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Search results for: recurrent neural networks
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A new fuzzy model of multi-criteria decision support based on Bayesian networks for the urban areas' decarbonization planning
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A new approach to inter-layer sharing providing differentiated protection services in survivable IP-MPLS/WDM networks
PublicationArtykuł omawia zagadnienie ochrony transmisji o charakterze połączeniowym w sieciach wielowarstwowych IP-MPLS/WDM. W szczególności prezentuje nową metodę współdzielenia międzywarstwowego zasobów ścieżek zabezpieczających gwarantującą szybkie odtwarzanie uszkodzonych połączeń (nawet o 40% szybciej w porównaniu z powszechnie stosowaną metodą).
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ADAPTIVE PREDICTIONS OF THE EURO/ZŁOTY CURRENCY EXCHANGE RATE USING STATE SPACE WAVELET NETWORKS AND FORECAST COMBINATIONS
PublicationThe paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day- ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space...
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Knowledge management in the IPv6 migration process
PublicationThere are many reasons to deploy IPv6 protocol with IPv4 address space depletion being the most obvious. Unfortunately, migration to IPv6 protocol seems slower than anticipated. To improve pace of the IPv6 deployment, authors of the article developed an application that supports the migration process. Its main purpose is to help less experienced network administrators to facilitate the migration process with a particular target...
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland – Swietokrzyskie Voivodeship
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Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples
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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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Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"
PublicationThe purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers’ recordings are presented in the form of two-dimensional spectrogram images and...
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Application of Bayesian networks for inferring cause–effect relations from gene expression profiles of cancer versus normal cells
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Fiber optic interface channels for united data and power supply transmission for neutral interaction application in signal transmission networks
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Thermal analysis and experimental verification of permanent magnet synchronous motor by combining lumped-parameter thermal networks with analytical method
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Dispersive Delay Structures With Asymmetric Arbitrary Group-Delay Response Using Coupled-Resonator Networks With Frequency-Variant Couplings
PublicationThis article reports the design of coupled-resonatorbased microwave dispersive delay structures (DDSs) with arbitrary asymmetric-type group delay response. The design process exploits a coupling matrix representation of the DDS circuit as a network of resonators with frequency-variant couplings (FVCs). The group delay response is shaped using complex transmission zeros (TZs) created by dispersive cross-couplings. We also present an...
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International Journal of Computer Networks & Communications (IJCNC)
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Rating by detection: an artifact detection protocol for rating EEG quality with average event duration
PublicationQuantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real EEG optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically...
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Neurocontrolled Car Speed System
PublicationThe features of the synthesis of neural controllers for the car speed control system are considered in this article. The task of synthesis is to determine the weight coefficients of neural networks that provide the implementation of proportional and proportional-integralderivative control laws. The synthesis of controllers is based on an approach that uses a reversed model of the standard. A model of the car speed control system with...
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Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction
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Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Deep Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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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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Optymalizacja zasad koegzystencji sieci standardów Bluetooth i IEEE 802.11 = Optimization of Bluetooth and IEEE 802.11 networks co-existence
PublicationZ uwagi na rosnącą popularność standardów Bluetooth (BT) i IEEE 802.11b (Wi-Fi ) można się z nimi spotkać praktycznie wszędzie. Gwałtowny wzrost liczby urządzeń różnych technologii ma także swoje negatywne strony. Stosowanie coraz większej liczby urządzeń różnych systemów radiokomunikacyjnych powoduje wzrost poziomu zaburzeń elektromagnetycznych. W konsekwencji działanie różnych sieci bezprzewodowych pracujących w bliskim zasięgu...
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Signals of the NB-IoT network generated by using the radiocommunication tester R&S CMW500
Open Research DataThe published dataset contains signals of the NB-IoT networks generated in the controlled conditions by using the Rohde&Schwarz CMW500 radiocommunication tester and recorded by USRP-X310 device. A test signals of the NB-IoT networks operating in the in-band, standalone and guard-band modes were captured.
