Search results for: data modeling

Authors: Noor M. Abdulfattah, Ahmed M. Khalafallah, Nabil A. Kartam

Abstract:

Despite the evident benefits of building information modeling (BIM) to the construction industry, it faces significant implementation challenges in the State of Kuwait. This study investigates the awareness of construction stakeholders of BIM implementation challenges, and identifies various solutions to overcome these challenges. Specifically, the main objectives of this study are to: (1) characterize the barriers that deter utilization of BIM, (2) examine the awareness of engineers, architects, and construction stakeholders of these barriers, and (3) identify practical solutions to facilitate BIM utilization. A questionnaire survey was designed to collect data on the aforementioned objectives from local companies and senior BIM experts. It was found that engineers are highly aware of BIM implementation barriers. In addition, it was concluded from the questionnaire that the biggest barrier is the lack of BIM training. Based on expert feedback, the study concluded with a number of recommendations on how to overcome the barriers of BIM utilization. This should prove useful to the construction industry stakeholders and can lead to significant changes to design and construction practices.

Keywords: Building information modeling, construction, challenges, information technology.

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Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data in Education, Hadoop, Learning Analytics.

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Authors: Engin Yesil, Leon Urbas

Abstract:

Modeling of complex dynamic systems, which are very complicated to establish mathematical models, requires new and modern methodologies that will exploit the existing expert knowledge, human experience and historical data. Fuzzy cognitive maps are very suitable, simple, and powerful tools for simulation and analysis of these kinds of dynamic systems. However, human experts are subjective and can handle only relatively simple fuzzy cognitive maps; therefore, there is a need of developing new approaches for an automated generation of fuzzy cognitive maps using historical data. In this study, a new learning algorithm, which is called Big Bang-Big Crunch, is proposed for the first time in literature for an automated generation of fuzzy cognitive maps from data. Two real-world examples; namely a process control system and radiation therapy process, and one synthetic model are used to emphasize the effectiveness and usefulness of the proposed methodology.

Keywords: Big Bang-Big Crunch optimization, Dynamic Systems, Fuzzy Cognitive Maps, Learning.

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Authors: T. Arakawa, H. Sato

Abstract:

There are many drivers who feel right A pillar of Japanese right-hand-drive car preventing visibility on turning right or left at intersection. On the other hand, there is a report that almost pedestrian accident is caused by the delay of finding pedestrian by drivers and this is found by drivers’ eye movement. Thus, we developed the evaluation method of quantification using drivers’ eye movement data by least squares estimation and we applied this method to commercial vehicle and evaluation the visibility. It is suggested that visibility of vehicle can be quantified and estimated by linear model obtained from experimental eye fixation data and information of vehicle dimensions.

Keywords: Eye fixation, modeling, obstacle feeling, right A pillar.

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Authors: Vinay Chandwani, Vinay Agrawal, Ravindra Nagar

Abstract:

Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.

Keywords: Artificial neural networks, Genetic algorithms, Back-propagation algorithm, Ready Mix Concrete, Slump value.

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Authors: Alicia Y. C. Tang, Rukaini Abdullah, Sharifuddin M. Zain, Noorsaadah A. Rahman

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This paper discusses the causal explanation capability of QRIOM, a tool aimed at supporting learning of organic chemistry reactions. The development of the tool is based on the hybrid use of Qualitative Reasoning (QR) technique and Qualitative Process Theory (QPT) ontology. Our simulation combines symbolic, qualitative description of relations with quantity analysis to generate causal graphs. The pedagogy embedded in the simulator is to both simulate and explain organic reactions. Qualitative reasoning through a causal chain will be presented to explain the overall changes made on the substrate; from initial substrate until the production of final outputs. Several uses of the QPT modeling constructs in supporting behavioral and causal explanation during run-time will also be demonstrated. Explaining organic reactions through causal graph trace can help improve the reasoning ability of learners in that their conceptual understanding of the subject is nurtured.

Keywords: Qualitative reasoning, causal graph, organicreactions, explanation, QPT, modeling constructs.

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Authors: Yang Bao, Shi Wei Deng, Wang Qun Lin

Abstract:

This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSql), and gives 6 data cleaning methods based on these algorithms.

Keywords: Data cleaning, dependency rules, violation data discovery, data repair.

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Authors: Chaw Su Mon

Abstract:

Abstract— The paper presents a preliminary study on modeling and estimation of basic wind speed ( extreme wind gusts ) for the consideration of vulnerability and design of building in Ayeyarwady Region. The establishment of appropriate design wind speeds is a critical step towards the calculation of design wind loads for structures. In this paper the extreme value analysis of this prediction work is based on the anemometer data (1970-2009) maintained by the department of meteorology and hydrology of Pathein. Statistical and probabilistic approaches are used to derive formulas for estimating 3-second gusts from recorded data (10-minute sustained mean wind speeds).

Keywords: Basic Wind Speed, Building, Gusts, Statistical and probabilistic approaches

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Authors: G.Kabashi, K.Kadriu, A.Gashi, S.Kabashi, G, Pula, V.Komoni

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This paper focuses on PSS/E modeling of wind farms of Doubly-fed Induction Generator (DFIG) type and their impact on issues of power system operation. Since Wind Turbine Generators (WTG) don-t have the same characteristics as synchronous generators, the appropriate modeling of wind farms is essential for transmission system operators to analyze the best options of transmission grid reinforcements as well as to evaluate the wind power impact on reliability and security of supply. With the high excepted penetration of wind power into the power system a simultaneous loss of Wind Farm generation will put at risk power system security and reliability. Therefore, the main wind grid code requirements concern the fault ride through capability and frequency operation range of wind turbines. In case of grid faults wind turbines have to supply a definite reactive power depending on the instantaneous voltage and to return quickly to normal operation.

Keywords: Power System transients, PSS/E dynamic simulationDouble-fed Induction Generator.

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Authors: Yeqing Guan, Fen Yang

Abstract:

To learn about China-s future energy demand, this paper first proposed GM(1,1) model group based on recursive solutions of parameters estimation, setting up a general solving-algorithm of the model group. This method avoided the problems occurred on the past researches that remodeling, loss of information and large amount of calculation. This paper established respectively all-data-GM(1,1), metabolic GM(1,1) and new information GM (1,1)model according to the historical data of energy consumption in China in the year 2005-2010 and the added data of 2011, then modeling, simulating and comparison of accuracies we got the optimal models and to predict. Results showed that the total energy demand of China will be 37.2221 billion tons of equivalent coal in 2012 and 39.7973 billion tons of equivalent coal in 2013, which are as the same as the overall planning of energy demand in The 12th Five-Year Plan.

Keywords: energy demands, GM(1, 1) model group, least square estimation, prediction

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Authors: Ayudhia P. Gusti, Semin

Abstract:

It is known that total operating cost of a vessel is dominated by the cost of fuel consumption. How to reduce the fuel cost of ship so that the operational costs of fuel can be minimized is the question that arises. As the basis of these kinds of problem, sailing speed determination is an important factor to be considered by a shipping company. Optimal speed determination will give a significant influence on the route and berth schedule of ships, which also affect vessel operating costs. The purpose of this paper is to clarify some important issues about ship speed optimization. Sailing speed, displacement, sailing time, and specific fuel consumption were obtained from shipping log data to be further analyzed for modeling the speed optimization. The presented speed optimization model is expected to affect the fuel consumption and to reduce the cost of fuel consumption.

Keywords: Maritime transportation, reducing fuel, shipping log data, speed optimization.

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Authors: Thitiporn Supasri, Natanee Vorayos, Piriya Thongchiew

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Electrification is a complex process and governed by various parameters.  Modeling of power plant’s target efficiency or target heat rate is often formulated and compared with the actual values. This comparison not only implies the performance of the power plant but also reflects the energy losses possibly inherited in some of related equipment and processes. The current modeling of Coal-fired Mae Moh power plant was formulated at the first commissioning. Some of equipments were replaced due to its life time. Relatively outdated for 20 years, the utilization of the model is not accomplished. This work has focused on the development of the performance analysis model of aforementioned power plant according to the most updated and current working conditions. The model is more appropriate and shows accuracy in its analysis.  Losses are detected and measures are introduced such that reduction in energy consumption, related cost, and also environment impacts can be anticipated.

Keywords: Performance analysis model, Power plant modeling, Target heat rate, Target efficiency.

