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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublicationIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublicationEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublicationGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublicationFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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On the use of leading safety indicators in maritime and their feasibility for Maritime Autonomous Surface Ships
PublicationAlthough the safety of prospective Maritime Autonomous Surface Ships will largely depend on their ability to detect potential hazards and react to them, the contemporary scientific literature lacks the analysis of how to achieve this. This could be achieved through an application of leading safety indicators. The aim of the performed study was to identify the research directions of leading safety indicators in three safety-critical...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublicationWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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The effects of relational and psychological capital on work engagement: the mediation of learning goal orientation
PublicationPurpose – This paper proposes a research model in which learning goal orientation (LGO) mediates the impacts of relational capital and psychological capital (PsyCap) on work engagement. Design/methodology/approach – Data obtained from 475 managers and employees in the manufacturing and service industries in Poland were utilized to assess the linkages given above. Common method variance was controlled by the unmeasured latent method...
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublicationPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Wisdom from Experience Paradox: Organizational Learning, Mistakes, Hierarchy and Maturity Issues
PublicationOrganizations often perceive mistakes as negligence and low-performance indicators, yet they can be a precious learning resource. However, organizations cannot learn from mistakes if they have not accepted them. This study aimed to explore how organizational hierarchy and maturity levels influence the relationship between mistakes acceptance and the ability to change. A sample composed of 380 Polish employees working in knowledge-driven...
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Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces
PublicationCoding metasurfaces have been introduced as efficient tools allowing meticulous control over the electromagnetic (EM) scattering. One of their relevant application areas is radar cross section (RCS) reduction, which principally relies on the diffusion of impinging EM waves. Despite its significance, careful control of the scattering properties poses a serious challenge at the level of practical realization. This article is concerned...
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Leading with Understanding: Cultivating Positive Relationships between Neurotypical Leaders and Neurodivergent Employees
PublicationNeurodivergent employees have atypical needs that require distinctive leadership approaches. In this study, the specific nature of a relationship between neurodivergent employees and their neurotypical leaders is explored through the lens of the Leader-Member-Exchange (LMX) theory. This two-phased qualitative study builds on 12 semi-structured interviews with neurodivergent employees and an unstructured focus group with 15 individuals...
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Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublicationThis research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
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Application of the Flipped Learning Methodology at a Business Process Modelling Course – A Case Study
PublicationFlipped learning has been known for a long time, but its modern use dates back to 2012, with the publication of Bergmann and Saams. In the last decade, it has become an increasingly popular learning method. Every year, the number of publications on implementing flipped learning experiments is growing, just as the amount of research on the effectiveness of this educational method. The aim of the article is to analyze the possibilities...
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Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
PublicationInstalling photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based...
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Gender as a Moderator of the Double Bias of Mistakes – Knowledge Culture and Knowledge Sharing Effects
PublicationThere is no learning without mistakes. The essence of the double bias of mistakes is the contradiction between an often-declared positive attitude towards learning from mistakes, and negative experiences when mistakes occur. Financial and personal consequences, shame, and blame force desperate employees to hide their mistakes. These adverse outcomes are doubled in organizations by the common belief that managers never make mistakes,...
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Comparison of Deep Neural Network Learning Algorithms for Mars Terrain Image Segmentation
PublicationThis paper is dedicated to the topic of terrain recognition on Mars using advanced techniques based on the convolutional neural networks (CNN). The work on the project was conducted based on the set of 18K images collected by the Curiosity, Opportunity and Spirit rovers. The data were later processed by the model operating in a Python environment, utilizing Keras and Tensorflow repositories. The model benefits from the pretrained...
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection
PublicationThis study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. The primary objective is to explore an enhanced method for estimating FH probability, surpassing the currently recommended Dutch Lipid Clinic Network (DLCN) Score. The models were trained using the largest Polish cohort...
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Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem
PublicationWe are increasingly striving to introduce modern artificial intelligence techniques in medicine and elevate medical care, catering to both patients and specialists. An essential aspect that warrants concurrent development is the protection of personal data, especially with technology's advancement, along with addressing data disparities to ensure model efficacy. This study assesses various domain adaptation techniques and federated...
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LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques
PublicationDeception detection is regarded as a concern for everyone in their daily lives and affects social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception or lie detection systems are non-intrusive, cost-effective, and mobile by identifying facial expressions. Over the last decade, numerous studies have been conducted on deception detection using several advanced techniques....
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublicationBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts,...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublicationIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublicationThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
PublicationThe Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublicationIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublicationEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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Technology-Enhanced Environmental Learning: Co-design of Educational Mobile Application Case
PublicationThe process of co-creating an educational mobile application to support environmentally friendly behavior is presented in this paper. The research material consisted of quantitative data collected on the application during the first testing phase by early adopters. The results suggest that the most frequently used features of the app were related to transport and educational activities. While women tended to split their time between...
