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Search results for: training methods
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Using Eye-tracking to get information on the skills acquisition by the radiology residents
PublicationThis paper describes the possibility of monitoring the progress of knowledge and skills acquisition by the students of radiology. It is achieved by an analysis of a visual attention distribution patterns during image-based tasks solving. The concept is to use the eye-tracking data to recognize the way how the radiographic images are read by recognized experts, radiography residents involved in the training program, and untrained...
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Method for Clustering of Brain Activity Data Derived from EEG Signals
PublicationA method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets,...
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublicationA Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...
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Multi-Aspect Quality Assessment Of Mobile Image Classifiers For Companion Applications In The Publishing Sector
PublicationThe paper presents the problem of quality assessment of image classifiers used in mobile phones for complimentary companion applications. The advantages of using this kind of applications have been described and a Narrator on Demand (NoD) functionality has been described as one of the examples, where the application plays an audio file related to a book page that is physically in front of the phone's camera. For such a NoD application,...
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Neural networks and deep learning
PublicationIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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Bridge Ergonomic Design: A Review
PublicationHuman error remains the most common cause of marine incidents and it is worth emphasizing that navigator’s performance is directly affected by ergonomic factors on the bridge. Studies regarding influence of bridge design and work environment on the operator are rare, thus the main purpose of this paper is to fill in this gap. Documents issued by recognized organizations, research publications and additional sources were reviewed...
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Lessons learned from developing an Industry 4.0 mobile process management system supported by Artificial Intelligence
PublicationResearch, development and innovation (RDI) projects are undertaken in order to improve existing, or develop new, more efficient products and services. Moreover, the goal of innovation is to produce new knowledge through research, and disseminating it through education and training. In this line of thinking, this paper reports and discusses the lessons learned from the undertaken project, regarding three areas: machine learning...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublicationThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Obtaining a Well-Trained Artificial Intelligence Algorithm from Cross-Validation in Endoscopy
PublicationThe article shortly discusses endoscopic video analysis problems and artificial intelligence algorithms supporting it. The most common method of efficiency testing of these algorithms is to perform intensive cross-validation. This allows for accurately evaluate their performance of generalization. One of the main problems of this procedure is that there is no simple and universal way of obtaining a specific instance of a well-trained...
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An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
PublicationThe topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected...
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Antecedents to Achieve Kanban Optimum Benefits in Software Companies
PublicationIn 2004, Kanban successfully entered into the Agile and Lean realm. Since then software companies have been increasingly using it in software development teams. The goal of this study is to perform an empirical investigation on antecedents considered as important for achieving optimum benefits of Kanban use and to discuss the practical implications of the findings. We conducted an online survey with software professionals from...
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Difference in Perceived Speech Signal Quality Assessment Among Monolingual and Bilingual Teenage Students
PublicationThe user perceived quality is a mixture of factors, including the background of an individual. The process of auditory perception is discussed in a wide variety of fields, ranging from engineering to medicine. Many studies examine the difference between musicians and non-musicians. Since musical training develops musical hearing and other various auditory capabilities, similar enhancements should be observable in case of bilingual...
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CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image
PublicationThe paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about...
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DOROTKA, czyli Doskonalenie Organizacji, ROzwoju oraz Tworzenia Kursów Akademickich przez Internet.
PublicationW artykule zaprezentowano dedykowaną platformę wspierającą kształcenie na odległość opracowaną i uruchomioną w ramach projektu Leonardo da Vinci TeleCAD (Teleworkers Training for CAD System Users, 1998-2001), wykorzystywaną w latach 2000-2003 do wspomagania przedmiotu Podstawy Informatyki na Wydziale Inżynierii Lądowej Politechniki Gdańskiej. Przedstawiono również, bazujący na wieloletnich doświadczeniach, model DOROTKA (Doskonalenie...
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Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification
PublicationThis article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and...
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Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation
PublicationCost-efficient multi-objective design optimization of antennas is presented. The framework exploits auxiliary data-driven surrogates, a multi-objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order...
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Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling
PublicationFull-wave electromagnetic (EM) analysis has become one of the major design tools for contemporary antenna structures. Although reliable, it is computationally expensive which makes automated simulation-driven antenna design (e.g., parametric optimization) difficult. This difficulty can be alleviated by utilization of fast and accurate replacement models (surrogates). Unfortunately, conventional data-driven modeling of antennas...
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Paradygmat jakościowy w analizie interakcji międzykulturowych – interpretacja na bazie wybranych teorii psychologicznych
PublicationIntercultural interactions in a multicultural work environment are a peculiar type of social interactions. The results of prior research on the effects of interactions in such environment are inconclusive. The majority of the previous studies have emphasized problems, applied a quantitative methodology and interpreted the results with regard to social identity and categorization theory, information-processing theory and intergroup contact...
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Cloud solutions as a platform for building advanced learning platform, that stimulate the real work environment for project managers
PublicationImproving skills of managers and executives require, that during the transfer of knowledge (in different ways: during studies, trainings, workshops and other forms of education) it is necessary to use tools and solutions that are (or will be) used in real world environments, where people being educated are working or will work. Cloud solutions allow educational entities (universities, training companies, trainers, etc.) to provide...
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Improved Uniform Sampling in Constrained Domains for Data-Driven Modelling of Antennas
PublicationData-driven surrogate modelling of antenna structures is an attractive way of accelerating the design process, in particular, parametric optimization. In practice, construction of surrogates is hindered by curse of dimensionality as well as wide ranges of geometry parameters that need to be covered in order to make the model useful. These difficulties can be alleviated by constrained performance-driven modelling with the surrogate...
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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
PublicationThis work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...
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Ranking Speech Features for Their Usage in Singing Emotion Classification
PublicationThis paper aims to retrieve speech descriptors that may be useful for the classification of emotions in singing. For this purpose, Mel Frequency Cepstral Coefficients (MFCC) and selected Low-Level MPEG 7 descriptors were calculated based on the RAVDESS dataset. The database contains recordings of emotional speech and singing of professional actors presenting six different emotions. Employing the algorithm of Feature Selection based...
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Investigating Feature Spaces for Isolated Word Recognition
PublicationThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
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Neural Modelling of Steam Turbine Control Stage
PublicationThe paper describes possibility of steam turbine control stage neural model creation. It is of great importance because wider application of green energy causes severe conditions for control of energy generation systems operation Results of chosen steam turbine of 200 MW power measurements are applied as an example showing way of neural model creation. They serve as training and testing data of such neural model. Relatively simple...
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Rapid Multi-band Patch Antenna Yield Estimation Using Polynomial Chaos-Kriging
PublicationYield estimation of antenna systems is important to check their robustness with respect to the uncertain sources. Since the Monte Carlo sampling-based real physics simulation model evaluations are computationally intensive, this work proposes the polynomial chaos-Kriging (PC-Kriging) metamodeling technique for fast yield estimation. PC-Kriging integrates the polynomial chaos expansion (PCE) as the trend function of Kriging metamodel...
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Marketing office management with leadership competences application in a manufacturing and service company
PublicationThe article presents a description of comprehensive leadership competencies in a company, with the emphasis on managing the marketing department. The publication commences with the indication of the leadership in the light of this literature, explaining what leadership competencies are, how they are identified and what competencies we distinguish. It also explains the purpose of applying leadership...
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Engineering education for smart grid systems in the quasi-industrial environment of the LINTE^2 laboratory
PublicationSmart grid systems are revolutionising the electric power sector, integrating advanced technologies to enhance efficiency, reliability and sustainability. It is important for higher education to equip the prospective smart grid professional with the competencies enabling them to navigate through the related complexities and drive innovation. To achieve this, interdisciplinary education programmes are necessary, addressing inter...
