Search results for: fracture classification

Authors: Reza Hedayati, Meysam Jahanbakhshi

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Fracture Mechanics is used to predict debonding propagation in adhesive joint between aluminum and composite plates. Three types of loadings and two types of glass-epoxy composite sequences: [0/90]2s and [0/45/-45/90]s are considered for the composite plate and their results are compared. It was seen that generally the cases with stacking sequence of [0/45/-45/90]s have much shorter lives than cases with [0/90]2s. It was also seen that in cases with λ=0 the ends of the debonding front propagates forward more than its middle, while in cases with λ=0.5 or λ=1 it is vice versa. Moreover, regardless of value of λ, the difference between the debonding propagations of the ends and the middle of the debonding front is very close in cases λ=0.5 and λ=1. Another main conclusion was the non-dimensionalized debonding front profile is almost independent of sequence type or the applied load value.

Keywords: fatigue, debonding, Paris law, APDL, adhesive

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Authors: M. Safa, A. Sabet, S. MacGillivray, M. Davidson, K. Kaczmarczyk, C. T. Haas, G. E. Gibson, D. Rayside

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To address construction project requirements and specifications, scholars and practitioners need to establish a taxonomy according to a scheme that best fits their need. While existing characterization methods are continuously being improved, new ones are devised to cover project properties which have not been previously addressed. One such method, the Project Definition Rating Index (PDRI), has received limited consideration strictly as a classification scheme. Developed by the Construction Industry Institute (CII) in 1996, the PDRI has been refined over the last two decades as a method for evaluating a project's scope definition completeness during front-end planning (FEP). The main contribution of this study is a review of practical project classification methods, and a discussion of how PDRI can be used to classify projects based on their readiness in the FEP phase. The proposed model has been applied to 59 construction projects in Ontario, and the results are discussed.

Keywords: project classification, project definition rating index (PDRI), risk, project goals alignment

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Authors: Ouafae Baida, Najma Hamzaoui, Maha Akbib, Abdelfettah Sedqui, Abdelouahid Lyhyaoui

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Numerous scientific works present various methods to analyze the data for several domains, specially the comparison of classifications. In our recent work, we presented a new approach to help the user choose the best classification method from the results obtained by every method, by basing itself on the distances between the trees of classification. The result of our approach was in the form of a dendrogram contains methods as a succession of connections. This approach is much needed in phylogeny analysis. This discipline is intended to analyze the sequences of biological macro molecules for information on the evolutionary history of living beings, including their relationship. The product of phylogeny analysis is a phylogenetic tree. In this paper, we recommend the use of a new method of construction the phylogenetic tree based on comparison of different classifications obtained by different molecular genes.

Keywords: hierarchical classification, classification methods, structure of tree, genes, phylogenetic analysis

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Authors: Bineet Kumar

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Many civil engineering infrastructures frequently encounter repetitive loading during their service life. Due to the inherent complexity observed in concrete, like quasi-brittle materials, understanding the fatigue behavior in concrete still posesa challenge. Moreover, the fracture process zone characteristics ahead of the crack tip have been observed to be different in fatigue loading than in the monotonic cases. Therefore, it is crucial to comprehend the energy dissipation associated with the fracture process zone (FPZ) due to repetitive loading. It is well known that stiffness degradation due to cyclic loadingprovides a better understanding of the fracture behavior of concrete. Under repetitive load cycles, concrete members exhibit a two-stage stiffness degradation process. Experimentally it has been observed that the stiffness decreases initially with an increase in crack length and subsequently increases. In this work, an attempt has been made to propose an analytical expression to predict energy dissipation and later the stiffness degradation as a function of crack length. Three-point bend specimens have been considered in the present work to derive the formulations. In this approach, the expression for the resultant stress distribution below the neutral axis has been derived by correlating the bending stress with the cohesive stresses developed ahead of the crack tip due to the existence of the fracture process zone. This resultant stress expression is utilized to estimate the dissipated energydue to crack propagation as a function of crack length. Further, the formulation for the stiffness degradation has been developed by relating the dissipated energy with the work done. It can be used to predict the critical crack length and fatigue life. An attempt has been made to understand the influence of stress amplitude on the damage pattern by using the information on the rate of stiffness degradation. It has been demonstrated that with the increase in the stress amplitude, the damage/FPZ proceeds more in the direction of crack propagation compared to the damage in the direction parallel to the span of the beam, which causes a lesser rate of stiffness degradation for the incremental crack length. Further, the effect of loading frequency has been investigated in terms of stiffness degradation. Under low-frequency loading cases, the damage/FPZ has been found to spread more in the direction parallel to the span, in turn reducing the critical crack length and fatigue life. In such a case, a higher rate of stiffness degradation has been observed in comparison to the high-frequency loading case.

Keywords: fatigue life, fatigue, fracture, concrete

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: power spectral density, 3D EEG model, brain balancing, kNN

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Authors: Khaled Harrar, Rachid Jennane

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The purpose of this study is the discrimination of 28 postmenopausal with osteoporotic femoral fractures from an age-matched control group of 28 women using texture analysis based on fractals. Two pre-processing approaches are applied on radiographic images; these techniques are compared to highlight the choice of the pre-processing method. Furthermore, the values of the fractal dimension are compared to those of the fractal signature in terms of the classification of the two populations. In a second analysis, the BMD measure at proximal femur was compared to the fractal analysis, the latter, which is a non-invasive technique, allowed a better discrimination; the results confirm that the fractal analysis of texture on calcaneus radiographs is able to discriminate osteoporotic patients with femoral fracture from controls. This discrimination was efficient compared to that obtained by BMD alone. It was also present in comparing subgroups with overlapping values of BMD.

Keywords: osteoporosis, fractal dimension, fractal signature, bone mineral density

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Authors: P. Abachi, T. W. Coyle, P. S. Musavi Gharavi

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By applying coating onto a structural component, the corrosion and/or wear resistance requirements of the surface can be fulfilled. Since the layer adhesion of the coating influences the mechanical integrity of the coat/substrate interface during the service time, it should be examined accurately. At the present work, the tensile bonding strength of the 316 stainless steel plasma sprayed coating on aluminum substrate was determined by using tensile adhesion test, TAT, specimen. The interfacial fracture toughness was specified using four-point bend specimen containing a saw notch and modified chevron-notched short-bar (SB) specimen. The coating microstructure and fractured specimen surface were examined by using scanning electron- and optical-microscopy. The investigation of coated surface after tensile adhesion test indicates that the failure mechanism is mostly cohesive and rarely adhesive type. The calculated value of critical strain energy release rate proposes relatively good interface status. It seems that four-point bending test offers a potentially more sensitive means for evaluation of mechanical integrity of coating/substrate interfaces than is possible with the tensile test. The fracture toughness value reported for the modified chevron-notched short-bar specimen testing cannot be taken as absolute value because its calculation is based on the minimum stress intensity coefficient value which has been suggested for the fracture toughness determination of homogeneous parts in the ASTM E1304-97 standard. 

Keywords: bonding strength, four-point bend test, interfacial fracture toughness, modified chevron-notched short-bar specimen, plasma sprayed coating, tensile adhesion test

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Authors: Med. A. Djeridane, M. Ferhat, H. A. Benhorma, O. Bouledroua

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This research is dedicated to exploring the failure of a turbine blade within a gas transportation plant, with a primary focus on conducting a comprehensive examination through advanced metallurgical and mechanical analyses of the identified failed blade. Crafted from the nickel superalloy Inconel IN738LC, the turbine engine had accumulated approximately 61,000 operational hours before the blades failed, causing severe damage to the transportation plant and necessitating a prolonged shutdown. The investigative procedure commenced with an in-depth visual inspection of the blade surfaces, succeeded by fractography analysis of the fracture surfaces, microstructural investigations, chemical analysis, and hardness measurements. The findings unveiled distinctive fatigue marks on the fracture surface. Critical microstructural changes were identified as a consequence of the blade's operation at high temperatures. The investigation determined that the crack initiation resulted from coating damage at the leading edge, subsequently propagating through fatigue. Ultimately, due to a reduction in cross-sectional area, the fracture was completed. This comprehensive analysis sheds light on the intricate factors contributing to turbine blade failure and offers valuable insights for enhancing operational reliability in similar environments.

Keywords: gas turbine, blade failure, TCP phases, fatigue, quantitative analysis

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Authors: Brian E. Usibe

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This paper addresses the problem of a linear crack located in the middle of a homogeneous elastic media under normal tension-compression harmonic loading. The problem of deformation of the fractured media is solved using the direct finite element numerical procedure, including the analysis of the dynamic field variables of the problem. A finite element algorithm that satisfies the unilateral Signorini contact constraint is also presented for the solution of the contact interaction of the crack faces and how this accounts for the qualitative and quantitative changes in the solution when determining the dynamic fracture parameter.

Keywords: harmonic loading, linear crack, fracture parameter, wave number, FEA, contact interaction

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Authors: Minho Kwon, Seonghyeok Lee, Wooyoung Jung

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In recent years, with increase of construction of skyscraper structures, the study of concrete materials to improve their weight and performance has been emerging as a key of research area. Typically, the concrete structures has disadvantage of increasing the weight due to its mass in comparison to the strength of the materials. Therefore, in order to improve such problems, the light-weight aggregate concrete and high strength concrete materials have been studied during the past decades. On the other hand, the study of light-weight aggregate concrete materials has lack of data in comparison to the concrete structure using high strength materials, relatively. Consequently, this study presents the performance characteristics of light-weight aggregate concrete materials due to the material properties and strength. Also, this study conducted the experimental tests with respect to normal and lightweight aggregate materials, in order to indentify the tensile crack failure of the concrete structures. As a result, the Crack Mouth Opening Displacement (CMOD) from the experimental tests was constructed and the fracture energy using inverse problem analysis was developed from the force-CMOD relationship in this study, respectively.

Keywords: lightweight aggregate concrete, crack mouth opening displacement, inverse analysis, fracture energy

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Authors: Michael Bach

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Extensive weld repair was carried out in 2009 after a leak was detected in the aluminum 5052 vessel of the National Research Universal (NRU) reactor. This was the second vessel installed since 1974. In support of the NRU vessel leak repair and fitness for service assessments, an estimate of property changes due to irradiation exposure is required to extend the service of the reactor until 2018. In order to fully evaluate the property changes in the vessel wall, an Iodine-125 rod (I rod) made from the same material and irradiated in the NRU reactor from 1974 1991, was retrieved and sectioned for microstructure characterization and mechanical testing. The different sections of the I rod were exposed to various levels of thermal neutron fluences from 0 to a maximum of 11.9 x 1022 n/cm2. The end of life thermal neutron fluence of the NRU vessel is estimated to be 2.2 x 1022 n/cm2 at 35 years of service. Tensile test and fracture toughness test was performed on the I-rod material at various axial locations. The changes in tensile properties were attributed primarily to the creation of finely dispersed Mg-Si precipitates that harden the material and reduced the ductility. Despite having a reduction in fracture toughness, the NRU vessel is still operation at the current fluence levels.

Keywords: aluminum alloy, fitness-for-service assessment , fracutre toughness, nuclear reactor, precipitate strengthening, radiation damage, tensile strength

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: data envelopment analysis, interval DEA, efficiency classification, efficiency prediction

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Carolyn Whyte, Tina Halai, Sonita Koshal

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Aim: There are many methods available to assess the potential difficulty of third molar surgery. This study investigated various factors to assess whether they had a bearing on the difficulties encountered. Study design: A retrospective study was completed of 62 single mandibular third molar teeth removed under day case general anaesthesia between May 2016 and August 2016 by 3 consultant oral surgeons. Method: Data collection was by examining the OPG radiographs of each tooth and recording the necessary data. This was depth of impaction, angulation, bony impaction, point of application in relation to second molar, root morphology, Pell and Gregory classification and Winters Lines. This was completed by one assessor and verified by another. Information on medical history, anxiety, ethnicity and age were recorded. Case notes and surgical entries were examined for any difficulties encountered. Results: There were 5 cases which encountered surgical difficulties which included fracture of root apices (3) which were left in situ, prolonged bleeding (1) and post-operative numbness >6 months(1). Four of the 5 cases had Pell and Gregory classification as (B) where the occlusal plane of the impacted tooth is between the occlusal plane and the cervical line of the adjacent tooth. 80% of cases had the point of application as either coronal or apical one third (1/3) in relation to the second molar. However, there was variability in all other aspects of assessment in predicting difficulty of removal. Conclusions: Of the cases which encountered difficulties they all had at least one predictor of potential complexity but these varied case by case.

Keywords: impaction, mandibular third molar, radiographic assessment, surgical removal

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Authors: Nasar Aldian Ambark Shashoa

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Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.

