Search results for: modelling techniques
2566 Investigating the Effectiveness of Iranian Architecture on Sustainable Space Creation
Authors: Mansour Nikpour, Mohsen Ghasemi, Elahe Mosavi, Mohd Zin Kandar
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lack of convenience condition is one of the problems in open spaces in hot and dry regions. Nowadays parks and green landscapes was designed and constructed without any attention to convenience condition. If this process continues, Citizens will encounter with some problems. Harsh climatic condition decreases the efficiency of people-s activities. However there is hard environment condition in hot and dry regions, Convenience condition has been provided in Iranian traditional architecture by using techniques and methods. In this research at the first step characteristics of Iranian garden that can effect on creating sustainable spaces were investigated through observation method. Pleasure space in cities will be created with using these methods and techniques in future cities. Furthermore the comparison between Iranian garden and landscape in today-s cities demonstrate the effectiveness of Iranian garden characteristics on sustainable spaces. Iranian architects used simple and available methods for creating open architectural spaces. In addition desirable conditions were provided with taking in to account both physically and spiritually. Parks and landscapes in future cities can be designed and constructed with respect to architectural techniques that used in Iranian gardens in hot and arid regions.Keywords: Iranian garden, convenience condition, landscape, sustainable
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18332565 Comparison of Different Discontinuous PWM Technique for Switching Losses Reduction in Modular Multilevel Converters
Authors: Kaumil B. Shah, Hina Chandwani
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The modular multilevel converter (MMC) is one of the advanced topologies for medium and high-voltage applications. In high-power, high-voltage MMC, a large number of switching power devices are required. These switching power devices (IGBT) considerable switching losses. This paper analyzes the performance of different discontinuous pulse width modulation (DPWM) techniques and compares the results against a conventional carrier based pulse width modulation method, in order to reduce the switching losses of an MMC. The DPWM reference wave can be generated by adding the zero-sequence component to the original (sine) reference modulation signal. The result of the addition gives the reference signal of DPWM techniques. To minimize the switching losses of the MMC, the clamping period is controlled according to the absolute value of the output load current. No switching is generated in the clamping period so overall switching of the power device is reduced. The simulation result of the different DPWM techniques is compared with conventional carrier-based pulse-width modulation technique.Keywords: Modular multilevel converter, discontinuous pulse width modulation, switching losses, zero-sequence voltage.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8922564 Modeling of the Process Parameters using Soft Computing Techniques
Authors: Miodrag T. Manić, Dejan I. Tanikić, Miloš S. Stojković, Dalibor M. ðenadić
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The design of technological procedures for manufacturing certain products demands the definition and optimization of technological process parameters. Their determination depends on the model of the process itself and its complexity. Certain processes do not have an adequate mathematical model, thus they are modeled using heuristic methods. First part of this paper presents a state of the art of using soft computing techniques in manufacturing processes from the perspective of applicability in modern CAx systems. Methods of artificial intelligence which can be used for this purpose are analyzed. The second part of this paper shows some of the developed models of certain processes, as well as their applicability in the actual calculation of parameters of some technological processes within the design system from the viewpoint of productivity.Keywords: fuzzy logic, manufacturing, neural networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18932563 Biometric Methods and Implementation of Algorithms
Authors: Parvinder S. Sandhu, Iqbaldeep Kaur, Amit Verma, Samriti Jindal, Shailendra Singh
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Biometric measures of one kind or another have been used to identify people since ancient times, with handwritten signatures, facial features, and fingerprints being the traditional methods. Of late, Systems have been built that automate the task of recognition, using these methods and newer ones, such as hand geometry, voiceprints and iris patterns. These systems have different strengths and weaknesses. This work is a two-section composition. In the starting section, we present an analytical and comparative study of common biometric techniques. The performance of each of them has been viewed and then tabularized as a result. The latter section involves the actual implementation of the techniques under consideration that has been done using a state of the art tool called, MATLAB. This tool aids to effectively portray the corresponding results and effects.Keywords: Matlab, Recognition, Facial Vectors, Functions.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31732562 IMDC: An Image-Mapped Data Clustering Technique for Large Datasets
Authors: Faruq A. Al-Omari, Nabeel I. Al-Fayoumi
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In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Keywords: Data clustering, Data mining, Image-mapping, Pattern discovery, Predictive analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14862561 Machine Learning Techniques for COVID-19 Detection: A Comparative Analysis
Authors: Abeer Aljohani
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The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.
Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3612560 Using Data Mining Techniques for Estimating Minimum, Maximum and Average Daily Temperature Values
Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias
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Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.
Keywords: regression algorithms, supervised machine learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34032559 Automatic Visualization Pipeline Formation for Medical Datasets on Grid Computing Environment
Authors: Aboamama Atahar Ahmed, Muhammad Shafie Abd Latiff, Kamalrulnizam Abu Bakar, Zainul AhmadRajion
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Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Keywords: Visualization, Grid computing, Medical datasets, visualization techniques, thin clients, Globus toolkit, VTK.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17422558 A Classical Method of Optimizing Manufacturing Systems Using a Number of Industrial Engineering Techniques
Authors: John M. Ikome, Martha E. Ikome, Therese Van Wyk
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Productivity optimization of a company can significantly increase the company’s output and productivity which can be in the form of corrective actions of ineffective activities, process simplification, and reduction of variations, responsiveness, and reduction of set-up-time which are all under the classification of waste within the manufacturing environment. Deriving a means to eliminate a number of these issues has a key importance for manufacturing organization. This paper focused on a number of industrial engineering techniques which include a cause and effect diagram, to identify and optimize the method or systems being used. Based on our results, it shows that there are a number of variations within the production processes that can significantly disrupt the expected output.
Keywords: Optimization, fishbone diagram, productivity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9892557 End-to-End Spanish-English Sequence Learning Translation Model
Authors: Vidhu Mitha Goutham, Ruma Mukherjee
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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.
Keywords: Attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4552556 Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques
Authors: Faisal Alshuwaier, Ali Areshey
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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound (BB) method to simplify the texts.
Keywords: Extraction, Max-Prod, Fuzzy Relations, Text Mining, Memberships, Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21692555 Full-genomic Network Inference for Non-model organisms: A Case Study for the Fungal Pathogen Candida albicans
Authors: Jörg Linde, Ekaterina Buyko, Robert Altwasser, Udo Hahn, Reinhard Guthke
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Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
Keywords: Pathogen, network inference, text-mining, Candida albicans, LASSO, mutual information, reverse engineering, linear regression, modelling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16572554 Certain Data Dimension Reduction Techniques for application with ANN based MCS for Study of High Energy Shower
Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta
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Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.Keywords: EAS, Shower, Core, ANN, Location.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15942553 Optimising Business Rules in the Services Sector
Authors: Alan Dormer
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Business rules are widely used within the services sector. They provide consistency and allow relatively unskilled staff to process complex transactions correctly. But there are many examples where the rules themselves have an impact on the costs and profits of an organisation. Financial services, transport and human services are areas where the rules themselves can impact the bottom line in a predictable way. If this is the case, how can we find that set of rules that maximise profit, performance or customer service, or any other key performance indicators? The manufacturing, energy and process industries have embraced mathematical optimisation techniques to improve efficiency, increase production and so on. This paper explores several real world (but simplified) problems in the services sector and shows how business rules can be optimised. It also examines the similarities and differences between the service and other sectors, and how optimisation techniques could be used to deliver similar benefits.Keywords: Business rules, services, optimisation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16042552 An Automated Stock Investment System Using Machine Learning Techniques: An Application in Australia
Authors: Carol Anne Hargreaves
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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.
Keywords: Machine learning, stock market trading, logistic principal component analysis, automated stock investment system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10742551 Identifying Blind Spots in a Stereo View for Early Decisions in SI for Fusion based DMVC
Authors: H. Ali, K. Hameed, N. Khan
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In DMVC, we have more than one options of sources available for construction of side information. The newer techniques make use of both the techniques simultaneously by constructing a bitmask that determines the source of every block or pixel of the side information. A lot of computation is done to determine each bit in the bitmask. In this paper, we have tried to define areas that can only be well predicted by temporal interpolation and not by multiview interpolation or synthesis. We predict that all such areas that are not covered by two cameras cannot be appropriately predicted by multiview synthesis and if we can identify such areas in the first place, we don-t need to go through the script of computations for all the pixels that lie in those areas. Moreover, this paper also defines a technique based on KLT to mark the above mentioned areas before any other processing is done on the side view.
Keywords: Side Information, Distributed Multiview Video Coding, Fusion, Early Decision.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13202550 On the Use of Image Processing Techniques for the Estimation of the Porosity of Textile Fabrics
Authors: Ahmet Çay, Savvas Vassiliadis, Maria Rangoussi, Işık Tarakçıoğlu
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This paper presents a novel approach to assessing textile porosity by the application of the image analysis techniques. The images of different types of sample fabrics, taken through a microscope when the fabric is placed over a constant light source,transfer the problem into the image analysis domain. Indeed, porosity can thus be expressed in terms of a brightness percentage index calculated on the digital microscope image. Furthermore, it is meaningful to compare the brightness percentage index with the air permeability and the tightness indices of each fabric type. We have experimentally shown that there exists an approximately linear relation between brightness percentage and air permeability indices.
Keywords: Textile fabrics, porosity, air permeability, image analysis, light transmission.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32552549 Evaluating the Effectiveness of Memory Overcommit Techniques on KVM-based Hosting Platform
Authors: Chin-Hung Li
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Determining how many virtual machines a Linux host could run can be a challenge. One of tough missions is to find the balance among performance, density and usability. Now KVM hypervisor has become the most popular open source full virtualization solution. It supports several ways of running guests with more memory than host really has. Due to large differences between minimum and maximum guest memory requirements, this paper presents initial results on same-page merging, ballooning and live migration techniques that aims at optimum memory usage on KVM-based cloud platform. Given the design of initial experiments, the results data is worth reference for system administrators. The results from these experiments concluded that each method offers different reliability tradeoff.Keywords: Kernel-based Virtual Machine, Overcommit, Virtualization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31072548 An Empirical Evaluation of Performance of Machine Learning Techniques on Imbalanced Software Quality Data
Authors: Ruchika Malhotra, Megha Khanna
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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15072547 Deficiencies of Lung Segmentation Techniques using CT Scan Images for CAD
Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani
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Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the dataset of 19 patients consisting of a total of 917 images. We obtained datasets of 11 patients from Ackron University, USA and of 8 patients from AGA Khan Medical University, Pakistan. After testing the algorithms against datasets, the deficiencies of each algorithm have been highlighted.Keywords: Computer Aided Diagnosis (CAD), MathematicalMorphology, Medical Image Analysis, Region Growing, Segmentation, Thresholding,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23292546 Ozone Therapy and Pulsed Electromagnetic Fields Interplay in Controlling Tumor Growth, Symptom and Pain Management: A Case Report
Authors: J. F. Pollo Gaspary, F. Peron Gaspary, E. M. Simão, R. Concatto Beltrame, G. Orengo de Oliveira, M. S. Ristow Ferreira, F. Sartori Thies, I. F. Minello, F. dos Santos de Oliveira
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Background: The immune system has evolved several mechanisms to protect the host against cancer, and it has now been suggested that the expansion of its functions may prevent tumor growth and control the symptoms of cancer patients. Two techniques, ozone therapy and pulsed electromagnetic fields (PEMF), are independently associated with an increase in the immune system functions and they maybe help palliative care of patients in these conditions. Case Report: A patient with rectal adenocarcinoma with metastases decides to interrupt the clinical chemotherapy protocol due to refractoriness and side effects. As a palliative care alternative treatment it is suggested to the patient the use of ozone therapy associated with PEMF techniques. Results: The patient reports an improvement in well-being, in autonomy and in pain control. Imaging tests confirm a pause in tumor growth despite more than 60 days without using classic treatment. These results associated with palliative care alternative treatment stimulate the return to the chemotherapy protocol. Discussion: This case illustrates that these two techniques can contribute to the control of tumor growth and refractory symptoms, such as pain, probably by enhancing the immune system. Conclusions: The potential use of the combination of these two therapies, ozone therapy and PEMF therapy, can contribute to palliation of cancer patients, alone or in combination with pharmacological therapies. The conduct of future investigations on this paradigm can elucidate how much these techniques contribute to the survival and well-being of these patients.
Keywords: Cancer, complementary and alternative medicine, ozone therapy, palliative care, PEMF Therapy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9582545 Multiple Input Multiple Output Detection Using Roulette Wheel Based Ant Colony Optimization Technique
Authors: B. Rebekka, B. Malarkodi
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This paper describes an approach to detect the transmitted signals for 2×2 Multiple Input Multiple Output (MIMO) setup using roulette wheel based ant colony optimization technique. The results obtained are compared with classical zero forcing and least mean square techniques. The detection rates achieved using this technique are consistently larger than the one achieved using classical methods for 50 number of attempts with two different antennas transmitting the input stream from a user. This paves the path to use alternative techniques to improve the throughput achieved in advanced networks like Long Term Evolution (LTE) networks.Keywords: MIMO, ant colony optimization, roulette wheel, soft computing, LTE.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10602544 Exploring Performance-Based Music Attributes for Stylometric Analysis
Authors: Abdellghani Bellaachia, Edward Jimenez
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Music Information Retrieval (MIR) and modern data mining techniques are applied to identify style markers in midi music for stylometric analysis and author attribution. Over 100 attributes are extracted from a library of 2830 songs then mined using supervised learning data mining techniques. Two attributes are identified that provide high informational gain. These attributes are then used as style markers to predict authorship. Using these style markers the authors are able to correctly distinguish songs written by the Beatles from those that were not with a precision and accuracy of over 98 per cent. The identification of these style markers as well as the architecture for this research provides a foundation for future research in musical stylometry.
Keywords: Music Information Retrieval, Music Data Mining, Stylometry.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16672543 Controlling the Angle of Attack of an Aircraft Using Genetic Algorithm Based Flight Controller
Authors: S. Swain, P. S Khuntia
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In this paper, the unstable angle of attack of a FOXTROT aircraft is controlled by using Genetic Algorithm based flight controller and the result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. In addition, the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. are improved by using Genetic Algorithm. It was established that the error by using GA is very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system.Keywords: Angle of Attack, Genetic Algorithm, Performance Indices, PID Controller.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17542542 Biospeckle Techniques in Quality Evaluation of Indian Fruits
Authors: MD Zaheer Ansari, A.K. Nirala
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In this study spatial-temporal speckle correlation techniques have been applied for the quality evaluation of three different Indian fruits namely apple, pear and tomato for the first time. The method is based on the analysis of variations of laser light scattered from biological samples. The results showed that crosscorrelation coefficients of biospeckle patterns change subject to their freshness and the storage conditions. The biospeckle activity was determined by means of the cross-correlation functions of the intensity fluctuations. Significant changes in biospeckle activity were observed during their shelf lives. From the study, it is found that the biospeckle activity decreases with the shelf-life storage time. Further it has been shown that biospeckle activity changes according to their respiration rates.
Keywords: Biospeckle, cross-correlation, respiration, shelf-life.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24732541 Joint Use of Factor Analysis (FA) and Data Envelopment Analysis (DEA) for Ranking of Data Envelopment Analysis
Authors: Reza Nadimi, Fariborz Jolai
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This article combines two techniques: data envelopment analysis (DEA) and Factor analysis (FA) to data reduction in decision making units (DMU). Data envelopment analysis (DEA), a popular linear programming technique is useful to rate comparatively operational efficiency of decision making units (DMU) based on their deterministic (not necessarily stochastic) input–output data and factor analysis techniques, have been proposed as data reduction and classification technique, which can be applied in data envelopment analysis (DEA) technique for reduction input – output data. Numerical results reveal that the new approach shows a good consistency in ranking with DEA.Keywords: Effectiveness, Decision Making, Data EnvelopmentAnalysis, Factor Analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24082540 Data Oriented Model of Image: as a Framework for Image Processing
Authors: A. Habibizad Navin, A. Sadighi, M. Naghian Fesharaki, M. Mirnia, M. Teshnelab, R. Keshmiri
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This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.
