Search results for: Rule Based Modeling

Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

Abstract:

The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: ElectroMyoGraphic (EMG) signals, Experimental approach, Handwriting process, Radial Basis Functions (RBF) neural networks, Velocity Modeling.

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Authors: Suraiya Jabin, Kamal K. Bharadwaj

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In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Keywords: Censored Production Rule, Data Mining, GeneticAlgorithm, Learning Classifier System, Machine Learning, PittsburgApproach, , Reinforcement learning.

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Authors: Hidemi Ogasawara, Kiyoshi Akama, Hiroshi Mabuchi

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The purpose of this paper is to propose a framework for constructing correct parallel processing programs based on Equivalent Transformation Framework (ETF). ETF regards computation as In the framework, a problem-s domain knowledge and a query are described in definite clauses, and computation is regarded as transformation of the definite clauses. Its meaning is defined by a model of the set of definite clauses, and the transformation rules generated must preserve meaning. We have proposed a parallel processing method based on “specialization", a part of operation in the transformations, which resembles substitution in logic programming. The method requires “Memo-tree", a history of specialization to maintain correctness. In this paper we proposes the new method for the specialization-base parallel processing without Memo-tree.

Keywords: Parallel processing, Program correctness, Equivalent transformation, Specializer generation rule

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Authors: SeHun Lim, Sanghoon Kim, Sun-Tae Chung, Seongwon Cho

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Face authentication for access control is a face membership authentication which passes the person of the incoming face if he turns out to be one of an enrolled person based on face recognition or rejects if not. Face membership authentication belongs to the two class classification problem where SVM(Support Vector Machine) has been successfully applied and shows better performance compared to the conventional threshold-based classification. However, most of previous SVMs have been trained using image feature vectors extracted from face images of each class member(enrolled class/unenrolled class) so that they are not robust to variations in illuminations, poses, and facial expressions and much affected by changes in member configuration of the enrolled class In this paper, we propose an effective face membership authentication method based on SVM using class discriminating features which represent an incoming face image-s associability with each class distinctively. These class discriminating features are weakly related with image features so that they are less affected by variations in illuminations, poses and facial expression. Through experiments, it is shown that the proposed face membership authentication method performs better than the threshold rule-based or the conventional SVM-based authentication methods and is relatively less affected by changes in member size and membership.

Keywords: Face Authentication, Access control, member ship authentication, SVM.

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Authors: Lan Luo

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In China’s construction industry, it is a normal practice to separately subcontract the decoration engineering part from construction engineering, and Building Information Modeling (BIM) is also done separately. Application of BIM in decoration engineering should be integrated with other disciplines, but Chinese current practice makes this very difficult and complicated. Currently, there are three barriers in the auditing of BIM application in decoration engineering in China: heavy workload; scarcity of qualified professionals; and lack of literature concerning audit contents, standards, and methods. Therefore, it is significant to perform research on what (contents) should be evaluated, in which phase, and by whom (professional qualifications) in BIM application in decoration construction so that the application of BIM can be promoted in a better manner. Based on this consideration, four principles of BIM auditing are proposed: Comprehensiveness of information, accuracy of data, aesthetic attractiveness of appearance, and scheme optimization. In the model audit, three methods should be used: Collision, observation, and contrast. In addition, BIM auditing at six stages is discussed and a checklist for work items and results to be submitted is proposed. This checklist can be used for reference by decoration project participants.

Keywords: Audit, evaluation, dimensions, methods, standards, building information modeling application, decoration engineering projects.

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Authors: Zhidong Zhang, Zhi-Chao Zhang

Abstract:

This study examined the mental health and behavioral problems in early adolescence with the instrument of Achenbach System of Empirically Based Assessment (ASEBA). The purpose of the study was stratified sampling method was used to collect data from 1975 participants. Multiple regression models and hierarchical regression models were applied to examine the relations between the background variables and internalizing problems, and the ones between students’ performance and internalizing problems. The results indicated that several background variables as predictors could significantly predict the anxious/depressed problem; reading and social study scores could significantly predict the anxious/depressed problem. However the class as a hierarchical macro factor did not indicate the significant effect. In brief, the majority of these models represented that the background variables, behaviors and academic performance were significantly related to the anxious/depressed problem.

