Effects of freeze-thaw cycles on granite failure using acoustic
Rock subjected to freeze-thaw (F-T) cycles may experience alterations in structural integrity and potentially impact its strength. This study investigates the effects of F-T cycles on granite by analyzing the acoustic emission (AE) signals recorded during uniaxial compression tests, characterizing the damage responses of the granite influenced by repeated …
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(PDF) The Effect of Class Distribution on Classifier Learning: An
This comparison allows us to isolate and quantify the effect that the training set's class distribution has on learning and contrast the performance of the classifiers on the minority and majority ...
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The effect of metamorphism on the aggregate properties of …
Granitic rocks are durable materials sought after for the production of road and railroad aggregates. Granitic bedrock commonly, however, includes gabbroic components, which may enhance or decrease the aggregate performance. This study evaluates the variation in resistance to fragmentation (Los Angeles value, LA) and wear/abrasion (micro-Deval value, …
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On AE-Based Classification of the Real-Time Damage Evolution …
It's full of challenges to make a real-time observation and determination of three crack stresses for brittle granite with subtle deformation, damage accumulation and a jumping drop of stress under axial compression tests (UCT).
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Classifier design for computer-aided diagnosis: effects of finite
A classifier is designed with case samples drawn from the patient population. Generally, the sample size available for classifier design is limited, which introduces variance and bias into the … Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers ...
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Effects of heating on some physical properties of granite, Shandong
In this paper, granite samples are heated from 25 °C to 800 °C. The quality, color (L ⁎ a ⁎ b ⁎), roughness, thermal properties, and P-wave velocity of the samples after heat treatment were tested.The reasons for the testing of these physical parameters are as follows: (1) to study any possible thermal damage of granite by using the L ⁎ a ⁎ b ⁎ value of the specimen surface …
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Effect of mesoscopic heterogeneity on hydraulic fracturing
Granite is primarily composed of minerals such as quartz, mica, and feldspar. From a mesoscopic perspective, variations in mineral particle size, mineral content, and pore structure contribute to the rock's heterogeneity (Gao et al., 2023).These structural differences significantly influence the mechanical properties of granite (Guo et al., 2019; Wang et al., 2024; Feng et al., 2021; Yin et …
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Classifier effects on human categorization: the role of shape
Frontier in Psychology, 2010. This paper explores the effect of classifiers on young children's conceptual structures. For this purpose we studied Mandarin Chinese-and German-speaking 3-and 5-year-olds on non-lexical classification, novel-noun label extension, and inductive inference of novel properties.
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Scissors, Paper, Stone": Perceptual Foundations of Noun Classifier …
Shape classifiers are highly unusual in the way they reinforce the child's discovery of the physical world. Functional classifiers, which m ark m ore arbitrary vcategorizations, may not be so helpful to disadvantaged children. In addition, classifiers are governed by shifting discourse needs rather than object qualities.
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CFD elucidation of microscopic particles in a low-volumetric classifier
CFD elucidation of microscopic particles in a low-volumetric classifier towards effects of Stokes number and density ratio. ... This effect was irrespective of the incoming air velocity. ... [35] utilized CFD software and a discrete phase model (DPM) to simulate a Stone Powder Separator (SPS). The study involved modeling the airflow pattern and ...
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Effects of temperature on mechanical properties of granite under
Temperature is one of important factors influencing mechanical properties of rocks. To explore the evolution law of fracture characteristics of rocks at different temperatures, semi …
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Mechanical properties and macro–micro failure mechanisms of …
This paper investigates the coupled effects of inclination and temperature on the physical and mechanical properties of granite, as well as its macro and micro fracture …
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Application Of Spiral Classifier In Granite Processing
The application of spiral classifier in granite processing is mainly to separate and classify granite ore according to particle size through its classification function. The following are the specific application steps of spiral classifiers in granite processing. The application steps of spiral classifier in granite processing 1. Crushing and ...
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The effect of data complexity on classifier performance
The research area of Software Defect Prediction (SDP) is both extensive and popular, and is often treated as a classification problem. Improvements in classification, pre-processing and tuning techniques, (together with many factors which can influence model performance) have encouraged this trend. However, no matter the effort in these areas, it …
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The Effects of Class Imbalance and Training Data Size on …
The Effects of Class Imbalance and Training Data Size on Classifier Learning: An Empirical Study ...
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Effect of Immersion Duration on Shear Behavior of Granite Fractures
An understanding of shear behavior of fractured rocks subjected to different durations of groundwater immersion is essential for practical rock engineering. Direct shear tests were conducted on fractured granite samples after different immersion durations of 1, 3, 6 and 12 months, and the initially dry granite fracture samples were also tested as reference. The …
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Automatic classification of granite tiles through colour and texture
We discuss the development of an expert system for automatic classification of granite tiles. We propose new approaches to granite classification based on combined colour and texture analysis. We evaluate the performance of different visual descriptors and classifiers. Combination of colour and texture features proves highly effective in discriminating granite …
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Study of the degradation and microstructural characteristics of …
As shown in Table 1, the average density for the granite porphyry samples was 2.549 g/cm 3, and the average wave velocity was 3786 m/s.The samples required for this …
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Agitation effect on particle dispersion and separation in an …
Fluidized bed separators are an evolved version of hydraulic classifiers that have been extensively used in beneficiation of coarse coal slime and mineral sands (He et al., 2020, Kumar et al., 2009).Underflow discharge is controlled in a fluidized bed separator to achieve a stable fluidized bed where particles are separated on the basis of their hindered settling …
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Effects of Classifier Structures and Training Regimes on …
The string recognition results compare favorably to the best ones reported in the literature though we totally ignored the geometric context. The best results were obtained using a support vector classifier, but the neural classifiers and discriminative density models show better trade-off between accuracy and computational overhead.
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Derivative Classification Training (Final Exam) Flashcards
Derivative classification does not have the same impact and effects as original classification. False. Which of the following is true concerning derivative classification? Derivative classifiers are responsible for analyzing and evaluating information to …
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Mechanical Centrifugal Air Classifiers
When higher surface moisture is present in stone sand (2.5–3.0%), the fines stick to the rock and larger air classifiers are required with more airflow than usual to be effective. When the surface moisture is very high (3.5–4.0% …
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New insights into the continuous-discontinuous failure …
Tensile failure in brittle rocks is crucial for the stability of rock engineering applications. However, a comprehensive quantitative evaluation of continuous-discontinuous tensile failure …
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Effects of Training Samples and Classifiers on Classification of
We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier.
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Making Use of NXt to Nothing: The Effect of Class …
such as the effect of class imbalances on classifiers in order to keep the false positive rate as low as possible. The performance of machine learning classifiers depends on several factors such as the type of the classifier, choice of hyperparameters, and probably most important on the training data. A classifier always derives a
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On AE-Based Classification of the Real-Time Damage Evolution …
In this study, we investigated the strength, deformation, failure, and acoustic emission (AE) characteristics of granite during the five uniaxial incremental rates of (0.5, 1.0, …
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Using Granite to help organize conferences (and more!)
See how an ai-abstract-classifier runs locally, leverages Granite via Ollama, with an interface of AnythingLLM. It is a straightforward, secure, local, and private way to leverage an open source LLM to save hours of reading CFPs. ... Learn how you can use the Granite Models locally in these IBM Developer tutorials: Access the Granite models in ...
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Effect of Header-based Features on Accuracy of Classifiers …
subsets are used for classification to find the effect on accuracy of classifier. The minimum number of features with classifier is selected as result. The steps are as follows, 1) Input: Email datasets. 2) Extract Email header features. 3) Apply feature selection techniques. 4) Select subset of features generated by feature selection
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Effects of Mill Speed and Air Classifier Speed on Performance of …
The aim of this work was to observe the impact of the milling technique employed by the DESI 11 disintegrator on the properties of fly ash. This type of mill is a high-speed pin mill with two ...
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Training Neural Network Classifiers for Medical Decision Making: …
In this study the effect of a class imbalance in training data on the performance was evaluated for neural network based classifiers and the two-class classification problem. The confounding effects of other factors such as training sample size, number of features, and correlation between features were also considered.
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