Search results for: MRMC State unification Variable Prediction (MRSUP)
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3705

Search results for: MRMC State unification Variable Prediction (MRSUP)

3255 An Alternative Method for Generating Almost Infinite Sequence of Gaussian Variables

Authors: Nyah C. Temaneh, F. A. Phiri, E. Ruhunga

Abstract:

Most of the well known methods for generating Gaussian variables require at least one standard uniform distributed value, for each Gaussian variable generated. The length of the random number generator therefore, limits the number of independent Gaussian distributed variables that can be generated meanwhile the statistical solution of complex systems requires a large number of random numbers for their statistical analysis. We propose an alternative simple method of generating almost infinite number of Gaussian distributed variables using a limited number of standard uniform distributed random numbers.

Keywords: Gaussian variable, statistical analysis, simulation ofCommunication Network, Random numbers.

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3254 The Urban Transportation Systems in Two Cities Located in the Rio de Janeiro State, Brazil

Authors: D. I. De Souza, G. P. Azevedo, R. Rocha

Abstract:

The State of Rio de Janeiro, Brazil, will hold two important events in the nearby future. In 2014 it will have the final game of the Football World Cup, and in 2016 it will be holding the Olympic Games. Therefore, the public transportation system (mainly buses) is of a major concern to the Rio de Janeiro State authorities-. The main objective of this work is to compare the quality of service of the bus companies operating in the cities of ItaperunaandCampos, both cities situated in the state of Rio de Janeiro, Brazil. The outcome of thiscomparison, based on the opinion of the bus users, has shownthemdispleased with the quality of the service provided by the bus companies operating in both cities. It is urgent the need to find possible practical alternatives to minimize the consequences of the main problems detected in this work. With these practical alternatives available, we will be able to offer to the Rio de Janeiro State authorities- suggestions about possible solutions to the main problems identified in this survey, as well as the time of implantation and costs of these solutions.

Keywords: Public Transportation, Quality of Service, Riders' Opinion, Bus Companies, Practical Alternatives.

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3253 Elastic Stress Analysis of Annular Bi-Material Discs with Variable Thickness under Mechanical and Thermomechanical Loads

Authors: E. Çetin, A. Kurşun, Ş. Aksoy, M. Tunay Çetin

Abstract:

The closed form study deals with elastic stress analysis of annular bi-material discs with variable thickness subjected to the mechanical and thermomechanical loads. Those discs have many applications in the aerospace industry, such as gas turbines and gears. Those discs normally work under thermal and mechanical loads. Their life cycle can increase when stress components are minimized. Each material property is assumed to be isotropic. The results show that material combinations and thickness of profiles play an important role in determining the responses of bi-material discs and an optimal design of those structures. Stress distribution is investigated and results are shown as graphs.

Keywords: Bi-material discs, elastic stress analysis, mechanical loads, rotating discs.

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3252 Injury Prediction for Soccer Players Using Machine Learning

Authors: Amiel Satvedi, Richard Pyne

Abstract:

Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer.

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3251 Effects of Initial State on Opinion Formation in Complex Social Networks with Noises

Authors: Yi Yu, Vu Xuan Nguyen, Gaoxi Xiao

Abstract:

Opinion formation in complex social networks may exhibit complex system dynamics even when based on some simplest system evolution models. An interesting and important issue is the effects of the initial state on the final steady-state opinion distribution. By carrying out extensive simulations and providing necessary discussions, we show that, while different initial opinion distributions certainly make differences to opinion evolution in social systems without noises, in systems with noises, given enough time, different initial states basically do not contribute to making any significant differences in the final steady state. Instead, it is the basal distribution of the preferred opinions that contributes to deciding the final state of the systems. We briefly explain the reasons leading to the observed conclusions. Such an observation contradicts with a long-term belief on the roles of system initial state in opinion formation, demonstrating the dominating role that opinion mutation can play in opinion formation given enough time. The observation may help to better understand certain observations of opinion evolution dynamics in real-life social networks.

Keywords: Opinion formation, Deffuant model, opinion mutation, consensus making.

