Search
Filters
total: 16255
filtered: 12637
-
Catalog
- Publications 12637 available results
- Journals 364 available results
- Conferences 117 available results
- People 287 available results
- Projects 17 available results
- Research Equipment 1 available results
- e-Learning Courses 202 available results
- Events 16 available results
- Open Research Data 2614 available results
Chosen catalog filters
displaying 1000 best results Help
Search results for: high-performance alkali-activated concrete compressive strength cost and carbon emission machine learning algorithms steel fiber
-
Verification of Selected Calculation Methods Regarding Shear Strength in Reinforced and Prestressed Concrete Beams
PublicationThe purpose of this article was an attempt to compare selected calculation methods regarding shear strength in reinforced and prestressed concrete beams. Several calculation methods were tested. This included codes: PN-EN 1992-1-1:2008 [1], ACI 318- 14 [2] and fib Model Code for Concrete Structures 2010 [3]. The analysis also consists of methods published in technical literature. Calculations of shear strengths were made based...
-
Predicting Performance of Lightweight Concrete with Granulated Expanded Glass and Ash Aggregate by Means of Using Artificial Neural Networks
PublicationLightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure’s weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity...
-
Process of Medical Dataset Construction for Machine Learning-Multifield Study and Guidelines
PublicationThe acquisition of high-quality data and annotations is essential for the training of efficient machine learning algorithms, while being an expensive and time-consuming process. Although the process of data processing and training and testing of machine learning models is well studied and considered in the literature, the actual procedures of obtaining data and their annotations in collaboration with physicians are in most cases...
-
High catalytic performance of laccase wired to naphthylated multiwall carbon nanotubes
PublicationThe direct electrical connection of laccase on the electrode surface is a key feature in the design of efficient and stable biocathodes. However, laccases can perform a direct electron transfer only when they are in the preferable orientation toward the electrode. Here we report the investigation of the orientation of Laccase from Amano on multi-walled carbon nanotube surface modified with naphthalene group. Naphthylated multi...
-
Storage of High-Strength Steel Flux-Cored Welding Wires in Urbanized Areas
PublicationThe condition of the consumables is a key factor determining the waste reduction in the welding processes and the quality of the welded joint. The paper presents the results of tests of four types of fux-cored wires dedicated for welding high-strength steels, stored for 1 month and 6 months in Poland in two urbanized areas: in a large seaside city (Gdańsk) and in Warsaw, located in the center of the country. The wires were subjected...
-
Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublicationAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
-
THE COST ANALYSIS OF CORROSION PROTECTION SOLUTIONS FOR STEEL COMPONENTS IN TERMS OF THE OBJECT LIFE CYCLE COST
PublicationSteel materials, due to their numerous advantages - high availability, easiness of processing and possibility of almost any shaping are commonly applied in construction for carrying out basic carrier systems and auxiliary structures. However, the major disadvantage of this material is its high corrosion susceptibility, which depends strictly on the local conditions of the facility and the applied type of corrosion protection system....
-
Triaxial compression and shear strength characteristics of twostage concrete: an experimental study
PublicationThe research necessity stems from the need to understand and evaluate the performance of Two- Stage Concrete (TSC) under triaxial compression conditions, as prior studies have predominantly focused on uniaxial and biaxial testing of conventional concrete (CC). This study represents the first comprehensive investigation into the triaxial compressive strength and related mechanical properties of TSC, addressing a critical gap in...
-
Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublicationMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
-
Photocatalytic performance of alkali metal doped graphitic carbon nitrides and Pd-alkali metal doped graphitic carbon nitride composites
Publication -
Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
-
Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
-
Impact of Tensile and Compressive Stress on Classical and Acoustic Barkhausen Effects in Grain-Oriented Electrical Steel
PublicationIn this paper, we present the results of the investigation of impact of tensile and compressive stress on the classical Barkhausen effect, magnetoacoustic emission (MAE) signal properties, and B(H) hysteresis loops for grain-oriented (GO) electrical steel. Samples have been glued to a nonmagnetic steel bar and stressed within elastic range (±800 μdef) by means of four-point bending method. The samples were cut out in two directions...
-
Mechanical and structural behavior of high-strength low-alloy steel pad welded by underwater wet welding conditions
PublicationThe aim of the paper was to determine the metallurgical and mechanical behaviors of a high-strength low-alloy (HSLA) steel pad-welded specimen used in the structures of industrial and naval parts. Then to predict the metallurgical consequences (nature of the phases present) and the mechanical properties (hardness and impact strength) of the pad-welded steel obtained by underwater wet welding with different heat input values. The...
