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Wyniki wyszukiwania dla: SYSTEM TESTING , SENSITIVITY , COMPUTATIONAL MODELING , NEURAL NETWORKS , OBJECT DETECTION , DISTORTION , DATA MODELS
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The role and importance of WIMAX mobile system as a high-performance data transfer technology in wireless sensor networks for wide area monitoring applications
PublikacjaThe study discuses basic features and functional design of WiMAX Mobile system, based on the IEEE 802.16e (Release 1.5 Rev. 2.0) standard. The analysis has been made in terms of ability to use this system to transmit video stream related to monitoringof large agglomeration areas. What is more, the study includes comparison of technical parameters of WiMAX Mobile system with competitive systems such as: HSPA+ and UMTS-LTE, which...
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Training of Deep Learning Models Using Synthetic Datasets
PublikacjaIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Multiscaled Hybrid Features Generation for AdaBoost Object Detection
PublikacjaThis work presents the multiscaled version of modified census features in graphical objects detection with AdaBoost cascade training algorithm. Several experiments with face detector training process demonstrate better performance of such features over ordinal census and Haar-like approaches. The possibilities to join multiscaled census and Haar features in single hybrid cascade of strong classifiers are also elaborated and tested....
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Computational Approaches to Modeling Artificial Emotion – An Overview of the Proposed Solutions
PublikacjaCybernetic approach to modeling artificial emotion through the use of different theories of psychology is considered in this paper, presenting a review of twelve proposed solutions: ActAffAct, FLAME, EMA, ParleE, FearNot!, FAtiMA, WASABI, Cathexis, KARO, MAMID, FCM, and xEmotion. The main motivation for this study is founded on the hypothesis that emotions can play a definite utility role of scheduling variables in the construction...
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Technical State Assessment of Charge Exchange System of Self-Ignition Engine, Based On the Exhaust Gas Composition Testing
PublikacjaThis paper presents possible use of results of exhaust gas composition testing of self - ignition engine for technical state assessment of its charge exchange system under assumption that there is strong correlation between considered structure parameters and output signals in the form of concentration of toxic compounds (ZT) as well as unambiguous character of their changes. Concentration of the analyzed ZT may be hence considered...
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Multibeam data processing for 3D object shape reconstruction
PublikacjaThe technology of hydroacoustic scanning offers an efficient and widely-used source of geospatial information regarding underwater environments, providing measurement data which usually have the structure of irregular groups of points known as point clouds. Since this data model has known disadvantages, a different form of representation based on representing surfaces with simple geometric structures, such as edges and facets,...
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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublikacjaIn this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly,...
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Testing the Effect of Bathymetric Data Reduction on the Shape of the Digital Bottom Model
PublikacjaDepth data and the digital bottom model created from it are very important in the inland and coastal water zones studies and research. The paper undertakes the subject of bathymetric data processing using reduction methods and examines the impact of data reduction according to the resulting representations of the bottom surface in the form of numerical bottom models. Data reduction is an approach that is meant to reduce the size...
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The harmonic distortion of voltage waveforms in the ship's electrical power system
Dane BadawczeThe dataset is a part of the research results on the quality of supply voltage on bus bars of the ship's electrical power system's main switchboard in different states of ship exploitation. The attached dataset contains the results of a harmonic distortion analysis expressed by the total harmonic distortion (THD) coefficient of voltage waveforms recorded...
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Resolving conflicts in object tracking for automatic detection of events in video
PublikacjaW referacie przedstawiono algorytm rozwiązywania konfliktów w śledzeniu obiektów ruchomych. Proponowana metoda wykorzystuje predykcję stanu obiektu obliczaną przez filtry Kalmana oraz dopasowuje wykryte obiekty do struktur śledzących ich ruch na podstawie deskryptorów koloru i tekstury. Omówiono specyficzne sytuacje powodujące konflikty, takie jak rozdzielanie obiektów. Przedstawiono wyniki testów. Algorytm może być zastosowany...
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Automatic singing quality recognition employing artificial neural networks
PublikacjaCelem artykułu jest udowodnienie możliwości automatycznej oceny jakości technicznej głosów śpiewaczych. Pokrótce zaprezentowano w nim stworzoną bazę danych głosów śpiewaczych oraz zaimplementowane parametry. Przy pomocy sztucznych sieci neuronowych zaprojektowano system decyzyjny, który oceniono w pięciostopniowej skali jakość techniczną głosu. Przy pomocy metod statystycznych udowodniono, że wyniki generowane przez ten system...
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Differential models versus neural models in optimisation
PublikacjaW pracy porównano zastosowanie modeli różniczkowych i modeli neuronowych dla celów optymalizacji.