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Variable Resolution Machine Learning Optimization of Antennas Using Global Sensitivity Analysis
PublicationThe significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic...
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Predicting a passenger ship's response during evasive maneuvers using Bayesian Learning
PublicationThe rapidly advancing automation of the maritime industry – for instance, through onboard Decision Support Systems (DSS) – can facilitate the introduction of advanced solutions supporting the process of collision avoidance at sea. Nevertheless, relevant solutions that aim to correctly predict a ship's behavior in irregular waves are only available to a limited extent by omitting the impact of wave stochastics on resulting evasive...
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Release systems based on self-assembling RADA16-I hydrogels with a signal sequence which improves wound healing processes
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Wpływ olejów smarowych na wytrzymałość zmęczeniową stopów łożyskowych = Lubricating oil influence on fatigue strength of bearing alloys
PublicationBadano wpływ olejów smarowych na wytrzymałość zmęczeniową stopów łożyskowych. Obiektem badań były cienkościenne bimetalowe panwie łożyskowe poddawane zginaniu w różnych ośrodkach podczas testów zmęczeniowych na stanowisku badawczym SKMR-2. Zaobserwowano wyraźny wpływ rodzaju oleju i czynników związanych z wymuszeniami mechanicznymi i cieplnymi na wytrzymałość zmęczeniową warstwy ślizgowej. Przedstawiono wnioski z badań oraz program...
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Influence of Installation Effects on Pile Bearing Capacity in Cohesive Soils – Large Deformation Analysis Via Finite Element Method
PublicationIn this paper, the whole process of pile construction and performance during loading is modelled via large deformation finite element methods such as Coupled Eulerian Lagrangian (CEL) and Updated Lagrangian (UL). Numerical study consists of installation process, consolidation phase and following pile static load test (SLT). The Poznań site is chosen as the reference location for the numerical analysis, where series of pile SLTs...
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EVALUATING THE INFLUENCE OF BENDING STRESS ON A 1H18N9 STEEL SHAFT WEAR PROCESS IN A WATER LUBRICATED SLIDING BEARING WITH A RUBBER BUSHING
PublicationThe issue of excessive wear of shaft journals co-working with a rubber bearing has been unexplained so far. Premature and sometimes very intensive wear of ship sliding bearings in water conditions is the reason for carry out very expensive and more frequent than expected repairs. The authors (E. Piątkowska, W. Litwin) made an attempt to find a case that influences the value of this wear described in the paper “Attempt at Evaluating...
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Condition Monitoring of Horizontal Sieving Screens—A Case Study of Inertial Vibrator Bearing Failure in Calcium Carbonate Production Plant
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Prediction of metal deformation due to line heating; an alternative method of mechanical bending, based on artificial neural network approach
PublicationLine heating is one of the alternative methods of forming metals and this kind of forming uses the heating torch as a source of heat input. During the process, many parameters are considered like the size of the substrate, thickness, cooling method, source power intensity, the travel speed of the power source, the sequence of heating, and so on. It is important to analyze the factors affecting the...
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Comparison of endotracheal intubation performed with 3 devices by paramedics wearing chemical, biological, radiological, and nuclear personal protective equipment
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Release systems based on self-assembling RADA16-I hydrogels with a signal sequence which improves wound healing processes
PublicationSelf-assembling peptides can be used for the regeneration of severely damaged skin. They can act as scaffolds for skin cells and as a reservoir of active compounds, to accelerate scarless wound healing. To overcome repeated administration of peptides which accelerate healing, we report development of three new peptide biomaterials based on the RADA16-I hydrogel functionalized with a sequence (AAPV) cleaved by human neutrophil elastase...
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Ion recognition properties of new pyridine-2,6-dicarboxamide bearing propeller-like pendant residues: multi-spectroscopic approach
PublicationThe synthesis and ion binding properties of new amide derived from propeller-like tris(2-pyridyl)amine and 2,6-pyridinedicarboxylic acid chloride were described. Amide binds divalent metal cations: copper(II), nickel(II), zinc(II), and lead(II) in acetonitrile. In acetonitrile:water mixture (9:1 v/v) amide interacts only with copper(II) and nickel(II) cations forming complexes of 1:1 stoichiometry. It was found that the introduction...
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Modelling Long‐Term Transition from Coal‐Reliant to Low‐Emission Power Grid and District Heating Systems in Poland
PublicationEnergy systems require technological changes towards climate neutrality. In Poland, where the power system is dominated by outdated coal-fired power plants, efforts to minimize the environmental impact are associated with high costs. Therefore, optimal paths for the development of the energy sector should be sought in order to achieve ambitious long-term strategic goals, while minimizing the negative impact on the consumers’ home...