-
Optimization of Microwave Components Using Machine Learning and Rapid Sensitivity Analysis
PublicationRecent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately,...
-
Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublicationDue to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...
-
Antenna Optimization Using Machine Learning with Reduced-Dimensionality Surrogates
PublicationIn modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM)...
-
Prediction of cast-in-place concrete strength of the extradosed bridge deck based on temperature monitoring and numerical simulations
PublicationThe work is devoted to the implementation of a monitoring system for high performance concrete embedded in the span of an extradosed bridge deck using a modified maturity method augmented by numerical simulations conducted by the authors’ FEM code. The paper presents all research stages of bridge construction and considers the conclusions drawn from the results of laboratory tests, field measurements, and numerical calculations....
-
Triaxial compression and shear strength characteristics of two stage concrete: an experimental study
PublicationThe research necessity stems from the need to understand and evaluate the performance of Two- Stage Concrete (TSC) under triaxial compression conditions, as prior studies have predominantly focused on uniaxial and biaxial testing of conventional concrete (CC). This study represents the first comprehensive investigation into the triaxial compressive strength and related mechanical properties of TSC, addressing a critical gap in...
-
Assessing the attractiveness of human face based on machine learning
PublicationThe attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing...
-
Load effect impact on the exploitation of concrete machine foundations used in the gas and oil industry
PublicationMachine foundations is a critical topic in the gas and oil industry, which design and exploitation require extensive technical knowledge. Machine foundations are the constructions which are intended for mounting on it a specific type of machine. The foundation has to transfer dynamic and static load from machine to the ground. The primary difference between machine foundations and building foundations is that the machine foundations...
-
Large-Scale and Low-Cost Motivation of Nitrogen-Doped Commercial Activated Carbon for High-Energy-Density Supercapacitor
Publication -
A Machine Learning Approach for Estimating Overtime Allocation in Software Development Projects
PublicationOvertime planning in software projects has traditionally been approached with search-based multi-objective optimization algorithms. However, the explicit solutions produced by these algorithms often lack applicability and acceptance in the software industry due to their disregard for project managers' intuitive knowledge. This study presents a machine learning model that learns the preferred overtime allocation patterns from solutions...
-
Aspects of intergranular corrosion of AISI 321 stainless steel in high-carbon-containing environments
PublicationPurpose – The purpose of this paper is to present a case study of unexpected sensitization to intergranular corrosion of highly resistant AISI 321 steel in petrochemical conditions, where it was subjected to the simultaneous influence of elevated temperature of 250°C and vapors from the asphalt production process. Design/methodology/approach – Corrosion coupons were exposed in an installation carrying asphalt vapors. To identify...
-
Personal bankruptcy prediction using machine learning techniques
PublicationIt has become crucial to have an early prediction model that provides accurate assurance for users about the financial situation of consumers. Recent studies have focused on predicting corporate bankruptcies and credit defaults, not personal bankruptcies. Due to this situation, the present study fills the literature gap by comparing different machine learning algorithms to predict personal bankruptcy. The main objective of the...
-
Advanced numerical modelling for predicting residual compressive strength of corroded stiffened plates
PublicationAn advanced methodology for predicting the residual compressive strength of corroded stiffened plates is developed here using the non-linear finite element method. The non-uniform loss of a plate thickness is accounted for on a macro-scale. In contrast, mechanical properties are changed using the constitutive model to reflect the corrosion degradation impact on a micro-scale. Three different stiffened plate thicknesses are considered,...
-
Uniform corrosion monitoring of carbon steel in concrete
Publication -
Uniform corrosion monitoring of carbon steel in concrete.
PublicationW przypadku korozji prętów zbrojeniowych w żelbecie zaobserwowane zostały obszary wzmożonego zaatakowania. Pomiar prądowego i napięciowego szumu elektrochemicznego umożliwia wyznaczenie szybkości korozji ogólnej a zmiany parametrów statystycznych przebiegów szumowych umożliwiają detekcję występowania przestrzennych rejonów intensyfikacji korozji. Przedstawione rezultaty, oparte na wykorzystaniu odpowiedniego układu zastępczego,...
-
Designing acoustic scattering elements using machine learning methods
PublicationIn the process of the design and correction of room acoustic properties, it is often necessary to select the appropriate type of acoustic treatment devices and make decisions regarding their size, geometry, and location of the devices inside the room under the treatment process. The goal of this doctoral dissertation is to develop and validate a mathematical model that allows predicting the effects of the application of the scattering...
