Search results for: Design Algorithm

Authors: Raghad Jawad Ahmed

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Segmentation of a color image composed of different kinds of regions can be a hard problem, namely to compute for an exact texture fields. The decision of the optimum number of segmentation areas in an image when it contains similar and/or un stationary texture fields. A novel neighborhood-based segmentation approach is proposed. A genetic algorithm is used in the proposed segment-pass optimization process. In this pass, an energy function, which is defined based on Markov Random Fields, is minimized. In this paper we use an adaptive threshold estimation method for image thresholding in the wavelet domain based on the generalized Gaussian distribution (GGD) modeling of sub band coefficients. This method called Normal Shrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on sub band data energy that used in the pre-stage of segmentation. A quad tree is employed to implement the multi resolution framework, which enables the use of different strategies at different resolution levels, and hence, the computation can be accelerated. The experimental results using the proposed segmentation approach are very encouraging.

Keywords: Color image segmentation, Genetic algorithm, Markov random field, Scale space filter.

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Authors: A.Diaf, Z.Tigrine, H. Aburideh, D.Tassalit , F.Alaoui, B .Abbad

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The distillation process in the general sense is a relatively simple technique from the standpoints of its principles. When dedicating distillation to water treatment and specifically producing fresh water from sea, ocean and/ briny waters it is interesting to notice that distillation has no limitations or domains of applicability regarding the nature or the type of the feedstock water. This is not the case however for other techniques that are technologically quite complex, necessitate bigger capital investments and are limited in their usability. In a previous paper we have explored some of the effects of temperature on yield. In this paper, we continue building onto that knowledge base and focus on the effects of several additional engineering and design variables on productivity.

Keywords: Distillation, Desalination, Multi-Stage still, Solar Energy

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Authors: May Phyo Thwal, Khin Htay Kyi, Kyaw Swar Soe

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According to the density of the chips, designers are trying to put so any facilities of computational and storage on single chips. Along with the complexity of computational and storage circuits, the designing, testing and debugging become more and more complex and expensive. So, hardware design will be built by using very high speed hardware description language, which is more efficient and cost effective. This paper will focus on the implementation of 32-bit ALU design based on Verilog hardware description language. Adder and subtracter operate correctly on both unsigned and positive numbers. In ALU, addition takes most of the time if it uses the ripple-carry adder. The general strategy for designing fast adders is to reduce the time required to form carry signals. Adders that use this principle are called carry look- ahead adder. The carry look-ahead adder is to be designed with combination of 4-bit adders. The syntax of Verilog HDL is similar to the C programming language. This paper proposes a unified approach to ALU design in which both simulation and formal verification can co-exist.

Keywords: Addition, arithmetic logic unit, carry look-ahead adder, Verilog HDL.

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Authors: Z. Briqech, M. Abousetta

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Luneberg lens is a new generation of antennas that is developed in the last few years and inserts itself strongly in Microwaves, Communications and Telescopes area. The idea of this research is to improve the radiation pattern by decreasing the side lobes and increasing the main lobe. The new design is proposed to work in the X-band. The simulated result and analysis are presented.

Keywords: Communications, Microwaves, lens Antenna, Lunberg Lens Antenna.

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Authors: Sobhani U. Pilapitiya, Tharanga Peiris

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Software engineering curriculums generally consist of industry-based practices such as project-based learning (PBL) which mainly focuses on efficient and innovative product development. These approaches can be tailored and used in project-based modules in software engineering curriculums. However, there are very limited attempts in the area especially related to Sri Lankan context. This paper describes a tailored pedagogical approach and its results of using design sprint which can be used for project-based modules in software engineering (SE) curriculums. A controlled group of second year software engineering students was selected for the study. The study results indicate that all of the students agreed that the design sprint approach is effective in group-based projects and 83% of students stated that it minimized the re-work compared to traditional project approaches. The tailored process was effective, easy to implement and produced desired results at the end of the session while providing students an enjoyable experience.

