Search results for: overview of porosity classification
3475 U-Net Based Multi-Output Network for Lung Disease Segmentation and Classification Using Chest X-Ray Dataset
Authors: Jaiden X. Schraut
Abstract:
Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.Keywords: chest X-ray, deep learning, image segmentation, image classification
Procedia PDF Downloads 1353474 Case-Based Reasoning: A Hybrid Classification Model Improved with an Expert's Knowledge for High-Dimensional Problems
Authors: Bruno Trstenjak, Dzenana Donko
Abstract:
Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.Keywords: case based reasoning, classification, expert's knowledge, hybrid model
Procedia PDF Downloads 3643473 A Comparison of South East Asian Face Emotion Classification based on Optimized Ellipse Data Using Clustering Technique
Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam
Abstract:
In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering
Procedia PDF Downloads 5413472 Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning
Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul
Abstract:
In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.Keywords: electrocardiogram, dictionary learning, sparse coding, classification
Procedia PDF Downloads 3753471 Strong Microcapsules with Macroporous Polymer Shells
Authors: Eve S. A. Loiseau, Marion Frey, Yves Blickenstorfer, Fabian Niedermair, André R. Studart
Abstract:
Porous microcapsules have a broad range of applications that require a robust shell. We propose a new method to produce macroporous polymer capsules with controlled size, shell thickness, porosity and mechanical properties using co-flow flow-focusing glass capillary devices. The porous structure was investigated through SEM and the permeability through confocal microscopy. Compression tests on single capsules were performed. We obtained microcapsules with tailored permeability from open to close pores structures and able to withstand loads up to 150 g.Keywords: microcapsules, micromechanics, porosity, polymer shells
Procedia PDF Downloads 4433470 Semi-Automatic Method to Assist Expert for Association Rules Validation
Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen
Abstract:
In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.Keywords: association rules, rule-based classification, classification quality, validation
Procedia PDF Downloads 4313469 Spatial Audio Player Using Musical Genre Classification
Authors: Jun-Yong Lee, Hyoung-Gook Kim
Abstract:
In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing
Procedia PDF Downloads 4223468 Survey on Big Data Stream Classification by Decision Tree
Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi
Abstract:
Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.Keywords: big data, data streams, classification, decision tree
Procedia PDF Downloads 5133467 Documents Emotions Classification Model Based on TF-IDF Weighting Measure
Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees
Abstract:
Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms
Procedia PDF Downloads 4733466 Development of Wave-Dissipating Block Installation Simulation for Inexperienced Worker Training
Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto
Abstract:
In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks
Procedia PDF Downloads 883465 A Custom Convolutional Neural Network with Hue, Saturation, Value Color for Malaria Classification
Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi
Abstract:
Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images
Procedia PDF Downloads 833464 Reinforcement Learning for Classification of Low-Resolution Satellite Images
Authors: Khadija Bouzaachane, El Mahdi El Guarmah
Abstract:
The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.Keywords: classification, deep learning, reinforcement learning, satellite imagery
Procedia PDF Downloads 2013463 Using Self Organizing Feature Maps for Classification in RGB Images
Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami
Abstract:
Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image
Procedia PDF Downloads 4703462 A Hybrid Fuzzy Clustering Approach for Fertile and Unfertile Analysis
Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin
Abstract:
Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve
Procedia PDF Downloads 3273461 Overview and Pathophysiology of Radiation-Induced Breast Changes as a Consequence of Radiotherapy Toxicity
Authors: Monika Rezacova
Abstract:
Radiation-induced breast changes are a consequence of radiotherapy toxicity over the breast tissues either related to targeted breast cancer treatment or other thoracic malignancies (eg. lung cancer). This study has created an overview of different changes and their pathophysiology. The main conditions included were skin thickening, interstitial oedema, fat necrosis, dystrophic calcifications, skin retractions, glandular atrophy, breast fibrosis and radiation induced breast cancer. This study has performed focused literature search through multiple databases including pubmed, medline and embase. The study has reviewed English as well as non English publications. As a result of the literature the study provides comprehensive overview of radiation-induced breast changes and their pathophysiology with small focus on new development and prevention.Keywords: radiotherapy toxicity, breast tissue changes, breast cancer treatment, radiation-induced breast changes
Procedia PDF Downloads 1523460 Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
Authors: Jia Xin Low, Keng Wah Choo
Abstract:
This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification
Procedia PDF Downloads 3383459 Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Authors: Walid Cherif
Abstract:
Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification
Procedia PDF Downloads 4593458 Statistical Wavelet Features, PCA, and SVM-Based Approach for EEG Signals Classification
Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh
Abstract:
The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine
Procedia PDF Downloads 6273457 Lipschitz Classifiers Ensembles: Usage for Classification of Target Events in C-OTDR Monitoring Systems
Authors: Andrey V. Timofeev
Abstract:
This paper introduces an original method for guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with a probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if the cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers is relevant in the multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble
Procedia PDF Downloads 4873456 Direct Electrophoretic Deposition of Hierarchical Structured Electrode Supercapacitor Application
Authors: Jhen-Ting Huang, Chia-Chia Chang, Hu-Cheng Weng, An-Ya Lo
Abstract:
In this study, Co3O4-CNT-Graphene composite electrode was deposited by electrophoretic deposition (EPD) method, where micro polystyrene spheres (PSs) were added for co-deposition. Applied with heat treatment, a hierarchical porosity is left in the electrode which is beneficial for supercapacitor application. In terms of charge and discharge performance, we discussed the optimal CNT/Graphene ratio, macroporous ratio, and the effect of Co3O4 addition on electrode capacitance. For materials characterization, scanning electron microscope (SEM), X-ray diffraction, and BET were applied, while cyclic voltammetry (CV) and chronopotentiometry (CP) measurements, and Ragone plot were applied as in-situ analyses. Based on this, the effects of PS amount on the structure, porosity and their effect on capacitance of the electrodes were investigated. Finally, the full device performance was examined with charge-discharge and electron impedance spectrum (EIS) methods. The results show that the EPD coating with hierarchical porosity was successfully demonstrated in this study. As a result, the capacitance was greatly enhanced by 2.6 times with the hierarchical structure.Keywords: supercapacitor, nanocarbon tub, graphene, metal oxide
Procedia PDF Downloads 1353455 A Review of Effective Gene Selection Methods for Cancer Classification Using Microarray Gene Expression Profile
Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali
Abstract:
Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile
Procedia PDF Downloads 4413454 Preliminary Study of Sediment-Derived Plastiglomerate: Proposal to Classification
Authors: Agung Rizki Perdana, Asrofi Mursalin, Adniwan Shubhi Banuzaki, M. Indra Novian
Abstract:
The understanding about sediment-derived plastiglomerate has a wide-range of merit in the academic realm. It can cover discussions about the Anthropocene Epoch in the scope of geoscience knowledge to even provide a solution for the environmental problem of plastic waste. Albeit its importance, very few research has been done regarding this issue. This research aims to create a classification as a pioneer for the study of sediment-derived plastiglomerate. This research was done in Bantul Regency, Daerah Istimewa Yogyakarta Province as an analogue of plastic debris sedimentation process. Observation is carried out in five observation points that shows three different depositional environments, which are terrestrial, fluvial, and transitional environment. The resulting classification uses three parameters and forms in a taxonomical manner. These parameters are composition, degree of lithification, and abundance of matrix respectively in advancing order. There is also a compositional ternary diagram which should be followed before entering the plastiglomerate nomenclature classification.Keywords: plastiglomerate, classification, sedimentary mechanism, microplastic
Procedia PDF Downloads 1253453 Use of Interpretable Evolved Search Query Classifiers for Sinhala Documents
Authors: Prasanna Haddela
Abstract:
Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm
Procedia PDF Downloads 1073452 Classification Systems of Peat Soils Based on Their Geotechnical, Physical and Chemical Properties
Authors: Mohammad Saberian, Reza Porhoseini, Mohammad Ali Rahgozar
Abstract:
Peat is a partially carbonized vegetable tissue which is formed in wet conditions by decomposition of various plants, mosses and animal remains. This restricted definition, including only materials which are entirely of vegetative origin, conflicts with several established soil classification systems. Peat soils are usually defined as soils having more than 75 percent organic matter. Due to this composition, the structure of peat soil is highly different from the mineral soils such as silt, clay and sand. Peat has high compressibility, high moisture content, low shear strength and low bearing capacity, so it is considered to be in the category of problematic. Since this kind of soil is generally found in many countries and various zones, except for desert and polar zones, recognizing this soil is inevitably significant. The objective of this paper is to review the classification of peats based on various properties of peat soils such as organic contents, water content, color, odor, and decomposition, scholars offer various classification systems which Von Post classification system is one of the most well-known and efficient system.Keywords: peat soil, degree of decomposition, organic content, water content, Von Post classification
Procedia PDF Downloads 5903451 Natural Frequency Analysis of a Porous Functionally Graded Shaft System
Authors: Natural Frequency Analysis of a Porous Functionally Graded Shaft System
Abstract:
The vibration characteristics of a functionally graded (FG) rotor model having porosities and micro-voids is investigated using three-dimensional finite element analysis. The FG shaft is mounted with a steel disc located at the midspan. The shaft ends are supported on isotropic bearings. The FG material is composed of a metallic (stainless-steel) and ceramic phase (zirconium oxide) as its constituent phases. The layer wise material property variation is governed by power law. Material property equations are developed for the porosity modelling. Python code is developed to assign the material properties to each layer including the effect of porosities. ANSYS commercial software is used to extract the natural frequencies and whirl frequencies for the FG shaft system. The obtained results show the influence of porosity volume fraction and power-law index, on the vibration characteristics of the ceramic-based FG shaft system.Keywords: Finite element method, Functionally graded material, Porosity volume fraction, Power law
Procedia PDF Downloads 1953450 Overview of a Quantum Model for Decision Support in a Sensor Network
Authors: Shahram Payandeh
Abstract:
This paper presents an overview of a model which can be used as a part of a decision support system when fusing information from multiple sensing environment. Data fusion has been widely studied in the past few decades and numerous frameworks have been proposed to facilitate decision making process under uncertainties. Multi-sensor data fusion technology plays an increasingly significant role during people tracking and activity recognition. This paper presents an overview of a quantum model as a part of a decision-making process in the context of multi-sensor data fusion. The paper presents basic definitions and relationships associating the decision-making process and quantum model formulation in the presence of uncertainties.Keywords: quantum model, sensor space, sensor network, decision support
Procedia PDF Downloads 2193449 INRAM-3DCNN: Multi-Scale Convolutional Neural Network Based on Residual and Attention Module Combined with Multilayer Perceptron for Hyperspectral Image Classification
Authors: Jianhong Xiang, Rui Sun, Linyu Wang
Abstract:
In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification
Procedia PDF Downloads 733448 Effect of Rice Husk Ash on Strength and Durability of High Strength High Performance Concrete
Authors: H. B. Mahmud, Syamsul Bahri, Y. W. Yee, Y. T. Yeap
Abstract:
This paper reports the strength and durability properties of high strength high performance concrete incorporating rice husk ash (RHA) having high silica, low carbon content and appropriate fineness. In this study concrete containing 10%, 15% and 20% RHA as cement replacement and water to binder ratio of 0.25 were investigated. The results show that increasing amount of RHA increases the dosage of superplasticizer to maintain similar workability. Partial replacement of cement with RHA did not increase the early age compressive strength of concrete. However, concrete containing RHA showed higher compressive strength at later ages. The results showed that compressive strength of concrete in the 90-115 MPa range can be obtained at 28 curing days and the durability properties of RHA concrete performed better than that of control concrete. The water absorption of concrete incorporating 15% RHA exhibited the lowest value. The porosity of concrete is consistent with water absorption whereby higher replacement of RHA decreased the porosity of concrete. There is a positive correlation between reducing porosity and increasing compressive strength of high strength high performance concrete. The results also indicate that up to 20% of RHA incorporation could be advantageously blended with cement without adversely affecting the strength and durability properties of concrete.Keywords: compressive strength, durability, high performance concrete, rice husk ash
Procedia PDF Downloads 3353447 Dynamic Voltage Restorer Control Strategies: An Overview
Authors: Arvind Dhingra, Ashwani Kumar Sharma
Abstract:
Power quality is an important parameter for today’s consumers. Various custom power devices are in use to give a proper supply of power quality. Dynamic Voltage Restorer is one such custom power device. DVR is a static VAR device which is used for series compensation. It is a power electronic device that is used to inject a voltage in series and in synchronism to compensate for the sag in voltage. Inductive Loads are a major source of power quality distortion. The induction furnace is one such typical load. A typical induction furnace is used for melting the scrap or iron. At the time of starting the melting process, the power quality is distorted to a large extent especially with the induction of harmonics. DVR is one such approach to mitigate these harmonics. This paper is an attempt to overview the various control strategies being followed for control of power quality by using DVR. An overview of control of harmonics using DVR is also presented.Keywords: DVR, power quality, harmonics, harmonic mitigation
Procedia PDF Downloads 3693446 Comparative Study of Tensile Properties of Cast and Hot Forged Alumina Nanoparticle Reinforced Composites
Authors: S. Ghanaraja, Subrata Ray, S. K. Nath
Abstract:
Particle reinforced Metal Matrix Composite (MMC) succeeds in synergizing the metallic matrix with ceramic particle reinforcements to result in improved strength, particularly at elevated temperatures, but adversely it affects the ductility of the matrix because of agglomeration and porosity. The present study investigates the outcome of tensile properties in a cast and hot forged composite reinforced simultaneously with coarse and fine particles. Nano-sized alumina particles have been generated by milling mixture of aluminum and manganese dioxide powders. Milled particles after drying are added to molten metal and the resulting slurry is cast. The microstructure of the composites shows good distribution of both the size categories of particles without significant clustering. The presence of nanoparticles along with coarser particles in a composite improves both strength and ductility considerably. Delay in debonding of coarser particles to higher stress is due to reduced mismatch in extension caused by increased strain hardening in presence of the nanoparticles. However, higher addition of powder mix beyond a limit results in deterioration of mechanical properties, possibly due to clustering of nanoparticles. The porosity in cast composite generally increases with the increasing addition of powder mix as observed during process and on forging it has got reduced. The base alloy and nanocomposites show improvement in flow stress which could be attributed to lowering of porosity and grain refinement as a consequence of forging.Keywords: aluminium, alumina, nano-particle reinforced composites, porosity
Procedia PDF Downloads 243