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Activation Energy and Inclination Magnetic Dipole Influences on Carreau Nanofluid Flowing via Cylindrical Channel with an Infinite Shearing Rate
PublicationThe infinite shear viscosity model of Carreau fluid characterizes the attitude of fluid flow at a very high/very low shear rate. This model has the capacity for interpretation of fluid at both extreme levels, and an inclined magnetic dipole in fluid mechanics has its valuable applications such as magnetic drug engineering, cold treatments to destroy tumors, drug targeting, bio preservation, cryosurgery, astrophysics, reaction kinetics,...
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Dry bearing sliding layer transverse flexibility effects on real sliding distance for reciprocating microoscillatory movement of flatcontact surface.
PublicationW referacie omówiono wpływ właściwości mechanicznych i kształtu warstwy ślizgowej z bezsmarowego materiału łożyskowego. Wcześniej omówione i publikowane wyniki badań doświadczalnych i analizy numerycznej wyjaśniły i potwierdziły, dlaczego rzeczywista droga tarcia jest znacznie mniejsza od nominalnej w warunkach mikrooscylacji. Wyniki przeprowadzonej analizy numerycznej pozwoliły możliwie najlepiej ukształtować powierzchnię ślizgania...
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Ecological bearing systems for water turbines - two research programs aimed at making water turbines more "eco-friendly".
PublicationW pracy opisano próby wyeliminowania ropopochodnych środków smarowych z układów łożyskowania turbin wodnych. Próby dotyczą zastosowania bezsmarowych łożysk kierownic i smarowanych wodą łożysk wałów turbin. Wprowadzanie bezsmarowych łożysk kierownic wymaga stworzenia metod prognozowania ich trwałości w warunkach małych oscylacji. Do stosowania w łożyskach wałów zaproponowano smarowane wodą łożyska ceramiczne o oryginalnej konstrukcji,...
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Effect of water contamination of an environmentally acceptable lubricant based on synthetic esters on the wear and hydrodynamic properties of stern tube bearing
PublicationThe ecological aspects of the lubrication of stern tube bearings on ships represent a critical challenge today. Oil leakages are a typical issue, even for new systems, similarly to water ingress from the environment. One of the options for improving environmental protection is replacing mineral oil with an environmentally adaptable lubricant (EAL). This paper reports experimental investigations of the effect of water content in...
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Metoda energetyczno-czasowa oceny działania poprzecznych łożysk ślizgowych. Energy-time method of the estimation of work of slide bearing
PublicationZaproponowano interpretację wartościującą działania, które (podobnie jak przedstawione w mechanice klasycznej działania Hamiltona i Maupertiusa oraz działanie wynikające ze zmiany pędu ciała) jest rozpatrywane jako wielkość fizyczna o jednostce miary zwanej dżulosekundą [dżulxsekunda]. Przedstawiono oryginalną metodę analizy i oceny działania łożyska ślizgowego z uwzględnieniem jego niezawodności i bezpieczeństwa funkcjonowania....
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Ekonomična efektivnist` teplovoï pompi v sistemi opalennâ = Economical efficiency of heat pump system in heating system
PublicationW artykule opisano wynik porównawczych obliczeń kosztów ogrzewania budynku jednorodzinnego. Porównano koszty konwencjonalnego ogrzewania z kotłem olejowym jako źródłem ciepła z kosztami ogrzewania układem hybrydowym, w którym współpracują ze sobą dwa żródła ciepła: konwencjonalny kocioł olejowy i sprężarkowa pompa ciepła. Wykonano studium parametryczne kosztów ogrzewania z wykorzystaniem metody kosztów narastających.
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Finite element investigations of granular material behaviour during cyclic wall shearing under a constant normal stiffness condition
PublicationW artykule przedstawiono wyniki numnerycznej analizy zachowania sie materiałów granulowanych podczas cyklicznego ścinanai wzdłuz szorstkiej ściany z warunkiem stałej sztywności normalnej. Obliczenia wykonano przy zastosowaniu metody elementów skończonych i mikropolarnego nodelu hipopalstycznego. Obliczenia wykonano dla róznych sztywności, początkowych wskaźników porowatości i sredniej średnicy ziarna. Wyniki numeryczne porównano...
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WATER LUBRICATED MAIN SHAFT BEAR INGS WITH THREE LAYER BUSH – MODERN SOLUTION FOR MARINE INDUSTRY
PublicationWater lubricated sliding bearings are increasingly popular in marine and hydro power industry. Such popularity is partly due to their simple construction which also means a relatively affordable price. Properly designed and installed water lubricated bearings may well last for over a decade. During the last decade their traditional range has been expanded with new, modern products like three layer bearing bush. The work presents...
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Anna Hering doktor nauk farmaceutycznych
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Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
Publication"Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector...