Wyniki wyszukiwania - BazTech

1

A robust inversion of logging-while-drilling responses based on deep neural network

Zhu Gaoyang, Gao Muzhi, Wang Bin

Acta Geophysica

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2024

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Vol. 72, no. 1

129--139

EN

Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations.

2

Hybrid CNN-Ligru acoustic modeling using sincnet raw waveform for hindi ASR

Kumar Ankit, Aggarwal Rajesh Kumar

Computer Science

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2020

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T. 21 (4)

397-417

EN

Deep neural networks (DNN) currently play a most vital role in automatic speech recognition (ASR). The convolution neural network (CNN) and recurrent neural network (RNN) are advanced versions of DNN. They are right to deal with the spatial and temporal properties of a speech signal, and both properties have a higher impact on accuracy. With its raw speech signal, CNN shows its superiority over precomputed acoustic features. Recently, a novel first convolution layer named SincNet was proposed to increase interpretability and system performance. In this work, we propose to combine SincNet-CNN with a light-gated recurrent unit (LiGRU) to help reduce the computational load and increase interpretability with a high accuracy. Different configurations of the hybrid model are extensively examined to achieve this goal. All of the experiments were conducted using the Kaldi and Pytorch-Kaldi toolkit with the Hindi speech dataset. The proposed model reports an 8.0% word error rate (WER).

3

Wykrywanie anomalii w sieciach sterowanych programowo z wykorzystaniem dynamicznej telemetrii oraz głębokich sieci neuronowych

Rzym Grzegorz, Masny Amadeusz

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2019

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nr 7

635--638, CD

PL

Wraz ze wzrostem dostępności mocy obliczeniowej uczenie maszynowe w dzisiejszych czasach skupia się coraz bardziej na metodach głębokiego uczenia. Powszechna automatyzacja procesów skłania do przemyślenia nowoczesnych implementacji sieciowych w rozumieniu ich bezpieczeństwa i szybkiego, jak i dokładnego reagowania na awarie. Niniejszy artykuł opisuje wykorzystanie głębokich sieci neuronowych do wykrywania anomalii w ruchu sieciowym w sieciach sterowanych programowo (SDN). Dodatkowo, obrazuje szerszy pogląd na automatyzację monitorowania sieci z wykorzsytaniem dynamicznej telemetrii.

EN

With the increasing availability of computational power, nowadays machine learning focuses more and more on deep learning methods. The widespread automation of processes leads to the rethinking of modern network implementations in the understanding of their safety and quick and accurate response to failures. This article describes the use of deep neural networks to detect anomalies in network traffic in conjunction with SDN. In addition, it provides a broader view of network monitoring automation with the usage of dynamic telemetry.

4

Effect of Water Vapor to Explosives and Their Use for Improvised Explosive Devices

Hutov I., Ganev R., Godinyachki G., Grozev V.

Problemy Mechatroniki : uzbrojenie, lotnictwo, inżynieria bezpieczeństwa

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2013

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Vol. 4, Nr 4 (14)

77-83

EN

This report examines the stability of TNT and dinitronaphthalene and their mixtures after their thermal extraction from the shells of artillery munitions by means of water vapor and their use for improvised explosives with high power. The comparison of the baseline explosive mixture with the samples acquired after the water vapor extraction showed no significant differences in their characteristics. Similarly, each of the explosive mixture components (TNT and DNN) after the water vapor extraction, examined separately, did not show significant differences from the original TNT and DNN characteristics.