Wyniki wyszukiwania - BazTech

1

Spatial differentiation of the level of ecological development of polish voivodeship

Bluszcz Anna, Tobór-Osadnik Katarzyna, Wyganowska Małgorzata, Mansor Nur Suhaili, Awang Hapini

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 191

77--88

EN

Purpose: The aim of the research is the evaluation of the ecological state of development based on statistical data from voivodeships in Poland. Design/methodology/approach: The research uses selected methods of multivariate comparative analysis, in particular, linear ordering. The analysis of the differentiation of the level of ecological development by voivodeships in Poland made it possible to order the provinces according to the indicators that represent the state of the environmental situation. After the process of ordering, the process of grouping voivodeships was possible. The relevant calculations were made using QGIS and Statistica software. Findings: The result of the analysis presents a tree main cluster with similar voivodeships according to ecological situation. Practical implications: The presented methods enable continuous monitoring and control of progress in the implementation of the assumed ecological goals. Green development assessment methods can also help monitor progress towards the Sustainable Development Goals over time. This can help identify trends and patterns and provide feedback on the effectiveness of policies and programs. The results of the analyses may be a useful tool for monitoring and evaluating Poland's progress in achieving the assumed ecological goals of the European Union by 2030. Originality/value: These studies are a very useful tool in identifying the ecological situation and directing administrative activities to the appropriate regions in the country.

2

Analysis of the Effectiveness of Selected Machine Learning Algorithms in the Classification of Satellite Image Content Depending on the Size of the Training Sample

Kupidura Przemysław, Niemyski Stanisław

Teledetekcja Środowiska

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2024

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T. 64

24--38

EN

The article presents an analysis of the accuracy of 3 popular machine learning (ML) methods: Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), and Random Forest (RF) depending on the size of the training sample. The analysis involved performing the classification of the content of a Landsat 8 satellite image (divided into 6 basic land cover classes) in 10 different variants of the number of training samples (from 2664 to 34711 pixels), estimating individual results, and a comparative analysis of the obtained results. For each classification variant, an error matrix was developed and on their basis, accuracy metrics were calculated: f1-score, precision and recall (for individual classes) as well as overall accuracy and kappa index of agreement (generally for the entire classification). The analysis showed a stimulating effect of the size of the training sample on the accuracy of the obtained classification results in all analyzed cases, with the most sensitive to this factor being MLC, showing the best effectiveness with the largest training sample and the smallest - with the smallest, and the least SVM, characterized by the highest accuracy with the smallest training sample, comparing to other algorithms.

PL

Artykuł przedstawia analizę dokładności 3 popularnych metod uczenia maszynowego: Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) oraz Random Forest (RF) w zależności od liczebności próbki treningowej. Analiza polegała na wykonaniu klasyfikacji treści zdjęcia satelitarnego Landsat 8 (w podziale na 6 podstawowych klas pokrycia terenu) w 10 różnych wariantach liczebności próbek uczących (od 2664 do 34711 pikseli), oszacowaniu poszczególnych wyników oraz analizie porównawczej uzyskanych wyników. Dla każdego wariantu klasyfikacji opracowano macierz błędów, a na ich podstawie obliczono metryki dokładności: F1-score, precision and recall (dla pojedynczych klas) oraz ogólną dokładność i wskaźnik zgodności Kappa (ogólnie dla całej klasyfikacji). Analiza wykazała stymulujący wpływ rozmiaru próbki uczącej na dokładność uzyskiwanych wyników klasyfikacji we wszystkich analizowanych przypadkach, przy czym najbardziej wrażliwym na ten czynnik był MLC, wykazujący się najlepszą skutecznością przy największej próbce treningowej i najmniejszą - przy najmniejszej, a najmniej SVM, cechujący się największą dokładnością przy najmniejszej próbce treningowej, w porównaniu do pozostałych algorytmów.