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Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics
PublikacjaData-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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A framework for detection of selfishness in multihop mobile ad hoc networks
PublikacjaThe paper discusses the need for a fully-distributed selfishness detection mechanism dedicated for multihop wireless ad hoc networks which nodes may exhibit selfish forwarding behaviour. The main contribution of this paper is an introduction to a novel approach for detecting and coping with the selfish nodes. Paper describes a new framework based on Dempster-Shafer Theory called Dempster-Shafer Theory-based Selfishness Detection...
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublikacjaThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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Reliable data-driven modeling of high-frequency structures by means of nested kriging with enhanced design of experiments
PublikacjaData-driven (or approximation) surrogate models have been gaining popularity in many areas of engineering and science, including high-frequency electronics. They are attractive as a way of alleviating the difficulties pertinent to high computational cost of evaluating full-wave electromagnetic (EM) simulation models of microwave, antenna, and integrated photonic components and devices. Carrying out design tasks that involve massive...
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Testing sensitivity of A-type residual current devices to earth fault currents with harmonics
PublikacjaIn many applications, modern current-using equipment utilizes power electronic converters to control the consumed power and to adjust the motor speed. Such equipment is used both in industrial and domestic installations. A characteristic feature of the converters is producing distorted earth fault currents, which contain a wide spectrum of harmonics, including high-order harmonics. Nowadays, protection against electric shock in...
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Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments
PublikacjaKnowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, sensitivity analysis is used to limit the number of parameters....
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Open Data Capability Architecture - An Interpretive Structural Modeling Approach
PublikacjaDespite of increasing availability of open data as a vital organizational resource, large numbers of startups and organizations fail when it comes to utilizing open data effectively. This shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse....
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A neural network based system for soft fault diagnosis in electronic circuits
PublikacjaW artykule przedstawiono system do diagnostyki uszkodzeń parametrycznych w układach elektronicznych. W systemie zaimplementowano słownikową metodę lokalizacji uszkodzeń, bazującą na pomiarach w dziedzinie częstotliwości przeprowadzanych za pomocą analizatora transmitancji HP4192A. Rozważono główne etapy projektowania systemu: definiowanie modelu uszkodzeń, wybór optymalnych częstotliwosci pomiarowych, ekstrakcję cech diagnostycznych,...
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DEEP CONVOLUTIONAL NEURAL NETWORKS AS A DECISION SUPPORT TOOL IN MEDICAL PROBLEMS – MALIGNANT MELANOMA CASE STUDY
PublikacjaThe paper presents utilization of one of the latest tool from the group of Machine learning techniques, namely Deep Convolutional Neural Networks (CNN), in process of decision making in selected medical problems. After the survey of the most successful applications of CNN in solving medical problems, the paper focuses on the very difficult problem of automatic analyses of the skin lesions. The authors propose the CNN structure...
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Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublikacjaMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks
PublikacjaTraffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building...
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Derivation of Executable Test Models From Embedded System Models using Model Driven Architecture Artefacts - Automotive Domain
PublikacjaThe approach towards system engineering compliant to Model-Driven Architecture (MDA) implies an increased need for research on the automation of the model-based test generation. This applies especially to embedded real-time system development where safety critical requirements must be met by a system. The following paper presents a methodology to derive basic Simulink test models from Simulink system models so as to execute them...
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Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care
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Efficient Surrogate Modeling and Design Optimization of Compact Integrated On-Chip Inductors Based on Multi-Fidelity EM Simulation Models
PublikacjaHigh-performance and small-size on-chip inductors play a critical role in contemporary radio-frequency integrated circuits. This work presents a reliable surrogate modeling technique combining low-fidelity EM simulation models, response surface approximations based on kriging interpolation, and space mapping technology. The reported method is useful for the development of broadband and highly accurate data-driven models of integrated...
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Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks
PublikacjaThe estimation of systolic and diastolic blood pressure using artificial neural network is considered in the paper. The blood pressure values are estimated using pulse arrival time, and additionally RR intervals of ECG signal together with respiration signal. A single layer recurrent neural network with hyperbolic tangent activation function was used. The average blood pressure estimation error for the data obtained from 21 subjects...
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Deep neural networks for human pose estimation from a very low resolution depth image
PublikacjaThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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International Journal of Neural Networks
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IEEE TRANSACTIONS ON NEURAL NETWORKS
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Stirling engines - the state of technology development and computational models
PublikacjaStirling engines represent a technologically important solution in combined heat and power systems. Their use enables the achievement of over 90 percent efficiency in the management of the primary energy source with a very high durability of the device, mainly due to the lack of contact of the working gas with external factors and a very small number of mechanical components. The use of a Stirling engine may be equally important...
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Cooperative Data Transmission in Wireless Vehicular Networks
PublikacjaThe paper presents issues related to the cooperative transmission in wireless vehicular networks. Cooperative transmission involves the use of mobile terminals as relay stations to improve the transmission quality, improve network performance and reduce energy consumption. The paper presents the methods used to implement cooperative transmission and the types of cooperative networks.
