Search results for: classifiers accuracy

Authors: P. Bárta

Abstract:

Increasing demand on the performance of Subsea Production Systems (SPS) suggests a need for more detailed investigation of fluid behavior taking place in subsea equipment. Complete CFD cool down analyses of subsea equipment are very time demanding. The objective of this paper is to investigate a Locked CFD approach, which enables significant reduction of the computational time and at the same time maintains sufficient accuracy during thermal cool down simulations. The result comparison of a dead leg simulation using the Full CFD and the three LCFD-methods confirms the validity of the locked flow field assumption for the selected case. For the tested case the LCFD simulation speed up by factor of 200 results in the absolute thermal error of 0.5 °C (3% relative error), speed up by factor of 10 keeps the LCFD results within 0.1 °C (0.5 % relative error) comparing to the Full CFD.

Keywords: CFD, Locked Flow Field, Speed up of CFD simulation time, Subsea

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Authors: Parvinder S. Sandhu, Jagdeep Singh, Vikas Gupta, Mandeep Kaur, Sonia Manhas, Ramandeep Sidhu

Abstract:

Prediction of fault-prone modules provides one way to support software quality engineering. Clustering is used to determine the intrinsic grouping in a set of unlabeled data. Among various clustering techniques available in literature K-Means clustering approach is most widely being used. This paper introduces K-Means based Clustering approach for software finding the fault proneness of the Object-Oriented systems. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the categorization of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results are measured in terms of accuracy of prediction, probability of Detection and Probability of False Alarms.

Keywords: K-Means, Software Fault, Classification, ObjectOriented Metrics.

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Authors: Ding Guo-hao, Li Hua, Wang Wen-long

Abstract:

A parallel computational fluid dynamics code has been developed for the study of aerodynamic heating problem in hypersonic flows. The code employs the 3D Navier-Stokes equations as the basic governing equations to simulate the laminar hypersonic flow. The cell centered finite volume method based on structured grid is applied for spatial discretization. The AUSMPW+ scheme is used for the inviscid fluxes, and the MUSCL approach is used for higher order spatial accuracy. The implicit LU-SGS scheme is applied for time integration to accelerate the convergence of computations in steady flows. A parallel programming method based on MPI is employed to shorten the computing time. The validity of the code is demonstrated by comparing the numerical calculation result with the experimental data of a hypersonic flow field around a blunt body.

Keywords: Aerodynamic Heating, AUSMPW+, MPI, ParallelComputation

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Authors: Tayfun Gundogdu, Guven Komurgoz

Abstract:

This paper presents design, analysis and comparison of the different rotor type permanent magnet machines. The presented machines are designed as having same geometrical dimensions and same materials for comparison. The main machine parameters of interior and exterior rotor type machines including eddy current effect, torque-speed characteristics and magnetic analysis are investigated using MAXWELL program. With this program, the components of the permanent magnet machines can be calculated with high accuracy. Six types of Permanent machines are compared with respect to their topology, size, magnetic field, air gap flux, voltage, torque, loss and efficiency. The analysis results demonstrate the effectiveness of the proposed machines design methodology. We believe that, this study will be a helpful resource in terms of examination and comparison of the basic structure and magnetic features of the PM (Permanent magnet) machines which have different rotor structure.

Keywords: Motor design, Permanent Magnet, Finite-Elementmethod.

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Authors: Yanan Yang, Zhigang Wang, Xiang Chen

Abstract:

This paper presents the use of Legendre pseudospectral method for the optimization of finite-thrust orbital transfer for spacecrafts. In order to get an accurate solution, the System-s dynamics equations were normalized through a dimensionless method. The Legendre pseudospectral method is based on interpolating functions on Legendre-Gauss-Lobatto (LGL) quadrature nodes. This is used to transform the optimal control problem into a constrained parameter optimization problem. The developed novel optimization algorithm can be used to solve similar optimization problems of spacecraft finite-thrust orbital transfer. The results of a numerical simulation verified the validity of the proposed optimization method. The simulation results reveal that pseudospectral optimization method is a promising method for real-time trajectory optimization and provides good accuracy and fast convergence.