3

Sareenet : saree texture classification via region-based patch generation with an optimized efficient Aquila network

Ashok Krishnan, Priya Dharmaraj Karthika, Ahilan Appathurai, Jayapriya Palanisamy

Opto-Electronics Review

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2024

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Vol. 32, No. 1

art. no. e149164

EN

Online saree shopping has become a popular way for adolescents to shop for fashion. Purchasing from e-commerce is a huge time-saver in this situation. Female apparel has many difficult-to-describe qualities, such as texture, form, colour, print, and length. Research involving online shopping often involves studying consumer behaviour and preferences. Fashion image analysis for product search still faces difficulties in detecting textures based on query images. To solve the above problem, a novel deep learning-based SareeNet is presented to quickly classify the tactile sensation of a saree according to the user’s query. The proposed work consists of three phases: i) saree image pre-processing phase, ii) patch generation phase, and iii) texture detection and optimization for efficient classification. The input image is first denoised using a contrast stretching adaptive bilateral (CSAB) filter. The deep learning-based mask region-based convolutional neural network (Mask R-CNN) divides the region of interest into saree patches. A deep learning-based improved EfficientNet-B3 has been introduced which includes an optimized squeeze and excitation block to categorise 25 textures of saree images. The Aquila optimizer is applied within the squeeze and excitation block of the improved EfficientNet to normalise the parameters for improving the accuracy in saree texture classification. The experimental results show that SareeNet is effective in categorising texture in saree images with 98.1% accuracy. From the experimental results, the proposed improved EfficientNet-B3 improves overall accuracy by 2.54%, 0.17%, 2.06%, 1.78%, and 0.63%, for MobileNet, DenseNet201, ResNet152, and InspectionV3, respectively.

4

Evaluating the feasibility of thermographic images for predicting breast tumor stage using DCNN

Khomsi Zakaryae, El Fezazi Mohamed, Elouerghi Achraf, Bellarbi Larbi

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 1

99--104

EN

Early-stage and advanced breast cancer represent distinct disease processes. Thus, identifying the stage of tumor is a crucial procedure for optimizing treatment efficiency. Breast thermography has demonstrated significant advancements in non-invasive tumor detection. However, the accurate determination of tumor stage based on temperature distribution represents a challenging task, primarily due to the scarcity of thermal images labeled with the stage of tumor. This work proposes a transfer learning approach based on Deep Convolutional Neural Network (DCNN) with thermal images for predicting breast tumor stage. Various tumor stage scenarios including early and advanced tumors are embedded in a 3D breast model using the Finite Element Method (FEM) available on COMSOL Multiphysics software. This allows the generation of the thermal image dataset for training the DCNN model. A detailed investigation of the hyperparameters tuning process has been conducted to select the optimal predictive model. Thus, various evaluation metrics, including accuracy, sensitivity, and specificity, are computed using the confusion matrix. The results demonstrate the DCNN model's ability to accurately predict breast tumor stage from thermographic images, with an accuracy of 98.2%, a sensitivity of 98.8%, and a specificity of 97.7%. This study indicates the promising potential of thermographic images in enhancing deep learning algorithms for the non-invasive prediction of breast tumor stage.

PL

Wczesny i zaawansowany rak piersi stanowią odrębne procesy chorobowe. Dlatego też identyfikacja stadium nowotworu jest kluczową procedurą dla optymalizacji skuteczności leczenia. Termografia piersi wykazała znaczny postęp w nieinwazyjnym wykrywaniu nowotworów. Jednak dokładne określenie stopnia zaawansowania nowotworu na podstawie rozkładu temperatury stanowi trudne zadanie, głównie ze względu na niedobór obrazów termicznych oznaczonych stopniem zaawansowania nowotworu. W niniejszej pracy zaproponowano podejście uczenia transferowego oparte na głębokiej konwolucyjnej sieci neuronowej (DCNN) z obrazami termicznymi do przewidywania stadium guza piersi. Różne scenariusze stadium nowotworu, w tym guzy wczesne i zaawansowane, są osadzone w trójwymiarowym modelu piersi przy użyciu metody elementów skończonych (MES) dostępnej w oprogramowaniu COMSOL Multiphysics. Pozwala to na wygenerowanie zestawu danych obrazów termicznych do trenowania modelu DCNN. Przeprowadzono szczegółowe badanie procesu dostrajania hiperparametrów w celu wybrania optymalnego modelu predykcyjnego. W związku z tym różne wskaźniki oceny, w tym dokładność, czułość i swoistość, są obliczane przy użyciu macierzy pomyłek. Wyniki pokazują zdolność modelu DCNN do dokładnego przewidywania stadium guza piersi na podstawie obrazów termograficznych, z dokładnością 98,2%, czułością 98,8% i swoistością 97,7%. Badanie to wskazuje na obiecujący potencjał obrazów termograficznych w ulepszaniu algorytmów głębokiego uczenia się w celu nieinwazyjnego przewidywania stadium guza piersi.