Keywords: design sprint, project-based learning, software engineering, curriculum

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Authors: Roman Ružarovský, Radovan Holubek, Daynier Rolando Delgado Sobrino

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Currently is characterized production engineering together with the integration of industrial automation and robotics such very quick view of to manufacture the products. The production range is continuously changing, expanding and producers have to be flexible in this regard. It means that need to offer production possibilities, which can respond to the quick change. Engineering product development is focused on supporting CAD software, such systems are mainly used for product design. That manufacturers are competitive, it should be kept procured machines made available capable of responding to output flexibility. In response to that problem is the development of flexible manufacturing systems, consisting of various automated systems. The integration of flexible manufacturing systems and subunits together with product design and of engineering is a possible solution for this issue. Integration is possible through the implementation of CIM systems. Such a solution and finding a hyphen between CAD and procurement system ICIM 3000 from Festo Co. is engaged in the research project and this contribution. This can be designed the products in CAD systems and watch the manufacturing process from order to shipping by the development of methods and processes of integration, This can be modeled in CAD systems products and watch the manufacturing process from order to shipping to develop methods and processes of integration, which will improve support for product design parameters by monitoring of the production process, by creating of programs for production using the CAD and therefore accelerates the a total of process from design to implementation.

Keywords: CAD- Computer Aided Design, CAM- Computer Aided Manufacturing, CIM- Computer integrated manufacturing, iCIM 3000, integration, direct production from CAD.

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Authors: Shigeyuki Haruyama, Aidil Khaidir Bin Muhamad, Tadayuki Kyoutani, Dai-Heng Chen, Ken Kaminishi

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The product development process has undergone many changes concomitant with world progress in order to produce products that meet customer needs quickly and inexpensively. Analysis-Led Design (ALD) is one of the latest methods in the product development process. It focuses more on up-front engineering, a product quality optimization process that starts early in the conceptual design stage. Product development and manufacturing through ALD utilizes digital tools extensively for design, analysis and product optimization. This study uses computer-aided design (CAD) and finite element method (FEM) simulation to examine the modes of deformation of tubular members under axial loading. A multiple-combination impact absorption tubular member, referred to as a compress–expand member, is proposed as a substitute for the conventional thin-walled cylindrical tube to be used as a vehicle’s crash box. The study of deformation modes is crucial for evaluating the geometrical dimension limits by which a member can absorb energy efficiently.

Keywords: Analysis-led design, axial collapse, tubular member, finite element method, thin-walled cylindrical tube, compress-expand member, deformation modes.

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Authors: Do-Jin Jang, Sung-Ah Kim

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In designing a kinetic façade, it is hard for the designer to make digital models due to its complex geometry with motion. This paper aims to present a methodology of converting a point cloud of a physical model into a single digital model with a certain topology and motion. The method uses a Microsoft Kinect sensor, and color markers were defined and applied to three paper folding-inspired designs. Although the resulted digital model cannot represent the whole folding range of the physical model, the method supports the designer to conduct a performance-oriented design process with the rough physical model in the reduced folding range.

Keywords: Design media, kinetic façades, tangible user interface, 3D scanning.

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Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: Neural network, aerodynamic angles, virtual sensor, unmanned aerial vehicle, air data system, flight test.

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Authors: Thiang, Handry Khoswanto, Rendy Pangaldus

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Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

Keywords: Artificial neural network, back propagation, inverse kinematics, manipulator, robot.

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Authors: K. Aravindhan, Trishit Bandyopadhyay, Mahesh Mehendale, Supriya Kumar De

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The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.

Keywords: Decision Points, Iteration, Product Design, Rework.

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Authors: Mohammad Reza Pirestani, Hamid Reza Vosoghifar, Pegah Jazayeri

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In designing river intakes and diversion structures, it is paramount that the sediments entering the intake are minimized or, if possible, completely separated. Due to high water velocity, sediments can significantly damage hydraulic structures especially when mechanical equipment like pumps and turbines are used. This subsequently results in wasting water, electricity and further costs. Therefore, it is prudent to investigate and analyze the performance of lateral intakes affected by sediment control structures. Laboratory experiments, despite their vast potential and benefits, can face certain limitations and challenges. Some of these include: limitations in equipment and facilities, space constraints, equipment errors including lack of adequate precision or mal-operation, and finally, human error. Research has shown that in order to achieve the ultimate goal of intake structure design – which is to design longlasting and proficient structures – the best combination of sediment control structures (such as sill and submerged vanes) along with parameters that increase their performance (such as diversion angle and location) should be determined. Cost, difficulty of execution and environmental impacts should also be included in evaluating the optimal design. This solution can then be applied to similar problems in the future. Subsequently, the model used to arrive at the optimal design requires high level of accuracy and precision in order to avoid improper design and execution of projects. Process of creating and executing the design should be as comprehensive and applicable as possible. Therefore, it is important that influential parameters and vital criteria is fully understood and applied at all stages of choosing the optimal design. In this article, influential parameters on optimal performance of the intake, advantages and disadvantages, and efficiency of a given design are studied. Then, a multi-criterion decision matrix is utilized to choose the optimal model that can be used to determine the proper parameters in constructing the intake.

Keywords: Diversion structures lateral intake, multi criteria decision making, optimal design, sediment control.

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Authors: Kuo-Cheng Liu

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Effective estimation of just noticeable distortion (JND) for images is helpful to increase the efficiency of a compression algorithm in which both the statistical redundancy and the perceptual redundancy should be accurately removed. In this paper, we design a DCT-based model for estimating JND profiles of color images. Based on a mathematical model of measuring the base detection threshold for each DCT coefficient in the color component of color images, the luminance masking adjustment, the contrast masking adjustment, and the cross masking adjustment are utilized for luminance component, and the variance-based masking adjustment based on the coefficient variation in the block is proposed for chrominance components. In order to verify the proposed model, the JND estimator is incorporated into the conventional JPEG coder to improve the compression performance. A subjective and fair viewing test is designed to evaluate the visual quality of the coding image under the specified viewing condition. The simulation results show that the JPEG coder integrated with the proposed DCT-based JND model gives better coding bit rates at visually lossless quality for a variety of color images.

Keywords: Just-noticeable distortion (JND), discrete cosine transform (DCT), JPEG.

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Authors: Sudhir Kumar Tiwari

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The paper demonstrates a methodology that can be used at an early design stage of any conventional aircraft. This research activity assesses the feasibility derivation of methodology for aircraft loads estimation during the various phases of design for a transport category aircraft by utilizing potential of using commercial finite element analysis software, which may drive significant time saving. Early Design phase have limited data and quick changing configuration results in handling of large number of load cases. It is useful to idealize the aircraft as a connection of beams, which can be very accurately modelled using finite element analysis (beam elements). This research explores the correct approach towards idealizing an aircraft using beam elements. FEM Techniques like inertia relief were studied for implementation during course of work. The correct boundary condition technique envisaged for generation of shear force, bending moment and torque diagrams for the aircraft. The possible applications of this approach are the aircraft design process, which have been investigated.

Keywords: Multi-disciplinary optimization, aircraft load, finite element analysis, Stick Model.

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Authors: Michael Zimba, Darlison Nyirenda

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A novel copy-move image forgery, CMIF, detection method is proposed. The proposed method presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilized to extract robust features. The extracted features are invariant to additive noise, JPEG compression, and affine transformation. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. SATS is a better option than the common shift vector method because SATS is insensitive to affine transformation. Consequently, the proposed CMIF algorithm is not only fast but also more robust to attacks compared to the existing related CMIF algorithms. The experimental results show high detection rates, as high as 100% in some cases.

Keywords: Affine transformation, Radix sort, SATS, Virtual electrostatic field.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.

Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.

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Authors: Sufian Ashraf Mazhari, Surendra Kumar

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In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function based Neuro-Fuzzy rule base knowledge is not required. Hybrid Genetic Algorithm with generalized Pattern Search is used for tuning weights of radial basis function based Neuro- fuzzy controller. All the controllers are checked for butterfly trajectory tracking and results in the form of Cartesian and joint space errors are being compared. ANFIS based controller is showing better performance compared to Radial Basis Function based Neuro-Fuzzy Controller but rule base independency of RBF based Neuro-Fuzzy gives it an edge over ANFIS

Keywords: Neuro-Fuzzy, Robotic Control, RBFNF, ANFIS, Hybrid GA.

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Authors: Sergey M. Karabanov, Dmitry V. Suvorov, Dmitry Yu. Tarabrin

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The operating principle of magnetically controlled microelectromechanical system (MEMS) switches is based on controlling the beam movement under the influence of a magnetic field. Currently, there is a MEMS switch design with a flexible ferromagnetic electrode in the form of a fixed-terminal beam, with an electrode fastened on a straight or cranked anchor. The basic performance characteristics of magnetically controlled MEMS switches (service life, sensitivity, contact resistance, fast response) are largely determined by the flexible electrode design. To ensure the stable and controlled motion of the flexible electrode, it is necessary to provide the optimal design of a flexible electrode.

Keywords: MEMS switch, magnetic sensitivity, magnetic concentrator.

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: Satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization.

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Authors: Ki-Sung Park, Dae-Hee Lee, Dae-Ho Won, Yeon-Mo Yang

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Solar power plants(SPPs) have shown a lot of good outcomes in providing a various functions depending on industrial expectations by deploying ad-hoc networking with helps of light loaded and battery powered sensor nodes. In particular, it is strongly requested to develop an algorithm to deriver the sensing data from the end node of solar power plants to the sink node on time. In this paper, based on the above observation we have proposed an IEEE802.15.4 based self routing scheme for solar power plants. The proposed beacon based priority routing Algorithm (BPRA) scheme utilizes beacon periods in sending message with embedding the high priority data and thus provides high quality of service(QoS) in the given criteria. The performance measures are the packet Throughput, delivery, latency, total energy consumption. Simulation results under TinyOS Simulator(TOSSIM) have shown the proposed scheme outcome the conventional Ad hoc On-Demand Distance Vector(AODV) Routing in solar power plants.

Keywords: Solar Power Plants(SPPs), Self routing, Quality of Service(QoS), WPANs, WSNs, TinyOS, TOSSIM, IEEE802.15.4

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Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: Energy production, meteorological data, irradiance decomposition, solar photovoltaic system.

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Authors: Manpreet Singh, Keerat Kaur, Bhavdeep Singh

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With the hardware technology advancing, the cost of storing is decreasing. Thus there is an urgent need for new techniques and tools that can intelligently and automatically assist us in transferring this data into useful knowledge. Different techniques of data mining are developed which are helpful for handling these large size databases [7]. Data mining is also finding its role in the field of biotechnology. Pedigree means the associated ancestry of a crop variety. Genetic diversity is the variation in the genetic composition of individuals within or among species. Genetic diversity depends upon the pedigree information of the varieties. Parents at lower hierarchic levels have more weightage for predicting genetic diversity as compared to the upper hierarchic levels. The weightage decreases as the level increases. For crossbreeding, the two varieties should be more and more genetically diverse so as to incorporate the useful characters of the two varieties in the newly developed variety. This paper discusses the searching and analyzing of different possible pairs of varieties selected on the basis of morphological characters, Climatic conditions and Nutrients so as to obtain the most optimal pair that can produce the required crossbreed variety. An algorithm was developed to determine the genetic diversity between the selected wheat varieties. Cluster analysis technique is used for retrieving the results.

Keywords: Genetic diversity, pedigree, nutrients.

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Authors: Andreas Hammer

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Six peculiar architecture designs from the Frankfurt University will be discussed within this paper and their future potential of the adaptable and solar thin-film sheets implemented facades will be shown acting and reacting on climate/solar changes of their specific sites. The different aspects, as well as limitations with regard to technical and functional restrictions, will be named.  The design process for a “multi-purpose building”, a “high-rise building refurbishment” and a “biker’s lodge” on the river Rheine valley, has been critically outlined and developed step by step from an international studentship towards an overall energy strategy, that firstly had to push the design to a plus-energy building and secondly had to incorporate bionic aspects into the building skins design. Both main parameters needed to be reviewed and refined during the whole design process. Various basic bionic approaches have been given [e.g. solar ivy ^TM, flectofin ^TM or hygroskin ^TM, which were to experiment with, regarding the use of bendable photovoltaic thin film elements being parts of a hybrid, kinetic façade system.

Keywords: Energy-strategy, photovoltaic in building skins, bionic and bioclimatic design, plus-energy-buildings, solar gain, the harvesting façade, sustainable building concept, high-efficiency building skin, climate adaptive Building Shells (CABS).

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: V Krishnaveni, S Jayaraman, K Ramadoss

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The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.

Keywords: Electroencephalogram, Ocular Artifacts (OA), Independent Component Analysis (ICA), Mutual Information (MI), Mutual Information based Least dependent Component Analysis(MILCA)

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Authors: Fariba Azizi, Firoozeh Haghighi, Viliam Makis

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In this paper, we propose a method to model the relationship between failure time and degradation for a simple step stress test where underlying degradation path is linear and different causes of failure are possible. It is assumed that the intensity function depends only on the degradation value. No assumptions are made about the distribution of the failure times. A simple step-stress test is used to shorten failure time of products and a tampered failure rate (TFR) model is proposed to describe the effect of the changing stress on the intensities. We assume that some of the products that fail during the test have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the maximum likelihood estimates (MLEs) of the model parameters are obtained through an expectation-maximization (EM) algorithm by treating the causes of failure as missing values. The effect of incomplete information on the estimation of parameters is studied through a Monte-Carlo simulation. Finally, a real example is analyzed to illustrate the application of the proposed methods.

