Wykaz obszarów badawczych związanych z tagiem Sztuczna-inteligencja:
| # | Obszar badawczy | Dziedzina naukowa |
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| 1 |
Applications of Artificial Intelligence (AI, such as deep learning) in the field of technical and environmental sciences, including the analog systems diagnostics (for instance, audio amplifiers or RIAA correctors), exploration of human musical taste based on the analysis of acoustic features of songs, design of smart investment strategies for the stock market, monitoring the electrical energy consumption by the appliances based on the aggregated voltage and current signals, or design of secure IoT systems.
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| 2 |
The growing importance of projects in modern enterprises is accompanied by an increasing need for methods and tools supporting decisions. Computational technologies related to artificial intelligence methods, such as neural networks, fuzzy logic, and hybrid systems, are noteworthy.
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| 3 |
The research area covers the issues of image processing, computer vision, machine learning (including deep learning) and broadly understood artificial intelligence.
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| 4 |
1)Methods of detection and classification of heart abnormalities using deep learning techniques - concerns techniques and algorithms for processing ECG signals for automatic detection and classification of heart abnormalities using deep machine learning methods. It is planned to use a variety of deep convolutional neural networks for automatic analysis of the structure (waveform) of ECG signals, as well as mechanisms for detecting anomalies (deviations) and searching for information based on autoencoder neural networks. The work will be carried out in cooperation with the Department of Cardiology-Intensive Therapy and Internal Diseases of the Medical University of Karol Marcinkowski in Poznań. 2)Methods of detection and classification of heart abnormalities using heart rate variability (HRV) parameters and machine learning techniques - concerns techniques and algorithms for processing ECG signals for automatic detection and classification of heart abnormalities using machine learning methods. It is planned to use a wide variety of heart rate variability (HRV) parameters as well as new original heart rate asymmetry (HRA) parameters. The work will be carried out in cooperation with the Department of Cardiology-Intensive Therapy and Internal Diseases of the Medical University of Karol Marcinkowski in Poznań. 3)Passive radar for space object detection using signals recorded by antennas of the international network of radio telescopes LOFAR - The research area concerns the techniques and algorithms for processing signals recorded by the antennas of the network of radio telescopes LOFAR (Low-Frequency Array for radio astronomy) in order to use them for passive radiolocation of space objects: satellites in low orbits and so-called space debris. The considered system of passive radiolocation does not require the construction of dedicated transmitters, but uses the existing so-called illuminators of opportunity, e.g. FM, DAB + or TV DVB-T transmitters. After reflecting the signals from the objects, they are received by the antennas of the LOFAR system. The research in this area, carried out in cooperation with the Space Research Center of the Polish Academy of Sciences, is pioneering on a global scale. Three LOFAR stations are located in Poland. A single LOFAR station consists of many antennas creating a large radio telescope that can receive relatively weak signals. 4) Methods and algorithms for signal processing in passive radar for small unmanned aerial vehicles (drones) - concerns techniques and algorithms for signal processing dedicated to the passive radiolocation of small unmanned aerial vehicles (drones). The work will be carried out in cooperation with the Faculty of Power and Aeronautical Engineering of the Warsaw University of Technology at the airport in Sieraków near Przasnysz, recently purchased by the Warsaw University of Technology, where the Area Monitoring Laboratory with four antenna stations was built. Problems to be solved within the research area are related to detection of small flying objects with the use of specific features of signals reflected from the considered objects, estimation of their parameters, tracking, as well as classification of detected objects, in particular, the research is to focus on the possibility of distinguishing small drones from birds. 5) Optimization of methods and algorithms of people identification based on the EEG signal with the use of machine learning techniques - concerns the methods and algorithms of people identification based on the EEG signal with the use of machine learning techniques. It is planned to use the approach based both on the spectral features of the EEG signal in its individual bands, as well as the analysis of the EEG signal itself using the so-called deep learning techniques with convolutional neural networks. The work will include the selection and optimization of EEG signal parameters and classifiers used to identify people, the number of sessions necessary to train classifiers, the minimum number of electrodes used for identification, as well as the development of headbands/caps dedicated to collecting the EEG signal for the application under consideration, and testing the developed solutions under the conditions similar to their practical implementation. The research will be carried out in cooperation with the Nencki Institute of Experimental Biology of the Polish Academy of Sciences.
