Wyszukiwarka
Filtry
wszystkich: 13298
-
Katalog
- Publikacje 11085 wyników po odfiltrowaniu
- Czasopisma 435 wyników po odfiltrowaniu
- Konferencje 64 wyników po odfiltrowaniu
- Wydawnictwa 1 wyników po odfiltrowaniu
- Osoby 214 wyników po odfiltrowaniu
- Projekty 16 wyników po odfiltrowaniu
- Zespoły Badawcze 1 wyników po odfiltrowaniu
- Kursy Online 212 wyników po odfiltrowaniu
- Wydarzenia 11 wyników po odfiltrowaniu
- Dane Badawcze 1259 wyników po odfiltrowaniu
wyświetlamy 1000 najlepszych wyników Pomoc
Wyniki wyszukiwania dla: machine learning algorithm soil-structure interaction seismic risk assessment residual interstory drift seismic demand seismic failure probability
-
Modeling of a rock fall using the discrete element method and study of the seismic signal
PublikacjaW artykule przedstawiono opis fal sejsmicznych, zastosowanie dyskretnej metody elementów do opisu obrywu skalnego, opis programów obliczeniowych oraz obliczenia numeryczne wg. podanych programów obliczeniowych.
-
Modeling of a rock fall using the discrete element method and study of the seismic signal
PublikacjaMechanizm dużego spadającego bloku skalnego lub lawiny pozostaje nadal słabo rozpoznany zarówno w zakresie pęknięć jak i procesów propagacji. Jedynie ilościowe dane, które aktualnie są mierzone i uchwytne podczas spadania skały, zarejestrowane są jako sejsmiczne sygnały przez szereg sejsmografów. Właściwości sygnału (amplituda, trwanie, częstotliwość) są wyraźnie powiązane z cechami spadania (masa, wysokość spadania, odległość...
-
Expert system against machine learning approaches as a virtual sensor for ventricular arrhythmia risk level estimation
PublikacjaRecent advancements in machine learning have opened new avenues for preventing fatal ventricular arrhythmia by accurately measuring and analyzing QT intervals. This paper presents virtual sensor based on an expert system designed to prevent the risk of fatal ventricular arrhythmias associated with QT-prolonging treatments. The expert system categorizes patients into three risk levels based on their electrocardiogram-derived QT...
-
A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublikacjaTraffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced...
-
Earthquake-induced pounding between equal height multi-storey buildings considering soil-structure interaction
PublikacjaThe present paper investigates the coupled effect of the supporting soil flexibility and pounding between neighbouring, insufficiently separated equal height buildings under earthquake excitation. Two adjacent three-storey structures, modelled as inelastic lumped mass systems with different structural characteristics, have been considered in the study. The models have been excited using a suit of ground motions with different peak...
-
Relation between benchmark displacement velocity and seismic activity caused by underground longwall exploitation
Publikacja -
Assessment Of the Relevance of Best Practices in The Development of Medical R&D Projects Based on Machine Learning
PublikacjaMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper...
-
Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublikacjaWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
-
Behaviour of Colliding Multi-Storey Buildings under Earthquake Excitation Considering Soil-Structure Interaction
PublikacjaThis paper investigates the coupled effect of the supporting soil flexibility and pounding between neighbouring, insufficiently separated buildings under earthquake excitation. Two adjacent three-storey structures, modelled as inelastic lumped mass systems with different structural characteristics, have been considered in the study. The models have been excited using the time history of the Kobe earthquake of 1995. A nonlinear...
-
Failure of cold-formed beam: How does residual stress affect stability?
PublikacjaIn machine industry, stresses are often calculated using simple linear FEM analysis. Occasional failures of elements designed in such a way require recomputation by means of more sophisticated methods, eg. including plasticity and non-linear effects. It usually leads to investigation of failure causes and improvement of an element in order to prevent its unwanted behavior in the future. The study presents the case where both linear...
-
A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia
PublikacjaPrevious reviews have investigated machine learning (ML) models used to predict the risk of developing preeclampsia. However, they have not addressed the intended deployment of these models throughout pregnancy, nor have they detailed feature performance. This study aims to provide an overview of existing ML models and their intended deployment patterns and performance, along with identified features of high importance. This review...
-
TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
PublikacjaTensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing...
-
Speech Analytics Based on Machine Learning
PublikacjaIn 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...
-
Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn 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...