-
Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublicationIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
-
Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublicationMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
-
Modular machine learning system for training object detection algorithms on a supercomputer
PublicationW pracy zaprezentowano architekturę systemu służącego do tworzenia algorytmów wykorzystujących metodę AdaBoost i służących do wykrywania obiektów (np. twarzy) na obrazach. System został podzielony na wyspecjalizowane moduły w celu umożliwienia łatwej rozbudowy i efektywnego zrównoleglenia implementacji przeznaczonej dla superkomputera. Na przykład, system może być rozszerzony o nowe cechy i algorytmy ich ekstrakcji bez konieczności...
-
On the Application of Magnetoacoustic Emission for a Nondestructive Assessment of the Post Welding Heat Treatment of High Chromium Steel Weld Seams
PublicationThe paper analyses the possibility of post weld heat treatment (PWHT) quality assessment with the help of magnetoacoustic emission (MAE) signal measurements. Two welded superheater tubes, made of high chromium VM12 steel, were analysed—as welded and heat treated one. The analysed sample in the as welded state exhibited significantly higher hardness, accompanied by a big difference in the MAE signal intensity (of order of about...
-
Isothermal Calorimetry and Compressive Strength Tests of Mortar Specimens for Determination of Apparent Activation Energy
PublicationThe hydration process of cementitious materials involves a thermally activated reaction that depends on the composition of the mixture and the curing temperature. The main parameter affecting the temperature variation of cast-in-place concrete is the apparent activation energy, which can be used for the efficient prediction of the temperature evolution and maturity index of hardening concrete. This paper discusses two methods to...
-
Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
-
Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
-
Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
-
Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublicationThe study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...
-
Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms
Publication(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification...
-
Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublicationMaximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...
-
Self-organized multilayered graphene-boron doped diamond hybrid nanowalls for high performance electron emission devices
PublicationCarbon nanomaterials like nanotubes, nanoflakes/nanowalls and graphene have been used as electron sources due to their superior field electron emission (FEE) characteristics. Nevertheless, these materials show poor stability and a short lifetime, preventing them from being used in practical device applications. The intention of this study was to find an innovative nanomaterial, possessing both high robustness and reliable FEE behavior....
-
Sustainable utilization of sodium silicate-based lead glass sludge as an alkali-activator for alkali-activated slag: Performance, characterization, and Pb-stabilization
Publication -
Weldability of high strength steels in wet welding conditions
PublicationIn this paper are characterized problems of high strength steel weldability in underwater wet welding conditions. Water as a welding environment intensifies action of unfavourable factors which influence susceptibility to cold cracking of welded steel joints. The susceptibility to cold cracking of S355J2G3 steel and S500M steel in wet conditions was experimentally estimated (by using Tekken test). It was concluded that the steels...
-
Global Miniaturization of Broadband Antennas by Prescreening and Machine Learning
PublicationThe development of contemporary electronic components, particularly antennas, places significant emphasis on miniaturization. This trend is driven by the emergence of technologies such as mobile communications, the internet of things, radio-frequency identification, and implantable devices. The need for small size is accompanied by heightened demands on electrical and field properties, posing a considerable challenge for antenna...
-
Pin-on-Substrate Gap Waveguide: An Extremely Low-Cost Realization of High-Performance Gap Waveguide Components
PublicationConsidering the limitations of currently available technologies for the realization of microwave components and antennas, a trade-off between different factors including the efficiency and fabrication cost is required. The main objective of this letter is to propose a novel method for the realization of gap waveguides (GWGs) that take advantage of conventional PCB fabrication technology, thus are low cost and light weight. Moreover,...
-
How Machine Learning Contributes to Solve Acoustical Problems
PublicationMachine learning is the process of learning functional relationships between measured signals (called percepts in the artificial intelligence literature) and some output of interest. In some cases, we wish to learn very specific relationships from signals such as identifying the language of a speaker (e.g. Zissman, 1996) which has direct applications such as in call center routing or performing a music information retrieval task...
-
Predictions of cervical cancer identification by photonic method combined with machine learning
PublicationCervical 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...
-
Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublicationThis research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
-
Predicting emotion from color present in images and video excerpts by machine learning
PublicationThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
-
Determination of Mechanical Properties of P91 Steel by Means of Magnetic Barkhausen Emission
PublicationIn this work, an attempt at determination of mechanical properties by means of a method based on magnetic Barkhausen emission measurements was proposed. The specimens made of P91 steel were subjected to creep or plastic flow which were interrupted after a range of selected time periods in order to achieve specimens with an increasing level of strain. Subsequently, measurements of magnetic Barkhausen emission were carried out, and...