Results 21 to 30 of about 61 (58)
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2023
The topic of the final qualifying work: "The use of powder materials for the detection of hazardous substances." This work is devoted to the analysis of existing explosives simulators and the development of technology to improve the creation of a simulator based on graphite, melamine and 9-water aluminum nitrate. In this work, several tasks were set: 1.
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The topic of the final qualifying work: "The use of powder materials for the detection of hazardous substances." This work is devoted to the analysis of existing explosives simulators and the development of technology to improve the creation of a simulator based on graphite, melamine and 9-water aluminum nitrate. In this work, several tasks were set: 1.
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2019
The aim of the work is to develop a technical vision system for mobile underwater vehicles capable of detecting objects in changing conditions of the underwater environment. In this paper, we consider studies aimed at creating a vision system for underwater cameras, as well as a description of neural networks, their speed, size, and detection accuracy ...
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The aim of the work is to develop a technical vision system for mobile underwater vehicles capable of detecting objects in changing conditions of the underwater environment. In this paper, we consider studies aimed at creating a vision system for underwater cameras, as well as a description of neural networks, their speed, size, and detection accuracy ...
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РеконÑÑÑÑкÑÐ¸Ñ ÐС 35/6 ÐºÐ Ñ Ð¿ÐµÑеводом напÑÑÐ¶ÐµÐ½Ð¸Ñ 110/6 кÐ
2020ÐбÑекÑом ÑабоÑÑ Ð²ÑбÑана подÑÑанÑÐ¸Ñ â18 «ÐÑаÑнÑй ÑÑеÑголÑник» 35 кÐ. Ð¦ÐµÐ»Ñ ÑабоÑÑ Ð·Ð°ÐºÐ»ÑÑаеÑÑÑ Ð² модеÑнизаÑии подÑÑанÑии за ÑÑÐµÑ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐºÐ»Ð°ÑÑа напÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ Ð·Ð°Ð¼ÐµÐ½Ñ Ð¼Ð¾ÑалÑно ÑÑÑаÑевÑего обоÑÑÐ ...
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2023
The object of research in the work was convolutional neural networks for the development of an algorithm for detecting people in the aquatic environment at the swimming pool. A number of modern architectures were considered to solve this problem and their implementation was explored using the PyTorch library.
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The object of research in the work was convolutional neural networks for the development of an algorithm for detecting people in the aquatic environment at the swimming pool. A number of modern architectures were considered to solve this problem and their implementation was explored using the PyTorch library.
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СооÑÑÐ¶ÐµÐ½Ð¸Ñ ÑиÑÑем пиÑÑевого водоÑнабжениÑ
2019РпоÑобии опиÑÐ°Ð½Ñ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ оÑиÑÑки пиÑÑевой Ð²Ð¾Ð´Ñ Ð¸ Ð´Ð°Ð½Ñ Ð¾ÑÐ½Ð¾Ð²Ñ ÑаÑÑеÑа ÑооÑÑжений на водопÑоводнÑÑ ÑÑанÑиÑÑ . ÐоÑобие на английÑком ÑзÑке пÑедназнаÑено Ð´Ð»Ñ Ð¸Ð·ÑÑÐµÐ½Ð¸Ñ Ð´Ð¸ÑÑÐ¸Ð¿Ð»Ð¸Ð½Ñ Â«ÐодоÑнабР...
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2023
Ð ÑезÑлÑÑаÑе иÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±Ñл пÑоведен вÑÐ±Ð¾Ñ Ð¸ обоÑнование пеÑÑпекÑивного ÑÑ ÐµÐ¼Ð¾ÑÐµÑ Ð½Ð¸ÑеÑкого ÑеÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑеализаÑии пÑеобÑазоваÑÐµÐ»Ñ Ð¿Ð¾ÑÑоÑнного напÑÑÐ¶ÐµÐ½Ð¸Ñ Ñ Ð·Ð°Ð´Ð°Ð½Ð½Ñми Ñ Ð°ÑакÑеÑиÑÑиками; Ð¿Ñ ...