5

Advancing Electrical Losses Assessment Methods in Power Systems

Rexhepi Vezir, Rexhepi Shaqir

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 3

326--335

EN

The operation of modern power systems requires a sophisticated technological infrastructure to effectively manage and evaluate their parameters and performance. This infrastructure includes the generation, transmission and distribution power system components. This paper provides an overview of the loss evaluation to a part of Kosovo’s power system, substation with wind and photovoltaic (PV) energy sources integrated (SS Mramori, SS Kitka, and SS Kamenica) and the analysis of the loss assessment methods. One the assessment method in the research encompass simulated loss scenarios and their corresponding values in network components, employing the simulation based on the respective software tools. In current trends, power systems are visualized through the Supervisory Control and Data Acquisition (SCADA) platform. However, in Kosovo, although losses are integral to the SCADA system, they are represented as a overall value in the online mode, not encompassed depict losses per-components in real-time. This limitation hinders effective online power system optimization regarding the losses. As consequence, the purpose of this study is proposal a logical method developed through neural networks. The methodology incorporates various parameters, including as inputs variables; voltages, currents, active and reactive powers, and their computed values for extracting losses (X(x1, x2, ..., xn)). These parameters undergo systematic processing through hidden layers (Y(x1, x2, ..., xn)), leading to the classification of components within the power system. Finally, at the output stage (A(x1, x2, ..., xn)), an assessment is conducted based on the level of losses observed in the components of the power system. This implementation method promises significant benefits for transmission systems, impacting not only reducing losses, power quality but also yielding economic advantages.

6

Deep learning method for classifying items into categories for dutch auctions

Bobulski Janusz, Szymoniak Sabina

Computer Assisted Methods in Engineering and Science

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2024

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Vol. 31, no. 1

67--79

EN

Artificial Intelligence (AI) methods are widely used in our lives (phones, social media, self-driving cars, and e-commerce). In AI methods, we can find convolutional neural networks (CNN). First of all, we can use these networks to analyze images. This paper presents a method for classifying items into particular categories on an auction site. The technique prompts the seller to which category assign the item when creating a new auction. We choose a neural network with a number of image convolution layers as the best available approach to address this task. All tests were carried out in the Matlab environment using GPU and CPU. Then, the tested and verified solution was implemented in the TensorFlow environment with a CPU processor. Thanks to the cross-validation method, the effectiveness of the recognition system was fully verified in several stages. We obtained promising results. Consequently, we implemented the developed method by adding a new sales offer on the Clemens website.

7

Classification of cognitive states using clustering-split time series framework

Ramakrishna J. Siva, Ramasangu Hariharan

Computer Assisted Methods in Engineering and Science

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2024

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Vol. 31, no. 2

241--260

EN

Over the last two decades, functional Magnetic Resonance Imaging (fMRI) has provided immense data about the dynamics of the brain. Ongoing developments in machine learning suggest improvements in the performance of fMRI data analysis. Clustering is one of the critical techniques in machine learning. Unsupervised clustering techniques are utilized to partition the data objects into different groups. Supervised classification techniques applied to fMRI data facilitate the decoding of cognitive states while a subject is engaged in a cognitive task. Due to the high dimensional, sparse, and noisy nature of fMRI data, designing a classifier model for estimating cognitive states becomes challenging. Feature selection and feature extraction techniques are critical aspects of fMRI data analysis. In this work, we present one such synergy, a combination of Hierarchical Consensus Clustering (HCC) and the Statistics of Split Timeseries (SST) framework to estimate cognitive states. The proposed HCC-SST model’s performance has been verified on StarPlus fMRI data. The obtained experimental results show that the proposed classifier model achieves 99% classification accuracy with a smaller number of voxels and lower computational cost.

