Wyniki wyszukiwania - Biblioteka Nauki

1

Influence of the radiofrequency applicators arrangement on the sizes of ablative zones inside hepatic tumor

100%

Gas P. , Miaskowski A.

|

tom Vol. 73, nr 3

557--571

EN

Radiofrequency (RF) ablation is a popular therapeutic technique for heating solid tumors that are medically unsuitable for resection or other treatments. Thermal ablation applicators create high-frequency electromagnetic fields (EMFs) within the tumor site, which causes heating, coagulation, and ultimately death of the cancer cells. The aim of this study is the numerical analysis of the temperature distributions, ablation zones, and specific absorption rates (SAR) during RF ablation in relation to an ellipsoidal shaped tumor placed in the model of liver tissue. The source of heat is a three-element system of RF needle applicators operating at a frequency 100 kHz, with a given electrode potential, inserted into the tumor. In order to obtain an appropriate temperature distribution in the target area, the Laplace equation coupled with the Pennes equation were solved using the finite element method (FEM). The arrangement effect of three needle-type applicators on the resultant thermal profiles and the volumes of ablation zones were analyzed and compared. In addition, the ablation zones for various angles of the RF applicator placed in the center of the tumor were analyzed. The paper shows that in order to control temperature distribution and ablation zones the proposed system of RF applicators and the arrangement of electrodes can be successfully applied in hepatocellular carcinoma treatment.

2

Load balancing in parallel implementation of vascular network modeling

84%

Jurczuk K. , Krętowski M. , Bézy-Wendling J.

|

tom Z. 6

41-61

EN

In this paper, load balancing mechanisms in a parallel algorithm of vascular network development are investigated. The main attention is focused on the perfusion process (connection of new cells to vascular trees) as it is the most time demanding part of the vascular algorithm. We propose several techniques that aim at balancing load among processors, decreasing their idle time and reducing the communication overhead. The core solution is based on the centralized dynamic load balancing approach. The model behaviors are analyzed and a tradeoff between the different mechanisms is found. The proposed mechanisms are implemented on a computing cluster with the use of the message passing interface (MPI) standard. The experimental results show that the introduced improvements provide a more efficient solution and consequently further accelerate the simulation process.

PL

W artykule rozważane są mechanizmy zrównoważające obciążenie w równoległym algorytmie rozwoju sieci naczyń krwionośnych. Główną uwagę zwrócono na proces perfuzji (podłączanie nowych komórek do drzew krwionośnych) jako, że proces ten jest najbardziej czasochłonnym fragmentem rozpatrywanego algorytmu. Zaproponowane przez autorów rozwiązania mają na celu zrównoważenie obciążenia pomiędzy procesorami, skrócenie ich czasu bezczynności oraz zredukowanie narzutu komunikacyjnego. Jądro rozwiązania jest oparte na scentralizowanym dynamicznym podejściu równoważenia obciążenia. Zachowania modelu zostały przeanalizowane i kompromis pomiędzy różnymi technikami został zaproponowany. Przedstawione mechanizmy zostały zaimplementowane na klastrze obliczeniowym przy wykorzystaniu standardu MPI. Otrzymane rezultaty jednoznacznie pokazuja˛ iż wprowadzone usprawnienia zapewniają bardziej efektywne rozwiązanie co w konsekwencji pozwala na jeszcze większe przyśpieszenie procesu symulacji.

3

Applying Knowledge Distillation to Improve Weed Mapping With Drones

67%

Castellano G. , De Marinis P. , Vessio G.

|

tom Vol. 35

393--400

EN

Non-invasive remote sensing using UAVs can be used in precision agriculture to observe crops in visible and non-visible spectra. This paper investigates the effectiveness of state-of-the-art knowledge distillation techniques for mapping weeds with drones, an essential component of precision agriculture that employs remote sensing to monitor crops and weeds. The study introduces a lightweight Vision Transformer-based model that achieves optimal weed mapping capabilities while maintaining minimal computation time. The research shows that the student model effectively learns from the teacher model using the WeedMap dataset, achieving accurate results suitable for mobile platforms such as drones, with only 0.5 GMacs compared to 42.5 GMacs of the teacher model. The trained models obtained an F1 score of 0.863 and 0.631 on two data subsets, with a performance improvement of 2 and 7 points, respectively, over the undistilled model. The study results suggest that developing efficient computer vision algorithms on drones can significantly improve agricultural management practices, leading to greater profitability and environmental sustainability.

