Wyniki wyszukiwania - BazTech

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Artificial intelligence versus natural intelligence in mineral processing

Özkan Şafak Gökhan

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 5

art. no. 167501

EN

This article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, ML-Machine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use.

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Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey

Abdellaoui Meriem, Benabdelkader Souad, Assas Ouarda

Machine Graphics and Vision

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2023

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Vol. 32, No. 2

147--167

EN

Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.

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Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions

Shankar Ravi, Bangare Jyoti L., Kumar Ajay, Gupta Sandeep, Mehraj Haider, Kulkarni Shriram S.

Journal of Telecommunications and Information Technology

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2022

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nr 3

17--24

EN

This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.

4

Predicting the response to cardiac resynchronization therapy (CRT) using the deep learning approach

Nejadeh Mohammad, Bayat Peyman, Kheirkhah Jalal, Moladoust Hassan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

758--778

EN

Cardiac Resynchronization Therapy Defibrillator (CRT-D) is a method to improve heart rate variability and arrhythmia-related symptoms in heart failure patients. According to clinical reports, CRT is not entirely safe and risk-free like other surgery. It can reduce heart failure risks, shorten hospital stays, and enhance the patients’ quality of life. The present study aims to perform the proper selection of patients before surgery to avoid potential costs. This article focuses on the data collection of heart failure patients’ activities, the process of features effective extraction, and identifying an optimal pattern using a Deep Learning (DL) algorithm. Also, the main tasks of the proposed methods include the use of qualitative indicators for initial feature extraction, oversampling from minority class, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) hierarchical clustering, selecting features from Low error clusters, selecting samples from high error clusters, and classification using customized DL configuration. The research data collection consisted of 209 patients with 60 demographic, clinical, laboratory, ECG, and echo features. In addition, features were analyzed based on their significance in predicting CRT response status. The DL algorithm, which used dense layers and convolution for its architecture, was employed to heart failure patients optimally identify the treatment status. The proposed method predicted the response to cardiac resynchronization therapy with an error rate of 91.85% and an Area Under Curve (AUC) of 0.957 and a sensitivity of 94.22%.

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Analizy PVT jako skuteczne narzędzie w rękach inżyniera naftowego. Część 1: laboratoryjne badania PVT

Wojnicki M., Warnecki M., Kuśnierczyk J., Szuflita S.

Nafta-Gaz

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2018

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R. 74, nr 7

535--542

PL

W pierwszej części artykułu omówiono, wykonywane rutynowo, laboratoryjne analizy fazowe węglowodorowych płynów złożowych (tzw. badania PVT), tj.: badanie kontaktowe, badanie różnicowe, badanie odbioru gazu do stałej objętości i badanie separacji. Opisano krótko budowę typowej aparatury do prowadzenia badań PVT. Omówiono podstawy wykonywania poszczególnych badań wraz z wizualizacją ich przebiegu w postaci schematów oraz przedstawiono określane na ich podstawie istotne parametry płynów złożowych. Skomentowano również stosowność wykonywania tego typu analiz oraz ich znaczenie dla prowadzenia sprawnego i efektywnego wydobycia węglowodorów.

EN

The first part of the article discusses routine laboratory phase behavior studies of hydrocarbon reservoir fluids (so-called PVT tests) such as constant mass expansion, differential liberation, constant volume depletion and separation tests. The construction of typical equipment for conducting PVT is briefly described. The basics of performing PVT test is discussed along with the visualization of the process in the diagrams, and the important parameters of the reservoir fluids determined on their basis, are presented. The appropriateness of performing such analyzes and their significance for efficient and effective hydrocarbon production is also commented on.

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Ogólna architektura systemu semantycznej integracji geograficznych żródeł danych

Świderski M.

Studia Informatica

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2005

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Vol. 26, nr 3

29-38

PL

W artykule określone zostały wymagania, które stają przed systemem semantycznej integracji geoprzestrzennych źródeł danych w sieci Internet. Następnie przedstawiona została nowa architektura systemu, uwzględniająca relacyjno-obiektową specyfikę danych geoprzestrzennych, wykorzystująca w Schemacie Koncepcyjnym połączenie logiki deskrypcyjnej oraz Datalog, wnioskowanie w komponencie terminologicznym logiki deskrypcyjnej, zmodyfikowany algorytm przepisywania zapytań oraz dodanie opisu semantycznego do dokumentu WSDL usług Web.