8

Zastosowanie spektrometrii oscylacyjnej w analizie fitozwiązków i substancji odżywczych w produktach naturalnych i spożywczych

Mazurek Sylwester, Szostak Roman

Wiadomości Chemiczne

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2023

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[Z] 77, 7-8

745--774

EN

The prevalence of lifestyle diseases and trends related to healthy eating contribute to the constant search for chemical compounds with specific biological activity. Studies are conducted on plants and substances of natural origin that have been used in medicine for millennia. Techniques of vibrational spectroscopy are an underrated group of methods enabling direct analysis of plant raw material and food in their native forms. The presented examples of Arabidopsis tissues, various species and hybrids of poplar and Cistus herb classification, as well as quantitative analyses of active compounds in plant material and pharmaceutical products and determination of physicochemical parameters of common food (i.e. milk, yoghurts, pasta and flour), demonstrate the possibility of using vibrational spectroscopy for comprehensive analysis of samples of natural origin. Typical measurement techniques and chemometric methods are briefly described in this paper. The scheme of quantitative analysis based on vibrational spectra is shown and the impact of selected experimental parameters on the accuracy of the obtained results is discussed. The imaging techniques used to analyse the changes in plant tissue structures caused by genetic mutations were also presented.

9

Improving the credibility of the extracted position from a vast collection of job offers with machine learning ensemble methods

Drozda Paweł, Ropiak Krzysztof, Nowak Bartosz A., Talun Arkadiusz, Osowski Maciej

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2023

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nr 26(1)

125--140

EN

The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers.

10

Application of deep learning techniques in identification of the structure of selected road materials

Mazurek Grzegorz, Durlej Małgorzata, Šrámek Juraj

Structure and Environment

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2023

|

Vol. 15, no. 3

159--167

EN

In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects.

PL

W badaniach naukowych obserwuje się coraz większe zainteresowanie wykorzystaniem sztucznej inteligencji do poszukiwania rozwiązań trudnych problemów naukowych. W niniejszym artykule został zastosowany algorytm głębokiego uczenia z użyciem obrazów próbek materiałów wykorzystywanych do budowy nawierzchni drogowych. Fotografie przedstawiały przekroje losowych próbek wykonane za pomocą tomografu komputerowego. Do analizy wykorzystano próbki historyczne, znajdujące się w bazie danych zbierającej informacje z wielu lat. Analizę głębokiego uczenia wykonano przy użyciu niektórych elementów architektury sieci VGG16 i zaimplementowano, stosując język R. Dane uczące oraz treningowe poddano augmentacji oraz walidacji krzyżowej. W rezultacie uzyskano wysoki poziom 96,4% jakości identyfikacji rodzaju próbki oraz jej wybranych cech strukturalnych. Fotografie w zbiorze identyfikacyjnym zostały poprawnie zidentyfikowane pod względem struktury, typu mieszanki oraz uziarnienia. Wytrenowany model w bardzo dobry sposób zidentyfikował próbki w obszarze dziedziny trenowanego zbioru danych. W rezultacie taka metodyka może w przyszłości ułatwić identyfikację rodzaju mieszanki, jej podstawowych właściwości oraz defektów.

11

Patient specific channel optimization using entropy and CNN deep learning for epileptic seizure prediction

Cherouati Brahim, Senouci Mohamed

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

106--110

EN

Predicting epileptic seizures in advance improves greatly the life of epileptic patients. In this paper we present a new approach based on patient specific channel optimization using four different features namely entropy, variance, kurtosis and skewness. After selecting three best channels for each method, we then use Convolutional Neural Network (CNN) to classify raw EEG signal in order to discriminate between interictal and preictal state. With entropy, our method achieves a good degree of prediction in terms of accuracy 97.09%, sensitivity 97.67% and specificity 96.51% for patient 01 using channels 4, 8 and 20.

PL

Przewidywanie napadów padaczkowych z wyprzedzeniem znacznie poprawia życie chorych na padaczkę. W tym artykule prezentujemy nowe podejście oparte na optymalizacji kanałów specyficznych dla pacjenta przy użyciu czterech różnych metod, a mianowicie entropii, wariancji, kurtozy i skośności. Po wybraniu trzech najlepszych kanałów dla każdej z metod, wykorzystujemy Neuronową Sieć Konwolucyjną (CNN) do klasyfikacji surowego sygnału EEG w celu rozróżnienia pomiędzy stanem międzynapadowym i przednapadowym. Dzięki entropii nasza metoda osiąga dobry stopień predykcji w zakresie dokładności 97,09%, czułości 97,67% i specyficzności 96,51% dla pacjenta 01 przy użyciu kanałów 4, 8 i 20.

12

Machine learning to diagnose breast cancer

Ismaeel Ghassan Ahmad

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

10--12

EN

As the number of breast cancer diseases is increasing rapidly every year, new technologies are utilized to predict and diagnose this disease for better women's lives worldwide. The development of Machine Learning can be utilized to contribute in this sense and help in the early diagnosis of breast cancer. This paper aims to predict and diagnose breast cancer using Machine Learning techniques such as support vector Machine (SVM) and Decision -tree and Nearest neighbour (KNN). The results show the out performance of SVM over the other methods. These methods can be very helpful to predict the breast cancer disease ahead of time.

PL

Ponieważ liczba zachorowań na raka piersi gwałtownie rośnie z roku na rok, nowe technologie są wykorzystywane do przewidywania i diagnozowania tej choroby w celu poprawy życia kobiet na całym świecie. Rozwój uczenia maszynowego może być wykorzystany do wniesienia wkładu w tym sensie i pomocy we wczesnej diagnozie raka piersi. Niniejszy artykuł ma na celu przewidywanie i diagnozowanie raka piersi przy użyciu technik uczenia maszynowego, takich jak maszyna wektora nośnego (SVM) oraz drzewo decyzyjne i najbliższy sąsiad (KNN). Wyniki pokazują wydajność SVM w porównaniu z innymi metodami. Metody te mogą być bardzo pomocne w przewidywaniu zgonów na raka piersi z wyprzedzeniem.

13

Grey Wolf optimization based breast cancer detection using 1D Convolution LSTM classifier

Jebarani Esther P., Umadevi N.

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

1--9

EN

Women are particularly vulnerable to breast cancer. Breast cancer diagnosis has benefited greatly from the utilization of ultrasound imaging. Breast UltraSound (BUS) image segmentation remains a difficult challenge due to low image quality. Furthermore, BUS image segmentation, as well as classification, is an important stage in the analysis process. Initially, the image associated with breast cancer is gathered from MIAS database. The gathered image undergoes pre-processing operation using the adaptive median filtering technique. Subsequently, the segmentation is performed in the pre-processed images using the hybrid method consisting of GMM and K-Means. These segmented images undergo the feature extraction steps further where the features are extracted by utilizing the Gray Level Co-occurrence Matrix (GLCM). Grey Wolf Optimization (GWO) selects the optimal features for further classification using a novel 1D Convolution LSTM. Here, the pooling layer of 1D CNN is replaced by the LSTM. The objective function behind the optimal feature selection and classification is the accuracy maximization. Finally, the novel One Dimensional Convolution Long Short Term Memory (1 DCLSTM) classifies the outcome into normal, benign, and malignant, respectively. The proposed method is compared with the other state of art methods related to this research.

PL

Kobiety są szczególnie narażone na raka piersi. Diagnostyka raka piersi bardzo skorzystała na wykorzystaniu obrazowania ultrasonograficznego. Segmentacja obrazu UltraSound (BUS) piersi pozostaje trudnym wyzwaniem ze względu na niską jakość obrazu. Ponadto segmentacja obrazu BUS, a także klasyfikacja, jest ważnym etapem procesu analizy. Początkowo obraz związany z rakiem piersi pozyskiwany jest z bazy MIAS. Zgromadzony obraz jest poddawany wstępnemu przetwarzaniu przy użyciu techniki adaptacyjnego filtrowania medianowego. Następnie na wstępnie przetworzonych obrazach przeprowadzana jest segmentacja metodą hybrydową składającą się z GMM i K-Means. Te podzielone na segmenty obrazy przechodzą kolejne etapy ekstrakcji cech, w których cechy są wyodrębniane przy użyciu macierzy współwystępowania poziomu szarości (GLCM). Optymalizacja Gray Wolf (GWO) wybiera optymalne funkcje do dalszej klasyfikacji przy użyciu nowatorskiego rozwiązania 1D Convolution LSTM. W tym przypadku warstwa łączenia 1D CNN zostaje zastąpiona przez LSTM. Funkcją celu stojącą za optymalnym doborem i klasyfikacją cech jest maksymalizacja dokładności. Wreszcie, powieść jednowymiarowa pamięć krótkoterminowa z konwolucją jednowymiarową (1 DCLSTM) klasyfikuje wynik odpowiednio na normalny, łagodny i złośliwy. Proponowana metoda jest porównywana z innymi nowoczesnymi metodami związanymi z tymi badaniami.