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Performance‐driven modeling of compact couplers in restricted domains
PublicationFast surrogate models can play an important role in reducing the cost of EM-driven design closure of miniaturized microwave components. Unfortunately, construction of such models is challenging due to curse of dimensionality and wide range of geometry parameters that need to be included in order to make it practically useful. In this letter, a novel approach to design-oriented modeling of compact couplers is presented. Our method...
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Reduced-cost surrogate modeling of input characteristics and design optimization of dual-band antennas using response features
PublicationIn this article, a procedure for low-cost surrogate modeling of input characteristics of dual-band antennas has been discussed. The number of training data required for construction of an accurate model has been reduced by representing the antenna reflection response to the level of suitably defined feature points. The points are allocated to capture the critical features of the reflection characteristic, such as the frequencies...
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Low-Cost Multi-Objective Optimization Yagi-Uda Antenna in Multi-Dimensional Parameter Space
PublicationA surrogate-based technique for fast multi-objective optimization of a multi-parameter planar Yagi-Uda antenna structure is presented. The proposed method utilizes response surface approximation (RSA) models constructed using training samples obtained from evaluation of the low-fidelity antenna model. Utilization of the RSA models allowsfor fast determination of the best possible trade-offs between conflicting objectives in multi-objective...
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Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics
PublicationData-driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality which limits the number of independent parameters that can be accounted for in the modelling process. Recently, a performance-driven modelling technique has been proposed where the constrained domain of the...
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How to Sort Them? A Network for LEGO Bricks Classification
PublicationLEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises...
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From Knowledge based Vision Systems to Cognitive Vision Systems: A Review
PublicationComputer vision research and applications have their origins in 1960s. Limitations in computational resources inherent of that time, among other reasons, caused research to move away from artificial intelligence and generic recognition goals to accomplish simple tasks for constrained scenarios. In the past decades, the development in machine learning techniques has contributed to noteworthy progress in vision systems. However,...
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Video Classification Technology in a Knowledge-Vision-Integration Platform for Personal Protective Equipment Detection: An Evaluation
PublicationThis work is part of an effort for the development of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. This paper focuses on hazards resulted from the non-use of personal protective equipment (PPE), and examines a few supervised learning techniques to compose the proposed system for the purpose of recognition of three protective...
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Investigating Noise Interference on Speech Towards Applying the Lombard Effect Automatically
PublicationThe aim of this study is two-fold. First, we perform a series of experiments to examine the interference of different noises on speech processing. For that purpose, we concentrate on the Lombard effect, an involuntary tendency to raise speech level in the presence of background noise. Then, we apply this knowledge to detecting speech with the Lombard effect. This is for preparing a dataset for training a machine learning-based...
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NLITED - New Level of Integrated Techniques for Daylighting Education: Preliminary Data on the Use of an E-learning Platform
PublicationProject NLITED – New Level of Integrated Techniques for Daylighting Education - is an educational project for students and professionals. The project's objective is to create and develop an online eLearning platform with 32 eModules dedicated to daylight knowledge. The project also offers e-learners two summer school training where the theory is put into practice. The platform was launched on January 31, 2022. The paper...
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Toward Robust Pedestrian Detection With Data Augmentation
PublicationIn this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety. A popular and simple technique for improving robustness is using data...
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Trust triggers and barriers in intercultural teams
PublicationIntercultural teams are more and more popular nowadays — they constitute a serious challenge in terms of effective cooperation and trust building, however. The article presents the potential problems that can affect intercultural cooperation and stresses the power of trust in cultural diversity conditions. The ten-factor model of intercultural team trust is presented. The main aim was to answer the questions: what are the differences...
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On Reduced-Cost Design-Oriented Constrained Surrogate Modeling of Antenna Structures
PublicationDesign of contemporary antenna structures heavily relies on full-wave electromagnetic (EM) simulation models. Such models are essential to ensure reliability of evaluating antenna characteristics, yet, they are computationally expensive and therefore unsuitable for handling tasks that require multiple analyses, e.g., parametric optimization. The cost issue can be alleviated by using fast surrogate models. Conventional data-driven...