Keywords: analytical redundancy, fault detection, feature extraction, Bayesian approach

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Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: A. El Amri, M. El Yakhloufi Haddou, A. Khamlichi

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The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as fracture. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. The influences of thermal and mechanical loads cause damage. The temperature and strain rate dependent materials’ properties and their modelling are discussed. A Johnson-Cook Model of damage has been selected for the numerical simulations. Virtual software called the ABAQUS 6.11 is used for finite element analysis. This model was introduced in order to give information concerning crack initiation during thermal and mechanical loads.

Keywords: thermo-mechanical fatigue, failure, numerical simulation, fracture, damage

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Authors: Dina Kon Mushid, Kabutakapua Kakanda, Dibu Dave Mbako

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The failure of material usually involves elastoplastic deformation and fracturing. Continuum mechanics can effectively deal with plastic deformation by using a yield function and the flow rule. At the same time, it has some limitations in dealing with the fracture problem since it is a theory based on the continuous field hypothesis. The lattice model can simulate the fracture problem very well, but it is inadequate for dealing with plastic deformation. Based on the discretized virtual internal bond model (DVIB), this paper proposes a lattice model that can account for plasticity. DVIB is a lattice method that considers material to comprise bond cells. Each bond cell may have any geometry with a finite number of bonds. The two-body or multi-body potential can characterize the strain energy of a bond cell. The two-body potential leads to the fixed Poisson ratio, while the multi-body potential can overcome the limitation of the fixed Poisson ratio. In the present paper, the modified Stillinger-Weber (SW), a multi-body potential, is employed to characterize the bond cell energy. The SW potential is composed of two parts. One part is the two-body potential that describes the interatomic interactions between particles. Another is the three-body potential that represents the bond angle interactions between particles. Because the SW interaction can represent the bond stretch and bond angle contribution, the SW potential-based DVIB (SW-DVIB) can represent the various Poisson ratios. To embed the plasticity in the SW-DVIB, the plasticity is considered in the two-body part of the SW potential. It is done by reducing the bond stiffness to a lower level once the bond reaches the yielding point. While before the bond reaches the yielding point, the bond is elastic. When the bond deformation exceeds the yielding point, the bond stiffness is softened to a lower value. When unloaded, irreversible deformation occurs. With the bond length increasing to a critical value, termed the failure bond length, the bond fails. The critical failure bond length is related to the cell size and the macro fracture energy. By this means, the fracture energy is conserved so that the cell size sensitivity problem is relieved to a great extent. In addition, the plasticity and the fracture are also unified at the bond level. To make the DVIB able to simulate different Poisson ratios, the three-body part of the SW potential is kept elasto-brittle. The bond angle can bear the moment before the bond angle increment is smaller than a critical value. By this method, the SW-DVIB can simulate the plastic deformation and the fracturing process of material with various Poisson ratios. The elastoplastic SW-DVIB is used to simulate the plastic deformation of a material, the plastic fracturing process, and the tunnel plastic deformation. It has been shown that the current SW-DVIB method is straightforward in simulating both elastoplastic deformation and plastic fracture.

Keywords: lattice model, discretized virtual internal bond, elastoplastic deformation, fracture, modified stillinger-weber potential

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Authors: G. A. Rombach, A. Faron

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Crack formation and growth in reinforced concrete members are, in many cases, the cause of the collapse of technical structures. Such serious failures impair structural behavior and can also damage property and persons. An intensive investigation of the crack propagation is indispensable. Numerical methods are being developed to analyze crack growth in an element and to detect fracture failure at an early stage. For reinforced concrete components, however, further research and action are required in the analysis of shear cracks. This paper presents numerical simulations and continuum mechanical modeling of bending shear crack propagation in a three-dimensional reinforced concrete beam without transverse reinforcement. The analysis will provide a further understanding of crack growth and redistribution of inner forces in concrete members. As a numerical method to map discrete cracks, the extended finite element method (XFEM) is applied. The crack propagation is compared with the smeared crack approach using concrete damage plasticity. For validation, the crack patterns of real experiments are compared with the results of the different finite element models. The evaluation is based on single span beams under bending. With the analysis, it is possible to predict the fracture behavior of concrete members.

