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Wyniki wyszukiwania dla: deep neural network training benchmarking parallel computations caffe mkl
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Zastosowanie sieci neuronowych do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu
PublikacjaDetekcja impulsów w odebranym sygnale radiowym, zwłaszcza w obecności silnego szumu oraz trendu, jest trudnym zadaniem. Artykuł przedstawia propozycje rozwiązań wykorzystujących sieci neuronowe do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu. Na potrzeby realizacji tego zadania zaproponowano dwie architektury. W pracy przedstawiono wyniki badań wpływu kształtu impulsu, mocy zakłóceń szumowych oraz trendu...
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AUTOMATYCZNA KLASYFIKACJA MOWY PATOLOGICZNEJ
PublikacjaAplikacja przedstawiona w niniejszym rozdziale służy do automatycznego wykrywania mowy patologicznej na podstawie bazy nagrań. W pierwszej kolejności przedstawiono założenia leżące u podstaw przeprowadzonych badan wraz z wyborem bazy mowy patologicznej. Zaprezentowano również zastosowane algorytmy oraz cechy sygnału mowy, które pozwalają odróżnić mowę niezaburzoną od mowy patologicznej. Wytrenowane sieci neuronowe zostały następnie...
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Self-Association of Amphotericin B: Spontaneous Formation of Molecular Structures Responsible for the Toxic Side Effects of the Antibiotic
PublikacjaAmphotericin B (AmB) is a lifesaving antibiotic used to treat deep-seated mycotic infections. Both the pharmaceutical activity and highly toxic side effects of the drug rely on its interaction with biomembranes, which is governed by the molecular organization of AmB. In the present work we present detailed analysis of self-assembly of AmB molecules in different environments, interesting from the physiological standpoint, based...
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Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates
PublikacjaThe major bottleneck of electromagnetic (EM)-driven antenna design is the high CPU cost of massive simulations required by parametric optimization, uncertainty quantification, or robust design procedures. Fast surrogate models may be employed to mitigate this issue to a certain extent. Unfortunately, the curse of dimensionality is a serious limiting factor, hindering the construction of conventional data-driven models valid over...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks
PublikacjaIn the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point...
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Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublikacjaIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublikacjaBeta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural...
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Topology recognition and leader election in colored networks
PublikacjaTopology recognition and leader election are fundamental tasks in distributed computing in networks. The first of them requires each node to find a labeled isomorphic copy of the network, while the result of the second one consists in a single node adopting the label 1 (leader), with all other nodes adopting the label 0 and learning a path to the leader. We consider both these problems in networks whose nodes are equipped with...
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Toward Robust Pedestrian Detection With Data Augmentation
PublikacjaIn this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled. While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety. A popular and simple technique for improving robustness is using data...
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Architektury klasyfikatorów obrazów
PublikacjaKlasyfikacja obrazów jest zagadnieniem z dziedziny widzenia komputerowego. Polega na całościowej analizie obrazu i przypisaniu go do jednej lub wielu kategorii (klas). Współczesne rozwiązania tego problemu są w znacznej części realizowane z wykorzystaniem konwolucyjnych głębokich sieci neuronowych (convolutional neural network, CNN). W tym rozdziale opisano przełomowe architektury CNN oraz ewolucję state-of-the-art w klasyfikacji...
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Intelligent Autonomous Robot Supporting Small Pets in Domestic Environment
PublikacjaIn this contribution, we present preliminary results of the student project aimed at the development of an intelligent autonomous robot supporting small pets in a domestic environment. The main task of this robot is to protect a freely moving small pets against accidental stepping on them by home residents. For this purpose, we have developed the mobile robot which follows a pet and makes an alarm signal when a human is approaching....
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RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine
PublikacjaIn 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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A Canonical Switched Capacitor DC-DC Converter. A look at how the historical SC synthesis problem is getting solved
PublikacjaWe reconsider a solution to the historical problem in switched capacitor (SC) DC-DC converter synthesis. Specifically, we retackle the problem of constructing an SC twophase network realizing arbitrary voltage conversion ratio within rational bounds defined. Theoretical foundations are available in our earlier research [3] where a nonconstructive proof of existence was given. We briefly review and comment on known circuit solutions...
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Trust triggers and barriers in intercultural teams
PublikacjaIntercultural teams are more and more popular nowadays — they constitute a serious challenge in terms of effective cooperation and trust building, however. The article presents the potential problems that can affect intercultural cooperation and stresses the power of trust in cultural diversity conditions. The ten-factor model of intercultural team trust is presented. The main aim was to answer the questions: what are the differences...