Keywords: Expectation-maximization (EM) algorithm, cause of failure, intensity, linear degradation path, masked data, reliability function.

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Authors: YuriyTasinkevych, Ihor Trots, AndrzejNowicki, Marcin Lewandowski

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The paper presents the optimization problem for the multi-element synthetic transmit aperture method (MSTA) in ultrasound imaging applications. The optimal choice of the transmit aperture size is performed as a trade-off between the lateral resolution, penetration depth and the frame rate. Results of the analysis obtained by a developed optimization algorithm are presented. Maximum penetration depth and the best lateral resolution at given depths are chosen as the optimization criteria. The optimization algorithm was tested using synthetic aperture data of point reflectors simulated by Filed II program for Matlab® for the case of 5MHz 128-element linear transducer array with 0.48 mm pitch are presented. The visualization of experimentally obtained synthetic aperture data of a tissue mimicking phantom and in vitro measurements of the beef liver are also shown. The data were obtained using the SonixTOUCH Research systemequipped with a linear 4MHz 128 element transducerwith 0.3 mm element pitch, 0.28 mm element width and 70% fractional bandwidth was excited by one sine cycle pulse burst of transducer's center frequency.

Keywords: synthetic aperture method, ultrasound imaging, beamforming.

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Authors: T. Mokhele, O. Mutanga, F. Ahmed

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South Africa is one of the few countries that have stopped using the same Enumeration Areas (EAs) for census enumeration and dissemination. The advantage of this change is that confidentiality issue could be addressed for census dissemination as the design of geographic unit for collection is mainly to ensure that this unit is covered by one enumerator. The objective of this paper was to evaluate the performance of automated zone design output areas against non-zone design developed geographies using the 2001 census data, and 2011 census to some extent, as the main input. The comparison of the Automated Zone-design Tool (AZTool) census output areas with the Small Area Layers (SALs) and SubPlaces based on confidentiality limit, population distribution, and degree of homogeneity, as well as shape compactness, was undertaken. Further, SPSS was employed for validation of the AZTool output results. The results showed that AZTool developed output areas out-perform the existing official SAL and SubPlaces with regard to minimum population threshold, population distribution and to some extent to homogeneity. Therefore, it was concluded that AZTool program provides a new alternative to the creation of optimised census output areas for dissemination of population census data in South Africa.

Keywords: AZTool, enumeration areas, small areal layers, South Africa.

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Authors: Parvinder Singh Sandhu, Dalwinder Singh Salaria, Hardeep Singh

Abstract:

Software Reusability is primary attribute of software quality. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. In this paper, we have devised the framework of metrics that uses McCabe-s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component as input attributes and calculated reusability of the software component. Here, comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA approaches is performed to evaluate the reusability of software components and Fuzzy-GA results outperform the other used approaches. The developed reusability model has produced high precision results as expected by the human experts.

Keywords: Software Reusability, Software Metrics, Neural Networks, Genetic Algorithm, Fuzzy Logic.

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Authors: Mat Syai'in, Adi Soeprijanto, Takashi Hiyama

Abstract:

An optimal power flow (OPF) based on particle swarm optimization (PSO) was developed with more realistic generator security constraint using the capability curve instead of only Pmin/Pmax and Qmin/Qmax. Neural network (NN) was used in designing digital capability curve and the security check algorithm. The algorithm is very simple and flexible especially for representing non linear generation operation limit near steady state stability limit and under excitation operation area. In effort to avoid local optimal power flow solution, the particle swarm optimization was implemented with enough widespread initial population. The objective function used in the optimization process is electric production cost which is dominated by fuel cost. The proposed method was implemented at Java Bali 500 kV power systems contain of 7 generators and 20 buses. The simulation result shows that the combination of generator power output resulted from the proposed method was more economic compared with the result using conventional constraint but operated at more marginal operating point.

Keywords: Optimal Power Flow, Generator Capability Curve, Particle Swarm Optimization, Neural Network

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