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Search results for: marine engine fault diagnosis fault detection diesel engine machine learning ensemble learning extreme learning machines multi-class decomposition
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An application for a new type of pneumatic engine concept
PublicationHeavy trucks are often equipped with loading and unloading systems like dock levellers with swing lip or telescopic lip. Most of these devices require hydro-electrical energy supply systems (eg. the pump that presses the working substance to the actuators must be propelled by electric engine.) The space taken by pump with electric engine can be reduced on condition that a new type of pneumatic drive will be considered. It is possible...
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Open-transistor fault diagnostics in voltage-source inverters by analyzing the load current
PublicationA novel method is presented for the detection andisolation of open-transistor faults in voltage-source invertersfeeding low-power AC motors. The method is based onmonitoring two diagnostic signals, one indicating sustained nearzerovalues of output current and thus permitting fault detection,the other permitting the isolation of the particular transistorwhich went faulty. The latter signal is the ratio of the averagephase current...
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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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Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
PublicationIn recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design...
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Machine learning goes global: Cross-sectional return predictability in international stock markets
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Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview
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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems
PublicationTe feld of Big Data is rapidly developing with a lot of ongoing research, which will likely continue to expand in the future. A crucial part of this is Knowledge Discovery from Data (KDD), also known as the Knowledge Discovery Process (KDP). Tis process is a very complex procedure, and for that reason it is essential to divide it into several steps (Figure 1). Some authors use fve steps to describe this procedure, whereas others...
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Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing
PublicationIt is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology...
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Love your mistakes!—they help you adapt to change. How do knowledge, collaboration and learning cultures foster organizational intelligence?
PublicationPurpose: The study aims to determine how the acceptance of mistakes is related to adaptability to change in a broad organizational context. Therefore it explores how knowledge, collaboration, and learning culture (including “acceptance of mistakes”) might help organizations overcome their resistance to change. Methodology: The study uses two sample groups: students aged 18–24 (330 cases) and employees aged >24 (326 cases) who work...
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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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Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublicationThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
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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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Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning
PublicationText-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the...
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A semi-Markov model of fuel combustion process in a Diesel engine
PublicationW artykule przedstawiono czterostanowy model procesu spalania w przestrzeniach roboczych (cylindrach) silników o zapłonie samoczynnym w formie procesu semimarkowskiego, dyskretnego w stanach i ciągłego w czasie. Wartościami tego procesu są stany odpowiadające powszechnie akceptowanym rodzajom spalania w tego rodzaju silnikach a mianowicie takie stany procesu jak: spalanie pełne (całkowite i zupełne), spalanie niezupełne, spalanie...
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Energy-time method for assesment of main diesel engine operation
PublicationW publikacji przedstawiono rozwinięcie prezentowanej w literaturze metody ilościowej oceny działania na przykładzie okrętowego silnika głównego z zapłonem samoczynnym. Według tej interpretacji, działanie silnika może zostać przedstawione jako wielkość fizyczna. W tym aspekcie, na przykładzie wybranego układu funkcjonalnego, silnika dokonano oceny przydatności tej wielkości do opisu własności niezawodnościowych tego układu.
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Analysis of start energy of Stirling engine type alpha
PublicationThe Stirling engine type alpha is composed of two cylinders (expansion space E and compression space C), regenerator that forms the space between the cylinders and the buffer space (under the pistons). Before the start-up and as a result of long-term operation, the average pressure in the working space (above the pistons) and in the buffer space is the same. However, in the initial phase of operation, the average pressure in the...
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An Adaptive Network Model for a Double Bias Perspective on Learning from Mistakes within Organizations
PublicationAlthough making mistakes is a crucial part of learning, it is still often being avoided in companies as it is considered as a shameful incident. This goes hand in hand with a mindset of a boss who dominantly believes that mistakes usually have negative consequences and therefore avoids them by only accepting simple tasks. Thus, there is no mechanism to learn from mistakes. Employees working for and being influenced by such a boss...
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Modeling of the internal combustion engine cooling system
PublicationThe article concerns computer modelling of processes in cooling systems of internal combustion engines. Modelling objectives and existing commercial programs are presented. It also describes Author’s own method of binding graphs used to describe phenomena in the cooling system of a spark ignition engine. The own model has been verified by tests on the engine dynamometer. An example of using a commercial program for experimental...
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Using LSTM networks to predict engine condition on large scale data processing framework
PublicationAs the Internet of Things technology is developing rapidly, companies have an ability to observe the health of engine components and constructed systems through collecting signals from sensors. According to output of IoT sensors, companies can build systems to predict the conditions of components. Practically the components are required to be maintained or replaced before the end of life in performing their assigned task. Predicting...
