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Wyświetlanie 1-2 z 2
Tytuł:
FPGA Implementation of Neural Nets
Autorzy:
Kumari, B A Sujatha
Kulkarni, Sudarshan Patil
Sinchana, C. G.
Powiązania:
https://bibliotekanauki.pl/articles/27311922.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
artificial neural network
Spartan-6
field programmable gate arrays (FPGAs)
convolutional neural network
Opis:
The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distributed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 599--604
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm : A Field Study
Autorzy:
Zare, Sajad
Ghotbiravandi, Mohammad Reza
Elahishirvan, Hossein
Ahsaeed, Mostafa Ghazizadeh
Rostami, Mina
Esmaeili, Reza
Powiązania:
https://bibliotekanauki.pl/articles/176392.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
noise
modeling
NIHL
noise induced hearing loss
neural network algorithm
Opis:
The aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure level of noise exposure: Group 1 (LAeq < 70 dB), Group 2 (LAeq 70-80 dB), and Group 3 (LAeq > 85 dB). The results obtained for Group 1 indicate that the hearing thresholds at the frequencies of 8 kHz and 1 kHz had the maximum effect on the development of hearing loss. In Group 2, the factors with maximum weight were the hearing threshold at 4 kHz and the worker’s age. In Group 3, maximum weight was found for the factors of hearing threshold at a frequency of 4 kHz and duration of work experience. The article also reports the results of hearing loss modeling on combined data from the three groups. The study shows that neural data mining classification algorithms can be an effective tool for the identification of hearing hazards and greatly help in designing and conducting hearing conservation programs in the industry.
Źródło:
Archives of Acoustics; 2020, 45, 2; 303-311
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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