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Wyświetlanie 1-15 z 117
Tytuł:
Dynamic states equations of transport pipeline in deep-sea mining
Autorzy:
Sobota, Jerzy
Jianxin, Xia
Kirichenko, Evgeniy
Powiązania:
https://bibliotekanauki.pl/articles/2073867.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
górnictwo głębinowe
rurociąg głębinowy
rurociąg transportowy
deep-sea mining
vertical pipeline
stability of deep-sea pipeline
Opis:
The transport pipeline of lifting the underwater minerals to the surface of the water onto the ship during the movement of the vessel takes in the water a curved deformed shape. Analysis of the state of stability of the pipeline showed that if the flow velocity of fluid in the pipeline exceeds a certain critical value Vkr, then its small random deviations from the equilibrium position may develop into deviations of large amplitude. The cause of instability is the presence of the centrifugal force of the moving fluid mass, which occurs in places of curvature of the axis of the pipeline and seeks to increase this curvature when the ends of the pipeline are fixed. When the critical flow velocity is reached, the internal force factors become unable to compensate for the action of centrifugal force, as a result of that a loss of stability occurs. Equations describing this dynamic state of the pipeline are presented in the article.
Źródło:
Archives of Mining Sciences; 2021, 66, 3; 385--392
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Layout Optimizations of Operational Amplifiers in Deep Submicron
Autorzy:
Shi, Jun
Powiązania:
https://bibliotekanauki.pl/articles/226453.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Layout Optimization
deep submicron (DSM)
Operational Amplifier
place-and-route
Opis:
Operational amplifies (op amps) are an integral part of many analog and mixed-signal systems. Op amps with vastly different levels of complexity are used to realize functions ranging from DC bias generation to high-speed amplification or filtering. The design of op amps continues to pose a challenge as the supply voltage and transistor channel lengths scale down with each generation of CMOS technologies. The thesis deals with the analysis, design and layout optimization of CMOS op amps in deep Submicron (DSM) from a study case. Finally, layout optimizations of op amps will be given, in which propose optimization techniques to mitigate these DSM effects in the place-and-route stage of VLSI physical design.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 2; 287-293
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Theory I: Deep networks and the curse of dimensionality
Autorzy:
Poggio, T.
Liao, Q.
Powiązania:
https://bibliotekanauki.pl/articles/200623.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep network
shallow network
convolutional neural network
function approximation
deep learning
sieci neuronowe
aproksymacja funkcji
uczenie głębokie
Opis:
We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation of bone structures with the use of deep learning techniques
Autorzy:
Krawczyk, Zuzanna
Starzyński, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2173574.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
semantic segmentation
U-net
FCN
ResNet
computed tomography
technika deep learning
głęboka nauka
segmentacja semantyczna
tomografia komputerowa
Opis:
The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136751
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation of bone structures with the use of deep learning techniques
Autorzy:
Krawczyk, Zuzanna
Starzyński, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2128158.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
semantic segmentation
U-net
FCN
ResNet
computed tomography
technika deep learning
głęboka nauka
segmentacja semantyczna
tomografia komputerowa
Opis:
The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136751, 1--8
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected technical issues of deep neural networks for image classification purposes
Autorzy:
Grochowski, Michał
Kwasigroch, A.
Mikołajczyk, A.
Powiązania:
https://bibliotekanauki.pl/articles/200871.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep neural network
deep learning
image classification
batch normalization
transfer learning
dropout
sieć neuronowa
klasyfikacja obrazów
normalizacja
transfer nauki
uczenie głębokie
Opis:
In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 2; 363-376
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep Image Features in Music Information Retrieval
Autorzy:
Gwardys, G.
Grzywczak, D.
Powiązania:
https://bibliotekanauki.pl/articles/226400.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
music information retrieval
deep learning
genre classification
convolutional neural networks
transfer learning
Opis:
Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model trained on CNN were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition. The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform image classification and was reused to perform genre classification using spectrograms images. Harmonic/percussive separation was applied, because it is characteristic for musical genre. At final stage, the evaluation of various strategies of merging Support Vector Machines (SVMs) was performed on well known in MIR community - GTZAN dataset. Even though, the model was trained on natural images, the results achieved in this study were close to the state-of-the-art.
Źródło:
International Journal of Electronics and Telecommunications; 2014, 60, 4; 321-326
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Theory II: Deep learning and optimization
Autorzy:
Poggio, T.
Liao, Q.
Powiązania:
https://bibliotekanauki.pl/articles/201787.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
convolutional neural networks
loss surface
optimization
uczenie głębokie
sieć neuronowa
optymalizacja
Opis:
The landscape of the empirical risk of overparametrized deep convolutional neural networks (DCNNs) is characterized with a mix of theory and experiments. In part A we show the existence of a large number of global minimizers with zero empirical error (modulo inconsistent equations). The argument which relies on the use of Bezout theorem is rigorous when the RELUs are replaced by a polynomial nonlinearity. We show with simulations that the corresponding polynomial network is indistinguishable from the RELU network. According to Bezout theorem, the global minimizers are degenerate unlike the local minima which in general should be non-degenerate. Further we experimentally analyzed and visualized the landscape of empirical risk of DCNNs on CIFAR-10 dataset. Based on above theoretical and experimental observations, we propose a simple model of the landscape of empirical risk. In part B, we characterize the optimization properties of stochastic gradient descent applied to deep networks. The main claim here consists of theoretical and experimental evidence for the following property of SGD: SGD concentrates in probability – like the classical Langevin equation – on large volume, ”flat” minima, selecting with high probability degenerate minimizers which are typically global minimizers.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 775-787
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
International Seabed Authority and Environmental Deep-Sea Stewardship – Principles Governing the Protection and Use of Seabed Resources
Autorzy:
Nyka, Maciej
Powiązania:
https://bibliotekanauki.pl/articles/1954778.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Deep Sea Stewardship
Deep Sea Mining
International Seabed Authority
Future Generations rights
common heritage of mankind
Nauki Humanistyczne i Społeczne
Opis:
Koncepcje nawiązujące do sprawiedliwości ekologicznej wydają się być głęboko zakorzenione w prawnych regulacjach działalności w Obszarze. Konwencja Narodów Zjednoczonych o prawie morza wielokrotnie podkreśla konieczność ochrony ekologicznego i ekonomicznego interesu obecnych i przyszłych pokoleń przy prowadzeniu działalności wydobywczej w obszarze dna morskiego. W świetle coraz bardziej rychłej perspektywy rozpoczęcia działalności wydobywczej w obszarze dna morskiego morza pełnego konieczną staje się refleksja nad stopniem przygotowania Międzynarodowej Organizacji Dna Morskiego do realizacji swoich funkcji w zakresie reprezentowania interesu ludzkości.
The concept of intergenerational justice is deeply rooted into the regulation of activities in the Area. United Nations Convention on the Law of the Sea on many occasions stresses the need to protect the marine environment in the interest of both contemporary and future generations. One of the institutions with vast competences in this field is the International Seabed Authority. With a perspective on inevitable commercial exploration and exploitation of seabed resourcesthere is a need to answer the question if the Seabed Authority is properly prepared both in the field of law and policy to act as a steward of a mankind?Which instruments has been developed to ensure sustainable use of seabed resources and which of them are at the disposal of the International Seabed Authority? Finally, are the standards of the protection of seabed environment sufficient to satisfy the needs of contemporary consumption without diminishing the ability of the mankind in the future to freely choose their path of development? We can assume that environmental standards which would protect the interest of future people would also secure the sustainability in contemporary use of common heritage at the Seas. International Seabed Authority and international law of the sea can play important role in this process by shaping the future of the mankind with actions undertaken today.
Źródło:
Prawo Morskie; 2020, XXXIX; 9-20
0860-7338
Pojawia się w:
Prawo Morskie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
Autorzy:
Goay, Chan Hong
Cheong, Zheng Quan
Low, Chen En
Ahmad, Nur Syazreen
Goh, Patrick
Powiązania:
https://bibliotekanauki.pl/articles/2200709.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
adaptive sampling
deep neural networks
deep learning
power-ground plane
Z-parameters
Opis:
This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the nonnormalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a squareshaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 793--798
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Is the research of deep-seated geological structures useful from practical point of view?
Autorzy:
Jankowski, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/703640.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
pure and applied geophysics
sedimentary basin
deep natural gas
Opis:
Similarities and differences between basic and application-oriented research in geophysics are discussed. Particular attention is drawn to the fact that some results of basic research may turn out to be useful from practical point of view. This is illustrated by the example of the lithosphere investigations in Poland. Poland is a site of two huge sedimentary basins, the Polish Basin and the Carpathians. Surveys of the lower parts of these basins, as performed with two methods, explosive seismology and electromagnetic sounding, revealed the presence of porous rocks, which may suggest their reservoir-like nature.
Źródło:
Nauka; 2009, 4
1231-8515
Pojawia się w:
Nauka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks
Autorzy:
Prajapati, Hardik K.
Joshi, Rutvij
Powiązania:
https://bibliotekanauki.pl/articles/2200710.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
Deep learning
Convolutional Neural Network (CNN)
LEACH
Opis:
Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 799--805
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning-based framework for tumour detection and semantic segmentation
Autorzy:
Kot, Estera
Krawczyk, Zuzanna
Siwek, Krzysztof
Królicki, Leszek
Czwarnowski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/2173573.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
medical imaging
tumour detection
semantic segmentation
image fusion
technika deep learning
głęboka nauka
obrazowanie medyczne
wykrywanie guza
segmentacja semantyczna
połączenie obrazu
Opis:
For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136750
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning: theory and practice
Autorzy:
Cichocki, A.
Poggio, T.
Osowski, S.
Lempitsky, V.
Powiązania:
https://bibliotekanauki.pl/articles/202346.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
networks
theory
practice
uczenie głębokie
sieci
teoria
praktyka
Opis:
This Special Section of the Bulletin of the Polish Academy of Sciences on Technical Sciences is devoted to theoretical aspects of deep machine learning as well as practical applications in some areas of signal and image processing, particularly in bioengineering.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 757-759
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning-based framework for tumour detection and semantic segmentation
Autorzy:
Kot, Estera
Krawczyk, Zuzanna
Siwek, Krzysztof
Królicki, Leszek
Czwarnowski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/2128156.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
medical imaging
tumour detection
semantic segmentation
image fusion
technika deep learning
głęboka nauka
obrazowanie medyczne
wykrywanie guza
segmentacja semantyczna
połączenie obrazu
Opis:
For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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