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Zastosowanie algorytmu ewolucyjnego do uczenia neuronowego regulatora napięcia generatora synchronicznego. Evolutionary algorithm for training a neural network of synchronous generator voltage controller
PublicationNajpopularniejsza metoda uczenia wielowarstwowych sieci neuronowych -metoda wstecznej propagacji błędu - charakteryzuje się słabą efektywnością. Z tego względu podejmowane są próby stosowania innych metod do uczenia sieci. W pracy przedstawiono wyniki uczenia sieci realizującej regulator neuronowy, za pomocą algorytmu ewolucyjnego. Obliczenia symulacyjne potwierdziły dobrą zbieżność algorytmu ewolucyjnego w tym zastosowaniu.
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Computational intelligence methods in production management
PublicationThis chapter presents a survey of selected computational intelligence methods used in production management. This group of methods includes, among others, approaches based on the artificial neural networks, the evolutionary algorithms, the fuzzy logic systems and the particle swarm optimization mechanisms. From the abovementioned methods particularly noteworthy are the evolutionary and the particle swarm algorithms, which are successfully...
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Bartosz Puchalski dr inż.
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Journal of Sensor and Actuator Networks
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Multicast Traffic Throughput Maximization through Joint Dynamic Modulation and Coding Schemes Assignment, and Transmission Power Control in Wireless Sensor Networks
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn 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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Methods of Artificial Intelligence for Prediction and Prevention Crisis Situations in Banking Systems
PublicationIn this paper, a support vector machine has been studied due to prediction of bank crisis. To prevent outcomes of crisis situations, artificial neural networks have been characterized as applied to stock market investments, as well as to test the credibility of the bank's customers. Finally, some numerical experiments have been presented.
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Cluster Computing-The Journal of Networks Software Tools and Applications
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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublicationTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
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Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition
PublicationThe idea that synchronous neural activity underlies cognition has driven an extensive body of research in human and animal neuroscience. Yet, insufficient data on intracranial electrical connectivity has precluded a direct test of this hypothesis in a whole-brain setting. Through the lens of memory encoding and retrieval processes, we construct whole-brain connectivity maps of fast gamma (30-100 Hz) and slow theta (3-8 Hz) spectral...
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Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublicationThe aim of this paper is to examine the new method of obtaining the simulation-based results using backpropagation of errors artificial neural networks. The primary motivation to conduct the research was to determine an alternative, more efficient and less timeconsuming method which would serve to achieve the results of daylight simulations. Three daylight metrics: Daylight Factor, Daylight Autonomy and Daylight Glare Probability have...
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Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
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Creating a radiological database for automatic liver segmentation using artificial intelligence.
PublicationImaging in medicine is an irreplaceable stage in the diagnosis and treatment of cancer. The subsequent therapeutic effect depends on the quality of the imaging tests performed. In recent years we have been observing the evolution of 2D to 3D imaging for many medical fields, including oncological surgery. The aim of the study is to present a method of selection of radiological imaging tests for learning neural networks.
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On a Method of Efficiency Increasing in Kaplan Turbine
PublicationThis paper presents a method of increasing efficiency in Kaplan-type turbine. The method is based on blade profile optimisation together with modelling the interaction between rotor and stator blades. Loss coefficient was chosen as the optimisation criterion, which is related directly to efficiency. Global optimum was found by means of Genetic Algorithms, and Artificial Neural Networks were utilised for approximations to reduce...
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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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Classifying Emotions in Film Music - A Deep Learning Approach
PublicationThe paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the...
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Speech Analytics Based on Machine Learning
PublicationIn this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
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Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
PublicationThe 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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Automatic music set organizatio based on mood of music / Automatyczna organizacja bazy muzycznej na podstawie nastroju muzyki
PublicationThis work is focused on an approach based on the emotional content of music and its automatic recognition. A vector of features describing emotional content of music was proposed. Additionally, a graphical model dedicated to the subjective evaluation of mood of music was created. A series of listening tests was carried out, and results were compared with automatic mood recognition employing SOM (Self Organizing Maps) and ANN (Artificial...
