Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "sztuczna sztuczna inteligencja" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-10 z 10
    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
    Tytuł:
    Intelligent system supporting technological process planning for machining and 3D printing
    Autorzy:
    Rojek, Izabela
    Mikołajewski, Dariusz
    Kotlarz, Piotr
    Macko, Marek
    Kopowski, Jakub
    Powiązania:
    https://bibliotekanauki.pl/articles/2090703.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial intelligence
    AI
    intelligent system
    technological process
    machining
    3D printing
    sztuczna inteligencja
    system inteligentny
    proces technologiczny
    obróbka skrawaniem
    druk 3D
    Opis:
    The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company’s employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136722, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent system supporting technological process planning for machining and 3D printing
    Autorzy:
    Rojek, Izabela
    Mikołajewski, Dariusz
    Kotlarz, Piotr
    Macko, Marek
    Kopowski, Jakub
    Powiązania:
    https://bibliotekanauki.pl/articles/2173593.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial intelligence
    AI
    intelligent system
    technological process
    machining
    3D printing
    sztuczna inteligencja
    system inteligentny
    proces technologiczny
    obróbka skrawaniem
    druk 3D
    Opis:
    The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company’s employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136722
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modern methods in the field of machine modelling and simulation as a research and practical issue related to Industry 4.0
    Autorzy:
    Rojek, Izabela
    Macko, Marek
    Mikołajewski, Dariusz
    Sága, Milan
    Burczyński, Tadeusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2086825.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Industry 4.0
    Internet of Things
    artificial intelligence
    models
    AI
    simulation
    IoT
    Przemysł 4.0
    internet rzeczy
    internet przedmiotów
    sztuczna inteligencja
    modele
    symulacja
    Opis:
    Artificial intelligence (AI) is changing many areas of technology in the public and private spheres, including the economy. This report reviews issues related to machine modelling and simulations concerning further development of mechanical devices and their control systems as part of novel projects under the Industry 4.0 paradigm. The challenges faced by the industry have generated novel technologies used in the construction of dynamic, intelligent, flexible and open applications, capable of working in real time environments. Thus, in an Industry 4.0 environment, the data generated by sensor networks requires AI/CI to apply close-to-real-time data analysis techniques. In this way industry can face both fresh opportunities and challenges, including predictive analysis using computer tools capable of detecting patterns in the data based on the same rules that can be used to formulate the prediction.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136717, 1--12
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Power quality analysis in electrical drives and a case study of artificial intelligence prediction algorithm for fault deterrent electrical drives
    Autorzy:
    Kumarasamy, Vishnu Murthy
    Loganathan, Ashok Kumar
    Powiązania:
    https://bibliotekanauki.pl/articles/2173673.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    voltage sag
    voltage swell
    voltage imbalance
    machine learning
    inverter drives
    artificial intelligence
    wzrost napięcia
    spadek napięcia
    asymetria napięcia
    uczenie maszynowe
    napędy falownikowe
    sztuczna inteligencja
    Opis:
    Since electrical drives have become an integral element of any industrial sector, power quality difficulties have been well expected, and delivering genuine quality of the same has proven to be a difficult challenge. Since power quality relies on load side non-linearity and high semiconductor technology consumption, it is a serious concern. The efficiency of the drive segment employed in the sector is increasingly becoming a topic of discussion in today’s market. Numerous reviews of available literature have found problems with the load side as well as with electrical drive proficiency, as a result of the issues listed above. A high level of power quality vulnerability is simply too much. Even the most advanced technology has its limits when it comes to drive operation. Research on the grid-side quality issues of electrical drives is the focus of this article. After field testing of grid power quality, each parametric analysis is performed to identify crucial parameters that can cause industrial drives to fail. Based on this discovery, a machine learning strategy was developed and an artificial intelligence technique was proposed to administer the fault deterrent prediction algorithm. An accurate forecast of anomalous behavior on the grid side ensures safe and dependable grid operation such that shutdown or failure probability is minimized to a greater extent by the results. Additional information gleaned from historical data will prove useful to equipment manufacturers in the future, providing a solution to this problem.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141180
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modern methods in the field of machine modelling and simulation as a research and practical issue related to Industry 4.0
    Autorzy:
    Rojek, Izabela
    Macko, Marek
    Mikołajewski, Dariusz
    Sága, Milan
    Burczyński, Tadeusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2090720.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Industry 4.0
    Internet of Things
    artificial intelligence
    models
    AI
    simulation
    IoT
    Przemysł 4.0
    czwarta rewolucja przemysłowa
    internet rzeczy
    internet przedmiotów
    sztuczna inteligencja
    modele
    symulacja
    Opis:
    Artificial intelligence (AI) is changing many areas of technology in the public and private spheres, including the economy. This report reviews issues related to machine modelling and simulations concerning further development of mechanical devices and their control systems as part of novel projects under the Industry 4.0 paradigm. The challenges faced by the industry have generated novel technologies used in the construction of dynamic, intelligent, flexible and open applications, capable of working in real time environments. Thus, in an Industry 4.0 environment, the data generated by sensor networks requires AI/CI to apply close-to-real-time data analysis techniques. In this way industry can face both fresh opportunities and challenges, including predictive analysis using computer tools capable of detecting patterns in the data based on the same rules that can be used to formulate the prediction.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136717, 1--12
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Environmental analysis of a product manufactured with the use of an additive technology – AI-based vs. traditional approaches
    Autorzy:
    Dostatni, Ewa
    Dudkowiak, Anna
    Rojek, Izabela
    Mikołajewski, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2204511.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    additive manufacturing
    AM
    eco-design
    life cycle assessment
    LCA
    artificial intelligence
    AI
    neural networks model
    produkcja dodatkowa
    zielony design
    szacowanie cyklu życia
    sztuczna inteligencja
    model sieci neuronowej
    Opis:
    This paper attempts to conduct a comparative life cycle environmental analysis of alternative versions of a product that was manufactured with the use of additive technologies. The aim of the paper was to compare the environmental assessment of an additive-manufactured product using two approaches: a traditional one, based on the use of SimaPro software, and the authors’ own concept of a newly developed artificial intelligence (AI) based approach. The structure of the product was identical and the research experiments consisted in changing the materials used in additive manufacturing (from polylactic acid (PLA) to acrylonitrile butadiene styrene (ABS)). The effects of these changes on the environmental factors were observed and a direct comparison of the effects in the different factors was made. SimaPro software with implemented databases was used for the analysis. Missing information on the environmental impact of additive manufacturing of PLA and ABS parts was taken from the literature for the purpose of the study. The novelty of the work lies in the results of a developing concurrent approach based on AI. The results showed that the artificial intelligence approach can be an effective way to analyze life cycle assessment (LCA) even in such complex cases as a 3D printed medical exoskeleton. This approach, which is becoming increasingly useful as the complexity of manufactured products increases, will be developed in future studies.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144478
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Solar chargers based on new dye-based photovoltaic modules and new supercapacitors
    Autorzy:
    Plebankiewicz, Ireneusz
    Bogdanowicz, Krzysztof. A.
    Kwaśnicki, Paweł
    Skunik-Nuckowska, Magdalena
    Rączka, Patryk M.
    Kulesza, Paweł
    Iwan, Agnieszka
    Przybył, Wojciech
    Powiązania:
    https://bibliotekanauki.pl/articles/27311422.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    dye-sensitized solar cell
    supercapacitor
    redox-active electrolyte
    current source
    energy storage system
    Artificial Intelligence
    superkondensator
    elektrolit o działaniu redoks
    system magazynowania energii
    sztuczna inteligencja
    barwnikowe ogniwo słoneczne
    aktywne źródło
    Opis:
    Electricity storage is one of the best-known methods of balancing the energy supply and demand at a given moment. The article presents an innovative solution for the construction of an electric energy storage device obtained from an innovative photovoltaic panel made of new dye-based photovoltaic modules and newly developed supercapacitors – which can be used as an emergency power source. In the paper, for the first time, we focused on the successful paring of new dye-sensitized solar cell (DSSC) with novel supercapacitors. In the first step, a microprocessor stand was constructed using Artificial Intelligence algorithms to control the parameters of the environment, as well as the solar charger composed of six DSSC cells with the dimensions of 100_100 mm and 126 CR2032 coin cells with a total capacitance of 60 F containing redox-active aqueous electrolyte. It was proven that the solar charger store enough energy to power, i.e. SOS transmitter or igniters, using a 5 V signal.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 4; art. no. e146452
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Coherent structures and flow control: genesis and prospect
    Autorzy:
    Gad-El-Hak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/201471.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    smart wings
    coherent structures
    reactive flow control
    adaptive control
    machine-learning control
    futuristic control systems
    microsensors
    microactuators
    artificial intelligence
    turbulent shear flows
    history of flow control
    history of coherent structures
    inteligentne skrzydła
    kontrola adaptacyjna
    mikroczujniki
    sztuczna inteligencja
    Opis:
    The genesis of both coherent structures and reactive flow control strategies is explored. Futuristic control systems that utilize microsensors and microactuators together with artificial intelligence to target specific coherent structures in a transitional or turbulent flow are considered. Of possible interest to the readers of this journal is the concept of smart wings, to be briefly discussed early in the article.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 411-444
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-10 z 10

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies