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Search results for: HIGH-PERFORMANCE ALKALI-ACTIVATED CONCRETE COMPRESSIVE STRENGTH COST AND CARBON EMISSION MACHINE LEARNING ALGORITHMS STEEL FIBER
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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...
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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....
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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...
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Photocatalytic performance of alkali metal doped graphitic carbon nitrides and Pd-alkali metal doped graphitic carbon nitride composites
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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...
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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...
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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...
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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...
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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)...
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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...
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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...
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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....
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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...
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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...
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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,...
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Large-Scale and Low-Cost Motivation of Nitrogen-Doped Commercial Activated Carbon for High-Energy-Density Supercapacitor
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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...
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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...
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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,...
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Uniform corrosion monitoring of carbon steel in concrete
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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....
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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...
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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...
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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,...
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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...
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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...
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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...
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Introduction to the special issue on machine learning in acoustics
PublicationWhen 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...
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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...
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublicationA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
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Boron doped Nanocrystalline Diamond-Carbon Nanospike Hybrid Electron Emission Source
PublicationElectron emission signifies an important mechanism facilitating the enlargement of devices that have modernized large parts of science and technology. Today, the search for innovative electron emission devices for imaging, sensing, electronics, and high-energy physics continues. Integrating two materials with dissimilar electronic properties into a hybrid material is an extremely sought-after synergistic approach envisioning a...
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Real and Virtual Instruments in Machine Learning – Training and Comparison of Classification Results
PublicationThe continuous growth of the computing power of processors, as well as the fact that computational clusters can be created from combined machines, allows for increasing the complexity of algorithms that can be trained. The process, however, requires expanding the basis of the training sets. One of the main obstacles in music classification is the lack of high-quality, real-life recording database for every instrument with a variety...
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MACHINE LEARNING APPLICATIONS IN RECOGNIZING HUMAN EMOTIONS BASED ON THE EEG
PublicationThis study examined the machine learning-based approach allowing the recognition of human emotional states with the use of EEG signals. After a short introduction to the fundamentals of electroencephalography and neural oscillations, the two-dimensional valence-arousal Russell’s model of emotion was described. Next, we present the assumptions of the performed EEG experiment. Detail aspects of the data sanitization including preprocessing,...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo 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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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublicationIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
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Ultimate strength of stiffened plates subjected to compressive load and spatially distributed mechanical properties
PublicationThe present study deals with the ultimate strength of stiffened plates subjected to spatially distributed mechanical properties and compressive load. Normally, mean values of mechanical properties based on tensile tests are used to validate the numerical assessment with experimental results. However, mechanical properties may vary within a single specimen. To investigate the impact of that, random fields of yield stress and Young...
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Machine Learning and Electronic Noses for Medical Diagnostics
PublicationThe 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...
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Characterization of Highly Filled Glass Fiber/Carbon Fiber Polyurethane Composites with the Addition of Bio-Polyol Obtained through Biomass Liquefaction
PublicationThis work aims to investigate the process of obtaining highly filled glass and carbon fiber composites. Composites were manufactured using previously obtained cellulose derived polyol, polymeric methylene diphenyl diisocyanate (pMDI). As a catalyst, dibutyltin dilaurate 95% and Dabco® 33-LV were used. It was found that the addition of carbon and glass fibers into the polymer matrix causes an increase in the mechanical properties...
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Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
PublicationHigh-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the...