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3250 Armed Groups and Intra State Conflict: A Study on the Egyptian Case

Authors: Ghzlan Mahmoud Abdel Aziz

Abstract:

This case study aims to identify the intrastate conflicts between the nation state and armed groups. Nowadays, most wars weaken states against armed groups. Thus, it is very important to negotiate with such groups in order to reinforce the law for the protection of victims. These armed groups are the cause of conflicts and they are related with many of humanitarian issues that result out of conflicts. In this age of rivalry; terrorists, insurgents, or transnational criminal parties have surfaced to the top as a reaction to these armed groups in an effort to set up a new world order. Moreover, the intra state conflicts became increasingly treacherous than the interstate conflicts, particularly when nation state systems deal with armed groups which try to influence the state. The unexpected upraising of the Arab Spring during 2011 in parts of the Middle East and North Africa formed various patterns of conflicts. The events of the Arab Spring resulted in current and long term change across the region. Significant modifications in the level, strength and period of armed conflict around the world have been made. Egypt was in the center of these events. It has fought back the armed groups under the name of terrorism and spread common disorder and violence among civilians. On this note, this study focuses on the problem of the transformation in the methods of organized violence within one state rather than between two state or more and analyzes the objectives, strategies, and internal composition of armed groups and the environments that foster them, with a focus on the Egyptian case.

Keywords: Armed groups, conflicts, Egyptian armed forces, intrastate conflicts.

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3249 Implementation of Generalized Plasticity in Load-Deformation Behavior of Foundation with Emphasis on Localization Problem

Authors: A. H. Akhaveissy

Abstract:

Nonlinear finite element method with eight noded isoparametric quadrilateral element is used for prediction of loaddeformation behavior including bearing capacity of foundations. Modified generalized plasticity model with non-associated flow rule is applied for analysis of soil-footing system. Also Von Mises and Tresca criterions are used for simulation of soil behavior. Modified generalized plasticity model is able to simulate load-deformation including softening behavior. Localization phenomena are considered by different meshes. Localization phenomena have not been seen in the examples. Predictions by modified generalized plasticity model show good agreement with laboratory data and theoretical prediction in comparison the other models.

Keywords: Localization phenomena, Generalized plasticity, Non-associated Flow Rule

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3248 The Roles of Natural and Anthropogenic Factors of Ecological State in the Lake Peipsi

Authors: Galya Kapanen, Jaan–Mati Punning, Irina Blinova, Külli Kangur

Abstract:

In this paper we discuss the problems of the long-term management policy of Lake Peipsi and the roles of natural and anthropogenic factors in the ecological state of the lake. The reduction of the pollution during the last 15 years could not give significant changes of the chemical composition of the water, what implicates the essential role that natural factors have on the ecological state of lake. One of the most important factors having impact on the hydrochemical cycles and ecological state is the hydrological regime which is clearly expressed in L. Peipsi. The absence on clear interrelations of climate cycles and nutrients suggest that complex abiotic and biotic interactions, which take place in the lake ecosystem, plays a significant role in the matter circulation mechanism within lake.

Keywords: Lake Peipsi, ecosystem, eutrophication, waterfluctuation, NAO.

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3247 Vibration Induced Fatigue Assessment in Vehicle Development Process

Authors: Fatih Kagnici

Abstract:

Improvement in CAE methods has an important role for shortening of the vehicle product development time. It is provided that validation of the design and improvements in terms of durability can be done without hardware prototype production. In recent years, several different methods have been developed in order to investigate fatigue damage of the vehicle. The intended goal among these methods is prediction of fatigue damage in a short time with reduced costs. This study developed a new fatigue damage prediction method in the automotive sector using power spectrum densities of accelerations. This study also confirmed that the weak region in vehicle can be easily detected with the method developed in this study which results were compared with conventional method.

Keywords: Fatigue damage, Power spectrum density, Vibration induced fatigue, Vehicle development

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3246 A Multivariate Moving Average Control Chart for Photovoltaic Processes

Authors: Chunchom Pongchavalit

Abstract:

For the electrical metrics that describe photovoltaic cell performance are inherently multivariate in nature, use of a univariate, or one variable, statistical process control chart can have important limitations. Development of a comprehensive process control strategy is known to be significantly beneficial to reducing process variability that ultimately drives up the manufacturing cost photovoltaic cells. The multivariate moving average or MMA chart, is applied to the electrical metrics of photovoltaic cells to illustrate the improved sensitivity on process variability this method of control charting offers. The result show the ability of the MMA chart to expand to as any variables as needed, suggests an application with multiple photovoltaic electrical metrics being used in concert to determine the processes state of control.

Keywords: The multivariate moving average control chart, Photovoltaic processes control, Multivariate system.

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3245 A Study of Wind Speed Characteristic in PI Controller based DFIG Wind Turbine

Authors: T. Unchim, A. Oonsivilai

Abstract:

The Wind Turbine Modeling in Wind Energy Conversion System (WECS) using Doubly-Fed Induction Generator (DFIG) PI Controller based design is presented. To study about the variable wind speed. The PI controller performs responding to the dynamic performance. The objective is to study the characteristic of wind turbine and finding the optimum wind speed suitable for wind turbine performance. This system will allow the specification setting (2.5MW). The output active power also corresponding same the input is given. And the reactive power produced by the wind turbine is regulated at 0 Mvar. Variable wind speed is optimum for drive train performance at 12.5 m/s (at maximum power coefficient point) from the simulation of DFIG by Simulink is described.

