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Wyniki wyszukiwania dla: hearing impairment
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Machine learning techniques combined with dose profiles indicate radiation response biomarkers
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DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
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Developing Novel Solutions to Realise the European Energy - Information Sharing & Analysis Centre
PublikacjaFor more effective decision making in preparation for and response to cyberevents in the energy sector, multilevel situation awareness, from technical to strategic is essential. With an uncertain picture of evolving threats, sharing of the latest cybersecurity knowledge among all sector stakeholders can inform and improve decisions and responses. This paper describes two novel solutions proposed during the formation of the European...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn 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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OPERATING CONDITIONS OF SLIDE BEARINGS OF MILLS USED IN KGHM POLSKA MIEDŹ S.A.
PublikacjaThe paper contains the results from a tec hnical analysis of the conditions of the operation of hydrodynamic bearings supporting the drums of ore processing mills at KGHM Polska Mied ź S.A. A theoretical analysis was performed on the grounds of onsite examination and measurem ents of principal dimensions of the bearings of interest. The computer simulation covered the characteristics of the oil film in the bearings...
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The effects of relational and psychological capital on work engagement: the mediation of learning goal orientation
PublikacjaPurpose – 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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Social Media and Knowledge Sharing – What Do We Know So Far?
PublikacjaThe aim of this paper is to examine previous studies on topic of social media and how it influences knowledge sharing online and thereafter establish respective body of knowledge. The background investigation has been organized as a theoretical review with qualitative premises. The multi-layered Systematic Literature Review process has been utilized and carried out to fetch the most relevant peer-reviewed researches in the past....
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The Mediation Function of Job Satisfaction's between Organizational Culture Dimensions and Knowledge Sharing
PublikacjaIt is commonly acknowledged that organizational culture is a valuable element of intellectual capital and as a hidden source of competitive advantage can considerably affect the achieving of strategic business goals. The axiological dimension of organizational culture is mostly identified with a set of shared assumptions and values, while work practices mainly define its behavioral dimension. Both these dimensions influence, among...
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublikacjaIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
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Improved estimation of dynamic modulus for hot mix asphalt using deep learning
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Effects of mutual learning in physical education to improve health indicators of Ukrainian students
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Rapid naming ability and its relationship to reading in Polish-speaking children with dyslexia
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Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect
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Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems
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Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection
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Machine Learning and data mining tools applied for databases of low number of records
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Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
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Enhancing diaphragmatic defect repair and regeneration: How biomaterials leading the way to progress?
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Shaking table experimental study on the effectiveness of polymer bearings for seismic isolation of structures
PublikacjaSeismic isolation has been recognised to be a very effective way of protecting structures from damage during earthquakes. It allows us to extend the natural period of the structure and therefore avoid resonance with the ground motion. Moreover, by increasing damping in the isolation devices, more energy can be dissipated and thus the structural response can be further reduced. The aim of this paper is to show the results of the...
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Klocki samopompujące w łożyskach hydrodynamicznych = Self-pumping pads in the hydrodynamic bearings
PublikacjaWe współpracy z firmą ALSTOM HYDRO (Szwajcaria), zespół Katedry Konstrukcji i Eksploatacji Maszyn Politechniki Gdańskiej przeprowadził symulacje przepływowe kilku rozwiązań konstrukcyjnych mających na celu zwiększenie niezawodności turbin wodnych. W artykule opisano jedno z takich rozwiązań - tak zwane łożysko z klockami samopompującymi. Głównym celem jest uzyskanie przepływu oleju w układzie smarowania łożyska hydrodynamicznego...
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Diagnostic model of compression-ignition engine slide bearings for controlling the changes of their state
PublikacjaW artykule przedstawiono koncepcję umożliwiającą sterowanie procesami zmian stanów eksploatacyjnych silnika na podstawie modelu diagnostycznego łożysk ślizgowych. Jako model łożyska ślizgowego przyjęty został topologiczny model diagnostyczny, pozwalający na pełne i dokładne wykorzystanie oleju smarowego jako jednego z nośników informacji o stanie technicznym łożyska. Przedstawiona została przykładowa interpretacja stanów eksploatacyjnych...