Keywords: Data oriented modelling, image, clustering, segmentation, classification, ADBT and image processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17812539 Crafting of Paper Cutting Techniques for Embellishment of Fashion Textiles
Authors: A. Vaidya-Soocheta, K. M. Wong-Hon-Lang
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Craft and fashion have always been interlinked. The combination of both often gives stunning results. The present study introduces ‘Paper Cutting Craft Techniques’ like the Japanese –Kirigami, Mexican –PapelPicado, German –Scherenschnitte, Polish –Wycinankito in textiles to develop innovative and novel design structures as embellishments and ornamentation. The project studies various ways of using these paper cutting techniques to obtain interesting features and delicate design patterns on fabrics. While paper has its advantages and related uses, it is fragile rigid and thus not appropriate for clothing. Fabric is sturdy, flexible, dimensionally stable and washable. In the present study, the cut out techniques develop creative design motifs and patterns to give an inventive and unique appeal to the fabrics. The beauty and fascination of lace in garments have always given them a nostalgic charm. Laces with their intricate and delicate complexity in combination with other materials add a feminine touch to a garment and give it a romantic, mysterious appeal. Various textured and decorative effects through fabric manipulation are experimented along with the use of paper cutting craft skills as an innovative substitute for developing lace or “Broderie Anglaise” effects on textiles. A number of assorted fabric types with varied textures were selected for the study. Techniques to avoid fraying and unraveling of the design cut fabrics were introduced. Fabrics were further manipulated by use of interesting prints with embossed effects on cut outs. Fabric layering in combination with assorted techniques such as cutting of folded fabric, printing, appliqué, embroidery, crochet, braiding, weaving added a novel exclusivity to the fabrics. The fabrics developed by these innovative methods were then tailored into garments. The study thus tested the feasibility and practicability of using these fabrics by designing a collection of evening wear garments based on the theme ‘Nostalgia’. The prototypes developed were complemented by designing fashion accessories with the crafted fabrics. Prototypes of accessories add interesting features to the study. The adaptation and application of this novel technique of paper cutting craft on textiles can be an innovative start for a new trend in textile and fashion industry. The study anticipates that this technique will open new avenues in the world of fashion to incorporate its use commercially.Keywords: Collection, fabric cutouts, nostalgia, prototypes.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25242538 In situ Real-Time Multivariate Analysis of Methanolysis Monitoring of Sunflower Oil Using FTIR
Authors: Pascal Mwenge, Tumisang Seodigeng
Abstract:
The combination of world population and the third industrial revolution led to high demand for fuels. On the other hand, the decrease of global fossil 8fuels deposits and the environmental air pollution caused by these fuels has compounded the challenges the world faces due to its need for energy. Therefore, new forms of environmentally friendly and renewable fuels such as biodiesel are needed. The primary analytical techniques for methanolysis yield monitoring have been chromatography and spectroscopy, these methods have been proven reliable but are more demanding, costly and do not provide real-time monitoring. In this work, the in situ monitoring of biodiesel from sunflower oil using FTIR (Fourier Transform Infrared) has been studied; the study was performed using EasyMax Mettler Toledo reactor equipped with a DiComp (Diamond) probe. The quantitative monitoring of methanolysis was performed by building a quantitative model with multivariate calibration using iC Quant module from iC IR 7.0 software. 15 samples of known concentrations were used for the modelling which were taken in duplicate for model calibration and cross-validation, data were pre-processed using mean centering and variance scale, spectrum math square root and solvent subtraction. These pre-processing methods improved the performance indexes from 7.98 to 0.0096, 11.2 to 3.41, 6.32 to 2.72, 0.9416 to 0.9999, RMSEC, RMSECV, RMSEP and R2Cum, respectively. The R2 value of 1 (training), 0.9918 (test), 0.9946 (cross-validation) indicated the fitness of the model built. The model was tested against univariate model; small discrepancies were observed at low concentration due to unmodelled intermediates but were quite close at concentrations above 18%. The software eliminated the complexity of the Partial Least Square (PLS) chemometrics. It was concluded that the model obtained could be used to monitor methanol of sunflower oil at industrial and lab scale.
Keywords: Biodiesel, calibration, chemometrics, FTIR, methanolysis, multivariate analysis, transesterification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9042537 Surface Defects Detection for Ceramic Tiles UsingImage Processing and Morphological Techniques
Authors: H. Elbehiery, A. Hefnawy, M. Elewa
Abstract:
Quality control in ceramic tile manufacturing is hard, labor intensive and it is performed in a harsh industrial environment with noise, extreme temperature and humidity. It can be divided into color analysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defects detection is still based on the judgment of human operators while most of the other manufacturing activities are automated so, our work is a quality control enhancement by integrating a visual control stage using image processing and morphological operation techniques before the packing operation to improve the homogeneity of batches received by final users.
Keywords: Quality control, Defects detection, Visual control, Image processing, Morphological operation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6610