4

Wybrane aspekty badań symulacyjnych strat w rzeczywistych łączach satelitarnych

67%

Wilk-Jakubowski J. Ł.

|

tom T. 6

135--151

PL

Na konkurencyjność oraz innowacyjność gospodarki światowej wpływa stan zaawansowania technologii TIK. Jednym z rozwiązań podnoszenia jakości świadczonych usług jest dążenie do optymalizacji współczesnych systemów łączności satelitarnej. Z punktu widzenia ich pracy, w celu zapewnienia zadowalającej pracy łącza satelitarnego konieczna jest odpowiednia moc nadajnika. W praktyce moc ta uzależniona jest od wielu czynników, jak chociażby: współrzędne geograficzne miejsca odbioru, ukształtowanie terenu, częstotliwość, lokalizacja anteny (ustawienie anteny), tłumienie sygnału w wolnej przestrzeni, wymagany poziom dostępności łącza. Czynniki te przekładają się na bilans energetyczny łącza satelitarnego, który uzależniony jest ponadto od parametrów technicznych systemu (zwłaszcza: apertury anteny, sprawności anteny oraz całkowitych strat w łączu). W artykule przeanalizowano wpływ całkowitych strat w łączu w zakresie 0,1-0,7 dB na jakość odbioru mikrofalowego sygnału satelitarnego w obszarze Kielc (z uwzględnieniem szumów interferencyjnych), co pozwala przedstawić wpływ tych strat na odbiór sygnału w skrajnie niekorzystnych warunkach.

EN

To ensure satisfactory operation of satellite links in the direction of the satellite-to-earth is essential to get an adequate power of the transmitter. In practice, this power depends on many factors such as: geographical coordinates, terrain, frequency, antenna aperture, antenna efficiency, coupling loss, the signal attenuation in space – in the atmosphere, the required level of availability of bandwidth, etc. Moreover in order to ensure a correct balance, properly conducted link budget requires consideration of extreme weather conditions (precipitation etc.). It is necessary to determine the required transmitter power (or antenna performance) as a function of many factors. The article analyzes the impact of the coupling loss on the receiving satellite signal and presents the results of modeling of these losses in the actual satellite links.

5

Extending Word2Vec with domain-specific labels

67%

Švaňa M.

|

tom Vol. 30

157--160

EN

Choosing a proper representation of textual data is an important part of natural language processing. One option is using Word2Vec embeddings, i.e., dense vectors whose properties can to a degree capture the “meaning” of each word. One of the main disadvantages of Word2Vec is its inability to distinguish between antonyms. Motivated by this deficiency, this paper presents a Word2Vec extension for incorporating domain-specific labels. The goal is to improve the ability to differentiate between embeddings of words associated with different document labels or classes. This improvement is demonstrated on word embeddings derived from tweets related to a publicly traded company. Each tweet is given a label depending on whether its publication coincides with a stock price increase or decrease. The extended Word2Vec model then takes this label into account. The user can also set the weight of this label in the embedding creation process. Experiment results show that increasing this weight leads to a gradual decrease in cosine similarity between embeddings of words associated with different labels. This decrease in similarity can be interpreted as an improvement of the ability to distinguish between these words.

6

Analyzing longitudinal Data in Knowledge Graphs utilizing shrinking pseudo-triangles

67%

Dörpinghaus J. , Weil V. , Binnewitt J.

|

tom Vol. 30

323--327

EN

This paper aims to analyze longitudinal data in knowledge graphs. Knowledge graphs play a central role for linking different data. While multiple layers for data from different sources are considered, there is only very limited research on longitudinal data in knowledge graphs. However, knowledge graphs are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers and time points within graphs impact methods and algorithms developed for single-purpose networks. This manuscript investigates the impact of a modeling of longitudinal data in multiple layers on retrieval algorithms. In particular, (a) we propose a first draft of a generic model for longitudinal data in multi-layer knowledge graphs, (b) we develop an experimental environment to evaluate a generic retrieval algorithm on random graphs inspired by computational social sciences. We present a knowledge graph generated on German job advertisements comprising data from different sources, both structured and unstructured, on data between 2011 and 2021. The data is linked using text mining and natural language processing methods. We further (c) present two different shrinking techniques for structured and unstructured layers in knowledge based on graph structures like triangles and pseudo-triangles. The presented approach (d) shows that on the one hand, the initial research questions, on the other hand the graph structures and topology have a great impact on the structures and efficiency for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations, we will (e) make suggestions for additional research on particular graph structures that have a great impact on the analysis of knowledge graph structures.

7

One-shot federated learning with self-adversarial data

67%

Danilenka A. , Bogacka K. , Wasielewska-Michniewska K.

|

tom Vol. 35

919--924

EN

Federated learning (FL) is a decentralized approach that aims at training a global model with the help of multiple devices, without collecting or revealing individual clients' data. The training of a federated model is conducted in communication rounds. Still, in certain scenarios, numerous communication rounds are impossible to perform. In such cases, a one-shot FL is utilized, where the number of communication rounds is limited to one. In this article, the idea of one-shot FL is enhanced with the usage of adversarial data, exploring and illustrating the possibilities to improve the performance of resulting global models, including scenarios with non-IID data, for image classification datasets: MNIST and CIFAR-10.