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Hyperspectral image segmentation for estimation of biomass at reclaimed heaps
Pikl, Miroslav ; Zemek, František
This paper presents the preliminary results from a study that aims at estimation of above ground biomass and soil carbon content at reclaimed mining heaps in the Sokolov region. Two image segmentation methods are presented. We applied maximal likelihood (ML) and neural network (NN) classifi ers on airborne hyperspectral data. Th e objective of this part of the study was to prepare a land cover classifi cation of the region. Th e main focus was paid to discrimination of six classes with prevailing forest species cover. Th e classifi cation accuracy of the training sites was 93.75 % for NN and 79.12 % for ML respectively. But ML outperformed NN in overall classifi cation accuracy with 61.54 % compared to 40.9 % of NN. Th e more accurate results of the ML classifi er are probably infl uenced by properties of the training samples. Th e larger size of the training samples derived for ML enabled better representation of class histograms. Th e lower overall NN accuracy could result from high spatial resolution of HS data.
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Business Intelligence principles and their use in questionnaire investigation
Hanuš, Václav ; Maryška, Miloš (advisor) ; Novotný, Ota (referee)
This thesis is oriented on practical usage of tools for data mining and business intelligence. Main goals are processing of source data to suitable form and test use of chosen tool on the test case. As input data I used database which was created as result of processing forms from research to verify the level of IT and economics knowledge among Czech universities. These data was modified into the form, which allows processing them via data mining tools included in Microsoft SQL Server 2008. I choose two cases for verification the potentials of these tools. First case was focused on clustering using Microsoft Clustering algorithm. Main task was to sort the universities into the clusters by comparing their attributes which was amounts of credits of each knowledge group. I had to deal with two problems. It was necessary to reduce the number of groups of subjects, otherwise there was a danger of creation too many clusters which I couldn't put the name on. Another problem was unequal value of credits in each group and this problem caused another problem with weights of these groups. Solution was at the end quite simple. I put together similar groups to bigger formation with more general category. For unequal value, I used parameter for each of new group and transform it to scale 0-5. Second case was focused on prediction task using Microsoft Logistic Regresion algorithm and Microsoft Neural Network algorithm. In this case was the goal to predict the number of presently studying students. I had a historical data from years 2001-2009. A predictive model was processed based on them and I could compare the prediction with real data. In this case, it was also necessary to transform the source data, otherwise it couldn't be processed by tested tool. Original data was placed into the view instead of table and contained not only wished objects but more types of these. For example divided by a sex. Solution was in creation of new table in database where only relevant objects for test case were placed. Last problem come up when I tried to use prediction model to predict data for year 2010 for which there wasn't real data in the table. Software reported an error and couldn't make prediction. During my research on the Microsoft technical support I find some threads which refer to similar problem, so it's possible that this is a system error whit will be fix in forthcoming actualization. Fulfillment of these cases provided me enough clues to determine abilities of these tools from Microsoft. After my former school experience with data mining tools from IBM (former SSPS) and SAS, I can recognize, if tested tools can match these software from major data mining supplier on the market and if it can be use for serious deployment.
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Mobile robot motion planner via neural network
Krejsa, Jiří ; Věchet, Stanislav
Motion planning is essential for mobile robot successful navigation. There are many algorithms for motion planning under various constraints. However, in some cases the human can still do a better job, therefore it would be advantageous to create a planner based on data gathered from the robot simulation when humans do the planning. The paper presents the method of using the neural network to transfer the previously gained knowledge into the machine learning based planner. In particular the neural network task is to mimic the planner based on finite state machine. The tests proved that neural network can successfully learn to navigate in constrained environment.
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Character recognition system
HANZLÍK, Ondřej
"The thesis proposes a system for recognition of printed text (OCR), which uses neural network for recognizing letters. The neural network is implemented using the program RapidMiner. To control the neural network is using the processes created by program RapidMiner. These processes are run directly from a Java application. RapidMiner is implemented into the java application and using its libraries is started directly from java application." directly from a Java application. RapidMiner is implemented into the java application and using its libraries is started directly from java application."
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