Temat: Blast Theory - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Dramaturgia nowych mediów i performans postcyfrowy. Projektowanie doświadczeń użytkownika we współczesnych sztukach performatywnych
New media dramaturgy and postdigital performance. User experience design of contemporary performing arts
Autorzy:: Kłeczek, Jakub
Powiązania:: https://bibliotekanauki.pl/articles/2036491.pdf
Data publikacji:: 2021-12-15
Wydawca:: Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:: post-digital performance
new media dramaturgy
user experience design
performing arts
Interrobang
Blast Theory
Opis:: The paper aims to answer the question – to what extent is the current reflection on user experience design in performing arts still valid? The text discusses the concept of post-digital performance (Causey); and the phenomenon of user experience design in the face of new media dramaturgy (Eckersall, Grehan, Scheer). From the perspective of these concepts and phenomena, I describe two works (To Like or Not to Like by Interrobang and Karen by Blast Theory). The text complements the discussion on performance artists’ approaches to media technologies. In this paper, I describe the changes in designing the relationships of performers and users (individualization and personalization) and the contexts of everyday media practices in artists’ strategies.
Źródło:: Przestrzenie Teorii; 2021, 36; 109-125
2450-5765
Pojawia się w:: Przestrzenie Teorii
Dostawca treści:: Biblioteka Nauki

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Tytuł:: Blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network
Autorzy:: Temeng, Victor Amoako
Ziggah, Yao Yevenyo
Arthur, Clement Kweku
Powiązania:: https://bibliotekanauki.pl/articles/1839067.pdf
Data publikacji:: 2021
Wydawca:: Główny Instytut Górnictwa
Tematy:: artificial intelligence
blast-induced noise level
emotional neural network
limbic system theory
sztuczna inteligencja
poziom hałasu wywołanego wybuchem
sieć neuronowa
teoria układu limbicznego
Opis:: Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.
Źródło:: Journal of Sustainable Mining; 2021, 20, 1; 28-38
2300-1364
2300-3960
Pojawia się w:: Journal of Sustainable Mining
Dostawca treści:: Biblioteka Nauki

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