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Nieliniowa statyka 6-parametrowych powłok sprężysto plastycznych. Efektywne obliczenia MES
PublikacjaGłównym zagadnieniem omawianym w monografii jest sformułowanie sprężysto-plastycznego prawa konstytutywnego w nieliniowej 6-parametrowej teorii powłok. Wyróżnikiem tej teorii jest występujący w niej w naturalny sposób tzw. stopień 6 swobody, czyli owinięcie (drilling rotation). Podstawowe założenie pracy to przyjęcie płaskiego stanu naprężenia uogólnionego na ośrodek typu Cosseratów. Takie podejście stanowi oryginalny aspekt opracowania....
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublikacjaCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
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Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond
PublikacjaFiber-Reinforced Polymers (FRP) were developed as a new method over the past decades due to their many beneficial mechanical properties, and they are commonly applied to strengthen masonry structures. In this paper, the Artificial Neural Network (ANN), K-fold Cross-Validation (KFCV) technique, Multivariate Adaptive Regression Spline (MARS) method, and M5 Model Tree (M5MT) method were utilized to predict the ultimate strength of...
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Fragmentation of Hydrographic Big Data Into Subsets During Reduction Process
PublikacjaThe article presented problems of fragmentation of hydrographic big data into smaller subsets during reduction process. Data reduction is a processing of reduce the value of the data set, in order to make them easier and more effective for the goals of the analysis. The main aim of authors is to create new reduction method. The article presented the first stage of this method – fragmentation of bathymetric data into subsets. It...
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MACHINE LEARNING SYSTEM FOR AUTOMATED BLOOD SMEAR ANALYSIS
PublikacjaIn this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones - the healthy blood cells (erythrocytes) and the pathologic (echinocytes). The separated blood cells are analyzed in terms of their most important features by the eigenfaces method. The features are the...
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Dynamic Bankruptcy Prediction Models for European Enterprises
PublikacjaThis manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and...
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Instance segmentation of stack composed of unknown objects
PublikacjaThe article reviews neural network architectures designed for the segmentation task. It focuses mainly on instance segmentation of stacked objects. The main assumption is that segmentation is based on a color image with an additional depth layer. The paper also introduces the Stacked Bricks Dataset based on three cameras: RealSense L515, ZED2, and a synthetic one. Selected architectures: DeepLab, Mask RCNN, DEtection TRansformer,...
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Towards Cancer Patients Classification Using Liquid Biopsy
PublikacjaLiquid biopsy is a useful, minimally invasive diagnostic and monitoring tool for cancer disease. Yet, developing accurate methods, given the potentially large number of input features, and usually small datasets size remains very challenging. Recently, a novel feature parameterization based on the RNA-sequenced platelet data which uses the biological knowledge from the Kyoto Encyclopedia of Genes and Genomes, combined with a classifier...
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Laboratory investigation with subbottom parametric echosounder SES-2000 standard with an emphasis on reflected pure signals analysis
PublikacjaThe main goal of the paper is to describe correlations between measurements results of trials taken on Gulf of Gdańsk bottom sounded with parametric echosounder SES-2000 Standard and laboratory research where collected during survey sediments were measured. Stationary tests took place at Gdansk University of Technology where 30 meters long 1.8 meter deep and 3 meters wide water tank is located. Main lobe of antenna was directed...
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Integracja bezprzewodowych heterogenicznych sieci IP dla poprawy efektywności transmisji danych na morzu
PublikacjaWraz ze wzrostem istotności środowiska morskiego w naszym codziennym życiu np. w postaci zwiększonego wolumenu transportu realizowanego drogą morską. czy zintensyfikowanych prac dotyczących obserwacji i monitoringu środowiska morskiego, wzrasta również potrzeba opracowania efektywnych systemów komunikacyjnych dedykowanych dla tego środowiska. Heterogeniczne systemy łączności bezprzewodowej integrowane na poziomie warstwy sieciowej...
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Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
PublikacjaIntroduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological...
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Model-Based Adaptive Machine Learning Approach in Concrete Mix Design
PublikacjaConcrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete...
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Otwarte zasoby edukacyjne - przegląd inicjatyw w Polsce i na świecie
PublikacjaOtwarte zasoby edukacyjne (OZE) to materiały szkoleniowe oraz narzędzia wspierające zarówno uczenie, jak i nauczanie. Zjawisko to nierozerwalnie łączy się z szerszym pojęciem otwartej edukacji (OE), które postuluje zniesienie barier w nauczaniu tak, aby uczący się mogli zdobywać wiedzę zgodnie ze swoimi potrzebami edukacyjno-szkoleniowymi. Celem artykułu jest zapoznanie czytelników z zagadnieniem otwartych zasobów edukacyjnych,...
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Deep Instance Segmentation of Laboratory Animals in Thermal Images
PublikacjaIn this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal...