-
Seismic Response of High-Rise Buildings Equipped with Base Isolation and Non-Traditional Tuned Mass Dampers
PublikacjaOne of the methods in structural control is the application of combinational control systems in order to reduce the response of structures during earthquakes. The aim of the present paper is to verify the effectiveness of a hybrid control strategy, combining base isolation and non-traditional tuned mass dampers (TMDs) (i.e., TMDs with dashpots directly connected to the ground) in suppressing structural vibrations of high-rise buildings....
-
Data Reduction Algorithm for Machine Learning and Data Mining
Publikacja -
Shaking table experimental study on pounding between adjacent structures founded on different soil types
PublikacjaThe aim of this study is to extensively investigate the effect of the soil type on the response of colliding structures based on shaking table experimental tests. Two single-storey models of steel buildings with different dynamic parameters were considered in this study. Three pounding scenarios were taken into account by applying different seismic gaps (0.5 cm, 1 cm and 1.5 cm as well as the no pounding case). First, the effect...
-
Raw data of AuAg nanoalloy plasmon resonances used for machine learning method
Dane BadawczeRaw data used for machine learning process. UV-vis measurements of AuAg alloyed nanostructures created from thin films. Plasmonic band position dependence on fabrication parameters. Small presentation reviewing achieved structures and their properties.
-
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...
-
The structure of demand and its economic consequences for urban transport
PublikacjaThe structure of demand for urban transport services, determined by the share of passengers entitled to free and reduced fares, is an important factor influencing its economic condition. The aim of this article is to present the results of research into the structure of demand for urban transport as well as its economic consequences. The article begins by considering the nature of the determinants and consequences of the public...
-
Consequences of neglecting the appropriate safety conditions of structures exposed to seismic excitations - damage examples after the Kaliningrad earthquake
PublikacjaW artykule przedstawiono przykłady uszkodzeń budowli znajdujących się w złym stanie technicznym, które powstały w wyniku trzęsienia ziemi o epicentrum w Obwodzie Kaliningradzkim. Przykłady te jednoznacznie pokazują, iż zaniedbywanie stanu technicznego konstrukcji narażonych na wstrząsy sejsmiczne może prowadzić do poważnych konsekwencji powodując znaczne straty materialne lub ofiary śmiertelne.
-
Effect of soil on the capacity of viscous dampers between adjacent buildings
PublikacjaThis study investigated the seismic pounding of two adjacent buildings considering soil–structure interaction (SSI). A comprehensive parametric study of buildings with different heights was performed to reveal the pounding-involved behaviour considering the soil effect. Wavelet transform has been conducted to gain insight into the differences in the frequency contents of the impact forces between fixed- and flexible-base adjacent...
-
Machine Learning Techniques in Concrete Mix Design
PublikacjaConcrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which...
-
An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
-
Risk of power cables insulation failure due to the thermal effect of solar radiation
PublikacjaLow-voltage, as well as high-voltage power cable lines, are usually buried in the ground. The ampacity of the power cables in the ground mainly depends on the thermal resistivity of the soil, which may vary in a wide range. A common practice in power cable systems performance is to supply them from a pole of an overhead line. If so, a section of the line is located in free air and can be directly exposed to solar radiation. In...
-
An investigation on residual stress and fatigue life assessment of T-shape welded joints
PublikacjaThis paper aims to quantitatively evaluate the residual stress and fatigue life of T-type welded joints with a multi-pass weld in different direction. The main research objectives of the experimental test were to test the residual stress by changing direction along with multiple wielding passes and determine the fatigue life of the welded joints. The result shows that compressive residual stress increases in the sample gradually...
-
Usefulness of the voltamperometric method for assessment of the purity of soil
PublikacjaThis paper presents a voltammetric analysis of soil. It shows that the voltammetry is a useful method for the assessment of soil contaminated with heavy metal ions. There are two major problems with voltammetry analysis: the diffusion coefficient and the measurement system. The paper contains a short literature study of mathematical equations and a study of differences between soil and water measurements. Suggestions of the solution...
-
How Machine Learning Contributes to Solve Acoustical Problems
PublikacjaMachine 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...
-
Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublikacjaCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
-
Assessing the attractiveness of human face based on machine learning
PublikacjaThe 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...
