Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

In this work the radial basis function neural network architecture is used to model the dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which do not require knowledge of the governing equations are described and compared. In the first method, the neural network methodology generates the full model of the system that is able to predict the process outputs at any spatial point. Past values of the process inputs and the coordinates of the specific location provide the input information to the model. The second method uses empirical basis functions produced by the Singular Value Decomposition (SVD) on the snapshot matrix to describe the spatial behavior of the system, while the neural network model is used to estimate only the temporal coefficients. The models produced by both methods are then implemented in Model Predictive Control (MPC) configurations, suitable for constrained DPSs. The accuracies of the modeling methodologies and the efficiencies of the proposed MPC formulations are tested in a tubular reactor and produce encouraging results.     

The politics and science behind GMO acceptance

The question of nutritional quality has arisen in the International Community over the last few years along with other important issues such as population aging, multipopulation societies, and political conflicts. The nutritional issue is questioned both quantitatively and qualitatively. It is well known that the planet faces enormous problems with food that is available. Nowadays 20% of the population consumes approximately 80% of the produced energy and natural resources. During the last 15 years, a series of food scares and crises (BSE, dioxin, foot and mouth disease, bird flu) have seriously undermined public confidence in food producers and operators and their capacity to produce safe food. As a result, food safety has become a top priority of the European legislative authorities. Genetically Modified Organisms (GMOs) is the new food safety concern which despite the intense reactions from Non Governmental Organizations and consumer organizations have entered our lives with inadequate legislative measures to protect consumers from their consumption. The GMO issue will be the issue for discussion in the long run not only for the European Community but also for the international community as far as scientific, economical, political, ideological, ethical, and human issues are concerned. These issues are discussed in this paper along with a case of study of GM fish.     

A metaheuristic approach for the sequencing by hybridization problem with positive and negative errors

This work introduces a metaheuristic method for the reconstruction of the DNA string from its l-mer content in the presence of large amounts of positive and negative errors. The procedure consists of three parts: the formulation of the problem as an asymmetric traveling salesman problem (ATSP), a technique for handling the positive errors and an optimization algorithm that solves the formulated problem. The optimization algorithm is a variation of the threshold accepting method with intense local search and its function is controlled by a size diminishing shell. The optimization algorithm is used consecutively on ATSPs of continuously decreasing sizes till it reaches a final solution. The proposed method provides solutions of better quality compared to algorithms in the recent bibliography.     

Safety and Security in Multiagent Systems: Report on the 2nd SASEMAS workshop (SASEMAS'05)

As intelligent autonomous agents and multiagent systems' applications become more pervasive, it becomes increasingly more important to understand the risks associated with using these systems. Incorrect or inappropriate agent behaviour can have harmful effects including financial cost, loss of data, and injury to humans or systems. Thus, security and safety are two central issues when developing and deploying such systems.However, the process of developing safe and secure multiagent systems, and verifying and validating them, is much more difficult than for conventional software systems. This is due to many agent-related aspects, such as the complex and rich multiagent environments, the risks involved in such environments, and the characteristics that can be found in agent systems such as learning, dynamic reacting and adapting. Hence, new and different techniques and perspectives are required to assist with the development and deployment of such systems.The Safety and Security in Multiagent Systems (SASEMAS) workshop presents new developments, and lessons learned from real world cases, and it provides a forum for the exchange of ideas and discussion on areas related to security and safety in multiagent systems.     

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   

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.     

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.