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Wyświetlanie 1-15 z 32
Tytuł:
Ethical challenges of technoscience – information technologies
Etyczne wyzwania technonauki - techniki informacyjne
Autorzy:
Morawski, Roman Z.
Powiązania:
https://bibliotekanauki.pl/articles/2120312.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
technoscience
ethics
information technology
measurements
artificial intelligence
Źródło:
Nauka; 2022, 2; 7-33
1231-8515
Pojawia się w:
Nauka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm
Autorzy:
Bilski, P.
Bobiński, P.
Krajewski, A.
Witomski, P.
Powiązania:
https://bibliotekanauki.pl/articles/177879.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wood boring insects identification
artificial intelligence classification
accelerometer
Opis:
The paper presents an application of signal processing and computational intelligence methods to detect presence of the wood boring insects larvae in the wooden constructions (such as the furniture of buildings). Such insects are one of the main sources of the degradation in such objects, therefore they should be detected as quickly as possible, before inflicting serious damage. The presented work involved the acoustic monitoring for detecting the presence of the larvae inside pieces of wood. An accelerometer was used to record the sound, further analyzed by a computer algorithm extracting features important for artificial-intelligence (AI) based classification employed to detect the old house borer’s (Hylotrupes bajulus L.) activity. The presented task is difficult, as the sounds made by the larvae are of relatively low amplitude and the background noise caused by people, electrical appliances or other sources may significantly degrade the accuracy of detection. The classification of sounds is needed to separate sources of noise which deteriorate the proper larva detection and should be suppressed if possible. The employed classification was based on features defined in the time domain followed by the support vector machine used as the binary classifier. The results allowed us to assess the effectiveness of the old house borer’s detection by the acoustic analysis enhanced with the AI algorithm.
Źródło:
Archives of Acoustics; 2017, 42, 1; 61-70
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Manufacturing lead time prediction for extrusion tools with the use of Neural Networks
Autorzy:
Sajko, Nika
Kovacic, Simon
Ficko, Mirko
Palcic, Iztok
Klancnik, Simon
Powiązania:
https://bibliotekanauki.pl/articles/407038.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
manufacturing lead time
Neural Network
Artificial Intelligence
extrusion
Opis:
Due to fast-paced technical development, companies are forced to modernise and update their equipment, as well as production planning methods. In the ordering process, the customer is interested not only in product specifications, but also in the manufacturing lead time by which the product will be completed. Therefore, companies strive towards setting an appealing but attainable manufacturing lead date. Manufacturing lead time depends on many different factors; therefore, it is difficult to predict. Estimation of manufacturing lead time is usually based on previous experience. In the following research, manufacturing lead time for tools for aluminium extrusion was estimated with Artificial Intelligence, more precisely, with Neural Networks. The research is based on the following input data; number of cavities, tool type, tool category, order type, number of orders in the last 3 days and tool diameter; while the only output data are the number of working days that are needed to manufacture the tool. An Artificial Neural Network (feed-forward neural network) was noted as a sufficiently accurate method and, therefore, appropriate for implementation in the company
Źródło:
Management and Production Engineering Review; 2020, 11, 3; 48-55
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recycling-oriented eco-design methodology based on decentralised artificial intelligence
Autorzy:
Dostatni, E.
Powiązania:
https://bibliotekanauki.pl/articles/406989.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
eco-design
methodology
decentralised artificial intelligence
agent-based technology
Opis:
In recent years, due to the growing importance of eco-design and tightening EU regulations entrepreneurs are required to implement activities related to environmental protection. It influences the development of methods and tools enabling the implementation of eco-design into practice, which are increasingly used by modern information technologies. They are based on intelligent solutions that allows them to better match the requirements of designers and allows for the automation of processes, and in some cases they are able to do the work themselves, replacing designers. Details are useful in areas that require calculations, comparisons and making choices, which is the process of eco-design. The paper describes methodology of pro-ecological product design oriented towards recycling, based on agent technology, enables the design of environmentally friendly products including recycling. The description of the methodology was preceded by a literature analysis on the characteristics of tools supporting eco-design and the process of its development was presented. The proposed methodology can be used at the design stage of devices to select the best product in terms of ecology. It is based on the original set of recycling indicators, used to evaluate the recycling of the product, ensure the ability to operate in a distributed design environment, and the use of data from various CAD systems, allows full automation of calculations and updates (without user participation).
Źródło:
Management and Production Engineering Review; 2018, 9, 3; 79-89
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sex and age differences in facial emotions expressions measured by artificial intelligence
Autorzy:
Gablíková, Mária
Halamová, Júlia
Powiązania:
https://bibliotekanauki.pl/articles/2130052.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
facial expressions
artificial intelligence
advertisement
age
sex
stereotypes
facial appearance
Opis:
Our aim was to test existing sex and age stereotypes related to emotional expressivity, gender and age. This was a complex analysis of facial expressions of all basic emotions (anger, disgust, fear, happiness, sadness, and surprise) to everyday life stimuli observing a large sample (2,969 unique participants creating 39,694 recordings) using an Emotion Artificial Intelligence. Our data partially support emotion-specific stereotype that women express more affiliate emotions and men express more dominant emotions except for sadness. There were found correlations of emotion expression with age, however intensity and frequency of emotion expression did not follow the same pattern. Not eliminating the differences between men and women in the baseline facial appearance resulted in men expressing dominant emotions (anger and disgust) more intensively, and women expressing more affiliative emotions (happiness, fear, and surprise). To sum up, facial appearance can be one of the origins of the existing gender stereotypic socialisation stereotype.
