Search results for: MANAGEMENT TRAINING
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Daylight Appraisal Classes For Achitecture Students A Survey Combined With A Practical Assessment For Educational Training Recommendations
PublicationThe main objectives of this article are: (i) to present the relations between architecture students' subjective assessment of daylight in classrooms and the objective evaluation of daylit conditions using daylight simulations tools, (ii) to formulate guidelines and recommendations on daylight appraisal methods and tools which may be useful in architectural training. The methodology used includes an evaluation of the results of...
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Tomasz Korczyński mgr
PeopleFrom February 2007 I'm working as a psychologist in Prison Service. I was working in almost all types of prisons with juvenile inmates, recidivist, “dangerous” inmates, women and first time offenders. I'm responsible for carrying out psychological consultations, testing, crisis intervention, suicide prevention. Other duties include promoting a healthy institutional environment to improve staff and inmate safety, delivering direct...
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Analysis of Denoising Autoencoder Properties Through Misspelling Correction Task
PublicationThe paper analyzes some properties of denoising autoencoders using the problem of misspellings correction as an exemplary task. We evaluate the capacity of the network in its classical feed-forward form. We also propose a modification to the output layer of the net, which we called multi-softmax. Experiments show that the model trained with this output layer outperforms traditional network both in learning time and accuracy. We...
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Evaluation of sound event detection, classification and localization in the presence of background noise for acoustic surveillance of hazardous situations
PublicationAn evaluation of the sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for separating foreground events from the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier...
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Managing competence and certifying persons responsible for functional safety = Zarządzanie kompetencjami i certyfikacja osób odpowiedzialnych za bezpieczeństwo funkcjonalne
PublicationThis article emphasizes that knowledge and competences of managers, engineers and specialists dealing with safety-related technologies for hazardous industry should be appropriately shaped in the technical education processes and training programmes fulfilling some quality requirements and assessment criteria. It concerns especially persons dealing with the functional safety solutions in the design and operation of the electric,...
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Classification of Music Genres Based on Music Separation into Harmonic and Drum Components . Klasyfikacja gatunków muzycznych wykorzystująca separację instrumentów muzycznych
PublicationThis article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector...
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Anna Maria Trzaskowska dr inż.
PeopleAnna Maria Trzaskowska has been employed as an Assistant Professor at the Faculty of Management and Economics at the Department of Informatics in Management since 2017. For over 2 years she was the Deputy Head of the Department. A member of the Board of the Pomeranian Branch of the Polish Information Processing Society. Current scientific interests fall within the areas of agile methods, lean management in higher education, digitization...
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A survey of neural networks usage for intrusion detection systems
PublicationIn recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commer‐ cial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested...
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Uniform sampling in constrained domains for low-cost surrogate modeling of antenna input characteristics
PublicationIn this letter, a design of experiments technique that permits uniform sampling in constrained domains is proposed. The discussed method is applied to generate training data for construction of fast replacement models (surrogates) of antenna input characteristics. The modeling process is design-oriented with the surrogate domain spanned by a set of reference designs optimized with respect to the performance figures and/or operating...
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Viewpoint independent shape-based object classification for video surveillance
PublicationA method for shape based object classification is presented.Unlike object dimension based methods it does not require any system calibration techniques. A number of 3D object models are utilized as a source of training dataset for a specified camera orientation. Usage of the 3D models allows to perform the dataset creation process semiautomatically. The background subtraction method is used for the purpose of detecting moving objects...
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Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement
PublicationA variety of surrogate modelling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up re-liable design-ready surrogates of modern microwave components. The difficulty of the model-ing task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across...
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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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The evolution of education spaces - from plan as generator to regenerative architecture, virtual rooms and green campuses
PublicationThe study programmes are often considered the main formative factors in the process of educating future architects. Another highly influential component is the architectural characteristics of learning spaces, and consequently the impact of the physical built environment on the quality of education has been widely discussed. However, not often do we realise that the characteristics of education spaces correlate with the organisational...
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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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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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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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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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Three-objective antenna optimization by means of kriging surrogates and domain segmentation
PublicationIn this paper, an optimization framework for multi-objective design of antenna structures is discussed which exploits data-driven surrogates, a multi-objective evolutionary algorithm, response correction techniques for design refinement, as well as generalized domain segmentation. The last mechanism is introduced to constrain the design space region subjected to sampling, which permits reduction of the number of training data samples...
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RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine
PublicationIn this letter, it is shown how direction-of-arrival (DoA) estimation for electronically steerable parasitic array radiator (ESPAR) antennas, which are designed to be integrated within wireless sensor network nodes, can be improved by applying support vector classification approach to received signal strength (RSS) values recorded at an antenna's output port. The proposed method relies on ESPAR antenna's radiation patterns measured...