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Computational Analysis of Transformational Organisational Change with Focus on Organisational Culture and Organisational Learning: An Adaptive Dynamical Systems Modeling Approach
PublicationTransformative Organisational Change becomes more and more significant both practically and academically, especially in the context of organisational culture and learning. However computational modeling and formalization of organisational change and learning processes are still largely unexplored. This chapter aims to provide an adaptive network model of transformative organisational change and translate a selection of organisational...
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Diagnosis of fully differential circuits based on a fault dictionary implemented in the microcontroller systems
PublicationPrzedstawiono nową koncepcję testera wbudowanego bist przeznaczonego do diagnostyki w pełni różnicowych układów analogowych implementowanych w mikrosystemach mieszanych sygnałowo. w trakcie testowania mierzona jest amplituda i faza wyjściowego napięcia różnicowego. procedura detekcji i lokalizacji uszkodzeń bazuje na słowniku uszkodzeń przechowywanym w pamięci programu mikrokontrolera. korzystną cechą przestrzeni pomiarowej wyznaczonej...
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AUTOMATIC LEARNING OF STRATEGY AND RULES IN CARD GAMES USING IMAGE FROM CAMERA
PublicationBelow work tries to answer a question: if it is possible to replace real human with computer system in social games. As a subject for experiments, card games were chosen, because they require a lot of player interaction (playing and taking cards), while their rules are easy to present in form of clear list of statements. Such a system, should allow real players to play without constant worrying about guiding or helping computer...
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Co-gasification of waste biomass-low grade coal mix using downdraft gasifier coupled with dual-fuel engine system: Multi-objective optimization with hybrid approach using RSM and Grey Wolf Optimizer
PublicationThe looming global crisis over increasing greenhouse gases and rapid depletion of fossil fuels are the motivation factors for researchers to search for alternative fuels. There is a need for more sustainable and less polluting fuels for internal combustion engines. Biomass offers significant potential as a feed material for gasification to produce gaseous fuel. It is carbon neutral, versatile, and abundant on earth. The present...
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Fault diagnosing system of wheeled tractors
PublicationA defect of complex wheeled tractor assembles most frequently negative influence on exploitation efficiency, safety and exhaust gases emission. Structure complexity of wheeled tractors requires more and more advanced diagnostic methods for identification of their serviceable possibilities as well in manufacturing step as in exploitation. In classical diagnosing methods of wheeled tractor defects states mapping by measured diagnostic...
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Visual Content Learning in a Cognitive Vision Platform for Hazard Control (CVP-HC)
PublicationThis work is part of an effort for the development of a Cognitive Vision Platform for Hazard Control (CVP-HC) for applications in industrial workplaces, adaptable to a wide range of environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment (PPE). Given the results of previous analysis of supervised techniques for the problem of classification of a few PPE (boots, hard hats, and gloves...
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Improving sensitivity of residual current transformers to high frequency earth fault currents
PublicationFor protection against electric shock in low voltage systems residual current devices are commonly used. However, their proper operation can be interfered when high frequency earth fault current occurs. Serious hazard of electrocution exists then. In order to detect such a current, it is necessary to modify parameters of residual current devices, especially the operating point of their current transformer. The authors proposed...
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Behavior of residual current devices at earth fault currents with DC component
PublicationLow-voltage electrical installations are increasingly saturated with power electronic converters. Due to very high popularity of photovoltaic (PV) installations and the spread of electric vehicles (EV) as well as their charging installations, DC–AC and AC–DC converters are often found in power systems. The transformerless coupling of AC and DC systems via power electronic converters means that an electrical installation containing...
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Sensorless field oriented control for five-phase induction motors with third harmonic injection and fault insensitive feature
PublicationThe paper presents a solution for sensorless field oriented control (FOC) system for five-phase induction motors with improved rotor flux pattern. In order to obtain the advantages of a third harmonic injection with a quasi-trapezoidal flux shape, two vector models, α1–β1 and α3–β3, were transformed into d1– q1, d3– q3 rotating frames, which correlate to the 1st and 3rd harmonic plane respectively. A linearization approach of the...
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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublicationIn this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...
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Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis
PublicationWe proposed to apply a specific machine learning technique called Self-Organising Maps (SOM) to identify similarities in the performance of muscles around human temporomandibular joint (TMJ). The performance was assessed by measuring muscle activation with the use of surface electromyography (sEMG). SOM algorithm used in the study was able to find clusters of data in sEMG test results. The SOM analysis was based on processed sEMG...
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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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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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A consensus-based approach to the distributed learning
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Prototype selection algorithms for distributed learning
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