http://dspace.vsb.cz:80 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Thu, 23 May 2013 12:24:59 GMT 2013-05-23T12:24:59Z http://hdl.handle.net/10084/96356 Influence of cutting parameters on heat-affected zone after laser cutting Petrů, Jana; Zlámal, Tomáš; Čep, Robert; Monková, Katarína; Monka, Peter The article deals with a method of thermal cutting of materials, specifically with laser technology. The theoretical part describes its principles, capabilities and use of laser in the process of machining, mainly the procedure of cutting material using a laser beam. The experimental part is focused on cutting the cobalt alloy by a continuous CO2 laser and the influence of technological parameters during cutting on the final quality of the cutting area. Suitable technological parameters were determined on the basis of metallographic analysis, and the lowest heat influence on the material was achieved. Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10084/96356 2011-01-01T00:00:00Z http://hdl.handle.net/10084/96355 The balancing of VOC concentration fluctuations by adsorption/desorption process on activated carbon Kuboňová, Lenka; Obalová, Lucie; Skovranek, Lukáš; Troppová, Ivana Volatile organic compounds (VOCs) are mostly toxic and carcinogenic substances. The technologies for cleaning of exhaust gases containing the constant concentrations of VOCs are commercially available. However, if concentration fluctuations occur in the range of several orders of magnitude, it can cause problems for a subsequent gas cleaning e.g. by thermal or catalytic oxidation. The balancing of VOC concentrations in flue gases can be a great simplification of a subsequent reduction of VOC emissions from sources with time-variable concentrations. Paint shops belong to the important sources of VOCs and are an example of periodic processes with time-variable concentrations of VOCs. One of the main aims was to experimentally determine the conditions, such as the minimal mean residence time, to balance out the fluctuations of inlet VOC concentrations at the laboratory model. After that, the verification of obtained results was applied for a real exhaust gas from a paint shop. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/10084/96355 2013-01-01T00:00:00Z http://hdl.handle.net/10084/96352 Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets Koloseni, David; Lampinen, Jouni; Luukka, Pasi In this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the training phase of the classifier. The earlier version of differential evolution classifier that applied individually optimized distance measure for each new data set to be classified is generalized here so, that instead of optimizing a single distance measure for the given data set, we take a further step by proposing an approach where distance measures are optimized individually for each feature of the data set to be classified. In particular, distance measures for each feature are selected optimally from a predefined pool of alternative distance measures. The optimal distance measures are determined by differential evolution algorithm, which is also determining the optimal values for all free parameters of the selected distance measures in parallel. After determining the optimal distance measures for each feature together with their optimal parameters, we combine all featurewisely determined distance measures to form a single total distance measure, that is to be applied for the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; A sample belongs to the class represented by the nearest prototype vector when measured with the above referred optimized total distance measure. During the training process the differential evolution algorithm determines optimally the class vectors, selects optimal distance metrics for each data feature, and determines the optimal values for the free parameters of each selected distance measure. Based on experimental results with nine well known classification benchmark data sets, the proposed approach yield a statistically significant improvement to the classification accuracy of differential evolution classifier. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/10084/96352 2013-01-01T00:00:00Z http://hdl.handle.net/10084/96351 A profile based network intrusion detection and prevention system for securing cloud environment Gupta, Sanchika; Kumar, Padam; Abraham, Ajith Cloud computing provides network based access to computing and data storage services on a pay per usage model. Cloud provides better utilization of resources and hence a reduced service access cost to individuals. Cloud services include software as a service, platform as a service, and infrastructure as a service. Cloud computing virtually and dynamically distributes the computing and data resources to a variety of users, based on their needs, with the use of virtualization technologies. As Cloud computing is a shared facility and is accessed remotely, it is vulnerable to various attacks including host and network based attacks (Brown 2012, and Grance 2009) and hence requires immediate attention. This paper identifies vulnerabilities responsible for well-known network based attacks on cloud and does a critical analysis on the security measures available in cloud environment. This paper focuses on a nonconventional technique for securing cloud network from malicious insiders and outsiders with the use of network profiling. With network profiling, a profile is created for each virtual machine (VM) in cloud that describes network behavior of each cloud user (an assigned VM). The behavior gathered is then used for determination (detection) of network attacks on cloud. The novelty of the approach lies in the early detection of network attacks with robustness and minimum complexity. The proposed technique can be deployed with minimal changes to existing cloud environment. An initial prototype implementation is verified and tested on private cloud with a fully functional implementation under progress Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/10084/96351 2013-01-01T00:00:00Z