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Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling
PublikacjaFull-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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Naval mine detection system based of FPGA circuit
PublikacjaElectrochemical processes take place in a metal object immersed into sea water even if an anticorrosive coating is applied [1]. As a result, flowing field appears around the object. There are naval mines between many other objects situated in the sea. Naval mines can be put in the seabed in order to be more difficult to detect by sonars. Such a mine is located on the line demarking two environments of different electrical conductivity....
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Electrical and noise responses of carbon nanotube networks enhanced by UV light for nitrogen dioxide sensing
Dane BadawczeNetworks consisting of randomly oriented carbon nanotubes (CNN) were investigated toward nitrogen dioxide detection by means of electrical and low-frequency noise measurements. UV-activation of CNN layers improved gas sensitivity and reduced the limit of detection, especially by employing 275 nm-LED. This data set includes DC resistance measurements...
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublikacjaThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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Robust Object Detection with Multi-input Multi-output Faster R-CNN
PublikacjaRecent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. However, recently it was shown that similarly competitive results could be achieved with a much smaller cost, by using multi-input multi-output architecture (MIMO). In this work,...
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Robust Object Detection with Multi-input Multi-output Faster R-CNN
PublikacjaRecent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. However, recently it was shown that similarly competitive results could be achieved with a much smaller cost, by using multi-input multi-output architecture (MIMO). In this work,...
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Testing the impact of simplifications adopted in models describing wave loads on ship
PublikacjaThe wave-ship system in naval architecture is generally assumed to be a linear one. This allows for the computation of a transfer function of ship response to waves including wave loads on ship. The transfer function and the wave spectral density function enable the development of the stochastic process of wave loads on ship, corresponding to the irregular wave generating that loads. If the mathematical models of wave load transfer...
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Testing the impact of simplifications adopted in models describing wave loads on ship
PublikacjaThe wave-ship system in naval architecture is generally assumed to be a linear one. This allows for the computation of a transfer function of ship response to waves including wave loads on ship. The transfer function and the wave spectral density function enable the development of the stochastic process of wave loads on ship, corresponding to the irregular wave generating that loads. If the mathematical models of wave load transfer...
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Categorization of emotions in dog behavior based on the deep neural network
PublikacjaThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Fundamentals of Physics-Based Surrogate Modeling
PublikacjaChapter 1 was focused on data-driven (or approximation-based) modeling methods. The second major class of surrogates are physics-based models outlined in this chapter. Although they are not as popular, their importance is growing because of the challenges related to construction and handling of approximation surrogates for many real-world problems. The high cost of evaluating computational models, nonlinearity of system responses,...
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Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
PublikacjaThe diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) - the most popular kind of deep learning algorithms...
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Comprehensive distortion compensation of grid-connected inverter currents
PublikacjaThe paper presents a comprehensive approach to the compensation of phase current distortion of grid-connected power inverters. Four different sources of current distortion are addressed: imperfect grid synchronization caused by the distortion in the grid voltages, voltage fluctuations in the dc link, time delays in the estimation of grid voltages, and the distortion of inverter output voltages due to dead-time effects and related...
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Reduced order models in computational electromagnetics (in memory of Ruediger Vahldieck)
PublikacjaThis paper reviews research of Ruediger Vahldieck's group and the group at the Gdansk University of Technology in the area of model order reduction techniques for accelerating full-wave simulations. The applications of reduced order models to filter design as well as of local and nested(multilevel) macromodels for solving 3D wave equations and wave-guiding problems using finite difference and finite element methods are discussed.
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Architectural Modifications to Enhance Steganalysis with Convolutional Neural Networks
PublikacjaThis paper investigates the impact of various modifications introduced to current state-of-the-art Convolutional Neural Network (CNN) architectures specifically designed for the steganalysis of digital images. Usage of deep learning methods has consistently demonstrated improved results in this field over the past few years, primarily due to the development of newer architectures with higher classification accuracy compared to...
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Assessment of Therapeutic Progress After Acquired Brain Injury Employing Electroencephalography and Autoencoder Neural Networks
PublikacjaA method developed for parametrization of EEG signals gathered from participants with acquired brain injuries is shown. Signals were recorded during therapeutic session consisting of a series of computer assisted exercises. Data acquisition was performed in a neurorehabilitation center located in Poland. The presented method may be used for comparing the performance of subjects with acquired brain injuries (ABI) who are involved...
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Computational Mathematics and Modeling
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Evolving neural network as a decision support system — Controller for a game of “2048” case study
PublikacjaThe paper proposes an approach to designing the neuro-genetic self-learning decision support system. The system is based on neural networks being adaptively learned by evolutionary mechanism, forming an evolved neural network. Presented learning algorithm enables for a selection of the neural network structure by establishing or removing of connections between the neurons, and then for a finding the beast suited values of the network...