Keywords: DFIG, wind speed, PI controller, the output power.

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3244 Application of GA Optimization in Analysis of Variable Stiffness Composites

Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

Abstract:

Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: Beam structures, layerwise, optimization, variable angle tow, neural network

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3243 Tuning of Thermal FEA Using Krylov Parametric MOR for Subsea Application

Authors: A. Suleng, T. Jelstad Olsen, J. Šindler, P. Bárta

Abstract:

A dead leg is a typical subsea production system component. CFD is required to model heat transfer within the dead leg. Unfortunately its solution is time demanding and thus not suitable for fast prediction or repeated simulations. Therefore there is a need to create a thermal FEA model, mimicking the heat flows and temperatures seen in CFD cool down simulations. This paper describes the conventional way of tuning and a new automated way using parametric model order reduction (PMOR) together with an optimization algorithm. The tuned FE analyses replicate the steady state CFD parameters within a maximum error in heat flow of 6 % and 3 % using manual and PMOR method respectively. During cool down, the relative error of the tuned FEA models with respect to temperature is below 5% comparing to the CFD. In addition, the PMOR method obtained the correct FEA setup five times faster than the manually tuned FEA.

Keywords: CFD, convective heat, FEA, model tuning, subseaproduction

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3242 Metabolic Predictive Model for PMV Control Based on Deep Learning

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: Deep learning, indoor quality, metabolism, predictive model.

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3241 New Graph Similarity Measurements based on Isomorphic and Nonisomorphic Data Fusion and their Use in the Prediction of the Pharmacological Behavior of Drugs

Authors: Irene Luque Ruiz, Manuel Urbano Cuadrado, Miguel Ángel Gómez-Nieto

Abstract:

New graph similarity methods have been proposed in this work with the aim to refining the chemical information extracted from molecules matching. For this purpose, data fusion of the isomorphic and nonisomorphic subgraphs into a new similarity measure, the Approximate Similarity, was carried out by several approaches. The application of the proposed method to the development of quantitative structure-activity relationships (QSAR) has provided reliable tools for predicting several pharmacological parameters: binding of steroids to the globulin-corticosteroid receptor, the activity of benzodiazepine receptor compounds, and the blood brain barrier permeability. Acceptable results were obtained for the models presented here.

Keywords: Graph similarity, Nonisomorphic dissimilarity, Approximate similarity, Drug activity prediction.

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3240 On-Line Impulse Buying and Cognitive Dissonance: The Moderating Role of the Positive Affective State

Authors: G. Mattia, A. Di Leo, L. Principato

Abstract:

The purchase impulsiveness is preceded by a lack of self-control: consequently, it is legitimate to believe that a consumer with a low level of self-control can result in a higher probability of cognitive dissonance. Moreover, the process of purchase is influenced by the pre-existing affective state in a considerable way. With reference to on-line purchases, digital behavior cannot be merely ascribed to the rational sphere, given the speed and ease of transactions and the hedonistic dimension of purchases. To our knowledge, this research is among the first cases of verification of the effect of moderation exerted by the positive affective state in the on-line impulse purchase of products with a high expressive value such as a smartphone on the occurrence of cognitive dissonance. To this aim, a moderation analysis was conducted on a sample of 212 impulsive millennials buyers. Three scales were adopted to measure the constructs of interest: IBTS for impulsivity, PANAS for the affective state, Sweeney for cognitive dissonance. The analysis revealed that positive affective state does not affect the onset of cognitive dissonance.

Keywords: Cognitive dissonance, impulsive buying, online shopping, online consumer behavior.

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3239 Performance Analysis of Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System

Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

Abstract:

This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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3238 Use of Information Technology in the Government of a State

Authors: Pavel E. Golosov, Vladimir I. Gorelov, Oksana L. Karelova

Abstract:

There are visible changes in the world organization, environment and health of national conscience that create a background for discussion on possible redefinition of global, state and regional management goals. Authors apply the sustainable development criteria to a hierarchical management scheme that is to lead the world community to non-contradictory growth. Concrete definitions are discussed in respect of decision-making process representing the state mostly. With the help of system analysis it is highlighted how to understand who would carry the distinctive sign of world leadership in the nearest future.

Keywords: Decision-making, information technology, public administration.

