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Tytuł:
Artificial intelligence-powered pulse sequences in nuclear magnetic resonance and magnetic resonance imaging: historical trends, current innovations and perspectives
Autorzy:
Tokarz, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/35508129.pdf
Data publikacji:
2024
Wydawca:
Radomskie Towarzystwo Naukowe
Tematy:
artificial intelligence
machine learning
evolutionary algorithm
artificial neural network
nuclear magnetic resonance
magnetic resonance imaging
pulse sequence
shaped pulse
sztuczna inteligencja
uczenie maszynowe
algorytm ewolucyjny
sztuczna sieć neuronowa
magnetyczny rezonans jądrowy
rezonans magnetyczny
sekwencja impulsów
impuls ukształtowany
Opis:
This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.
Źródło:
Scientiae Radices; 2024, 3, 1; 30-52
2956-4808
Pojawia się w:
Scientiae Radices
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
System oceny zdrowotności zbóż dla gospodarstw rolnych
Cereal health assessment system for farms
Autorzy:
Golka, Wiesław
Szechyńska-Hebda, Magdalena
Golka, Adrian
Góral, Tomasz
Bomberski, Aleksander
Kowalska, Jolanta
Powiązania:
https://bibliotekanauki.pl/articles/51594648.pdf
Data publikacji:
2024-03-29
Wydawca:
Centrum Doradztwa Rolniczego w Brwinowie. Oddział w Poznaniu
Tematy:
sztuczna sieć neuronowa
aplikacja
choroby zbóż
diagnozy
artificial neural network
application
cereal diseases
diagnoses
Opis:
Sztuczne sieci neuronowe (SSN) mogą znaleźć zastosowanie w identyfikacji i ocenie zagrożeń na plantacjach czynnikami abiotycznymi i biotycznymi. Celem badań było opracowanie intuicyjnego i dostępnego dla rolnika systemu do wykrywania i identyfikacji chorób roślin zbożowych, opartego na działaniu SSN. Podstawą systemu są: 1/ biblioteka wzorców chorób roślin zbożowych; 2/ oprogramowanie dla modułu publicznego i eksperckiego służące do oceny zdrowotności roślin; 3/ aplikacja typu ‘helpdesk’ dla kontaktu rolników z doradcami i ekspertami. Docelowym rezultatem działania systemu jest uruchomienie Centrum Informacyjnego Zdrowia Roślin. Niniejsze opracowanie zawiera opis systemu i działania jego elementów składowych.
Artificial neural networks (ANN) are becoming more and more common tools in identifying and assessing plantations in order to abiotic and biotic threat factors. The aim of the research was to develop an intuitive system based on ANN for detecting and identifying crop diseases, that can be commonly available to each farmer. The basis of the system are: 1/a database with patterns of cereal plant diseases; 2/ software for the public and expert application, used to assess plant health; 3/ 'helpdesk' application for direct contact between farmers, advisors, and experts. The target result of the system is the launch of the Plant Health Information Center. This work contains a description of the system and the operation of its components.
Źródło:
Zagadnienia Doradztwa Rolniczego; 2024, 115, 1; 98-118
1232-3578
2719-8901
Pojawia się w:
Zagadnienia Doradztwa Rolniczego
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction
Autorzy:
Zou, Baoping
Chibawe, Musa
Hu, Bo
Deng, Yansheng
Powiązania:
https://bibliotekanauki.pl/articles/27312113.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
budowa
tunel
osiadanie gruntu
regresja liniowa wielokrotna
sieć neuronowa sztuczna
tunnel
construction
ground settlement
multiple linear regression
artificial neural network
Opis:
Ground settlement during and after tunnelling using TBM results in varying dynamic and static load action on the geo-stratum. It is an undesirable effect of tunnel construction causing damage to the surface and subsurface infrastructure, safety risk, and increased construction cost and quality issues. Ground settlement can be influenced by several factors, like method of tunnelling, tunnel geometry, location of tunnelling machine, machine operational parameters, depth & its changes, and mileage of recording point from starting point. In this study, a description and evaluation of the performance of the artifcial neural network (ANN) was undertaken and a comparison with multiple linear regression (MLR) was carried out on ground settlement prediction. The performance of these models was evaluated using the coefficient of determination R2, root mean square error (RMSE) and mean absolute percentage error (MAPE). For ANN model, the R2, RMSE and MAPE were calculated as 0.9295, 4.2563 and 3.3372, respectively, while for MLR, the R2, RMSE and MAPE, were calculated as 0.5053, 11.2708, 6.3963 respectively. For ground settlement prediction, both ANN and MLR methods were able to predict significantly accurate results. It was further noted that the ANN performance was higher than that of the MLR.