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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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OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.
PublicationIn the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stage for fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy...
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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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Rapid surrogate-assisted statistical analysis of compact microstrip couplers
PublicationIn this paper, a technique for low-cost statistical analysis and yield estimation of compact microwave couplers has been presented. The analysis is executed at the level of a fast surrogate model representing selected characteristic points of the coupler response that are critical to determine satisfaction/violation of the prescribed design specifications. Because of less nonlinear dependence of the characteristic points on geometry...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
PublicationRecently gathered image datasets and the new capabilities of high-performance computing systems have allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels instead of specific features. The principle of operation of deep neural networks resembles more and more what we believe to be happening...
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From Creative Thinking Techniques to Innovative Design Solutions - The Educators' Perspective
PublicationThe article presents a structure and basic tasks of a new original academic course, which was inaugurated in 2015 at the Faculty of Architecture in Gdańsk University of Technology and organized for the first year students of Spatial Planning.The title of the course was ‘Garden Cities and the Gardens in the Cities. A Course with Elements of Training Creativity’. The aim of the course was to encourage the participants to develop...
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Human-computer interaction approach applied to the multimedia system of polysensory integration
PublicationIn the paper an approach of utilizing an interaction between the human and computer in a therapy of dyslexia and other sensory disorders is presented. Bakker's neuropsychological concept of dyslexia along with therapy methods are reviewed in the context of the Multimedia System of Polysensory Integration, proposed at the Multimedia Systems Department of Gdansk Univ. of Technology. The system is presented along with the training...
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Monitoring Parkinson's disease patients employing biometric sensors and rule-based data processing
PublicationArtykuł prezentuje automatyczny system wykrywania pogorszenia zdrowia pacjentów z chorobą Parkinsona opracowany w ramach projektu PERFORM.The paper presents how rule-based processing can be applied to automatically evaluate the motor state of Parkinson's Disease patients. Automatic monitoring of patients by using biometric sensors can provide assessment of the Parkinson's Disease symptoms. All data on PD patients' state are compared...
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TeleCAD w kształceniu studentów Wydziału Inżynierii Lądowej Politechniki Gdańskiej
PublicationPrzedstawiono system TeleCAD opracowany w ramach projektu Leonardo da Vinci - Teleworkers Training for CAD Systems Users (1998-2001). Głównym celem projektu było stworzenie środowiska obsługi kursów programu AutoCAD bazującego na Internecie jako medium do komunikacji między uczestnikami oraz do dostarczania materiałów kursowych. W artykule zaprezentowano również system służący do oceny jakości szkoleń na odległość. Szkolenie TeleCAD...
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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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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network
PublicationThe idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated...
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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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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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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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Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations
PublicationEvaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the...
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Theory vs. practice. Searching for a path of practical education
PublicationThe introduction of a three-tier model of higher education (the Bologna model) has led to considerable changes in the 1st- and 2nd-tier technical courses at universities. At present, a student with a bachelor’s degree can be employed in his / her profession after completing only 7 semesters of study. A search is under way for methods of combining theoretical knowledge taught at universities with practical knowledge gained afterwards....
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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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Koncepcja logistycznego usprawnienia magazynu cross-dockowego przedsiębiorstwa X
PublicationCelem artykułu jest przedstawienie koncepcji logistycznego usprawnienia magazynu cross- -dockowego przedsiębiorstwa X. W oparciu o informacje dotyczące stanu faktycznego maga- zynu zostały przeprowadzone badania procesów magazynowych. Po przeprowadzeniu identyfikacji problemów zostały zaproponowane usprawnienia wybranego procesu magazynowego, które polegały na propozycji zmiany sposobu zagospodarowania magazynu. Do tego...
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Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
PublicationNumerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending...
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Project-Based Collaborative Research and Training Roadmap for Manufacturing Based on Industry 4.0
PublicationThe importance of the economy being up to date with the latest developments, such as Industry 4.0, is more evident than ever before. Successful implementation of Industry 4.0 principles requires close cooperation of industry and state authorities with universities. A paradigm of such cooperation is described in this paper stemming from university partners with partly overlapping and partly complementary areas of expertise in manufacturing....
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublicationThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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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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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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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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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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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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Vehicle detector training with labels derived from background subtraction algorithms in video surveillance
PublicationVehicle detection in video from a miniature station- ary closed-circuit television (CCTV) camera is discussed in the paper. The camera provides one of components of the intelligent road sign developed in the project concerning the traffic control with the use of autonomous devices being developed. Modern Convolutional Neural Network (CNN) based detectors need big data input, usually demanding their manual labeling. In the presented...
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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...