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| 5 |
My main research interests include application of Artificial Intelligence and Machine Learning to the development of general-purpose human-like / human-level learning and problem-solving methods. Building such multi-purpose solving systems includes, among others, the concepts of explainability, universality, knowledge abstraction, task sharing, and continuality of the learning process. I’m also interested in the development of hybrid metaheuristic solution methods with application to games, dynamic and bi-level optimization problems, and human-machine cooperation in problem solving. For more information please visit http://www.mini.pw.edu.pl/~mandziuk
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| 6 |
Forecasting the RES generation and production of electricity in systems of various sizes using machine learning. Optimization in power engineering
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| 7 |
Research in the application of innovative techniques in the area of cybersecurity of information systems. The aim of the research is to develop mechanisms to increase the level of cybersecurity of computer systems, information networks or hardware implementations of systems (FPGA, ASIC, SoC) by applying innovative concepts such as Moving Target Defense, AI anomaly detection, software/hardware co-design. Examples of research problems are the development of methods and algorithms using SDN and NFV techniques to implement MTD mechanisms, the use of artificial intelligence and machine learning algorithms for anomaly detection, hardware acceleration of monitoring and processing of multigigabit network traffic.
Translated with www.DeepL.com/Translator (free version)
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| 8 |
1. Capacity of airspace, airports, ground handliig systems at airports. 2. Air traffic safety. 3. Civil aviation security, security screening systems. 4. Aerodrome traffic operations. 5. Air traffic organization. 6. Human factor in air transport. 7. Artificial intelligence in air transport. 8. Environmental impact of air transport.
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| 9 |
Research in the area of supply chain management (SC): flexibility and resilience of SC to internal phenomena and global disruption (natural disasters, wars, diseases, sanctions), management and shaping of SC, supply, distribution, risk management, development and/or the use of decision support tools in SC based on the machine learning, artificial intelligence, simulation.
Research in the field of city logistics: planning a cargo distribution system, developing innovative solutions for the last mile logistics, infrastructure designing for environmentally-friendly vehicles, including methods of locating charging stations for electric vehicles, developing and/or using decision support tools in urban logistics based on the machine learning, artificial intelligence, simulation.
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| 10 |
The research area is focused on non-conventional manufacturing technologies, including modeling of the electrical discharge machining, application of artificial intelligence in the optimization of EDM processes, additive manufacturing, optimization of SLS/SLM process, hybrid erosive-abrasive machining, finishing technologies, i.e., abrasive flow machining, magnetic-abrasive finishing, grinding.
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| 11 |
machine learning and artificial intelligence; autonomous systems; ML architectures, MLOps
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| 12 |
robotics; robot programming methods, robot control system architectures, robotic system metamodels; automatic code generation of robot controllers, task planning; vision servos; positional and force control; robot applications; artificial intelligence;
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| 13 |
Computer simulation of electrical devices (electromagnetic field, circuits and systems), construction of HV pulse generators, but also algorithms of image and three-dimensional data processing (thermal imaging defectoscopy, computed tomography, automation of diagnostics of electrical devices and systems). I am interested in evolutionary algorithms and neural networks from an algorithmic perspective. https://www.iem.pw.edu.pl/~jstar
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| 14 |
Bioinformatyka, genomika obliczeniowa, sztuczna inteligencja, uczenie maszynowe
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| 15 |
Bioinformatyka, genomika obliczeniowa, sztuczna inteligencja, uczenie maszynowe
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| 16 |
Polyrepresentation learning for image or text models. AI systems use numerous approaches to train data representations. Often, however, a single approach is based on one selected perspective related to the loss function used to train the representation. When looking for fundamental models, it may be beneficial to combine perspectives into a broader representation - a polyrepresentation.
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| 17 |
I invite doctoral students and researchers interested in harnessing the latest technologies in medicine to collaborate. My work is interdisciplinary, offering the opportunity to engage in both fundamental and applied research. The goal is not just to hone your analytical and programming skills but to make a genuine contribution to advancements in medical diagnostics. Moreover, the research is conducted in collaboration with real medical centers, such as hospitals and specialized clinics. This ensures that the outcomes of our studies have a realistic chance for rapid clinical implementation, enhancing their direct impact on the quality and effectiveness of medical care.
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| 18 |
First research field: artificial intelligence, machine learning, in particular, dimensionality reduction, data visualization, clustering, classification, self-organization, outlier detection, artificial neural networks, feature extraction and selection. Second research field: concurrent programming and the Java programming language.
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