MODELOWANIE PROCESÓW OCZYSZCZANIA SUROWEJ ROPY NAFTOWEJ WYKORZYSTUJĄCYCH DŹWIĘKI O NISKICH CZĘSTOTLIWOŚCIACH

These days new methods of purification of crude oil from paraffin are being sought to improve the quality of oil, reduce its cost and optimizethe processes of technological preparation of oil. The paper describes the method of conducting an experiment to study the impact of low-frequency sounds on oil samples of the Zhanazhol field, and presents the results of experiments, the input parameters of the experiment (exposure time and frequencyof infrasound), at which the maximum reduction of the kinematic viscosity of oil is achieved. Further study of processes in crude oil under the influenceof low-frequency sounds is planned to be investigated in COMSOL Multiphysics®.Obecnie poszukiwane są nowe metody oczyszczania surowej ropy naftowej z parafiny, prowadzące do obniżenia kosztów i optymalizacji procesów przetwarzania ropy naftowej. W artykule opisano sposób prowadzenia eksperyment polegającego na badającego wpływ dźwięków o niskiej częstotliwości na parametry próbek ropy naftowej pochodzące z pola Zhanazhola. Przedstawiono wyniki doświadczeń, parametry wejściowe eksperymentu (czas ekspozycji i częstotliwość infradźwięków), przy których osiągane jest maksymalne obniżenie lepkości kinematycznej ropy naftowej. Dalsze badania procesów zachodzących w ropie naftowej pod wpływem dźwięków o niskiej częstotliwości planowane są w środowisku COMSOL Multiphysics

APPLICATION OF MACHINE LEARNING FOR RECOGNIZING SURFACE WELDING DEFECTS IN VIDEO SEQUENCES

The paper offers a solution to the problem of detecting and recognizing surface defects in welded joints that appear during tungsten inert gas welding of metal edges. This problem belongs to the machine vision. Welding of stainless-steel edges is carried out automatically on the pipe production line. Therefore, frames of video sequences are investigated. Images of some welding defects are shown in the paper. An algorithm proposed by the authors is used to detect welding defects in the video sequence frames, the efficiency of which has been confirmed experimentally. The problem solution of welding defects recognition is based on the use of traditional machine learning methods: support vector machine and artificial neural network. To build classification models, a labeled dataset containing automatically extracted texture features from the areas of welding defects detected in the video sequences was created. An analysis was performed to identify the strength of the correlation of texture features between each other and the dependent variable in the dataset for dimensionality reduction of the feature vector. The models were trained and tested on datasets with different numbers of features. The quality of the classification models was evaluated based on the accuracy metric values. The best results were achieved by the classifier built using the support vector machine with a chi-square kernel on a training sample with two features. The build models allow automatic recognition of such welding defects as lack of fusion and metal oxidation. The computational experiments with real video sequences obtained with a digital camera confirmed the possibility of using the proposed solution for recognizing surface welding defects in the process of manufacturing stainless steel pipes