Keywords: Finite-thrust, Orbital transfer, Legendre pseudospectral method

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Authors: Osama Mohamed Elrajubi, Idris El-Feghi, Mohamed Abu Baker Saghayer

Abstract:

This paper describes an identification of specific shapes within binary images using the morphological Hit-or-Miss Transform (HMT). Hit-or-Miss transform is a general binary morphological operation that can be used in searching of particular patterns of foreground and background pixels in an image. It is actually a basic operation of binary morphology since almost all other binary morphological operators are derived from it. The input of this method is a binary image and a structuring element (a template which will be searched in a binary image) while the output is another binary image. In this paper a modification of Hit-or-Miss transform has been proposed. The accuracy of algorithm is adjusted according to the similarity of the template and the sought template. The implementation of this method has been done by C language. The algorithm has been tested on several images and the results have shown that this new method can be used for similar shape detection.

Keywords: Hit-or/and-Miss Operator/Transform, HMT, binary morphological operation, shape detection, binary images processing.

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Authors: Y. H. Tsai, Y. W. Chen

Abstract:

Facial features are frequently used to represent local properties of a human face image in computer vision applications. In this paper, we present a fast algorithm that can extract the facial features online such that they can give a satisfying representation of a face image. It includes one step for a coarse detection of each facial feature by AdaBoost and another one to increase the accuracy of the found points by Active Shape Models (ASM) in the regions of interest. The resulted facial features are evaluated by matching with artificial face models in the applications of physiognomy. The distance measure between the features and those in the fate models from the database is carried out by means of the Hausdorff distance. In the experiment, the proposed method shows the efficient performance in facial feature extractions and online system of physiognomy.

Keywords: Facial feature extraction, AdaBoost, Active shapemodel, Hausdorff distance

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Authors: Achraf El Allali, John R. Rose

Abstract:

A number of competing methodologies have been developed to identify genes and classify DNA sequences into coding and non-coding sequences. This classification process is fundamental in gene finding and gene annotation tools and is one of the most challenging tasks in bioinformatics and computational biology. An information theory measure based on mutual information has shown good accuracy in classifying DNA sequences into coding and noncoding. In this paper we describe a species independent iterative approach that distinguishes coding from non-coding sequences using the mutual information measure (MIM). A set of sixty prokaryotes is used to extract universal training data. To facilitate comparisons with the published results of other researchers, a test set of 51 bacterial and archaeal genomes was used to evaluate MIM. These results demonstrate that MIM produces superior results while remaining species independent.

Keywords: Coding Non-coding Classification, Entropy, GeneRecognition, Mutual Information.

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Authors: Iliana Marinova, Valentin Mateev

Abstract:

For investigations of electromagnetic field distributions in biological structures by Finite Element Method (FEM), a method for automatic 3D model building of human anatomical objects is developed. Models are made by meshed structures and specific electromagnetic material properties for each tissue type. Mesh is built according to specific FEM criteria for achieving good solution accuracy. Several FEM models of anatomical objects are built. Formulation using magnetic vector potential and scalar electric potential (A-V, A) is used for modeling of electromagnetic fields in human tissue objects. The developed models are suitable for investigations of electromagnetic field distributions in human tissues exposed in external fields during magnetic stimulation, defibrillation, impedance tomography etc.

Keywords: electromagnetic field, finite element method, humantissue.

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Authors: Essam Al Daoud

Abstract:

Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods.

Keywords: Feature selection, Intrusion detection, Support vector machine, Particle swarm.

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Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

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Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

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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 supportvectors 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.

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Authors: Reza Ziaie Moayad, Azam Kouhpeyma

Abstract:

In-situ testing is preferred to evaluate the liquefaction potential in cohesionless soils due to high disturbance during sampling. Although new in-situ methods with high accuracy have been developed, standard penetration test, the simplest and the oldest in-situ test, is still used due to the profusion of the recorded data. This paper reviews the Iranian standard of evaluating liquefaction potential in soils (codes 525) and compares the liquefaction assessment methods based on standard penetration test (SPT) results on cohesionless soil in this standard with the international standards. To this, methods for assessing liquefaction potential are compared with what is presented in standard 525. It is found that although the procedure used in Iranian standard of evaluating the potential of liquefaction has not been updated according to the new findings, it is a conservative procedure.

Keywords: cohesionless soil, liquefaction, SPT, Iranian liquefaction standard

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

Abstract:

This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: Machine learning, medical diagnosis, meningitis detection, gradient boosting.

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

Abstract:

Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: Fractal, micro-architecture analysis, multifractal, SVM, osteoporosis.

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Authors: Arash Moradkhani Roshandeh, Othman Che Puan

Abstract:

Access Management is the proactive management of vehicular access points to land parcels adjacent to all manner of roadways. Good access management promotes safe and efficient use of the transportation network. This study attempts to utilize archived data from the University Technology of Malaysia on-campus area to assess the accuracy with which access management display some benefits. Results show that usage of access management reduces delay and fewer crashes. Clustered development can improve walking, cycling and transit travel, reduce parking requirements and improve emergency responses. Effective Access Management planning can also reduce total roadway facility costs by reducing the number of driveways and intersections. At the end after presenting recommendations some of the travel impact, and benefits that can be derived if these suggestions are implemented have been summarized with the related comments.

Keywords: Access Management, Delay, Density, Traffic Flow

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Authors: B. Ramgopal Reddy, K. Ramji, B. Satyanarayana

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In this paper, free vibration analysis of carbon nanotube (CNT) reinforced laminated composite panels is presented. Three types of panels such as flat, concave and convex are considered for study. Numerical simulation is carried out using commercially available finite element analysis software ANSYS. Numerical homogenization is employed to calculate the effective elastic properties of randomly distributed carbon nanotube reinforced composites. To verify the accuracy of the finite element method, comparisons are made with existing results available in the literature for conventional laminated composite panels and good agreements are obtained. The results of the CNT reinforced composite materials are compared with conventional composite materials under different boundary conditions.

Keywords: CNT Reinforced Composite Panels, Effective ElasticProperties, Finite Element Method, Natural Frequency.

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Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan

Abstract:

This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.

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Authors: R. Amandi, A. Shahbazi, A. Mohebi, M. Bazargan, Y. Jaberi, P. Emadi, A. Valizade

Abstract:

The application of Neural Network for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. In our work, tachycardia features obtained are used for the training and testing of a Neural Network. In this study we are using Fuzzy Probabilistic Neural Networks as an automatic technique for ECG signal analysis. As every real signal recorded by the equipment can have different artifacts, we needed to do some preprocessing steps before feeding it to our system. Wavelet transform is used for extracting the morphological parameters of the ECG signal. The outcome of the approach for the variety of arrhythmias shows the represented approach is superior than prior presented algorithms with an average accuracy of about %95 for more than 7 tachy arrhythmias.

Keywords: Fuzzy Logic, Probabilistic Neural Network, Tachycardia, Wavelet Transform.

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

Abstract:

In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: Accelerometer, activity recognition, directional cosine matrix filter, gyroscope, Kalman filter, magnetometer.

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Authors: Mansour Tarafdar, Mohammad Saniee Abade

Abstract:

This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.

Keywords: Sampling, data streams, closed frequent item set mining.

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Authors: Kyu Chul Lee, Sung Hyun Yoo, Choon Ki Ahn, Myo Taeg Lim

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Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore , the number of centers will be considered since it affects the performance of approximation.

Keywords: Extended kalmin filter (EKF), classification problem, radial basis function networks (RBFN), finite impulse response (FIR)filter.

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Authors: Birmohan Singh, V. K. Jain

Abstract:

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support vector machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and an accuracy of 96% for mammogram images collected from digital database for screening mammography database.

Keywords: Architecture Distortion, GLCM Texture features, GLRLM Texture Features, Mammograms, Support Vector Machine.

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Authors: Saman M. Abdulla, Najla B. Al-Dabagh, Omar Zakaria

Abstract:

The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.

Keywords: Artificial Neural Network, Attack Features, MisuseIntrusion Detection System, Training Parameters.

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Authors: Sangram A. Sathe, Neha A. Madgulkar, Shruti S. Raut, S. P. Wadkar

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Transforaminal lumbar interbody fusion (TLIF) surgeries have nowadays became popular for treatment of degenerated spinal disorders. The interbody fusion technique like TLIF maintains load bearing capacity of the spine and a suitable disc height. Currently many techniques have been introduced to cure Spondylolisthesis. This surgery provides greater rehabilitation of degenerative spines. While performing this TLIF surgery existing methods use guideway, which is a troublesome surgery technique as the use of two separate instruments is required to perform this surgery. This paper presents a concept which eliminates the use of guideway. This concept also eliminates problems that occur like reverting the cage. The concept discussed in this paper also gives high accuracy while performing surgery.