14

Detection of epileptic seizures with the use of convolutional neural networks

Wiszniewski Przemysław, Kołodziej Marcin, Majkowski Andrzej, Rysz Andrzej

Przegląd Elektrotechniczny

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2023

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R. 99, nr 2

51--55

EN

The purpose of the article is to investigate whether the implementation of a CNN consisting of several layers will allow the effective detection of epileptic seizures. For the research, a publicly available database registered for 4 dogs and 8 people was used. The 1-second iEEG recordings were marked by a neurophysiologist as interictal, early seizure, and seizure. A CNN was trained for each patient individually. Coefficients such as precision, AUC, sensitivity, and specificity were calculated, and the results were compared with the best algorithms published in one of the contests on the Kaggle platform. The average accuracy for the recognition of seizures using CNN is 0.921, the sensitivity is 0.850, and the specificity is 0.927. For early seizures these values are 0.825, 0.782, and 0.828, respectively.

PL

Celem artykułu było zbadanie czy zastosowanie sieci CNN, składającej się z kilku warstw umożliwi skuteczną detekcję napadów epileptycznych. Na użytek badań zastosowano ogólnodostępną bazę danych zarejestrowaną dla 4 psów oraz 8 ludzi. Jednosekundowe zapisy sygnału iEEG zostały oznaczone przez neurofizjologa jako: międzynapadowe, wczesnonapadowe oraz napadowe. Zaproponowano strukturę sieci CNN, a następnie wytrenowano ją dla każdego pacjenta indywidualnie. Zostały wyliczone współczynniki takie jak: trafność, AUC, czułość, specyficzność. Następnie wyniki zostały porównane do osiągniętych w najlepszych algorytmach opublikowanych w konkursie na platformie Kaggle. Średnia skuteczność rozpoznawania napadów z wykorzystaniem sieci CNN wynosi 0.921, czułość 0.850, a specyficzność 0.927. Dla okresów wczesnonapadowych wartości te wynoszą odpowiednio 0.825, 0.782 i 0.828.

15

Many Faces of Singularities in Robotics

Dulęba Ignacy, Karcz-Dulęba Iwona

Pomiary Automatyka Robotyka

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2023

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R. 27, nr 1

19--26

EN

In this survey paper some issues concerning a singularity concept in robotics are addressed. Singularities are analyzed in the scope of inverse kinematics for serial manipulator, a motion planning task of nonholonomic systems and the optimal control covering a large area of practical robotic systems. An attempt has been made to define the term singularity, which is independent on a specific task. A few classifications of singularities with respect to different criteria are proposed and illustrated on simple examples. Singularities are analyzed from a numerical and physical point of view. Generally, singularities pose some problems in motion planning and/or control of robots. However, as illustrated on the example on force/momenta transformation in serial manipulators, they can also be desirable is some cases. Singularity detection techniques and some methods to cope with them are also provided. The paper is intended to be didactic and to help robotic researchers to get a general view on the singularity issue.

PL

W przeglądowym artykule przedstawiono wybrane zagadnienia dotyczące różnych koncepcji osobliwości spotykanych w robotyce. Analizowane są osobliwości w zadaniu odwrotnej kinematyki dla manipulatorów szeregowych, planowaniu ruchu układów nieholonomicznych oraz sterowaniu optymalnym. Rozważane zadania obejmują duży obszar praktycznych systemów robotycznych. Podjęto próbę zdefiniowania pojęcia osobliwości niezależne od konkretnego zadania. Zaproponowano kilka klasyfikacji osobliwości w zależności do różnych kryteriów oraz zilustrowanych na prostych przykładach. Osobliwości przeanalizowano z numerycznego i fizycznego punktu widzenia. Ogólnie, osobliwości stwarzają pewne problemy w planowaniu ruchu i/lub sterowaniu robotami. Jednakże, jak pokazano na przykładzie transformacji sił/momentów w manipulatorach szeregowych, w niektórych przypadkach mogą one być również użyteczne. Przedstawiono także techniki wykrywania osobliwości oraz metody radzenia sobie z nimi. Praca w założeniu ma charakter dydaktyczny i ma pomóc badaczom z kręgu robotyki uzyskać ogólny pogląd na zagadnienie osobliwości.