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3237 Throughflow Effects on Thermal Convection in Variable Viscosity Ferromagnetic Liquids

Authors: G. N. Sekhar, P. G. Siddheshwar, G. Jayalatha, R. Prakash

Abstract:

The problem of thermal convection in temperature and magnetic field sensitive Newtonian ferromagnetic liquid is studied in the presence of uniform vertical magnetic field and throughflow. Using a combination of Galerkin and shooting techniques the critical eigenvalues are obtained for stationary mode. The effect of Prandtl number (Pr > 1) on onset is insignificant and nonlinearity of non-buoyancy magnetic parameter M3 is found to have no influence on the onset of ferroconvection. The magnetic buoyancy number, M1 and variable viscosity parameter, V have destabilizing influences on the system. The effect of throughflow Peclet number, Pe is to delay the onset of ferroconvection and this effect is independent of the direction of flow.

Keywords: Ferroconvection, throughflow, temperature dependent viscosity, magnetic field dependent viscosity.

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3236 Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

Authors: Samir Brahim Belhaouari

Abstract:

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.

Keywords: Pattern recognition, Time series, k-Nearest Neighbor, k-means cluster, Gaussian Mixture Model, Classification

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3235 Application of Neural Network and Finite Element for Prediction the Limiting Drawing Ratio in Deep Drawing Process

Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini

Abstract:

In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).

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3234 Machine Learning Development Audit Framework: Assessment and Inspection of Risk and Quality of Data, Model and Development Process

Authors: Jan Stodt, Christoph Reich

Abstract:

The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: Audit, machine learning, assessment, metrics.

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3233 The Efficiency of Multimedia Educational Tools in Sport Gymnastics for The Students of Physical Education at Universities

Authors: G. Bago, P. Hedbávný

Abstract:

This contribution was developed from a research within the doctoral thesis. Its object was to create multimedia materials for sport gymnastics. Consequently we surveyed the influence of its practical application on the efficiency of schooling at a university. We verified the prescribed hypothesis of the efficiency of the teaching process using the method of single-factor experiment, where the entrance independent variable was the change of system of tuition and the outgoing dependent variable was the change of level of acquired motor skills. The results confirmed the positive impact of using multimedia materials on the efficiency of the teaching process. Further, with the aid of questionnaires, we evaluated how the tested subjects perceive the innovative methods in sport gymnastics. The responses showed that the students rate the application of multimedia materials very positively.

Keywords: efficiency of education, means of education, multimedia materials, sports gymnastics

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3232 Lexicon-Based Sentiment Analysis for Stock Movement Prediction

Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance.

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3231 Quintic Spline Solution of Fourth-Order Parabolic Equations Arising in Beam Theory

Authors: Reza Mohammadi, Mahdieh Sahebi

Abstract:

We develop a method based on polynomial quintic spline for numerical solution of fourth-order non-homogeneous parabolic partial differential equation with variable coefficient. By using polynomial quintic spline in off-step points in space and finite difference in time directions, we obtained two three level implicit methods. Stability analysis of the presented method has been carried out. We solve four test problems numerically to validate the derived method. Numerical comparison with other methods shows the superiority of presented scheme.

Keywords: Fourth-order parabolic equation, variable coefficient, polynomial quintic spline, off-step points, stability analysis.

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3230 Using Simulation for Prediction of Units Movements in Case of Communication Failure

Authors: J. Hodicky, P. Frantis

Abstract:

Command and Control (C2) system and its interfacethe Common Operational Picture (COP) are main means that supports commander in its decision making process. COP contains information about friendly and enemy unit positions. The friendly position is gathered via tactical network. In the case of tactical network failure the information about units are not available. The tactical simulator can be used as a tool that is capable to predict movements of units in respect of terrain features. Article deals with an experiment that was based on Czech C2 system that is in the case of connectivity lost fed by VR Forces simulator. Article analyzes maximum time interval in which the position created by simulator is still usable and truthful for commander in real time.

Keywords: command and control system, movement prediction, simulation

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3229 Lexicon-Based Sentiment Analysis for Stock Movement Prediction

Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Computational finance, sentiment analysis, sentiment lexicon, stock movement prediction.

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3228 Prediction the Limiting Drawing Ratio in Deep Drawing Process by Back Propagation Artificial Neural Network

Authors: H.Mohammadi Majd, M.Jalali Azizpour, M. Goodarzi

Abstract:

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: BPANN, deep drawing, prediction, limiting drawingratio (LDR), Levenberg–Marquardt algorithm

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3227 Application of EEG Wavelet Power to Prediction of Antidepressant Treatment Response

Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

Abstract:

In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: Alpha waves, antidepressant, treatment outcome, wavelet.

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3226 Fingerprint Verification System Using Minutiae Extraction Technique

Authors: Manvjeet Kaur, Mukhwinder Singh, Akshay Girdhar, Parvinder S. Sandhu

Abstract:

Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work.

Keywords: Biometrics, Minutiae, Crossing number, False Accept Rate (FAR), False Reject Rate (FRR).

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