Źródło:
Polish Psychological Bulletin; 2021, 52, 1; 83-96
0079-2993
Pojawia się w:
Polish Psychological Bulletin
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Use of Fuzzy Systems for Forecasting the Hardenability of Steel
Autorzy:
Sitek, W.
Irla, A.
Powiązania:
https://bibliotekanauki.pl/articles/356485.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
computational material science
artificial intelligence methods
materials design steels
modelling
simulation
Opis:
The goal of the research carried out was to develop the fuzzy systems, allowing the determination of the Jominy hardenability curve based on the chemical composition of structural steels for quenching and tempering. Fuzzy system was created to calculate hardness of the steel, based on the alloying elements concentrations, and to forecast the hardenability curves. This was done based on information from the PN-EN 10083-3: 2008. Examples of hardenability curves calculated for exemplar steels were presented. Results of the research confirmed that fuzzy systems are a useful tool in evaluation the effect of alloying elements on the properties of materials compared to conventional methods. It has been demonstrated the practical usefulness of the developed models which allows forecasting the steels’ Jominy hardenability curve.
Źródło:
Archives of Metallurgy and Materials; 2016, 61, 2A; 797-802
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques
Autorzy:
Eldeeb, Ahmed H.
Amr, Mohammed Nagah
Ibrahim, Amin S.
Kamel, Hesham
Fouad, Sara
Powiązania:
https://bibliotekanauki.pl/articles/2200729.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Deep learning
COVID-19
Artificial Intelligence
computed tomography
Convolutional Neural Networks
Opis:
The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 731--739
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm
Autorzy:
Lee, Min_Woo
Choi, Young-Sin
Kwon, Do-Hun
Cha, Eun-Ji
Kang, Hee-Bok
Jeong, Jae-In
Lee, Seok-Jae
Kim, Hwi-Jun
Powiązania:
https://bibliotekanauki.pl/articles/2176648.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Fe-based amorphous alloy
metalloid elements
artificial intelligence
coercivity
saturation magnetization
Opis:
Artificial intelligence operated with machine learning was performed to optimize the amount of metalloid elements (Si, B, and P) subjected to be added to a Fe-based amorphous alloy for enhancement of soft magnetic properties. The effect of metalloid elements on magnetic properties was investigated through correlation analysis. Si and P were investigated as elements that affect saturation magnetization while B was investigated as an element that affect coercivity. The coefficient of determination R2 (coefficient of determination) obtained from regression analysis by learning with the Random Forest Algorithm (RFR) was 0.95 In particular, the R2 value measured after including phase information of the Fe-Si-B-P ribbon increased to 0.98. The optimal range of metalloid addition was predicted through correlation analysis method and machine learning.
Źródło:
Archives of Metallurgy and Materials; 2022, 67, 4; 1539--1542
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and finite bit word length
Autorzy:
Słowik, A.
Powiązania:
https://bibliotekanauki.pl/articles/202324.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial intelligence
evolutionary algorithms
digital filters
minimal phase
finite bits word length
Opis:
In this paper an application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and with finite bit word length is presented. Four digital filters with infinite impulse response were designed using the proposed method. These digital filters possess: linearly falling characteristics, linearly growing characteristics, nonlinearly falling characteristics, and nonlinearly growing characteristics, and they are designed using bit words with an assumed length. This bit word length is connected with a processing register size. This register size depends on hardware possibilities where digital filter is to be implemented. In this paper, a modification of the mutation operator is introduced too. Due to this modification, better results were obtained in relation to the results obtained using the evolutionary algorithm with other mutation operators. The digital filters designed using the proposed method can be directly implemented in the hardware (DSP system) without any additional modifications.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2011, 59, 2; 125-135
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method
Autorzy:
Choi, Young-Sin
Kwon, Do-Hun
Lee, Min_Woo
Cha, Eun-Ji
Jeon, Junhyub
Lee, Seok-Jae
Kim, Jongryoul
Kim, Hwi-Jun
Powiązania:
https://bibliotekanauki.pl/articles/2174571.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Fe-based amorphous
soft magnetic properties
artificial intelligence
machine learning
random forest regression
Opis:
The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe80.5Si3.63B13.54C2.33 at.% and the highest saturation magnetization was obtained Fe81.83Si3.63B12.63C1.91at.% with R2 values of 0.74 and 0.878, respectively.