16

Experimental study of quartz classification in the enhanced gravity field using Falcon concentrator

Zhang Ling, Yang Lu, Hou Haochun, Zhao Yan, Lin Jun, Zhang Zeliang, Bu Caiyun, Zheng Xinran, Fu Dong

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 6

art. no. 175242

EN

The classification and separation of minerals happen in the traditional gravity separation simultaneously. This paper focuses on the classification performance of quartz particles in the enhanced gravity field. The classification efficiency of single quartz particles decreased then increased with the increase of rotational angular velocity, while it decreased with the increase of backwash water pressure. The classification efficiency of -0.5 +0.25mm, -0.25 +0.125mm, -0.125 +0.074mm, -0.074 +0.045mm and -0.045mm quartz was higher than the corresponding narrow size of -0.5mm quartz in general. The “fish-hook” phenomenon appeared in the partition curve of -0.5mm quartz under small/large rotational angular velocity and small backwash water pressure, and the dip point could be found in fine particles region, which indicated that the “fish-hook” was closely related with operating parameters and particle size. A medium rotational angular velocity and larger backwash water pressure could be helpful to avoid the appearance of “fish-hook” in fine particles region and achieve a better classification performance. This investigation is beneficial to understand the regularity of particle migration in the enhanced gravity field.

17

Classification techniques and parameter optimization of Cyclone Continuous Centrifugal Separator for hematite ore

Zhang Pei, Xie Haiyun, Jin Yanling, Chen Jialing, Zeng Peng, Li Yuanhong, Chen Luzheng, Liu Dianwen

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 1

art. no. 158864

EN

The Cyclonic Continuous Centrifugal Separator (CCCS) is a new type of separation equipment developed based on cyclonic continuous centrifugal separation technology and combined with the separation principle of the fluidized bed. Taking hematite as the research object, the main parameters and conditions of the best hematite classification were determined through the classification test by using CCCS. Based on the classification test, the significance order of each process parameter and their interaction with hematite classification efficiency of the underflow products was analyzed with the Response Surface Methodology, the optimal process parameter of hematite classification was obtained and a multiple regression equation was established. The optimized process conditions were as follows, feeding pressure 55.48 kPa, backwash pressure 9.79 kPa, and underflow pressure 31.94 kPa. Under these conditions, the average hematite ore classification efficiency of coarse fraction (-2~+0.15mm), medium fraction (-0.15~+0.074mm) and fine fraction (-0.074mm) were 85.08%, 65.10% and 51.41%, respectively, and the relative errors with the predicted values were 1.6%, 4.0% and 2.5%, respectively. The results showed that the analytical model has good predictive performance. This research provides a certain prospect for the application of Cyclonic Continuous Centrifugal Separation to hematite ore classification. it provides a reference for the application of the Response Surface Methodology in the classification of hematite by Cyclonic Continuous Centrifugal Separation.

18

Deep learning classification and recognition method for milling surface roughness combined with simulation data

Lu Lingli, Yi Huaian, Shu Aihua, Qin Jianhua, Lu Enhui

Metrology and Measurement Systems

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2023

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Vol. 30, nr 1

117--138

EN

To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.

19

Klasyfikacja metalowych pokryć samonośnych

Patoka Krzysztof

Materiały Budowlane

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2023

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nr 8

72--73

20

Criteria for the quality of the effects of waste management targets in 2015-2017 in the countries of the European Union

Kuźmiński Łukasz, Jaworski Jarosław, Miązek Paweł, Gawlik Andrzej, Jakubowska Agnieszka, Łopatka Agnieszka, Norek Tomasz

Journal of Sustainable Development of Transport and Logistics

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2023

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Vol. 8, No. 2

6--22

EN

For many years, the amount of waste generated on a global scale has shown an increasing tendency and their management and logistic is becoming a growing problem for most countries in the world. Waste management is an important issue to be addressed, as it concerns the three basic pillars of sustainable development: social, economic, and environmental. Therefore, it seems necessary to take initiatives to reduce the amount of waste generated and improve the waste management system. The article aims to analyse changes in the way of waste management and logistics in the European Union countries and the classification of these countries on the basis of the achieved effects in waste management. The article analyses three selected factors that reflect the effects of achieving environmental objectives in waste management. The cluster analysis method was used for the analysis. It found that EU countries differ in the quality of the results achieved in waste management, depending on the achievement of environmental management and sustainability objectives. In addition, the results of the analysis showed that the time factor has a significant impact on the classification of countries. High dynamics of the quality of effects in waste management were observed in the period under review.