a sphere of the same size but of conductivity ?= 10 mmho/cm (which for f>1000 is lower than that of biological tissues) hot spots occur inside for f>1000 MHz. Intense hot spots also occur inside spheres of radius 5 cm having the same electrical characteristics as those of biological tissues in the frequency region of 250 MHz相似文献   

Comparison of atmospheric correction methods using ASTER data for the area of Crete,Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.     

Taxonomy of quality metrics for assessing assurance of security correctness

Assurance is commonly considered as “something said or done to inspire confidence” (Webster dictionary). However, the level of confidence inspired from a statement or an action depends on the quality of its source. Similarly, the assurance that the deployed security mechanisms exhibit an appropriate posture depends on the quality of the verification process adopted. This paper presents a novel taxonomy of quality metrics pertinent for gaining assurance in a security verification process. Inspired by the systems security engineering capability maturity model and the common criteria, we introduce five ordinal quality levels for a verification process aimed at probing the correctness of runtime security mechanisms. In addition, we analyse the mapping between the quality levels and different capability levels of the following verification metrics families: coverage, rigour, depth and independence of verification. The quality taxonomy is part of a framework for the Security Assurance of operational systems. These metrics can also be used for gaining assurance in other areas such as legal and safety compliance. Furthermore, the resulting metrics taxonomy could, by identifying appropriate quality security requirements, assist manufacturers of information technology (IT) in developing their products or systems. Additionally, the taxonomy could also empower consumers in IT security product selection to efficaciously and effectively match their organisational needs, while IT security evaluators can use it as a reference point when forming judgments about the quality of a security product. We demonstrate the applicability of the proposed taxonomy through access control examples.     

Robust model predictive control for optimal continuous drug administration

In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise.     

Regioselective acylation of flavonoids catalyzed by lipase in low toxicity media

Flavonoid fatty esters were prepared by acylation of flavonoids (rutin and naringin) by fatty acids (C8, C10, C12), catalyzed by immobilized lipase from Candida antarctica in various solvent systems. The reaction parameters affecting the conversion of the enzymatic process, such as the nature of the organic solvent and acyl donor used, the water activity (a_w) of the system, as well as the acyl donor concentration have been investigated. At optimum reaction conditions, the conversion of flavonoids was 50—60% in tert‐butanol at a_w less than 0.11. In all cases studied, only flavonoid monoester was identified, which indicates that this lipase‐catalyzed esterification is regioselective.     

INFERENTIAL SENSORS FOR ON-LINE MONITORING OF TISSUE MACHINE QUALITY PROPERTIES

There are many processes in a pulp and paper mill where an on-line parameter analyzer cannot be used due to several reasons

•The analyzer is very expensive

•It cannot survive in the environment we want to use it

•It is not operational due to hardware problems, maintenance etc

•Such an analyzer does not exist in the market

In all these situations it would be great for the mill to have an alternative way of measuring those parameters in real-time. Neural network models can serve as virtual sensors that infer process parameters from other variables, which can be measured on-line. One excellent application of inferential sensors in the pulp and paper industry is the on-line prediction of paper properties, like tensile, stretch, brightness, opacity etc. In tissue machines, the most important quality parameter is softness, which is usually measured in a very subjective manner by the touch of a human finger. In this work we examine how neural networks can be deployed in order to build online virtual sensors for softness and other tissue quality properties. The results are promising and show that neural network technology can improve productivity and minimize out of specs production in a tissue machine, by providing accurate real time monitoring ofquality parameters.     

A simulated annealing algorithm for prioritized multiobjective optimization--implementation in an adaptive model predictive control configuration.

This paper presents a new stochastic algorithm for solving hierarchical multiobjective optimization problems. The algorithm is based on the simulated annealing concept and returns a single solution that corresponds to the lexicographic ordering approach. The algorithm optimizes simultaneously the multiple objectives by assigning a different initial temperature to each one, according to its position in the hierarchy. A major advantage of the proposed method is its low computational cost. This is very critical, particularly, for online applications, where the time that is available for decision making is limited. The method is tested in a number of benchmark problems, which illustrate its ability to find near-optimal solutions even in nonconvex multiobjective optimization problems. The results are comparable with those that are produced by state-of-the-art multiobjective evolutionary algorithms, such as the Nondominated Sorting Genetic Algorithm II. The algorithm is further applied to the solution of a large-scale problem that is formulated online, when a multiobjective adaptive model predictive control (MPC) configuration is adopted. This particular control scheme involves an adaptive discrete-time model of the system, which is developed using the radial-basis-function neural-network architecture. A key issue in the success of the adaptation strategy is the introduction of a persistent excitation constraint, which is transformed to a top-priority objective. The overall methodology is applied to the control problem of a pH reactor and proves to be superior to conventional MPC configurations.