-
Introduction to the special issue on machine learning in acoustics
PublikacjaWhen we started our Call for Papers for a Special Issue on “Machine Learning in Acoustics” in the Journal of the Acoustical Society of America, our ambition was to invite papers in which machine learning was applied to all acoustics areas. They were listed, but not limited to, as follows: • Music and synthesis analysis • Music sentiment analysis • Music perception • Intelligent music recognition • Musical source separation • Singing...
-
Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublikacjaThis 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...
-
Non-linear behaviour of base-isolated building supported on flexible soil under damaging earthquakes
PublikacjaSeismic isolation is a strategy to reduce damage of structures exposed to devastating earthquake excitations. Isolation systems, applied at the base of buildings, lower the fundamental frequency of the structure below the range of dominant frequencies of the ground motion as well as allow to dissipate more energy during structural vibrations. The effectiveness of the base-isolated buildings in damage reduction has been confirmed...
-
FIRE RISK ASSESSMENT IN INDUSTRIAL PREMISES
PublikacjaChapter presents different aspects of fire risk assessment in industrial sites
-
Assessment of unusual failure in crankshaft of heavy-duty truck engine
PublikacjaThe unusual premature failure of a heavy-duty truck engine crankshaft has been the subject of a rigorous study, and this manuscript describes it in detail. The failure was happened to begin with the growth of the crack from the surface defects, in the form of the clusters of non-metallic inclusions, in the lubrication hole zone of the first main journal, which was the stress concentration zone. A series of experiments including...
-
Ship Collision Risk Assessment Based on Collision Detection Algorithm
Publikacja -
Designing acoustic scattering elements using machine learning methods
PublikacjaIn 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...
-
Machine Learning and Electronic Noses for Medical Diagnostics
PublikacjaThe need for noninvasive, easy-to-use, and inexpensive methods for point-of-care diagnostics of a variety of ailments motivates researchers to develop methods for analyzing complex biological samples, in particular human breath, that could aid in screening and early diagnosis. There are hopes that electronic noses, that is, devices based on arrays of semiselective or nonselective chemical sensors, can fill this niche. Electronic...
-
APPLICATION OF QUANTITATIVE RISK ASSESSMENT TO SHIPS IN EMERGENCY CONDITIONS
PublikacjaThe paper is devoted to safety of ships in emergency conditions. The currently valid prescriptive method of safety assessment of ships in damage conditions is included in the SOLAS 2009 Part B-2 Ch.II-1 regulations. It is devoted to the design stage and difficult to apply in operation. A possible alternative described in this paper is a method based on assessment of performance of ships and risk assessment. Type of risk evaluation...
-
Personal bankruptcy prediction using machine learning techniques
PublikacjaIt 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...
-
Predicting emotion from color present in images and video excerpts by machine learning
PublikacjaThis 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...
-
Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublikacjaThe 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...
-
Assessment of student language skills in an e-learning environment
PublikacjaThis article presents the role of various assessment structures that can be used in a VLE. e-Learning language courses offer tutors a wide range of traditional and computer-generated formative and summative assessment procedures and tools. They help to evaluate each student’s progress, monitor their activities and provide varied support, which comes from the tutor, the course structure and materials as well as other participants....
-
Seismic damage diagnosis in adjacent steel and RC MRFs considering pounding effects through improved wavelet-based damage-sensitive feature
Publikacja -
FEM simulations applied to the failure analysis of RC structure under the influence of municipal sewage pressure
PublikacjaThe paper discusses a failure mechanism of reinforced concrete (RC) structure with steel cover that failed under the influence of municipal sewage pressure. To explain the reasons of failure, in-situ measurements, laboratory experiments and comprehensive Finite Element Method (FEM) computations were performed. Non-destructive in-situ scanning tests were carried out to determine quantity and cover thickness of embedded reinforcement...
-
Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublikacjaMachine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...
-
A Machine Learning Approach for Estimating Overtime Allocation in Software Development Projects
PublikacjaOvertime 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...
-
MACHINE LEARNING
Czasopisma -
Concrete mix design using machine learning
PublikacjaDesigning a concrete mix is a process of synthesizing many components, it is not a simple process and requires extensive technical knowledge. The design process itself focuses on obtaining the required strength of concrete. Very often designing a concrete mix takes into account the need to maintain the proper water-demand and frost-resistance features. The parameters that influence the concrete class most significantly are the...
-
Modeling lignin extraction with ionic liquids using machine learning approach
PublikacjaLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....