KONWOLUCYJNE SIECI NEURONOWE DO WCZESNEJ DIAGNOSTYKI KOMPUTEROWEJ DYSPLAZJI U DZIECI

The problem in ultrasound diagnostics hip dysplasiais the lack of experience of the doctor in case of incorrect orientation of the hip joint andultrasound head. The aim of this study was to evaluate the ability of the convolutional neural network (CNN) to classifyand recognize ultrasound imagingof thehip joint obtained at the correct and incorrect position of the ultrasound sensor head in the computer diagnosisofpediatricdysplasia. CNN's suchas GoogleNet, SqueezeNet, and AlexNet were selected for the study. The most optimal for the task is the useof CNN GoogleNet showed. In this CNN usedtransfer learning. At the same time, fine-tuning of the network and additional training on the databaseof 97 standards of ultrasonic images of the hip jointwere applied. Image type RGB 32 bit, 210 × 300 pixels are used. Fine-tuning has been performedthe lower layers of the structure CNN, in which 5 classesare allocated, respectively 4 classes of hip dysplasia types according to the Graf, and the Type ERROR ultrasound image, where position of the ultrasoundsensor head and of the hip joint in ultrasound diagnostics are incorrect orientation.It was found that the authenticity of training and testing is the highestfor the GoogleNet network:when classified in the training group accuracy is up to 100%, when classified in the test group accuracy–84.5%Problemem w diagnostyce ultrasonograficznej dysplazji stawu biodrowego jest brak doświadczenia lekarzy w zakresie nieprawidłowej orientacji stawu biodrowego i głowicy ultrasonograficznej. Celem tego badania była ocena zdolności konwolucyjnej sieci neuronowej (CNN) do klasyfikowania i rozpoznawania obrazów ultrasonograficznych stawu biodrowego uzyskanych przy prawidłowym i nieprawidłowym położeniu głowicy ultrasonograficznej we wspomaganej komputerowo diagnostyce dysplazji dziecięcej. Do badania wybrano sieci CNN, takie jak GoogleNet, SqueezeNet i AlexNet. Wykazano, że najbardziej optymalne dla tego zadania jest użycie CNN GoogleNet. Jednocześnie w CNN zastosowano metodologię uczenia transferowego. Zastosowano precyzyjne dostrojenie sieci i dodatkowe szkolenie na podstawie 97 próbek obrazów ultrasonograficznych stawu biodrowego, typ obrazu RGB 32 bity, 210 × 300 pikseli. Przeprowadzono dostrajanie dolnych warstw struktury CNN, w której zidentyfikowano 5 klas, odpowiednio 4 klasy typów dysplazji stawu biodrowego według Grafa oraz obraz ultrasonograficzny typu ERROR, w którym pozycja głowicy ultrasonograficznej i stawu biodrowego w diagnostyce ultrasonograficznej mają nieprawidłową orientację. Stwierdzono, że niezawodność szkolenia i testowania jest najwyższa dla sieci GoogleNet: podczas klasyfikacji w grupie szkoleniowej dokładność wynosi do 100%, podczas klasyfikacji w grupie testowej dokładność wynosi 84,5%

OPTYMALIZACJA PARAMETRÓW PROCESU CIĘCIA CZĘŚCI PRACUJĄCYCH W WARUNKACH OBCIĄŻEŃ CYKLICZNYCH

The paper is devoted to questions of technological fatigue life assurance of parts working in conditions of cyclic loads by optimization their cutting conditions for finish turning process. In order to solve the task of optimizing the parts cutting conditions, the corresponding software, based on the previously created mathematical model of the finishing turning process, was developed in the C# programming language. With the purpose of technological providing the necessary fatigue life of the part, taking into account the real conditions of its operation for the maximum productivity of the finishing turning process, the methodical recommendations for determining the optimal parts cutting conditions at the phase of production technological preparation are given. An example application of the proposed solution is presented.Artykuł poświęcony jest zagadnieniom zapewnienia technologicznej trwałości zmęczeniowej części pracujących w warunkach obciążeń cyklicznych, poprzez optymalizację ich warunków skrawania w procesie toczenia wykańczającego. W celu rozwiązania zadania optymalizacji warunków skrawania części, zostało opracowane odpowiednie oprogramowanie w języku C#, oparte na wcześniej stworzonym modelu matematycznym procesu toczenia wykańczającego. W celu technologicznego zapewnienia niezbędnej trwałości zmęczeniowej części, biorąc pod uwagę rzeczywiste warunki jej działania dla maksymalnej wydajności procesu toczenia wykańczającego, podano metodyczne zalecenia dotyczące określania optymalnych warunków skrawania części na etapie technologicznego przygotowania produkcji. Zaprezentowano przykładowe zastosowanie proponowanego rozwiązania

Development of data-mining technique for seismic vulnerability assessment

Assessment of seismic vulnerability of urbaninfrastructure is an actual problem, since the damage caused byearthquakes is quite significant. Despite the complexity of suchtasks, today’s machine learning methods allow the use of “fast”methods for assessing seismic vulnerability. The article proposesa methodology for assessing the characteristics of typical urbanobjects that affect their seismic resistance; using classification andclustering methods. For the analysis, we use kmeans and hkmeansclustering methods, where the Euclidean distance is used as ameasure of proximity. The optimal number of clusters isdetermined using the Elbow method. A decision-making model onthe seismic resistance of an urban object is presented, also themost important variables that have the greatest impact on theseismic resistance of an urban object are identified. The studyshows that the results of clustering coincide with expert estimates,and the characteristic of typical urban objects can be determinedas a result of data modeling using clustering algorithms

Development of data-mining technique for seismic vulnerability assessment

Assessment of seismic vulnerability of urbaninfrastructure is an actual problem, since the damage caused byearthquakes is quite significant. Despite the complexity of suchtasks, today’s machine learning methods allow the use of “fast”methods for assessing seismic vulnerability. The article proposesa methodology for assessing the characteristics of typical urbanobjects that affect their seismic resistance; using classification andclustering methods. For the analysis, we use kmeans and hkmeansclustering methods, where the Euclidean distance is used as ameasure of proximity. The optimal number of clusters isdetermined using the Elbow method. A decision-making model onthe seismic resistance of an urban object is presented, also themost important variables that have the greatest impact on theseismic resistance of an urban object are identified. The studyshows that the results of clustering coincide with expert estimates,and the characteristic of typical urban objects can be determinedas a result of data modeling using clustering algorithms

Metrological Aspects of Controlling the Rotational Movement Parameters of the Auger for Dewatering Solid Waste in a Garbage Truck

In the article, a device for measuring the parameters of the rotational movement of the auger for dewatering solid waste is proposed based on the analysis of signal processing methods and measurement of physical quantities. It can be used in the development of high-performance special vehicles for transporting waste as the main link in the structure of machines for the collection and primary processing of solid waste. The structural scheme of the means and block diagram of the microcontroller control program algorithm for implementation of the device for measuring the parameters of the rotational motion are proposed. The main technical characteristics of the proposed means are given. The results of experimental tests for measuring the parameters of rotational motion are shown. The results of experimental studies, which are given in the work, confirmed the reliability of the measured parameters

Metrological Aspects of Controlling the Rotational Movement Parameters of the Auger for Dewatering Solid Waste in a Garbage Truck

In the article, a device for measuring the parameters of the rotational movement of the auger for dewatering solid waste is proposed based on the analysis of signal processing methods and measurement of physical quantities. It can be used in the development of high-performance special vehicles for transporting waste as the main link in the structure of machines for the collection and primary processing of solid waste. The structural scheme of the means and block diagram of the microcontroller control program algorithm for implementation of the device for measuring the parameters of the rotational motion are proposed. The main technical characteristics of the proposed means are given. The results of experimental tests for measuring the parameters of rotational motion are shown. The results of experimental studies, which are given in the work, confirmed the reliability of the measured parameters

DOPASOWANIE ZGODNOŚCI W MODELACH 3D DLA DOPASOWANIA DŁONI 3D

Upper limb prosthetic is an area of medical research and development that aims to restore functionality and improve the quality of life of people affected by the loss of one or both upper limbs. The development and implementation of 3D scanning tools and analysis of 3D scanning data requires the use of specialized analysis methods that ensure the achievement of the required indicators. It should take into account the impact of the model resolution on the result. This paper is devoted to the analysis of finding matches between a point cloud of a hand model and another point cloud using Gromov-Wasserstein distance. For analysis, a subset of the MANO dataset was employed, containing a substantial volume of data and serving as a representative sample of the human population. The results obtained indicate the possibility of using this approach in the processing and analysis of three-dimensional data, which serves as one of the stages of designing individualized prostheses.Protetyka kończyn górnych to dziedzina badań i rozwoju medycznego mająca na celu przywrócenie funkcjonalności i poprawę jakości życia osób dotkniętych utratą jednej lub obu kończyn górnych. Opracowanie i wdrożenie narzędzi do skanowania 3D oraz analiza danych pochodzących ze skanowania 3D wymaga zastosowania specjalistycznych metod analizy, które zapewnią osiągnięcie wymaganych wskaźników. Należy przy tym uwzględnić wpływ rozdzielczości modelu na uzyskany wynik. Niniejszy artykuł poświęcony jest analizie znajdowania dopasowań między chmurą punktów modelu dłoni a inną chmurą punktów przy użyciu odległości Gromova-Wassersteina. Do analizy wykorzystano podzbiór zbioru danych MANO, który zawiera znaczną ilość danych i służy jako reprezentatywna próbka populacji ludzkiej. Uzyskane wyniki wskazują na możliwość wykorzystania tego podejścia w przetwarzaniu i analizie danych trójwymiarowych, które służą jako jeden z etapów projektowania zindywidualizowanych protez

OPTIMIZATION OF PARTS CUTTING PROCESS PARAMETERS WORKING IN CONDITIONS OF CYCLIC LOADS

The paper is devoted to questions of technological fatigue life assurance of parts working in conditions of cyclic loads by optimization their cutting conditions for finish turning process. In order to solve the task of optimizing the parts cutting conditions, the corresponding software, based on the previously created mathematical model of the finishing turning process, was developed in the C# programming language. With the purpose of technological providing the necessary fatigue life of the part, taking into account the real conditions of its operation for the maximum productivity of the finishing turning process, the methodical recommendations for determining the optimal parts cutting conditions at the phase of production technological preparation are given. An example application of the proposed solution is presented