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Document Agents with the Intelligent Negotiations Capability
PublicationThe paper focus is on augmenting proactive document-agents with built -in intelligence to enable them to recognize execution context provided by devices visited durning the business process, and to reach collaboration agreement despite of their conflicting requirements. We propose a solution based on neural networks to improve simple multi-issue negotiation between the document and the device, practically with no excessive cost...
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Neuronowa symulacja temperatury i ciśnienia pary w upuście parowego bloku energetycznego = Neural simulation of pressure and temperature fluctuations at steam extraction of power units with steam turbine
PublicationW artykule przedstawiono metodę symulacji neuronowej dla zastosowań w diagnostyce on-line bloków energetycznych. Model neuronowy opiera się na statycznych jednokierunkowych sieciach neuronowych (SSN) oraz na danych z parowego bloku energetycznego o mocy 200 MW. SSN obliczają wartości referencyjne parametrów cieplno-przepływowych dla aktualnego obciążenia obiektu. Określono wpływ architektury sieci i danych uczących na jakość symulacji...
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Zarządzanie (współzarządzanie) sieciowe i zarządzanie sieciami w wymiarze sprawiedliwości – wyzwania (15 stron) Governance network and networks governance in the justice system – challenges
PublicationCelem artykułu jest próba odpowiedzi na pytania czy w wymiarze sprawiedliwości jest miejsce i podstawa do wdrożenia zarządzania sieciowego (współzarządzania) oraz czy w działalności pomocniczej wymiaru sprawiedliwości istnieje potencjał do jego wdrożenia. W wymiarze sprawiedliwości istnieje duży potencjał do wykorzystania mechanizmów sieciowej współpracy. W ramach przestrzeni wymiaru sprawiedliwości współpraca międzyorganizacyjna...
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Towards Knowledge Sharing Oriented Adaptive Control
PublicationIn this paper, we propose a knowledge sharing oriented approach to enable a robot to reuse other robots' knowledge by adapting itself to the inverse dynamics model of the knowledge-sharing robot. The purpose of this work is to remove the heavy fine-tuning procedure required before using a new robot for a task via reusing other robots' knowledge. We use the Neural Knowledge DNA (NK-DNA) to help robots gain empirical knowledge and...
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Abdominal epilepsy in patient of schizophrenia - a diagnostic dilemma
PublicationAbdominal epilepsy is a rare and uncomman cause of recurrent abdominal pain. It is commonly occuring in children, but rarely in adolescent and elderly. Paroxysmal episodes of abdominal pain with neurological symptoms like dizziness, lethargy, and abnormal electroencephalogram and remarkable response to anticonvulsant confirms the diagnosis. Here we present a case of schizophrenia, who has repoted with recurrent abdominal pain...
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Control of the cultivation of cartilages for using in the biobearings.
PublicationBiotribologiczne charakterystyki biołożysk są zależne od procesu hodowli żywej tkanki chrząstki w bioreaktorze. Z kolei proces ten, jest wielowymiarowym procesem dynamicznym sterowanym za pomocą odpowiedniego układu automatycznej regulacji. Praca przedstawia prawo i algorytm sterowania takiego procesu. W tym celu zastosowano sztuczne sieci neuronowe (Artificial Neural Networks - ANN) i zaprezentowano wyniki obliczeń.
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Adaptive CAD-Model Construction Schemes
PublicationTwo advanced surrogate model construction techniques are discussed in this paper. The models employ radial basis function (RBF)interpolation scheme or artificial neural networks (ANN) with a new training algorithm. Adaptive sampling technique is applied withrespect to all variables. Histograms showing the quality of the models are presented. While the quality of RBF models is satisfactory, theperformance of the ANN models obtained...
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Sławomir Gajewski dr inż.
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Machine Learning in Multi-Agent Systems using Associative Arrays
PublicationIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...