Search results for: NEURAL NETWORK - Bridge of Knowledge

Search

Search results for: NEURAL NETWORK

Search results for: NEURAL NETWORK

  • OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.

    Publication

    In 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...

    Full text available to download

  • Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge

    Biomass management in terms of energy consumption optimization has become a recent challenge for developed countries. Nevertheless, the multiplicity of materials and operating parameters controlling energy consumption in wastewater treatment plants necessitates the need for sophisticated well-organized disciplines in order to minimize energy consumption and dissipation. Sewage sludge (SS) disposal management is the key stage of...

    Full text to download in external service

  • Cleaner energy for sustainable future using hybrid photovoltaics-thermoelectric generators system under non-static conditions using machine learning based control technique

    In addition to the load demand, the temperature difference between the hot and cold sides of the thermoelectric generator (TEG) module determines the output power for thermoelectric generator systems. Maximum power point tracking (MPPT) control is needed to track the optimal global power point as operating conditions change. The growing use of electricity and the decline in the use of fossil fuels have sparked interest in photovoltaic-TEG...

    Full text to download in external service

  • LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

    Open Research Data
    version 3.0 open access - series: LEGO

    The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the...

  • Embedded Representations of Wikipedia Categories

    Publication

    - Year 2021

    In this paper, we present an approach to building neural representations of the Wikipedia category graph. We test four different methods and examine the neural embeddings in terms of preservation of graphs edges, neighborhood coverage in representation space, and their influence on the results of a task predicting parent of two categories. The main contribution of this paper is application of neural representations for improving the...

    Full text to download in external service

  • IEEE International Symposium on Intelleligence in Neural & Biological Systems

    Conferences

  • Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction

    Publication

    - Scientific Reports - Year 2023

    This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...

    Full text available to download

  • Towards neural knowledge DNA

    Publication

    - JOURNAL OF INTELLIGENT & FUZZY SYSTEMS - Year 2017

    In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...

    Full text available to download

  • Neurocontrolled Car Speed System

    The 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...

    Full text available to download

  • Decision making process using deep learning

    Publication

    - Year 2019

    Endüstri 4.0, dördüncü endüstri devrimi veya Endüstriyel Nesnelerin İnterneti (IIoT) olarak adlandırılan sanayi akımı, işletmelere, daha verimli, daha büyük bir esneklikle, daha güvenli ve daha çevre dostu bir şekilde üretim yapma imkanı sunmaktadır. Nesnelerin İnterneti ile bağlantılı yeni teknoloji ve hizmetler birçok endüstriyel uygulamada devrim niteliği taşımaktadır. Fabrikalardaki otomasyon, tahminleyici bakım (PdM – Predictive...

  • Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2018

    In this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands...

    Full text available to download

  • Adding Intelligence to Cars Using the Neural Knowledge DNA

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2017

    In this paper we propose a Neural Knowledge DNA based framework that is capable of learning from the car’s daily operation. The Neural Knowledge DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers' classification based on their driving behaviour. The experimental...

    Full text available to download

  • Direct brain stimulation modulates encoding states and memory performance in humans

    Publication
    • Y. Ezzyat
    • J. E. Kragel
    • J. F. Burke
    • D. F. Levy
    • A. Lyalenko
    • P. Wanda
    • L. O'Sullivan
    • K. B. Hurley
    • S. Busygin
    • I. Pedisich... and 16 others

    - CURRENT BIOLOGY - Year 2017

    People often forget information because they fail to effectively encode it. Here, we test the hypothesis that targeted electrical stimulation can modulate neural encoding states and subsequent memory outcomes. Using recordings from neurosurgical epilepsy patients with intracranially implanted electrodes, we trained multivariate classifiers to discriminate spectral activity during learning that predicted remembering from forgetting,...

    Full text available to download

  • Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings

    Publication

    - Year 2022

    The paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...

    Full text available to download

  • Toward Intelligent Recommendations Using the Neural Knowledge DNA

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2021

    In this paper we propose a novel recommendation approach using past news click data and the Neural Knowledge DNA (NK-DNA). The Neural Knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for news recommendation tasks on the MIND benchmark dataset. By taking advantages of NK-DNA, deep...