Keywords: Behavioral problems, anxious/depression problems, empirical-based assessment, hierarchical modeling.

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Authors: Vladimir A. Fursov, Nikita E. Kozin

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In this work the opportunity of construction of the qualifiers for face-recognition systems based on conjugation criteria is investigated. The linkage between the bipartite conjugation, the conjugation with a subspace and the conjugation with the null-space is shown. The unified solving rule is investigated. It makes the decision on the rating of face to a class considering the linkage between conjugation values. The described recognition method can be successfully applied to the distributed systems of video control and video observation.

Keywords: Conjugation, Eigenfaces, Recognition.

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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.

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Authors: Ahmed, M., Yasser A., Mahmoud H., Ahmed, A., Abdulla M. S., Nazar, S.

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Strut-and-Tie Models (STM) for the design of concrete beams, comprising of struts, ties, nodes as the basic tools, is conceptually simple, but its realization for complex concrete structure is not straightforward and depends on flow of internal forces in the structure. STM technique has won wide acceptance for deep member and shear design. STM technique is a unified approach that considers all load effects (bending, axial, shear, and torsion) simultaneously, not just applicable to shear loading only. The present study is to portray Strut-and-Tie Modeling based on Load-Transfer-Mechanisms as a unified method to analyze, design and detailing for deep and slender concrete beams. Three shear span- effective depth ratio (a/ d) are recommended for the modeling of STM elements corresponding to dominant load paths. The study also discusses the research work conduct on effective stress of concrete, tie end anchorage, and transverse reinforcement demand under different load transfer mechanism. It is also highlighted that to make the STM versatile tool for design of beams applicable to all shear spans, the effective stress of concrete and, transverse reinforcement demand, inclined angle of strut, and anchorage requirements of tie bars is required to be correlated with respect to load transfer mechanism. The country code provisions are to be modified and updated to apply for generalized design of concrete deep and slender member using load transfer mechanism based STM technique. Examples available in literature are reanalyzed with refined STM based on load transfer mechanisms and results are compared. It is concluded from the results that proposed approach will require true reinforcement demand depending on dominant force transfer action in concrete beam.

Keywords: Deep member, Load transfer mechanism, Strut-and-Tie Model, Strut, Truss.

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Authors: K.M. Ngcobo, S.D. Eyono Obono

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Indigenous Knowledge (IK) has many social and economic benefits. However, IK is at the risk of extinction due to the difficulties to preserve it as most of the IK largely remains undocumented. This study aims to design a model of the factors affecting the adoption of Information and Communication Technologies (ICTs) for the preservation of IK. The proposed model is based on theoretical frameworks on ICT adoption. It was designed following a literature review of ICT adoption theories for households, and of the factors affecting ICT adoption for IK. The theory that fitted to the best all factors was then chosen as the basis for the proposed model. This study found that the Model of Adoption of Technology in Households (MATH) is the most suitable theoretical framework for modeling ICT adoption factors for the preservation of IK.

Keywords: Adoption factors, ICT adoption theories, Indigenous knowledge.

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Authors: Marc Sango, Olimpia Hoinaru, Christophe Gransart, Laurence Duchien

