DSpace at VSB - Technical University of OstravaThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.https://dspace.vsb.cz:4432016-07-25T20:15:12Z2016-07-25T20:15:12ZDesorption/ablation of lithium fluoride induced by extreme ultraviolet laser radiationBlejchař, TomášNevrlý, VáclavVašinek, MichalDostál, MichalKozubková, MiladaDlabka, JakubStachoň, MartinJuha, LiborBitala, PetrZelinger, ZdeněkPira, PeterWild, Janhttp://hdl.handle.net/10084/1119032016-07-22T11:54:15Z2016-01-01T00:00:00ZDesorption/ablation of lithium fluoride induced by extreme ultraviolet laser radiation
Blejchař, Tomáš; Nevrlý, Václav; Vašinek, Michal; Dostál, Michal; Kozubková, Milada; Dlabka, Jakub; Stachoň, Martin; Juha, Libor; Bitala, Petr; Zelinger, Zdeněk; Pira, Peter; Wild, Jan
The availability of reliable modeling tools and input data required for the prediction of surface removal rate from the lithium fluoride targets irradiated by the intense photon beams is essential for many practical aspects. This study is motivated by the practical implementation of soft X-ray (SXR) or extreme ultraviolet (XUV) lasers for the pulsed ablation and thin film deposition. Specifically, it is focused on quantitative description of XUV laser-induced desorption/ablation from lithium fluoride, which is a reference large band-gap dielectric material with ionic crystalline structure. Computational framework was proposed and employed here for the reconstruction of plume expansion dynamics induced by the irradiation of lithium fluoride targets. The morphology of experimentally observed desorption/ablation craters were reproduced using idealized representation (two-zone approximation) of the laser fluence profile. The calculation of desorption/ablation rate was performed using one-dimensional thermomechanic model (XUV-ABLATOR code) taking into account laser heating and surface evaporation of the lithium fluoride target occurring on a nanosecond timescale. This step was followed by the application of two-dimensional hydrodynamic solver for description of laser-produced plasma plume expansion dynamics. The calculated plume lengths determined by numerical simulations were compared with a simple adiabatic expansion (blast-wave) model.
2016-01-01T00:00:00ZA sanitization approach for hiding sensitive itemsets based on particle swarm optimizationLin, Jerry Chun-WeiLiu, QiankunFournier-Viger, PhilippeHong, Tzung-PeiVozňák, MiroslavZhan, Justinhttp://hdl.handle.net/10084/1119022016-07-21T11:05:32Z2016-01-01T00:00:00ZA sanitization approach for hiding sensitive itemsets based on particle swarm optimization
Lin, Jerry Chun-Wei; Liu, Qiankun; Fournier-Viger, Philippe; Hong, Tzung-Pei; Vozňák, Miroslav; Zhan, Justin
Privacy-preserving data mining (PPDM) has become an important research field in recent years, as approaches for PPDM can discover important information in databases, while ensuring that sensitive information is not revealed. Several algorithms have been proposed to hide sensitive information in databases. They apply addition and deletion operations to perturb an original database and hide the sensitive information. Finding an appropriate set of transactions/itemsets to be perturbed for hiding sensitive information while preserving other important information is a NP-hard problem. In the past, genetic algorithm (GA)-based approaches were developed to hide sensitive itemsets in an original database through transaction deletion. In this paper, a particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process. Each particle in the designed PSO2DT algorithm represents a set of transactions to be deleted. Particles are evaluated using a fitness function that is designed to minimize the side effects of sanitization. The proposed algorithm can also determine the maximum number of transactions to be deleted for efficiently hiding sensitive itemsets, unlike the state-of-the-art GA-based approaches. Besides, an important strength of the proposed approach is that few parameters need to be set, and it can still find better solutions to the sanitization problem than GA-based approaches. Furthermore, the pre-large concept is also adopted in the designed algorithm to speed up the evolution process. Substantial experiments on both real-world and synthetic datasets show that the proposed PSO2DT algorithm performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden (F-T-H), not to be hidden (N-T-H), and database similarity (DS).
2016-01-01T00:00:00ZFlow field in a downward diverging channel and its applicationVečeř, MarekWichterle, Kamilhttp://hdl.handle.net/10084/1119012016-07-21T08:54:57Z2016-01-01T00:00:00ZFlow field in a downward diverging channel and its application
Večeř, Marek; Wichterle, Kamil
The flow in a downward divergent channel turns out to be an interesting experimental setup for the observation of upward floating bubbles that appear to be levitating in view of the observer. A more detailed analysis of this flow and its characteristic parameters is necessary for better understanding of this phenomenon. The boundary layer theory was used to derive the velocity field for the experimental setup. The actual flow of a liquid in the presence of a bubble was studied experimentally by measuring the position of the bubble; the data were then statistically processed by an image analysis. Observation of the bubble positions distribution showed that it is reasonable to assume a flat velocity profile of the liquid in the channel and that the bubbles do not tend to move into the boundary layer. In our experiments, volume of the air bubbles floating in water was 200 mm3 and of that of bubbles floating in aqueous glycerin was 300 mm3. Thus, the experiment used in this work is suitable for reliable determination of instantaneous and average bubble rising velocities as well as of those of horizontal and vertical oscillations.
2016-01-01T00:00:00ZForecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithmBarak, SasanSadegh, S. Saeedehhttp://hdl.handle.net/10084/1119002016-07-21T08:34:21Z2016-01-01T00:00:00ZForecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm
Barak, Sasan; Sadegh, S. Saeedeh
Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA–ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA’s output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.
The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model’s MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done.
2016-01-01T00:00:00Z