    Full text available to download

  • Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks

    Publication
    • T. Dziubich
    • P. Białas
    • Ł. Znaniecki
    • J. Halman
    • J. Brzeziński

    - Year 2020

    One of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...

    Full text to download in external service

  • Generalised heart rate statistics reveal neurally mediated homeostasis transients

    Publication
    • D. Makowiec
    • B. Graff
    • W. Miklaszewski
    • D. Wejer
    • A. Kaczkowska
    • S. Budrejko
    • Z. R. Struzik

    - EPL-EUROPHYS LETT - Year 2015

    Distributions of accelerations and decelerations, obtained from increments of heart rate recorded during a head-up tilt table (HUTT) test provide short-term characterization of the complex cardiovascular response to a rapid controlled dysregulation of homeostasis. A generalised statistic is proposed for evaluating the neural reflexes responsible for restoring the homeostatic dynamics. An evaluation of the effects on heart rate...

    Full text to download in external service

  • Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits

    Publication

    The Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results...

    Full text available to download

  • Hotspot of human verbal memory encoding in the left anterior prefrontal cortex

    Publication

    - EBioMedicine - Year 2022

    Background: Treating memory and cognitive deficits requires knowledge about anatomical sites and neural activities to be targeted with particular therapies. Emerging technologies for local brain stimulation offer attractive therapeutic options but need to be applied to target specific neural activities, at distinct times, and in specific brain regions that are critical for memory formation. Methods: The areas that are critical...

    Full text available to download

  • Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"

    The 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...

    Full text to download in external service

  • Towards Knowledge Sharing Oriented Adaptive Control

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2022

    In 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...

    Full text available to download

  • Accuracy Investigations of Turbine Blading Neural Models Applied to Thermal and Flow Diagnostics

    Publication

    - Year 2016

    Possibility of replacing computional fluid dynamics simulations by a neural model for fluid flow and thermal diagnostics of steam turbines is investigated. Results of calculations of velocity magnitude of steam for 3D model of the stator of steam turbine is presented.

    Full text to download in external service

  • Special techniques and future perspectives: Simultaneous macro- and micro-electrode recordings

    Publication

    - Year 2019

    There are many approaches to studying the inner workings of the brain and its highly interconnected circuits. One can look at the global activity in different brain structures using non-invasive technologies like positron emission tomography (PET) or functional magnetic resonance imaging (fMRI), which measure physiological changes, e.g. in the glucose uptake or blood flow. These can be very effectively used to localize active patches...

    Full text to download in external service

  • Deep Learning: A Case Study for Image Recognition Using Transfer Learning

    Publication

    - Year 2021

    Deep 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,...

    Full text to download in external service

  • Deep Learning

    Publication

    - Year 2021

    Deep 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,...

    Full text to download in external service

  • Breast MRI segmentation by deep learning: key gaps and challenges

    Publication

    Breast 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...

    Full text available to download

  • Direct electrical brain stimulation of human memory: lessons learnt and future perspectives

    Publication

    Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental...

    Full text to download in external service

  • Language Models in Speech Recognition

    Publication

    - Year 2022

    This chapter describes language models used in speech recognition, It starts by indicating the role and the place of language models in speech recognition. Mesures used to compare language models follow. An overview of n-gram, syntactic, semantic, and neural models is given. It is accompanied by a list of popular software.

    Full text to download in external service

  • Application of spatial neural simulators of turbine blade rows to fluid flow diagnostics

    Publication

    - Year 2014

    This chapter presents the results of neural modelling of fluid flow in steam turbine row. In modelling working conditions of the flow channel varied, thus the aim of the work was to reconstruct the reference state - distributions of velocity, pressure, and losses in flow channel - with high accuracy for fluid flow diagnostics.

  • Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

    Publication

    - Year 2021

    This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de...

    Full text available to download

  • Problems of modelling toxic compounds emitted by a marine internal combustion engine in unsteady states

    Publication

    Contemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous...

    Full text available to download

  • Methods of Artificial Intelligence for Prediction and Prevention Crisis Situations in Banking Systems

    In 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.

  • How Can We Identify Electrophysiological iEEG Activities Associated with Cognitive Functions?