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Ontologies offer a means for representing and sharing information in many domains, particularly in complex domains. For example, it can be used for representing and sharing information of System Requirement Specification (SRS) of complex systems like the SRS of ERTMS/ETCS written in natural language. Since this system is a real-time and critical system, generic ontologies, such as OWL and generic ERTMS ontologies provide minimal support for modeling temporal information omnipresent in these SRS documents. To support the modeling of temporal information, one of the challenges is to enable representation of dynamic features evolving in time within a generic ontology with a minimal redesign of it. The separation of temporal information from other information can help to predict system runtime operation and to properly design and implement them. In addition, it is helpful to provide a reasoning and querying techniques to reason and query temporal information represented in the ontology in order to detect potential temporal inconsistencies. To address this challenge, we propose a lightweight 3-layer temporal Quality of Service (QoS) ontology for representing, reasoning and querying over temporal and non-temporal information in a complex domain ontology. Representing QoS entities in separated layers can clarify the distinction between the non QoS entities and the QoS entities in an ontology. The upper generic layer of the proposed ontology provides an intuitive knowledge of domain components, specially ERTMS/ETCS components. The separation of the intermediate QoS layer from the lower QoS layer allows us to focus on specific QoS Characteristics, such as temporal or integrity characteristics. In this paper, we focus on temporal information that can be used to predict system runtime operation. To evaluate our approach, an example of the proposed domain ontology for handover operation, as well as a reasoning rule over temporal relations in this domain-specific ontology, are presented.

Keywords: System Requirement Specification, ERTMS/ETCS, Temporal Ontologies, Domain Ontologies.

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Authors: R. Abdul Wahab, R. Mohd Fuad Tengku Aziz, N. Othman, S. Saleh, N. Razali, M. Al Baqir Zinal Abidin, M. Hanif Md Nasir

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This paper will discuss how we optimize our physical verification flow in our IC Design Department having various rule decks from multiple foundries. Our ultimate goal is to achieve faster time to tape-out and avoid schedule delay. Currently the physical verification runtimes and memory usage have drastically increased with the increasing number of design rules, design complexity, and the size of the chips to be verified. To manage design violations, we use a number of solutions to reduce the amount of violations needed to be checked by physical verification engineers. The most important functions in physical verifications are DRC (design rule check), LVS (layout vs. schematic), and XRC (extraction). Since we have a multiple number of foundries for our design tape-outs, we need a flow that improve the overall turnaround time and ease of use of the physical verification process. The demand for fast turnaround time is even more critical since the physical design is the last stage before sending the layout to the foundries.

Keywords: Physical verification, DRC, LVS, XRC, flow, foundry, runset.

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Authors: Sandipan Chakroborty, Goutam Saha

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A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. On a recent contribution by authors, it has been shown that the Inverted Mel- Frequency Cepstral Coefficients (IMFCC) is useful feature set for SI, which contains complementary information present in high frequency region. This paper introduces the Gaussian shaped filter (GF) while calculating MFCC and IMFCC in place of typical triangular shaped bins. The objective is to introduce a higher amount of correlation between subband outputs. The performances of both MFCC & IMFCC improve with GF over conventional triangular filter (TF) based implementation, individually as well as in combination. With GMM as speaker modeling paradigm, the performances of proposed GF based MFCC and IMFCC in individual and fused mode have been verified in two standard databases YOHO, (Microphone Speech) and POLYCOST (Telephone Speech) each of which has more than 130 speakers.

Keywords: Gaussian Filter, Triangular Filter, Subbands, Correlation, MFCC, IMFCC, GMM.

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Authors: Parisa Shooshtari, Gelareh Mohamadi, Behnam Molaee Ardekani, Mohammad Bagher Shamsollahi

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EEG signal is one of the oldest measures of brain activity that has been used vastly for clinical diagnoses and biomedical researches. However, EEG signals are highly contaminated with various artifacts, both from the subject and from equipment interferences. Among these various kinds of artifacts, ocular noise is the most important one. Since many applications such as BCI require online and real-time processing of EEG signal, it is ideal if the removal of artifacts is performed in an online fashion. Recently, some methods for online ocular artifact removing have been proposed. One of these methods is ARMAX modeling of EEG signal. This method assumes that the recorded EEG signal is a combination of EOG artifacts and the background EEG. Then the background EEG is estimated via estimation of ARMAX parameters. The other recently proposed method is based on adaptive filtering. This method uses EOG signal as the reference input and subtracts EOG artifacts from recorded EEG signals. In this paper we investigate the efficiency of each method for removing of EOG artifacts. A comparison is made between these two methods. Our undertaken conclusion from this comparison is that adaptive filtering method has better results compared with the results achieved by ARMAX modeling.