Keywords: Degenerative disc diseases, pedicle screw, spine, spondylolisthesis, transforaminal lumbar interbody fusion.

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Authors: M. Zare, S. Biau, M. Croq, Y. Roquelaure

Abstract:

A wide variety of observational methods have been developed to evaluate the ergonomic workloads in manufacturing. However, the precision and accuracy of these methods remain a subject of debate. The aims of this study were to develop biomechanical methods to evaluate ergonomic workloads and to compare them with observational methods.

Two observational methods, i.e. SCANIA Ergonomic Standard (SES) and Rapid Upper Limb Assessment (RULA), were used to assess ergonomic workloads at two simulated workstations. They included four tasks such as tightening & loosening, attachment of tubes and strapping as well as other actions. Sensors were also used to measure biomechanical data (Inclinometers, Accelerometers, and Goniometers).

Our findings showed that in assessment of some risk factors both RULA & SES were in agreement with the results of biomechanical methods. However, there was disagreement on neck and wrist postures. In conclusion, the biomechanical approach was more precise than observational methods, but some risk factors evaluated with observational methods were not measurable with the biomechanical techniques developed.

Keywords: Ergonomic, Observational Method, Biomechanical method, Workload.

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Authors: Zhongxu Chen, Jun Yang, Heyue Mao, Xiaoyu Zheng

Abstract:

Hemorrhage Disease of Grass Carp (HDGC) is a kind of commonly occurring illnesses in summer, and the extremely high death rate result in colossal losses to aquaculture. As the complex connections among each factor which influences aquiculture diseases, there-s no quit reasonable mathematical model to solve the problem at present.A BP neural network which with excellent nonlinear mapping coherence was adopted to establish mathematical model; Environmental factor, which can easily detected, such as breeding density, water temperature, pH and light intensity was set as the main analyzing object. 25 groups of experimental data were used for training and test, and the accuracy of using the model to predict the trend of HDGC was above 80%. It is demonstrated that BP neural network for predicating diseases in HDGC has a particularly objectivity and practicality, thus it can be spread to other aquiculture disease.

Keywords: Aquaculture, Hemorrhage Disease of Grass Carp, BP Neural Network

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Authors: Ahmed M. Farag, Wael F. Mohamed, Atef A. Ata, Burhamy M. Burhamy

Abstract:

In the present paper, an improved initial value numerical technique is presented to analyze the free vibration of symmetrically laminated rectangular plate. A combination of the initial value method (IV) and the finite differences (FD) devices is utilized to develop the present (IVFD) technique. The achieved technique is applied to the equation of motion of vibrating laminated rectangular plate under various types of boundary conditions. Three common types of laminated symmetrically cross-ply, orthotropic and isotropic plates are analyzed here. The convergence and accuracy of the presented Initial Value-Finite Differences (IVFD) technique have been examined. Also, the merits and validity of improved technique are satisfied via comparing the obtained results with those available in literature indicating good agreements.

Keywords: Free Vibrations, Initial Value, Finite Differences, Laminated plates.

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Authors: Melike Şah, Konstantin Y.Degtiarev

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This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.

Keywords: Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model.

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Authors: Surender Kumar Soni, Dhirendra Pratap Singh

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Wireless Sensor Networks can be used to monitor the physical phenomenon in such areas where human approach is nearly impossible. Hence the limited power supply is the major constraint of the WSNs due to the use of non-rechargeable batteries in sensor nodes. A lot of researches are going on to reduce the energy consumption of sensor nodes. Energy map can be used with clustering, data dissemination and routing techniques to reduce the power consumption of WSNs. Energy map can also be used to know which part of the network is going to fail in near future. In this paper, Energy map is constructed using the prediction based approach. Adaptive alpha GM(1,1) model is used as the prediction model. GM(1,1) is being used worldwide in many applications for predicting future values of time series using some past values due to its high computational efficiency and accuracy.

Keywords: Adaptive Alpha GM(1, 1) Model, Energy Map, Prediction Based Data Reduction, Wireless Sensor Networks

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