    Publication

    - Year 2023

    Electrophysiological activities of the brain are engaged in its various functions and give rise to a wide spectrum of low and high frequency oscillations in the intracranial EEG (iEEG) signals, commonly known as the brain waves. The iEEG spectral activities are distributed across networks of cortical and subcortical areas arranged into hierarchical processing streams. It remains a major challenge to identify these activities in...

    Full text to download in external service

  • Periodic and chaotic dynamics in a map‐based neuron model

    Map-based neuron models are an important tool in modeling neural dynamics and sometimes can be considered as an alternative to usually computationally costlier models based on continuous or hybrid dynamical systems. However, due to their discrete nature, rigorous mathematical analysis might be challenging. We study a discrete model of neuronal dynamics introduced by Chialvo in 1995. In particular, we show that its reduced one-dimensional...

    Full text available to download

  • Self Organizing Maps for Visualization of Categories

    Publication

    - Year 2012

    Visualization of Wikipedia categories using Self Organizing Mapsshows an overview of categories and their relations, helping to narrow down search domains. Selecting particular neurons this approach enables retrieval of conceptually similar categories. Evaluation of neural activations indicates that they form coherent patterns that may be useful for building user interfaces for navigation over category structures.

  • A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention

    Publication

    - IEEE Internet of Things Journal - Year 2019

    Together 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...

    Full text available to download

  • Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation

    Publication

    The 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...

  • Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition

    Publication
    • E. Solomon
    • J. Kragiel
    • M. R. Sperling
    • A. Sharan
    • G. Worrell
    • M. T. Kucewicz
    • C. S. Inman
    • B. Lega
    • K. A. Davis
    • J. M. Stein... and 5 others

    - Nature Communications - Year 2017

    The 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...

    Full text available to download

  • Fault detection in measuring systems of power plants

    Publication

    This paper describes possibility of forming diagnostic relations based on application of the artifical neural networks (ANNs), intended for the identifying of degradation of measuring instruments used in developed power systems. As an example a steam turbine high-power plant was used. And, simulative calculations were applied to forming diagnostic neural relations. Both degradation of the measuring instruments and simultaneously...

    Full text available to download

  • Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements

    Publication

    - ENERGIES - Year 2022

    This article presents the process of the construction and testing a remote, fully autonomous system for measuring the operational parameters of fans. The measurement results obtained made it possible to create and verify mathematical models using linear regression and neural networks. The process was implemented as part of the first stage of an innovative project. The article presents detailed steps of constructing a system to...

    Full text available to download

  • Creating a radiological database for automatic liver segmentation using artificial intelligence.

    Publication

    - EJSO-EUR J SURG ONC - Year 2022

    Imaging 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.

    Full text to download in external service

  • On a Method of Efficiency Increasing in Kaplan Turbine

    This 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...

  • Speech Analytics Based on Machine Learning

    Publication

    In 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...

    Full text to download in external service

  • Document Agents with the Intelligent Negotiations Capability

    Publication

    The 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...

  • Automatic music set organizatio based on mood of music / Automatyczna organizacja bazy muzycznej na podstawie nastroju muzyki

    This 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...

    Full text to download in external service

  • A compact smart sensor based on a neural classifier for objects modeled by Beaunier's model

    A new solution of a smart microcontroller sensor based on a simple direct sensor-microcontroller interface for technical objects modeled by two-terminal networks and by the Beaunier’s model of anticorrosion coating is proposed. The tested object is stimulated by a square pulse and its time voltage response is sampled four times by the internal ADC of microcontroller. A neural classifier based on measurement data classifies the...

    Full text available to download

  • Control of the cultivation of cartilages for using in the biobearings.

    Publication

    - Year 2004

    Biotribologiczne 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.

  • Adaptive CAD-Model Construction Schemes

    Two 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...

    Full text to download in external service

  • Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

    Publication

    - CYBERNETICS AND SYSTEMS - Year 2020

    ABSTRACT With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and...

    Full text available to download

  • Selection of Features for Multimodal Vocalic Segments Classification

    Publication

    English speech recognition experiments are presented employing both: audio signal and Facial Motion Capture (FMC) recordings. The principal aim of the study was to evaluate the influence of feature vector dimension reduction for the accuracy of vocalic segments classification employing neural networks. Several parameter reduction strategies were adopted, namely: Extremely Randomized Trees, Principal Component Analysis and Recursive...

    Full text to download in external service