Keywords: Ocular Artifacts, EEG, Adaptive Filtering, ARMAX

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Authors: Ali Haroonabadi, Mohammad Teshnehlab

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None of the processing models in the software development has explained the software systems performance evaluation and modeling; likewise, there exist uncertainty in the information systems because of the natural essence of requirements, and this may cause other challenges in the processing of software development. By definition an extended version of UML (Fuzzy- UML), the functional requirements of the software defined uncertainly would be supported. In this study, the behavioral description of uncertain information systems by the aid of fuzzy-state diagram is crucial; moreover, the introduction of behavioral diagrams role in F-UML is investigated in software performance modeling process. To get the aim, a fuzzy sub-profile is used.

Keywords: Fuzzy System, Software Development Model, Software Performance Evaluation, UML

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Authors: Hala Abdel hameed, Hazem M. El-Bakry, Torky Sultan

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There are many issues that affect modeling and designing real-time databases. One of those issues is maintaining consistency between the actual state of the real-time object of the external environment and its images as reflected by all its replicas distributed over multiple nodes. The need to improve the scalability is another important issue. In this paper, we present a general framework to design a replicated real-time database for small to medium scale systems and maintain all timing constrains. In order to extend the idea for modeling a large scale database, we present a general outline that consider improving the scalability by using an existing static segmentation algorithm applied on the whole database, with the intent to lower the degree of replication, enables segments to have individual degrees of replication with the purpose of avoiding excessive resource usage, which all together contribute in solving the scalability problem for DRTDBS.

Keywords: Database modeling, Distributed database, Real time databases, Replication

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Authors: Nafise Fareghzadeh

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Service identification is one of the main activities in the modeling of a service-oriented solution, and therefore errors made during identification can flow down through detailed design and implementation activities that may necessitate multiple iterations, especially in building composite applications. Different strategies exist for how to identify candidate services that each of them has its own benefits and trade offs. The approach presented in this paper proposes a selective identification of services approach, based on in depth business process analysis coupled with use cases and existing assets analysis and goal service modeling. This article clearly emphasizes the key activities need for the analysis and service identification to build a optimized service oriented architecture. In contrast to other approaches this article mentions some best practices and steps, wherever appropriate, to point out the vagueness involved in service identification.

Keywords: SOA, service identification, service taxonomy, service layer.

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Authors: Nittaya Kerdprasop, Narin Muenrat, Kittisak Kerdprasop

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A learning content management system (LCMS) is an environment to support web-based learning content development. Primary function of the system is to manage the learning process as well as to generate content customized to meet a unique requirement of each learner. Among the available supporting tools offered by several vendors, we propose to enhance the LCMS functionality to individualize the presented content with the induction ability. Our induction technique is based on rough set theory. The induced rules are intended to be the supportive knowledge for guiding the content flow planning. They can also be used as decision rules to help content developers on managing content delivered to individual learner.

Keywords: Decision rules, Knowledge induction, Learning content management system, Rough set.

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Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: Forecasting, Gaussian process, modeling, wind power.

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Authors: Z. Zainuddin, W. D. Wan Rosli, R. Lanouette, S. Sathasivam

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This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Keywords: Convergence, pulping modeling, neural networks, preconditioned conjugate gradient.

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Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim

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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.

Keywords: Face Alignment, AAM, facial feature detection, model matching.

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Authors: M. Ziółkowska, J. T. Duda, J. Milewska-Duda

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This paper describes an approach to the adsorption phenomena modeling aimed at specifying the adsorption mechanisms on localized or nonlocalized adsorbent sites, when applied to the nanocarbons. The concept comes from the fundamental thermodynamic description of adsorption equilibrium and is based on numerical calculations of the hydrogen adsorbed particles volume on the surface of selected nanocarbons: single-walled nanotube and nanocone. This approach enables to obtain information on adsorption mechanism and then as a consequence to take appropriate mathematical adsorption model, thus allowing for a more reliable identification of the material porous structure. Theoretical basis of the approach is discussed and newly derived results of the numerical calculations are presented for the selected nanocarbons.