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Ð ÑезÑлÑÑаÑе иÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±Ñл пÑоведен вÑÐ±Ð¾Ñ Ð¸ обоÑнование пеÑÑпекÑивного ÑÑ ÐµÐ¼Ð¾ÑÐµÑ Ð½Ð¸ÑеÑкого ÑеÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑеализаÑии пÑеобÑазоваÑÐµÐ»Ñ Ð¿Ð¾ÑÑоÑнного напÑÑÐ¶ÐµÐ½Ð¸Ñ Ñ Ð·Ð°Ð´Ð°Ð½Ð½Ñми Ñ Ð°ÑакÑеÑиÑÑиками; Ð¿Ñ ...
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2020
This work is devoted to the study of algorithms for optimal signal detection in conditions of a priori uncertainty. The study involves the software implementation of the algorithms under study and the calculation of their characteristic dependencies. Thus, in the first chapter of this paper, we present a theoretical material related to the criterion of
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This work is devoted to the study of algorithms for optimal signal detection in conditions of a priori uncertainty. The study involves the software implementation of the algorithms under study and the calculation of their characteristic dependencies. Thus, in the first chapter of this paper, we present a theoretical material related to the criterion of
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2022
ÐбÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â алгоÑиÑÐ¼Ñ Ð¾Ð¿ÑималÑного обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ñигнала неизвеÑÑной длиÑелÑноÑÑи. Ð¦ÐµÐ»Ñ ÑабоÑÑ â изÑÑение ÑазлиÑнÑÑ Ð°Ð»Ð³Ð¾ÑиÑмов обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ модиÑикаÑÐ¸Ñ Ð¾Ð¿ÑималÑного Ð´Ð»Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾Ñ
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ÐбÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â алгоÑиÑÐ¼Ñ Ð¾Ð¿ÑималÑного обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ñигнала неизвеÑÑной длиÑелÑноÑÑи. Ð¦ÐµÐ»Ñ ÑабоÑÑ â изÑÑение ÑазлиÑнÑÑ Ð°Ð»Ð³Ð¾ÑиÑмов обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ модиÑикаÑÐ¸Ñ Ð¾Ð¿ÑималÑного Ð´Ð»Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾Ñ
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2021
ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена пÑоблеме обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿Ð°ÐºÐµÑнÑÑ Ñигналов, в коÑоÑÑÑ Ð¿ÑеамбÑла ÑлиÑком мала Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸Ð»Ð¸ полноÑÑÑÑ Ð¾ÑÑÑÑÑÑвÑеÑ.
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ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена пÑоблеме обнаÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿Ð°ÐºÐµÑнÑÑ Ñигналов, в коÑоÑÑÑ Ð¿ÑеамбÑла ÑлиÑком мала Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸Ð»Ð¸ полноÑÑÑÑ Ð¾ÑÑÑÑÑÑвÑеÑ.
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2018
Рданной ÑабоÑе изÑÑена завиÑимоÑÑÑ Ð²Ð°Ð¶Ð½ÐµÐ¹Ñей ÑÑанÑпоÑÑной Ñ Ð°ÑакÑеÑиÑÑики гÑаÑена â пÑоводимоÑÑи, Ð¾Ñ Ð¿Ñиложенного Ð¼ÐµÑ Ð°Ð½Ð¸ÑеÑкого ÑаÑÑÑгиваÑÑего напÑÑжениÑ. ÐÑл пÑоведен ÑаÑÑÐµÑ Ð¿ÑоводимоÑÑи
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Рданной ÑабоÑе изÑÑена завиÑимоÑÑÑ Ð²Ð°Ð¶Ð½ÐµÐ¹Ñей ÑÑанÑпоÑÑной Ñ Ð°ÑакÑеÑиÑÑики гÑаÑена â пÑоводимоÑÑи, Ð¾Ñ Ð¿Ñиложенного Ð¼ÐµÑ Ð°Ð½Ð¸ÑеÑкого ÑаÑÑÑгиваÑÑего напÑÑжениÑ. ÐÑл пÑоведен ÑаÑÑÐµÑ Ð¿ÑоводимоÑÑи
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