Software for image recognition of domestic manufacturers dairy drinking products using Raspberry PI

С использованием каскада Хаара и одноплатного компьютера Raspberry Pi была создана программа по распознаванию образа молочного питьевого продукта в видеопотоке. Был проведён эксперимент, показавший целесообразность использования метода обучения модели, а также сделан вывод о резонности применения Raspberry Pi для достижения поставленной задачи. Using the Haar cascade and single-board computer Raspberry Pi, was created a program to recognize the image of a dairy drinking product in a video stream. An experiment that showed the feasibility of using the model training method was conducted, and a conclusion about the reasonableness of using Raspberry Pi to achieve the task was made

The results of reliability tests of semiconductor devices analysis using Python libraries

При сборе большого количества данных об электрических параметрах приборов важно рационально осуществлять анализ получаемых значений. Выводы удобно делать при оценке графических представлений результатов, однако вручную строить графики для каждого параметра является нецелесообразным, в этом могут помочь библиотеки языка программирования Python. Collecting a large amount of data on the electrical parameters of devices, it is important to analyze the obtained values rationally Conclusions can be done easily by a graphical presentation of the results; however, it is impractical to manually build graphs for each parameter. Python programming language libraries can help in this

Forecasting energy characteristics of photoelectric stations by the methods of learning solution trees

В статье представлены результаты прогнозирования выработки электроэнергии фотоэлектрическими (солнечными) электростанциями (ФЭС) методами обучения деревьев решений. Для прогнозирования были использованы деревянные модели, основаны на деревьях решений DecisionTree, GradientBoosting, RandomForest. Для оценки точности прогнозирования оценивались среднеквадратичная ошибка (MSE), средняя абсолютная ошибка (MAE), коэффициент детерминации (R2). At present, the results of forecasting the generation of electricity by photovoltaic (solar) power plants by methods of training decision trees. For forecasting, wooden models was used, based on decision trees DecisionTree, GradientBoosting, RandomForest. To assess the prediction accuracy, the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) was estimated

Polycrystalline solar cells electrical characteristics forecasting based on their degradation models

Представлены результаты ускоренных испытаний полупроводниковых фотоэлектрических (солнечных) элементов (СЭ) класса B на основе поликристаллического кремния. Установлено, что автомодельность эксперимента соблюдается при температурах до 150–160° С и уровнях освещения до 2000 Вт/м2. За 168 ч ускоренных испытаний относительная деградация КПД составила 5,8%. Установлена закономерность деградации коэффициента заполнения (ff) и тока короткого замыкания (Isc) СЭ в начальный и конечный момент времени испытаний.In this paper we present the results of accelerated tests B class based solar cells. It is established that the self-similarity of the experiment is observed at temperatures up to 150–160° C and lighting levels up to 2000 W/m2. During over 168 h of accelerated testing the relative degradation of efficiency was 5,8%. The pattern of degradation of the duty cycle (ff) and short-circuit current (Isc) of the solar cells at the initial and final time points of the tests is established

Geology, stratigraphy and palaeoenvironmental evolution of the Stephanorhinus kirchbergensis

The sedimentary succession exposed in the Gorzów Wielkopolski area includes Eemian Interglacial (MIS 5e) or Early Weichselian (MIS 5d–e) deposits. The sedimentary sequence has been the object of intense interdisciplinary study, which has resulted in the identification of at least two palaeolake horizons. Both yielded fossil remains of large mammals, alongside pollen and plant macrofossils. All these proxies have been used to reconstruct the environmental conditions prevailing at the time of deposition, as well as to define the geological context and the biochronological position of the fauna. Optically stimulated luminescence dating of the glaciofluvial layers of the GS3 succession to 123.6 ± 10.1 (below the lower palaeolake) and 72.0 ± 5.2 ka (above the upper palaeolake) indicate that the site formed during the Middle–Late Pleistocene (MIS 6 – MIS 5). Radiocarbon-dating of the lacustrine organic matter revealed a tight cluster of Middle Pleniglacial Period (MIS 3) ages in the range of ~41–32 ka cal bp (Hengelo – Denekamp Interstadials). Holocene organic layers have also been found, with C ages within a range of 4330–4280 cal bp (Neolithic). Pollen and plant macrofossil records, together with sedimentological and geochemical data, confirm the dating to the Eemian Interglacial.This research was supported by grant 0201/2048/18 ‘Life and death of extinctrhino (Stephanorhinus sp.) from Western Poland: a multiproxy palaeoenvironmental approach’ financed by the National Science Centre, Poland. LiDAR DTM data presented in this study were used under academic licences DIO.DFT.DSI.7211.1619.2015_PL_N and DIO.DFT.7211.9874. 2015_PL_N awarded to the Faculty of Earth Sciences and the Environmental Management University of Wrocław, in accordance with the Polish legal regulations of the administration of the Head Office of Land Surveying and Cartography