Keywords: Adsorption, mathematical modeling, nanocarbons, numerical analysis.

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: Markov process, milling system, availability modeling, rice milling plant.

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

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Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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Authors: Suhas K, Sripadaraja K

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This paper details few mechanical modeling and design issues of RF MEMS switches. We concentrate on an electrostatically actuated broad side series switch; surface micromachined with a crab leg membrane. The same results are extended to any complex structure. With available experimental data and fabrication results, we present the variation in dynamic performance and compliance of the switch with reference to few design issues, which we find are critical in deciding the dynamic behavior of the switch, without compromise on the RF characteristics. The optimization of pull in voltage, transient time and resonant frequency with regard to these critical design parameters are also presented.

Keywords: Microelectromechanical Systems (MEMS), RadioFrequency MEMS, Modeling, Actuators

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Authors: Wint Thida Zaw, Thinn Thu Naing

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One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so that an outcome variable can be predicted from the other or others. In this paper, the modeling of monthly rainfall prediction over Myanmar is described in detail by applying the polynomial regression equation. The proposed model results are compared to the results produced by multiple linear regression model (MLR). Experiments indicate that the prediction model based on MPR has higher accuracy than using MLR.

Keywords: Polynomial Regression, Rainfall Forecasting, Statistical forecasting.

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Authors: Taweechai Nuntawisuttiwong, Natasha Dejdumrong

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Geometric modeling plays an important role in the constructions and manufacturing of curve, surface and solid modeling. Their algorithms are critically important not only in the automobile, ship and aircraft manufacturing business, but are also absolutely necessary in a wide variety of modern applications, e.g., robotics, optimization, computer vision, data analytics and visualization. The calculation and display of geometric objects can be accomplished by these six techniques: Polynomial basis, Recursive, Iterative, Coefficient matrix, Polar form approach and Pyramidal algorithms. In this research, the coefficient matrix (simply called monomial form approach) will be used to model polynomial rectangular patches, i.e., Said-Ball, Wang-Ball, DP, Dejdumrong and NB1 surfaces. Some examples of the monomial forms for these surface modeling are illustrated in many aspects, e.g., construction, derivatives, model transformation, degree elevation and degress reduction.

Keywords: Monomial form, rectangular surfaces, CAGD curves, monomial matrix applications.

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Authors: Mehdi Salami-Kalajahi, Pejman Ganjeh-Anzabi, Vahid Haddadi-Asl, Mohammad Najafi

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In the following text, we show that by introducing universal kinetic scheme, the origin of rate retardation and inhibition period which observed in dithiobenzoate-mediated RAFT polymerization can be described properly. We develop our model by utilizing the method of moments, then we apply our model to different monomer/RAFT agent systems, both homo- and copolymerization. The modeling results are in an excellent agreement with experiments and imply the validity of universal kinetic scheme, not only for dithiobenzoate-mediated systems, but also for different types of monomer/RAFT agent ones.

Keywords: RAFT Polymerization, Mechanism, Kinetics, Moment Equations, Modeling.

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Authors: Chokri Jebali, Noureddine Boulejfen, Ali Gharsallah, Fadhel M. Ghannouchi

Abstract:

In this paper, a system level behavioural model for RF power amplifier, which exhibits memory effects, and based on multibranch system is proposed. When higher order terms are included, the memory polynomial model (MPM) exhibits numerical instabilities. A set of memory orthogonal polynomial model (OMPM) is introduced to alleviate the numerical instability problem associated to MPM model. A data scaling and centring algorithm was applied to improve the power amplifier modeling accuracy. Simulation results prove that the numerical instability can be greatly reduced, as well as the model precision improved with nonlinear model.

Keywords: power amplifier, orthogonal model, polynomialmodel , memory effects.

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Authors: W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sathasivam

Abstract:

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Keywords: Convergence, Modeling, Neural Networks, Preconditioned Conjugate Gradient.

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