Keywords: concrete damage plasticity, crack propagation, extended finite element method, fracture mechanics

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Authors: Thu Nguyen, Jane Daugherty-Luck

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Falx subdural hematoma is a life-threatening condition associated with high mortality. We present a patient case who had fallen with no head injury or loss of conspicuousness. She had tenderness along cervical and thoracic lumbar spine. CT head revealed falx subdural hematoma. The patient was managed medically. The pathophysiology of falx subdural hematoma is linked to laceration of bridging veins provoked by frontal or occipital impact. Posttraumatic subdural hematoma is commonly caused by inertia instead of facture or cerebral contusion resulting from direct impact. The theory is consistent with the lack of fracture in most cases in the literature. Our patient had neither contusion nor fracture.

Keywords: falx subdural hematoma, traumatic head injury, CT head scan, bridging veins, inertia

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Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: Karthik K. R, Viswanath V, Asraff A. K

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The first stage of a new-generation launch vehicle of ISRO makes use of large pressure vessels made of Aluminium alloy AA2219 to store fuel and oxidizer. These vessels have many weld joints that may contain cracks or crack-like defects during their fabrication. These defects may propagate across the vessel during pressure testing or while in service under the influence of tensile stresses leading to catastrophe. Though ductile materials exhibit significant stable crack growth prior to failure, it is not generally acceptable for an aerospace component. There is a need to predict the initiation of stable crack growth. The structural integrity of the vessel from fracture considerations can be studied by constructing the Failure Assessment Diagram (FAD) that accounts for both brittle fracture and plastic collapse. Critical crack sizes of the pressure vessel may be highly conservative if it is predicted from FAD alone. If the J-R curve for material under consideration is available apriori, the critical crack sizes can be predicted to a certain degree of accuracy. In this paper, a novel approach is proposed to predict the integrity of a weld in a pressure vessel made of AA2219 material. Fracture parameter ‘J-integral’ at the crack front, evaluated through finite element analyses, is used in the new procedure. Based on the simulation of tension tests carried out on SCT specimens by NASA, a cut-off value of J-integral value (J?ᵤₜ_ₒ??) is finalised. For the pressure vessel, J-integral at the crack front is evaluated through FE simulations incorporating different surface cracks at long seam weld in a cylinder and in dome petal welds. The obtained J-integral, at vessel level, is compared with a value of J?ᵤₜ_ₒ??, and the integrity of vessel weld in the presence of the surface crack is firmed up. The advantage of this methodology is that if SCT test data of any metal is available, the critical crack size in hardware fabricated using that material can be predicted to a better level of accuracy.

Keywords: FAD, j-integral, fracture, surface crack

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Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

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In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Souhir Chelly, Chahida Harizi, Aicha Hechaichi, Sihem Aissaoui, Leila Ben Ayed, Maha Bergaoui, Mohamed Kouni Chahed

Abstract:

Introduction: The International Classification of Primary Care (ICPC) is part of the morbidity classification system. It had 17 chapters, and each is coded by an alphanumeric code: the letter corresponds to the chapter, the number to a paragraph in the chapter. The objective of this study is to show the utility of this classification in the coding of the reasons for demand for care in Primary health care (PHC), its advantages and limits. Methods: This is a cross-sectional descriptive study conducted in 4 PHC in Ariana district. Data on the demand for care during 2 days in the same week were collected. The coding of the information was done according to the CISP. The data was entered and analyzed by the EPI Info 7 software. Results: A total of 523 demands for care were investigated. The patients who came for the consultation are predominantly female (62.72%). Most of the consultants are young with an average age of 35 ± 26 years. In the ICPC, there are 7 rubrics: 'infections' is the most common reason with 49.9%, 'other diagnoses' with 40.2%, 'symptoms and complaints' with 5.5%, 'trauma' with 2.1%, 'procedures' with 2.1% and 'neoplasm' with 0.3%. The main advantage of the ICPC is the fact of being a standardized tool. It is very suitable for classification of the reasons for demand for care in PHC according to their specificity, capacity to be used in a computerized medical file of the PHC. Its current limitations are related to the difficulty of classification of some reasons for demand for care. Conclusion: The ICPC has been developed to provide healthcare with a coding reference that takes into account their specificity. The CIM is in its 10th revision; it would gain from revision to revision to be more efficient to be generalized and used by the teams of PHC.

Keywords: international classification of primary care, medical file, primary health care, Tunisia

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Authors: B. S. Harish, M. B. Revanasiddappa

Abstract:

Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Behzad Behnia, Noah LaRussa-Trott

Abstract:

In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

Procedia PDF Downloads 142