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[popis] => Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.
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[klicova_slova] => cardiomyocyte development stages; densely connected convolutional network; deep learning; classification of object images; confocal microscopy
[klicova_slova_orig] => cardiomyocyte development stages; densely connected convolutional network; deep learning; classification of object images; confocal microscopy
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[citace_text] => ŠKRABÁNEK, P.; ZAHRADNÍKOVÁ, A. Automatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networks. PLoS One, 2019, vol. 14, no. 5, p. 1-18. ISSN: 1932-6203.
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[popis_en] => Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.
[klicova_slova_en] => cardiomyocyte development stages; densely connected convolutional network; deep learning; classification of object images; confocal microscopy
[vysledek_datum] => 2019-05-30T00:00:00+02:00
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[popis] => The production of mixed municipal waste changes due to the increase of separation rate, urbanization and other factors. The future changes in the legislation and technology development influences the way the waste is being treated. Thus the method, location and capacity of processing sites are unknown. The realization of future projects can be supported by the developing of transportation infrastructure. Such a feature may be represented by a robust transfer station grid, which can be designed to handle all possible future realizations and technological solutions (establishment of waste treatment facilities). The paper presents an approach utilising a mathematical model for the design of transfer stations. It is formulated as a multi-objective two-stage mixed-integer stochastic programming problem, where the trade-off between the environmental aspect and the economic viability is considered. The model is tested through a case study for the Czech Republic, where the waste treatment of over 6,000 municipalities is analysed. The solutions for different preferences are assessed throughout the principle called out-of-sample stability with 10,000 scenarios. The optimal decision consists of the robust transfer station grid with selected locations and their respective capacity. The particular solution with respect to the potential trade-off suggests to save 1.148 mil of travelled kilometres with only 0.77 mil EUR. The output in the form of decision support can serve possible stakeholders from the field of waste management to plan more sustainable projects.
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[klicova_slova] => Multi-objective model; Stochastic programming; Strategic planning; Transfer station; Waste treatment
[klicova_slova_orig] => Multi-objective model; Stochastic programming; Strategic planning; Transfer station; Waste treatment
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[popis_en] => The production of mixed municipal waste changes due to the increase of separation rate, urbanization and other factors. The future changes in the legislation and technology development influences the way the waste is being treated. Thus the method, location and capacity of processing sites are unknown. The realization of future projects can be supported by the developing of transportation infrastructure. Such a feature may be represented by a robust transfer station grid, which can be designed to handle all possible future realizations and technological solutions (establishment of waste treatment facilities). The paper presents an approach utilising a mathematical model for the design of transfer stations. It is formulated as a multi-objective two-stage mixed-integer stochastic programming problem, where the trade-off between the environmental aspect and the economic viability is considered. The model is tested through a case study for the Czech Republic, where the waste treatment of over 6,000 municipalities is analysed. The solutions for different preferences are assessed throughout the principle called out-of-sample stability with 10,000 scenarios. The optimal decision consists of the robust transfer station grid with selected locations and their respective capacity. The particular solution with respect to the potential trade-off suggests to save 1.148 mil of travelled kilometres with only 0.77 mil EUR. The output in the form of decision support can serve possible stakeholders from the field of waste management to plan more sustainable projects.
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[klicova_slova] => Circular economy; Robust optimization; Facility location; Waste treatment; Decision-dependent production; Network design
[klicova_slova_orig] => Circular economy; Robust optimization; Facility location; Waste treatment; Decision-dependent production; Network design
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[popis_en] => The paper presents a new approach to support the strategic decision-making in the area of municipal solid waste management applying modern circular economy principles. A robust two-stage integer non-linear program is developed. The primary goal tends to reduce the waste production. The generated waste should be preferably recycled as much as possible and the resultant residual waste might be used for energy recovery. Only some waste residues are appropriate for landfilling. The aim is to propose the near-optimal waste allocation for its suitable processing as well as waste transportation plan at an operational level. In addition, the key strategical decisions on waste treatment facilities location must be made. Since waste production is very often hard to predict, it is modeled as an uncertain decision-dependent quantity. To support the circular economy ideas, advertising and pricing principles are introduced and applied. Due to the size of available real-world data and complexity of the designed program, the presented model is linearized and uncertainty is handled by a robust optimization methodology. The model, data, and algorithm are implemented in MATLAB and Julia, using the state-of-the-art solvers. The computational result is a set of decisions providing a trade-off between the average performance and the immunization against the worst-case conditions.
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[nazev] => Optimization of Deposition Parameters of a DLC Layer Using (RF) PECVD Technology
[nazev_orig] => Optimization of Deposition Parameters of a DLC Layer Using (RF) PECVD Technology
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[popis] => The aim of this study was to carry out a planned experiment (DoE) in order to find a suitable combination of deposition parameters to increase the hardness of the coating. In a 20-round experiment, parameters of chamber pressure, gas flow and the power of an RF source were gradually changed. Subsequently, a micro hardness analysis was carried out using the Berkovich method. Using the DoE method, a combination of parameters was detected within which a comparable hardness of the coating was achieved as with doping of the hydrogen atoms coating to increase the number of Sp3 carbon bonds. Within the indentation test, a scratch test was carried out to determine the normal force required to break the coating.
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[klicova_slova_orig] => DLC, RF PECVD, Coating hardness, scrath test, DoE, Berkovich
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[popis] => The Quadratic Assignment Problem (QAP) is a classical NP-hard combinatorial optimization problem. In the paper will be presented suitable metaheuristic algorithm HC12. The algorithm is population based and uses a massive parallel search of the binary space which represents the solution space of the QAP. The presented implementation of the metaheuristic HC12 utilizes the latest GPU CUDA platform. The results are presented on standard test problems from the QAP library.
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[url] => https://dl.acm.org/citation.cfm?doid=3319619.3322088
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[identifikator] => ISBN 978-1-4503-6748-6
[identifikator_popis] => ISBN - GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion
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[citace_text] => MATOUŠEK, R.; DOBROVSKÝ, L.; KŮDELA, J. The quadratic assignment problem: metaheuristic optimization using HC12 algorithm. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, NY, USA: ACM, 2019. p. 153-154. ISBN: 978-1-4503-6748-6.
[citace_html] => MATOUŠEK, R.; DOBROVSKÝ, L.; KŮDELA, J. The quadratic assignment problem: metaheuristic optimization using HC12 algorithm. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, NY, USA: ACM, 2019. p. 153-154. ISBN: 978-1-4503-6748-6.
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author="Radomil {Matoušek} and Ladislav {Dobrovský} and Jakub {Kůdela}",
title="The quadratic assignment problem: metaheuristic optimization using HC12 algorithm",
booktitle="GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion",
year="2019",
pages="153--154",
publisher="ACM",
address="New York, NY, USA",
doi="10.1145/3319619.3322088",
isbn="978-1-4503-6748-6",
url="https://dl.acm.org/citation.cfm?doid=3319619.3322088"
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