Temat: Ga - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Microstructure evaluation of Ni-Mn-Ga alloy
Mikrostruktura stopu Ni-Mn-Ga
Autorzy:: Flaga, S.
Kata, D.
Sapiński, B.
Powiązania:: https://bibliotekanauki.pl/articles/368870.pdf
Data publikacji:: 2010
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: MSMA
stop Ni-Mn-Ga
Ni-Mn-Ga alloy
Opis:: This paper summarises the applications of MSMA materials, outlines the mechanism of magnetic shape memory effect and provides the procedure for microstructure evaluation of samples as well as the results of microstructure analysis using the example of the Ni-Mn-Ga alloy.
W artykule scharakteryzowano zastosowania materiałów z magnetyczną pamięcią kształtu (MSMA). Opisano mechanizm działania magnetycznej pamięci kształtu. Przedstawiono procedurę mikroskopowej oceny próbki i jej wyniki na przykładzie stopu Ni-Mn-Ga.
Źródło:: Mechanics and Control; 2010, 29, 1; 6-10
2083-6759
2300-7079
Pojawia się w:: Mechanics and Control
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Performance measurement with high-performance computer using HW-GA anomaly-detection algorithms for streaming data
Autorzy:: Fondaj, Jakup
Hasani, Zirije
Krrabaj, Samedin
Powiązania:: https://bibliotekanauki.pl/articles/27312908.pdf
Data publikacji:: 2022
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: time-series data
HW-GA
anomaly detection
big streaming data
Numenta
COVID-19 data set
high-performance computer
Libelium sensor data
e-dnevnik
Opis:: Anomaly detection for streaming real-time data is very important; more significant is the performance of an algorithm in order to meet real-time requirements. Anomaly detection is very crucial in every sector because, by knowing what is going wrong with data/digital systems, we can make decisions to help in every sector. Dealing with real-time data requires speed; for this reason, the aim of this paper is to measure the performance of our proposed Holt–Winters genetic algorithm (HW-GA) as compared to other anomaly-detection algorithms with a large amount of data as well as to measure how other factors such as visualization and the performance of the testing environment affect the algorithm’s performance. The experiments will be done in R with different data sets such as the as real COVID-19 and IoT sensor data that we collected from Smart Agriculture Libelium sensors and e-dnevnik as well as three benchmarks from the Numenta data sets. The real data has no known anomalies, but the anomalies are known in the benchmark data; this was done in order to evaluate how the algorithm works in both situations. The novelty of this paper is that the performance will be tested on three different computers (in which one is a high-performance computer); also, a large amount of data will be used for our testing, as will how the visualization phase affects the algorithm’s performance.
Źródło:: Computer Science; 2022, 23 (3); 395--410
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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