Źródło:
Archives of Metallurgy and Materials; 2022, 67, 4; 1459--1463
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent energy management system of a smart microgrid using multiagent systems
Autorzy:
Azeroual, Mohamed
Lamhamdi, Tijani
El Moussaoui, Hassan
El Markhi, Hassane
Powiązania:
https://bibliotekanauki.pl/articles/140667.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial intelligence
fault detection
microgrid
multi-agent system (MAS)
power distribution
smart grid
Opis:
The smart grid concept is predicated upon the pervasive use of advanced digital communication, information techniques, and artificial intelligence for the current power system, to be more characteristics of the real-time monitoring and controlling of the supply/demand. Microgrids are modern types of power systems used for distributed energy resource (DER) integration. However, the microgrid energy management, the control, and protection of microgrid components (energy sources, loads, and local storage units) is an important challenge. In this paper, the distributed energy management algorithm and control strategy of a smart microgrid is proposed using an intelligent multi-agent system (MAS) approach to achieve multiple objectives in real-time. The MAS proposed is developed with co-simulation tools, which the microgrid model, simulated using MATLAB/Simulink, and the MAS algorithm implemented in JADE through a middleware MACSimJX. The main study is to develop a new approach, able to communicate a multi-task environment such as MAS inside the S-function block of Simulink, to achieve the optimal energy management objectives.
Źródło:
Archives of Electrical Engineering; 2020, 69, 1; 23-38
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion
Autorzy:
Wilk-Kołodziejczyk, Dorota
Jaśkowiec, Krzysztof
Bitka, Adam
Pirowski, Zenon
Grudzień-Rakoczy, Małgorzata
Chrzan, Konrad
Małysza, Marcin
Doroszewski, Maciej
Powiązania:
https://bibliotekanauki.pl/articles/2134111.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial intelligence
material conversion
technological conversion
selection of parameters
prediction of mechanical properties
Opis:
The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.
Źródło:
Archives of Metallurgy and Materials; 2022, 67, 3; 1085--1091
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementation of artificial intelligence in estimating prime costs of producing machine elements
Zastosowanie sztucznej inteligencji w szacowaniu kosztów własnych wytwarzania elementów maszyn
Autorzy:
Więcek, D.
Powiązania:
https://bibliotekanauki.pl/articles/176226.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial intelligence
cost estimation
activity based costing
sztuczna inteligencja
szacowanie kosztów
rachunek kosztów działań
Opis:
Using methods of planning automating of production processes as well as artificial intelligence, the methods presented in this paper were constructed for identifying the set value of manufacturing process parameters, which are key to evaluating the costs of the designed elements. The proposed solutions were adapted for systems used under the conditions of unit and small-batch production.
W artykule przedstawiono metody pozwalające na określenie zbiorów wartości parametrów procesu wytwarzania. Mogą stanowić podstawę szacowania kosztów produkcji projektowanych elementów. Stosowano metody automatyzacji projektowania procesów produkcyjnych i metody sztucznej inteligencji. Zaproponowane rozwiązania dostosowano do systemów produkcyjnych funkcjonujących w warunkach produkcji jednostkowej i małoseryjnej.
Źródło:
Advances in Manufacturing Science and Technology; 2013, 37, 1; 43-53
0137-4478
Pojawia się w:
Advances in Manufacturing Science and Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Residual life estimation of fabricated humidity sensors using different artificial intelligence techniques
Autorzy:
Bhargava, C.
Aggarwal, J.
Sharma, P. K.
Powiązania:
https://bibliotekanauki.pl/articles/201564.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
composite material
artificial intelligence
humidity sensor
accelerated life testing
SEM
materiał kompozytowy
sztuczna inteligencja
czujnik wilgotności
Opis:
Background: a humidity sensor is used to sense and measure the relative humidity of air. A new composite system has been fabricated using environmental pollutants such as carbon black and low-cost zinc oxide, and it acts as a humidity sensor. Residual life of the sensor is calculated and an expert system is modelled. For properties and nature confirmation, characterization is performed, and a sensing material is fabricated. Methodology: characterization is performed on the fabricated material. Complex impedance spectroscopy (CIS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) are all used to confirm the surface roughness, its composite nature as well as the morphology of the composite. The residual lifetime of the fabricated humidity sensor is calculated by means of accelerated life testing. An intelligent model is designed using artificial intelligence techniques, including the artificial neural network (ANN), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: maximum conductivity obtained is 6.4£10−3 S/cm when zinc oxide is doped with 80% of carbon black. Conclusion: the solid composite obtained possesses good humidity-sensing capability in the range of 30–95%. ANFIS exhibits the maximum prediction accuracy, with an error rate of just 1.1%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 1; 147-154
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Early detection of major diseases in turmeric plant using improved deep learning algorithm
Autorzy:
Devisurya, V.
Devi Priya, R.
Anitha, N.
Powiązania:
https://bibliotekanauki.pl/articles/2173642.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial intelligence
computer vision
turmeric leaf diseases detection
sztuczna inteligencja
wizja komputerowa
wykrywanie chorób liści kurkumy
Opis:
Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 2; art. no. e140689
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 32

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