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RISS 인기검색어
- 검색키워드
- 저자 : Bieda, Igor (검색결과 4 건)
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A Systematic Mapping Study on Artificial Intelligence Tools Used in Video Editing
Bieda, Igor,Panchenko, Taras International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.3
From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.
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Matthias Bieda,Cindy Schmädicke,Andreas Wetzig,Andrés Lasagni 대한금속·재료학회 2013 METALS AND MATERIALS International Vol.19 No.1
The direct laser interference patterning method was used to fabricate periodic structures on flat and cylindrical specimens of austenitic stainless steel (X5CrNi18-10) and martensitic bearing steel (100Cr6). In such laser processing the characterization of materials is a central issue. Therefore, this paper places special emphasis on the chemical, metallurgical and mechanical characterization of the laser processed metals. During the patterning process of 100Cr6, the carbides in the martensitic structure appear as elongated needles and become aligned to the direction of melt flow. The chemical composition analysis before and after laser interference patterning did not reveal any significant changes to either material. However, the surface hardness of 100Cr6 decreased when energy densities above 2.0 J/cm2 were applied. For cylindrical specimens, the grating period changes with position along the circumference. The observed difference, however, is less than ~6.4% for rotation angles smaller than 20°.
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A Comparison of Scene Change Localization Methods over the Open Video Scene Detection Dataset
Panchenko, Taras,Bieda, Igor International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.6
Scene change detection is an important topic because of the wide and growing range of its applications. Streaming services from many providers are increasing their capacity which causes the industry growth. The method for the scene change detection is described here and compared with the State-of-the-Art methods over the Open Video Scene Detection (OVSD) - an open dataset of Creative Commons licensed videos freely available for download and use to evaluate video scene detection algorithms. The proposed method is based on scene analysis using threshold values and smooth scene changes. A comparison of the presented method was conducted in this research. The obtained results demonstrated the high efficiency of the scene cut localization method proposed by authors, because its efficiency measured in terms of precision, recall, accuracy, and F-metrics score exceeds the best previously known results.
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Neural Networks-Based Method for Electrocardiogram Classification
Maksym Kovalchuk,Viktoriia Kharchenko,Andrii Yavorskyi,Igor Bieda,Taras Panchenko International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9
Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.
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