A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

Volumetric Curved Planar Reformation for virtual endoscopy

Curved Planar Reformation (CPR) has proved to be a practical and widely used tool for the visualization of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for large, tubular, structures such as the trachea and the colon because abnormalities may be smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions and standard CPR for the surrounding tissue. This effectively combines gray scale contextual information with detailed color information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative to virtual endoscopy. Because the CPR and the volume rendering are tightly coupled, the projection method used has a significant effect on properties of the volume renderer, such as distortion and isometry. We describe and compare the different CPR projection methods and how they affect the volume rendering process. A version of the algorithm is also presented which makes use of importance driven techniques; this ensures the users attention is always focused on the area of interest and also improves the speed of the algorithm.     

A process model of cyberbullying in adolescence

Cyberbullying is an emerging form of aggression that utilizes information and communication technologies (ICTs). While cyberbullying incidents attract considerable attention, research on the causes and psychosocial predictors of cyberbullying is still limited. The present study used an integrated theoretical model incorporating empathy, moral disengagement, and social cognitions related to cyberbullying. Structured questionnaires were administered to 355 randomly selected adolescents (M = 14.7, SD = 1.20). Linear regression analysis showed that social norms, prototype similarity and situational self-efficacy directly predicted cyberbullying expectations. Multiple mediation modelling indicated that normative influences mediated the effects of moral disengagement and affective empathy on cyberbullying expectations. These findings provide valuable information regarding the effect of both distal and proximal risk factors for cyberbullying in adolescence, highlight the relationship between normative processes and moral self-regulation, and set the basis for related educational and preventive interventions.     

Application of three satellite techniques in support of precipitation forecasts of a NWP model

In this study, the efficiency of an integrated operational system, based on three satellite infrared techniques, to support nowcasting and very short range forecasting of high precipitation rates provided by a numerical weather prediction (NWP) model is examined. Three algorithms, one for the detection and tracking of convective cloud cells and another two for rainfall estimation, are applied on satellite sensor data in order to qualitatively and quantitatively verify the precipitation forecasts provided by a NWP model. The application of the detection and tracking algorithm aims at verifying qualitatively, in real time, the precipitation forecasts by monitoring the detected convective cloud cells, at the same time intervals that the model forecasts are given. The application of the rainfall estimation techniques on satellite sensor data is needed for both quantitative and qualitative cross‐comparisons with the model outputs. The developed tool is applied in a case of intense precipitation over Greece. The results of the application are promising and show the potential for the implementation of the integrated system as a support tool for nowcasting and very short range forecasting by performing real‐time validation of NWP precipitation forecasts.     

Use of Meteosat imagery to define clouds linked with floods in Greece

A cloud classification scheme has been developed with the aim of defining and monitoring clouds cells associated with heavy rain. In the first stage of the scheme, Meteosat images in the visible were corrected to account for varying illumination times and angles. In the second stage, analysis of Meteosat images in the visible, infrared and water vapour channels resulted in the assignment of spectral signatures to seven categories of cloud class. The analysis was supported by temperature and humidity profiles from radiosondes in the wider geographical area as well as by synoptic maps of the area. Finally, Meteosat images reflecting two flood incidents which occurred in Greece on 21 October 1994 and 12 January 1997 were classified on the basis of the defined cloud categories; cloud cells associated with heavy rain were clearly depicted on the classified images through the category of thick opaque convective clouds.     

An integrated cyber security risk management framework and risk predication for the critical infrastructure protection

Neural Computing and Applications - Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving...     

Satellite-observed features of a mesoscale convective complex over SE Europe

On 24 May 2009, a large size mesoscale convective system crossed south-eastern Europe causing severe weather, heavy precipitation, and strong wind. The system met both spatial and duration criteria of a mesoscale convective complex (MCC). The case was analysed using different image processing techniques based on the high spectral resolution of Meteosat Second Generation (MSG) satellite data. First, an automatic cloud tracking algorithm was applied on successive infrared images to objectively characterize the life cycle of the MCC and analyse the temporal evolution of several morphological, positional, and spectral parameters. Then, satellite data were processed and visualized as single channel, channel differences, RGB (Red-Green-Blue) composite, and ‘blended multi-layer’ images to reveal important information on the development and cloud-top structure and microphysics of the MCC. Lightning data were also used as a measure of intense convective activity in the MCC. The MCC exhibited extraordinary characteristics from the satellite point of view related to its evolution, vertical development, and structure. The results of this study showed how the use of different processing methods of multispectral MSG imagery can provide valuable information in both studying and nowcasting MCCs.     

CuO–CeO2 mixed oxide catalysts for the selective oxidation of carbon monoxide in excess hydrogen

A series of mixed oxide CuO–CeO₂ catalysts were prepared by coprecipitation and tested for the selective oxidation of carbon monoxide in the presence of excess hydrogen. These catalysts were found to be very active and exceptionally selective for this reaction and exhibited a good resistance towards CO₂ and H₂O. The catalytic performance of these non-noble metal containing catalysts is compared with that of other selective CO oxidation catalysts reported in literature.     

Stochastic model predictive control for constrained discrete-time Markovian switching systems

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.