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Real-time simulator of agricultural biogas plant
PublicationThis article presents a real-time simulator of an agricultural biogas plant. The project contains biogas and biomass circuits simulation, as well as heating circuit simulation with a complete control system and visualization interface of the whole process. The software tool used to simulate the plant work is CFD (Computational Fluid Dynamics), which enables a user to create and test simulation objects based on fundamental physical...
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Performance-Driven Inverse/Forward Modeling of Antennas in Variable-Thickness Domains
PublicationDesign of contemporary antenna systems is a challenging endeavor. The difficulties are partially rooted in stringent specifications imposed on both electrical and field characteristics, demands concerning various functionalities, but also constraints imposed upon the physical size of the radiators. Furthermore, conducting the design process at the level of full-wave electromagnetic (EM) simulations, otherwise dictated by reliability,...
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublicationPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublicationCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
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Labeler-hot Detection of EEG Epileptic Transients
PublicationPreventing early progression of epilepsy and sothe severity of seizures requires effective diagnosis. Epileptictransients indicate the ability to develop seizures but humansoverlook such brief events in an electroencephalogram (EEG)what compromises patient treatment. Traditionally, trainingof the EEG event detection algorithms has relied on groundtruth labels, obtained from the consensus...
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A COMPREHENSIVE REVIEW OF LIFE CYCLE ASSESSMENT AND ENERGY EFFICIENCY IN 3D PRINTING FOR CONSTRUCTION: CURRENT STATE, BENEFITS, LIMITATIONS, AND FUTURE OUTLOOK
PublicationThe agenda of Industry 4.0 strongly affects design and construction at all its phases, and three-Dimensional Printing (3DP) is an essential part of it. The emerging technology has the potential to become a more valid and accepted form of construction. This research is based on a literature review regarding the relationships between the concepts of Life Cycle Assessment (LCA) and energy efficiency for 3DP in construction research...
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Autoencoder application for anomaly detection in power consumption of lighting systems
PublicationDetecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...
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Multi-fidelity EM simulations and constrained surrogate modelling for low-cost multi-objective design optimisation of antennas
PublicationIn this study, a technique for low-cost multi-objective design optimisation of antenna structures has been proposed. The proposed approach is an enhancement of a recently reported surrogate-assisted technique exploiting variable-fidelity electromagnetic (EM) simulations and auxiliary kriging interpolation surrogate, the latter utilised to produce the initial approximation of the Pareto set. A bottleneck of the procedure for higher-dimensional...
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Triangulation-based Constrained Surrogate Modeling of Antennas
PublicationDesign of contemporary antenna structures is heavily based on full-wave electromagnetic (EM) simulation tools. They provide accuracy but are CPU-intensive. Reduction of EM-driven design procedure cost can be achieved by using fast replacement models (surrogates). Unfortunately, standard modeling techniques are unable to ensure sufficient predictive power for real-world antenna structures (multiple parameters, wide parameter ranges,...
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Accurate simulation-driven modeling and design optimization of compact microwave structures
PublicationCost efficient design optimization of microwave structures requires availability of fast yet reliable replacement models so that multiple evaluations of the structure at hand can be executed in reasonable timeframe. Direct utilization of full-wave electromagnetic (EM) simulations is often prohibitive. On the other hand, accurate data-driven modeling normally requires a very large number of training points and it is virtually infeasible...
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Developing students' spatial skills and teaching the history of architecture through structural drawing
PublicationThe method of “structural drawing" is used in teaching history of architecture in the Architectural Faculty of Gdańsk University of Technology. It is addressed to students of the first semester of study – so to the architectural beginners. There are three main goals of the structural drawing method used in that educational course: (1) developing the students’ spatial skills; (2) training architectural drawing ability; (3) teaching...