Wyniki wyszukiwania - BazTech

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Metody sztucznej inteligencji w zastosowaniu do przewidywania właściwości materiałów

Trzepieciński Tomasz

Stal, Metale & Nowe Technologie

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2023

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nr 11-12

22--29

PL

Naukowcy nieustannie dążą do lepszego zrozumienia, przewidywania i ulepszania pożądanych właściwości materiałów. Jednym z narzędzi, które można w tym celu wykorzystać, jest sztuczna inteligencja.

2

Zwiększanie potencjału sztucznej inteligencji generatywnej dzięki prompt engineering

Bistroń Marta, Piotrowski Zbigniew

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

341--344

PL

W artykule opisano różne techniki stosowane w algorytmach sztucznej inteligencji generatywnej, takie jak modele oparte na rozkładach prawdopodobieństwa, modele wariancyjne oraz modele sekwencyjne. Wyjaśniono podstawy tych technik oraz omówiono ich zastosowania w generowaniu obrazów, muzyki, tekstu czy mowy. Artykuł podkreśla znaczenie algorytmów generative AI jako narzędzi do twórczego generowania treści oraz prezentuje możliwe sposoby zwiększenia efektywności generowania tych treści z wykorzystaniem techniki prompt engineering.

EN

The article describes various techniques used in generative artificial intelligence algorithms, such as models based on probability distributions, variance models and sequential models. The basics of these techniques are explained and their applications in generating images, music, text or speech are discussed. The article emphasizes the importance of generative AI algorithms as tools for creative content generation and presents possible ways to increase the efficiency of generating this content using the prompt engineering technique.

3

Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

Dey Lipika, Jana Sudeshna, Dasgupta Tirthankar, Gupta Tanay

Annals of Computer Science and Information Systems

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2023

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Vol. 35

11--24

EN

Clinical notes that describe details about diseases, symptoms, treatments and observed reactions of patients to them, are valuable resources to generate insights about the effectiveness of treatments. Their role in designing better clinical decision making systems is being increasingly acknowledged. However, availability of clinical notes is still an issue due to privacy violation concerns. Hence most of the work done are on small datasets and neither the power of machine learning is fully utilized, nor is it possible to vaidate the models properly. With the availability of Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset for researchers though, the problem has been somewhat eased. In this paper we have presented an overview of our earlier work on designing deep neural models for prediction of outcomes and hospital stay for patients using MIMIC data. We have also presented new work on patient stratification and explanation generation for patient cohorts. This is early work targeted towards studying trajectories for treatment for different cohorts of patients, which can ultimately lead to discovery of low-risk models for individual patients to ensure better outcomes.

4

A nested autoencoder approach to automated defect inspection on textured surfaces

Oz Muhammed Ali Nur, Kaymakci Ozgur Turay, Mercimek Muharrem

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 3

515--523

EN

In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.

5

A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes

Huang Lei, Ren Hao, Chai Yi, Qu Jianfeng

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 1

29--43

EN

This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.

6

AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images

Rashid Nayeeb, Hossain Md Adnan Faisal, Ali Mohammad, Islam Sukanya Mumtahina, Mahmud Tanvir, Fattah Shaikh Anowarul

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1685--1701

EN

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

7

Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach

Siva Raja P. M., Rani Antony Viswasa

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

440--453

EN

In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach. Initially, the pre-processing stage is performed using the non-local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, information-theoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 %) when compared to other state-of-art methods.

8

Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning

Li Xueyi, Li Jialin, He David, Qu Yongzhi

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 3

403--410

EN

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.

PL

Pitting kół zębatych stanowi jedno z najczęstszych uszkodzeń przekładni mechanicznych. Do wykrywania takich uszkodzeń stosuje się sygnały emisji akustycznej (AE), które, ze względu na niższą wrażliwość na hałas otoczenia, stanowią skuteczniejsze narzędzie diagnostyczne niż tradycyjne sygnały wibracyjne. Wykrywalność zużycia guzełkowatego (pittingu) kół zębatych przy użyciu sygnałów AE i splotowych sieci neuronowych jest jednak niska. Aby rozwiązać ten problem, w niniejszym artykule zaproponowano nową metodę diagnozowania uszkodzeń kół zębatych za pomocą surowych sygnałów AE, którą nazwano augmented convolution sparse autoencoder (konwolucją rozszerzoną z wykorzystaniem autoenkodera rzadkiego, ACSAE). Jest to metoda samouczenia jednowymiarowych splotowych sieci neuronowych realizowanego za pomocą autoenkodera rzadkiego. Metoda ta wykorzystuje teorię wzmocnienia do zwiększania adaptacyjności i odporności sieci. Metoda ACSAE pozwala na automatyczne wyodrębnianie cech degradacji bezpośrednio z oryginalnych sygnałów AE bez konieczności ich konwersji do domeny czasu i częstotliwości. Walidację metody przeprowadzono na podstawie sygnałów AE otrzymanych w badaniach kół zębatych. Analiza wyników badań pittingu kół zębatych wskazuje, że proponowana metoda pozwala na skuteczną detekcję tego typu uszkodzeń, przy wskaźniku wykrywalności powyżej 98%. Analiza porównawcza pokazuje, że metoda ACSAE cechuje się większą trafnością diagnostyczną w wykrywaniu błędów montażowych kół zębatych w porównaniu z sieciami neuronowymi w pełni połączonymi, splotowymi i rekurencyjnymi.

9

A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing

Karim Ahmad M., Güzel Mehmet S., Tolun Mehmet R., Kaya Hilal, Çelebi Fatih V.

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

148--159

EN

This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved.

10

Głębokie sieci neuronowe i ich zastosowania w eksploracji danych

Osowski S.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2018

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nr 5

112--121

PL

Artykuł dotyczy podstaw działania typowych sieci neuronowych głębokich, do których zalicza się sieci konwolucyjne (CNN), autoenkoder czy sieć LSTM. Omówiono struktury tych sieci, ich algorytmy uczenia oraz dokonano przeglądu podstawowych zastosowań owych sieci w rozwiązywaniu różnego rodzaju zadań eksploracji danych, między innymi klasyfikacji, regresji, segmentacji danych, rekonstrukcji danych i wielu innych. Przedstawiono między innymi wyniki prac dotyczących bioinżynierii, w szczególności rozpoznawanie i klasyfikację obrazów mammograficznych.

EN

The paper presents the theoretical fundamentals of deep neural networks. The basic deep structures are discussed. They include convolutional neural networks (CNN), autoencoder (AE) and recurrent network called LSTM (Long short-term memory). This paper is concerned on presentation of their typical structures and learning algorithms. The review of some chosen applications of these solutions in such tasks as classification, regression, segmentation of data, reconstruction, text and speech recognition, etc., are discussed. Some chosen numerical results concerning classification problems will be also presented and discussed.