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Autoencoder application for anomaly detection in power consumption of lighting systems
PublikacjaDetecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...
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Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models
PublikacjaNon-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID- 19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These re- quirements limit the number...
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Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study
PublikacjaThis study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called...
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Visual Content Learning in a Cognitive Vision Platform for Hazard Control (CVP-HC)
PublikacjaThis work is part of an effort for the development of a Cognitive Vision Platform for Hazard Control (CVP-HC) for applications in industrial workplaces, adaptable to a wide range of environments. The paper focuses on hazards resulted from the nonuse of personal protective equipment (PPE). Given the results of previous analysis of supervised techniques for the problem of classification of a few PPE (boots, hard hats, and gloves...
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The Influence of Selecting Regions from Endoscopic Video Frames on The Efficiency of Large Bowel Disease Recognition Algorithms
PublikacjaThe article presents our research in the field of the automatic diagnosis of large intestine diseases on endoscopic video. It focuses on the methods of selecting regions of interest from endoscopic video frames for further analysis by specialized disease recognition algorithms. Four methods of selecting regions of interest have been discussed: a. trivial, b. with the deletion of characteristic, endoscope specific additions to the...
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Project-Based Collaborative Research and Training Roadmap for Manufacturing Based on Industry 4.0
PublikacjaThe 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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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublikacjaBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Human-Computer Interface Based on Visual Lip Movement and Gesture Recognition
PublikacjaThe multimodal human-computer interface (HCI) called LipMouse is presented, allowing a user to work on a computer using movements and gestures made with his/her mouth only. Algorithms for lip movement tracking and lip gesture recognition are presented in details. User face images are captured with a standard webcam. Face detection is based on a cascade of boosted classifiers using Haar-like features. A mouth region is located in...
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Food analysis using artificial senses.
PublikacjaNowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublikacjaTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Propagation of Acoustic Disturbances in Shallow Sea
PublikacjaPropagation of acoustic waves in shallow sea differs fundamentally from the same phenomenon occurring in deep sea in view of non-negligible distance from the sea bottom in the first case, where presence of two regions limiting the water layer results in the acoustic pressure distribution induced by a harmonic source has an interferential nature as a result of multi-path propagation of the acoustic signal. These interferential properties...
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Metal dusting phenomena of 501 AISI furnace tubes in refinery fractional distillation unit
PublikacjaThe purpose of this investigation was to conduct the failure analysis of 501 AISI furnace tubes places before distillation column in fractional distillation unit. The investigated furnace tubes were planned to work for ten years however after just two years of exploitation <30% of the material left. The observed corrosion process had the intense and complex character. The well-adhered shiny black deposits and deep, round pits were...
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A GPU Solver for Sparse Generalized Eigenvalue Problems with Symmetric Complex-Valued Matrices Obtained Using Higher-Order FEM
PublikacjaThe paper discusses a fast implementation of the stabilized locally optimal block preconditioned conjugate gradient (sLOBPCG) method, using a hierarchical multilevel preconditioner to solve nonHermitian sparse generalized eigenvalue problems with large symmetric complex-valued matrices obtained using the higher-order finite-element method (FEM), applied to the analysis of a microwave resonator. The resonant frequencies of the low-order...
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Online sound restoration system for digital library applications
PublikacjaAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
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Technical and economic conditions of supplying residential consumers with heat from nuclear power plant
PublikacjaThe reduction of CO2 emission requires technological shift towards carbon-free options. Cogeneration of electricity and heat based on coal and natural gas will experience generation cost increase, if the costs of emissions are imposed on the plant operators. Therefore, nuclear and renewable options should be considered. In this paper, we present the concept of electricity and heat supply system with nuclear condensing turbine adapted...
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Acoustic Detector of Road Vehicles Based on Sound Intensity
PublikacjaA method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions: parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement....
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Music information retrieval—The impact of technology, crowdsourcing, big data, and the cloud in art.
PublikacjaThe exponential growth of computer processing power, cloud data storage, and crowdsourcing model of gathering data bring new possibilities to music information retrieval (mir) field. Mir is no longer music content retrieval only; the area also comprises the discovery of expressing feelings and emotions contained in music, incorporating other than hearing modalities for helping this issue, users’ profiling, merging music with social...
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Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublikacjaThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
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Deep learning in the fog
PublikacjaIn the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high...
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Robustness in Compressed Neural Networks for Object Detection
PublikacjaModel compression techniques allow to significantly reduce the computational cost associated with data processing by deep neural networks with only a minor decrease in average accuracy. Simultaneously, reducing the model size may have a large effect on noisy cases or objects belonging to less frequent classes. It is a crucial problem from the perspective of the models' safety, especially for object detection in the autonomous driving...