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An introduction to lessons learned and best practices sharing as a chance for improvement in automative corporation
PublikacjaOmówiono szereg metodologii związanych z poprawą jakości funckjonowania organizacji i zarządzania w przemyśle samochodowym
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Fatigue resistance investigation of the mb46 plain bearings under conditions of dynamic loadings
PublikacjaOdporność materiału łożyskowego MB46 na pęknięcia zmęczeniowe badano na stanowisku SMOK, w którym generowane jest zmienne jednokierunkowe obciążenie badanego łożyska. Materiał łożyskowy MB46 składa sie z warstwy brązu CuPb22Sn 3 spiekanego na taśmie stalowej, stanowiącej podłoże. Na warstwę brązu nałożona jest galwanicznie cienka warstwa stopu SnCu6 stanowiąca powłokę. Każde z badanych łożysk poddawane było 20-godzinnemu testowi...
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Environmentally friedly propeller shaft support with the use of water lubricated foil bearings
PublikacjaW pracy przedstawiono podsumowanie prowadzonych badań w celu opracowania oryginalnej metodyki projektowania oraz technologii wykonania pierwszego w świecie łożyska foliowego smarowanego wodą. Zaprezentowano koncepcje nowego łożyska foliowego poprzecznego oraz opisano opracowany algorytm obliczeniowy wykorzystany do predykcji jego charakterystyk. W rezultacie prowadzonych prac opracowano i przebadano doświadczalnie na stanowisku...
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Concept for interpretation and assessment of slide bearings operation in Diesel enfines in probabilistic approach
PublikacjaW artykule zaproponowano 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''. Przedstawiono propozycję ilościowej interpretacji działania dowolnego poprzecznego łożyska ślizgowego, w którym zachodzą oddziaływania energetyczne...
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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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Application of Shuffled Frog-Leaping Algorithm for Optimal Software Project Scheduling and Staffing
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublikacjaHigher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer...
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Local and long range potentials for heparin‐protein systems for coarse‐grained simulations
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe 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
PublikacjaThe 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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Looking through the past: better knowledge retention for generative replay in continual learning
PublikacjaIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained...
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Leading with Understanding: Cultivating Positive Relationships between Neurotypical Leaders and Neurodivergent Employees
PublikacjaNeurodivergent 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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Gender as a Moderator of the Double Bias of Mistakes – Knowledge Culture and Knowledge Sharing Effects
PublikacjaThere 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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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublikacjaPlasmonic 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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Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces
PublikacjaCoding 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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Wisdom from Experience Paradox: Organizational Learning, Mistakes, Hierarchy and Maturity Issues
PublikacjaOrganizations 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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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing 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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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublikacjaEvery 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
PublikacjaThe 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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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublikacjaTo 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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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublikacjaBiochar 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
PublikacjaThis 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
PublikacjaIn 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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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublikacjaWe 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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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublikacjaGrasping 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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A Highly Scalable, Modular Architecture for Computer Aided Assessment e-Learning Systems
PublikacjaIn this chapter, the authors propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. The authors' research proved that such architecture, while well suited for didactic content distribution systems is ill-suited for knowledge...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
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E-Learning Service Management System For Migration Towards Future Internet Architectures
PublikacjaAs access to knowledge and continuous learning are among the most valuable assets in modern, technological society, it is hardly surprising that e-learning solutions can be counted amongst the most important groups of services being deployed in modern network systems. Based on analysis of their current state-of-the-art, we decided to concentrate our research and development work on designing and implementing a management system...
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Continuous learning as a method of raising qualifications – the perspective of workers, employers and training organizations
PublikacjaContinuous learning is discussed in strategic documents of Poland and the European Union. In Poland, the idea of continuous learning is not very popular. However, in the context of strong competition in the labour market and the progressive globalization processes, the skills issue takes on new meaning — both for employees and employers. In order to adapt skills to labour market needs it is necessary to conduct adequate studies...