Źródło:
Archives of Civil Engineering; 2023, 69, 2; 503--515
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel Parkinsons disease detection algorithm combined EMD, BFCC, and SVM classifier
Autorzy:
Boualoulou, Nouhaila
Mounia, Miyara
Nsiri, Benayad
Behoussine Drissi, Taoufiq
Powiązania:
https://bibliotekanauki.pl/articles/27313826.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
EMD
BFCC
MFCC
SVM
Parkinson’s disease
sztuczna sieć neuronowa
choroba Parkinsona
Opis:
Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease.
Źródło:
Diagnostyka; 2023, 24, 4; art. no. 2023404
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Denseformer for single image deraining
Autorzy:
Wang, Tianming
Wang, Kaige
Li, Qing
Powiązania:
https://bibliotekanauki.pl/articles/24987759.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
artificial intelligence
convolutional neural network
image deraining
sztuczna inteligencja
sieć neuronowa konwolucyjna
obraz pojedynczy
Opis:
Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software. As an image restoration task, image deraining can effectively restore the original information of the image, which is conducive to the downstream task. In recent years, with the development of deep learning technology, CNN and Transformer structures have shone brightly in computer vision. In this paper, we summarize the key to success of these structures in the past, and on this basis, we introduce the concept of a layer aggregation mechanism to describe how to reuse the information of the previous layer to better extract the features of the current layer. Based on this layer aggregation mechanism, we build the rain removal network called DenseformerNet. Our network strengthens feature promotion and encourages feature reuse, allowing better information and gradient flow. Through a large number of experiments, we prove that our model is efficient and effective, and expect to bring some illumination to the future rain removal network.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 4; 651--661
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementation of an artificial intelligence-based ECG acquisition system for the detection of cardiac abnormalities
Wdrożenie systemu pozyskiwania EKG opartego na sztucznej inteligencji w celu wykrywania nieprawidłowości serca
Autorzy:
Benba, Achraf
El Attaoui, Fatima Zahra
Sandabad, Sara
Powiązania:
https://bibliotekanauki.pl/articles/27315375.pdf
Data publikacji:
2023
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
electrocardiogram
arrhythmias
artificial intelligence
convolution neural network
elektrokardiogram
arytmia
sztuczna inteligencja
konwolucyjna sieć neuronowa
Opis:
The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.
Elektrokardiogram (EKG) to powszechny test, który mierzy aktywność elektryczną serca. W zapisie EKG można zauważyć kilka nieprawidłowości serca, w tym arytmie, które są jedną z głównych przyczyn śmiertelności sercowej na całym świecie. Celem społeczności naukowej jest dokładna i zautomatyzowana analiza układu sercowo-naczyniowego, zwłaszcza biorąc pod uwagę dojrzałość technologii sztucznej inteligencji i jej wkład w obszar zdrowia. Celem tych wysiłków jest stworzenie systemu akwizycji i wykorzystanie sztucznej inteligencji do klasyfikacji odczytów EKG. System ten składa się z dwóch części: pierwsza to akwizycja sygnału za pomocą modułu EKG AD8232; uzyskany sygnał jest pojedynczą pochodną, która została wzmocniona i przefiltrowana. Druga sekcja to klasyfikacja identyfikacji chorób serca; sugerowany model to głęboka konwolucyjna sieć neuronowa z 12 warstwami, która była w stanie sklasyfikować pięć typów uderzeń serca z bazy danych arytmii MIT-BIH. Wyniki były zachęcające i zbudowano system wbudowany.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 1; 22--25
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment
Autorzy:
Alnajjar, Hussein Y.H.
Üçüncü, Osman
Powiązania:
https://bibliotekanauki.pl/articles/27311558.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
artificial neural network
wastewater treatment
total phosphorus
total nitrogen
biological oxygen demand
sztuczna sieć neuronowa
oczyszczanie ścieków
fosfor całkowity
azot całkowity
zapotrzebowanie na tlen
Opis:
Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires signifi cant eff ort. However, the use of artifi cial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artifi cial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would affect effluent quality metrics. For ANN models, the correlation coefficients R-TRAINING and R-ALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged. This is a useful tool for plant management to enhance the quality of the treatment while enhancing the facility’s dependability and efficiency.
Źródło:
Archives of Environmental Protection; 2023, 49, 2; 16--29
2083-4772
2083-4810
Pojawia się w:
Archives of Environmental Protection
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting Young’s modulus of Indian coal measure rock using multiple regression and artificial neutral network
Autorzy:
Chakraborty, Sayantan
Bisai, Rohan
Roy, Rohit
Palaniappan, Sathish Kumar
Pal, Samir Kumar
Rao, Karanam Uma Maheshwar
Powiązania:
https://bibliotekanauki.pl/articles/2201429.pdf
Data publikacji:
2023
Wydawca:
Główny Instytut Górnictwa
Tematy:
sandstone
shale
multiple regression
outlier analysis
artificial neural network
piaskowiec
łupek ilasty
regresja wielokrotna
analiza odchyleń
sztuczna sieć neuronowa
Opis:
Accurate information on Young’s modulus (E) is required for simulating rock deformation in mines; on the other hand, it is very cumbersome to obtain in the laboratory and collecting drilled cores in sufficient amounts, especially in the case of soft rocks, is quite impossible. Empirical equations were deducted for - from easily determinable rock properties, and the final model was selected through different statistical strength parameter tests. The generalization of the equation was verified through the normal distribution tests of residues of the equation. R2 came to be 0.609 and was validated using an artificial neural network with an improved value of 0.73.
Źródło:
Journal of Sustainable Mining; 2023, 22, 1; 41--54
2300-1364
2300-3960
Pojawia się w:
Journal of Sustainable Mining
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Projekt stabilizatora impedancji sieci zasilającej
Design of the line impedance stabilization network
Autorzy:
Szewczyczak, Michał
Budnik, Krzysztof
Szymenderski, Jan
Powiązania:
https://bibliotekanauki.pl/articles/34655826.pdf
Data publikacji:
2023
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
stabilizator impedancji
sieć sztuczna
LISN
kompatybilność elektromagnetyczna
emisja przewodzona
Opis:
Artykuł omawia urządzenia nazywane stabilizatorem impedancji sieci zasilającej, który jest jednym z przyrządów pomiarowych niezbędnych w badaniach kompatybilności elektromagnetycznej. Pierwsza część pracy wprowadza w tematykę sztucznych sieci pomiarowych. Przedstawia zasadę działania stabilizatora impedancji sieci zasilającej oraz aktualny stan wiedzy o tych urządzeniach. Druga część pracy zawiera opis projektu prototypu jednoliniowego stabilizatora impedancji sieci zasilającej oraz kolejnych etapów jego fizycznej realizacji. Ostatnia część pracy poświęcona jest rozważaniom dotyczącym metod weryfikacji poprawności działania zbudowanego urządzenia oraz jego zgodności z założeniami projektowymi.
The article discusses devices called power supply impedance stabilizers, which are one of the measuring instruments necessary in electromagnetic compatibility tests. The first part of the work introduces the topic of artificial measurement networks. It presents the principle of operation of the power supply network impedance stabilizer and the current state of knowledge about these devices. The second part of the work contains a description of the prototype design of a single-line impedance stabilizer for the power supply network and the subsequent stages of its physical implementation. The last part of the work is devoted to considerations regarding methods of verifying the correct operation of the constructed device and its compliance with the design assumptions
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2023, 108; 41-53
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS
Autorzy:
Nazir, Awais
Khan, Safdar Abbas
Khan, Malak Adnan
Alam, Zaheer
Khan, Imran
Irfan, Muhammad
Rehman, Saifur
Nowakowski, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/27311430.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
wind energy conversion systems
WECS
robust control
maximum power point tracking
MPPT
sliding mode control
SMC
super-twisting algorithm
STA
high gain observer
artificial neural network
ANN
function fitting
backstepping
śledzenie maksymalnego punktu mocy
obserwator o dużym wzmocnieniu
sztuczna sieć neuronowa
dopasowanie funkcji
system konwersji energii wiatrowej
sterowanie odporne
sterowanie ślizgowe
algorytm super skręcania
Opis:
The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 5; art. no. e147063
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial neural network for solving the inverse kinematic model of a spatial and planar variable curvature continuum robot
Autorzy:
Ghoul, Abdelhamid
Kara, Kamel
Djeffal, Selman
Benrabah, Mahomed
Hadjili, Mohamed Laid
Powiązania:
https://bibliotekanauki.pl/articles/27309873.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
continuum robots
inverse kinematic model
artificial neural network
roboty kontinuum
odwrotny model kinematyczny
sztuczna sieć neuronowa
Opis:
In this paper, neural networks are presented to solve the inverse kinematic models of continuum robots. Firstly, the forward kinematic models are calculated for variable curvature continuum robots. Then, the forward kinematic models are implemented in the neural networks which present the position of the continuum robot’s end effector. After that, the inverse kinematic models are solved through neural networks without setting up any constraints. In the same context, to validate the utility of the developed neural networks, various types of trajectories are proposed to be followed by continuum robots. It is found that the developed neural networks are powerful tool to deal with the high complexity of the non-linear equations, in particular when it comes to solving the inverse kinematics model of variable curvature continuum robots. To have a closer look at the efficiency of the developed neural network models during the follow up of the proposed trajectories, 3D simulation examples through Matlab have been carried out with different configurations. It is noteworthy to say that the developed models are a needed tool for real time application since it does not depend on the complexity of the continuum robots' inverse kinematic models.
Źródło:
Archive of Mechanical Engineering; 2022, LXIX, 4; 595--613
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessment of Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl) of rock aggregates using gene expression programming and artificial neural networks
Autorzy:
Köken, Ekin
Powiązania:
https://bibliotekanauki.pl/articles/2203333.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
kruszywa skalne
sztuczna sieć neuronowa
siarczan magnezu
rock aggregates
aggregate properties
Los Angeles abrasion loss
magnesium sulphate soundness
gene expression programming
artificial neural network
Opis:
It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (M wl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and M wl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and M wl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.
Źródło:
Archives of Mining Sciences; 2022, 67, 3; 401--422
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality
Autorzy:
Agrawal, Seema
Kumar, Mahendra
Palwalia, D. K.
Powiązania:
https://bibliotekanauki.pl/articles/41176533.pdf
Data publikacji:
2022
Wydawca:
Politechnika Warszawska, Instytut Techniki Cieplnej
Tematy:
SAPF
shunt active power filter
THD
ANN
artificial neural network
AGPSO algorithm
PCC
bocznikowy filtr mocy czynnej
sztuczna sieć neuronowa
algorytmy
Opis:
This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.
Źródło:
Journal of Power Technologies; 2022, 102, 4; 121-131
1425-1353
Pojawia się w:
Journal of Power Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comprehensive analysis of reclamation of spent lubricating oil using green solvent: RSM and ANN approach
Autorzy:
Sarkar, Sayantan
Datta, Deepshikha
Chowdhury, Somnath
Das, Bimal
Powiązania:
https://bibliotekanauki.pl/articles/2173421.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
modelling
optimization
extraction-flocculation
artificial neural network
genetic algorithm
modelowanie
optymalizacja
sztuczna sieć neuronowa
algorytm genetyczny
Opis:
Waste lubricating oil (WLO) is the most significant liquid hazardous waste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil.
Źródło:
Chemical and Process Engineering; 2022, 43, 2; 119--135
0208-6425
2300-1925
Pojawia się w:
Chemical and Process Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Investigating the FSW parameter’s role on microstructure and mechanical properties of welding AZ31B–AA8110 alloy
Autorzy:
Dharmalingam, S.
Lenin, K.
Srinivasan, D.
Powiązania:
https://bibliotekanauki.pl/articles/2173552.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
AA8011–AZ31B alloy
FSW
friction stir welding
ANN-GA
artificial neural network based genetic algorithm
mechanical properties
stop AA8011–AZ31B
właściwości mechaniczne
zgrzewanie tarciowe z mieszaniem materiału zgorzeliny
algorytm genetyczny
sztuczna sieć neuronowa
Opis:
The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011–AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e140098, 1--7
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 228

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