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Authors: S. Houssaini, L. Bahi

Abstract:

The motorway segment between Tangier and Oued R’mel has experienced, since the beginning of building works, significant instability and landslides linked to a number of geological, hydrogeological and geothermic factors affecting the different formations. The landslides observed are not fully understood, despite many studies conducted on this segment. This study aims at producing new methods to better explain the phenomena behind the landslides, taking into account the geotechnical and geothermic contexts. This analysis builds up on previous studies and geotechnical data collected in the field. The final body of data collected shall be processed through the Plaxis software for a better and customizable view of the landslide problems in the area, which will help tofind solutions and stabilize land in the area.

Keywords: Landslides, modeling, risk, stabilization.

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Authors: Somayeh Komeylian

Abstract:

The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, adaptive antenna array, Deep Neural Network, LS-SVM optimization model, radial basis function, MSE.

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Authors: Yoonsuh Jung

Abstract:

As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an ‘optimal’ value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: Cross Validation, Parameter Averaging, Parameter Selection, Regularization Parameter Search.

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Authors: Brajesh Kumar Jha, Neeru Adlakha, M. N. Mehta

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Mathematical and computational modeling of calcium signalling in nerve cells has produced considerable insights into how the cells contracts with other cells under the variation of biophysical and physiological parameters. The modeling of calcium signaling in astrocytes has become more sophisticated. The modeling effort has provided insight to understand the cell contraction. Main objective of this work is to study the effect of voltage gated (Operated) calcium channel (VOC) on calcium profile in the form of advection diffusion equation. A mathematical model is developed in the form of advection diffusion equation for the calcium profile. The model incorporates the important physiological parameter like diffusion coefficient etc. Appropriate boundary conditions have been framed. Finite volume method is employed to solve the problem. A program has been developed using in MATLAB 7.5 for the entire problem and simulated on an AMD-Turion 32-bite machine to compute the numerical results.

Keywords: Ca2+ Profile, Advection Diffusion, VOC, FVM.

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Authors: N. Parimala, P. Pahwa

Abstract:

OLAP uses multidimensional structures, to provide access to data for analysis. Traditionally, OLAP operations are more focused on retrieving data from a single data mart. An exception is the drill across operator. This, however, is restricted to retrieving facts on common dimensions of the multiple data marts. Our concern is to define further operations while retrieving data from multiple data marts. Towards this, we have defined six operations which coalesce data marts. While doing so we consider the common as well as the non-common dimensions of the data marts.

Keywords: Data warehouse, Dimension, OLAP, Star Schema.

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: Crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest.

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Authors: W. K. Yap, V. Karri

Abstract:

This paper presents a hybrid electric scooter model developed and simulated using Matlab/Simulink. This hybrid scooter modeled has a parallel hybrid structure. The main propulsion units consist of a two stroke internal combustion engine and a hub motor attached to the front wheel of the scooter. The methodology used to optimize the energy and fuel consumption of the hybrid electric scooter is the multi-mode approach. Various case studies were presented to check the model and were compared to the literatures. Results shown that the model developed was feasible and valuable.

Keywords: Hybrid electric scooters, modeling and simulation, hybrid scooter energy management.

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Authors: S. Chuenchooklin, S. Taweepong, U. Pangnakorn

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This research was conducted in the Mae Sot Watershed where located in the Moei River Basin at the Upper Salween River Basin in Tak Province, Thailand. The Mae Sot Municipality is the largest urban area in Tak Province and situated in the midstream of the Mae Sot Watershed. It usually faces flash flood problem after heavy rain due to poor flood management has been reported since economic rapidly bloom up in recent years. Its catchment can be classified as ungauged basin with lack of rainfall data and no any stream gaging station was reported. It was attached by most severely flood events in 2013 as the worst studied case for all those communities in this municipality. Moreover, other problems are also faced in this watershed, such shortage water supply for domestic consumption and agriculture utilizations including a deterioration of water quality and landslide as well. The research aimed to increase capability building and strengthening the participation of those local community leaders and related agencies to conduct better water management in urban area was started by mean of the data collection and illustration of the appropriated application of some short period rainfall forecasting model as they aim for better flood relief plan and management through the hydrologic model system and river analysis system programs. The authors intended to apply the global rainfall data via the integrated data viewer (IDV) program from the Unidata with the aim for rainfall forecasting in a short period of 7-10 days in advance during rainy season instead of real time record. The IDV product can be present in an advance period of rainfall with time step of 3-6 hours was introduced to the communities. The result can be used as input data to the hydrologic modeling system model (HEC-HMS) for synthesizing flood hydrographs and use for flood forecasting as well. The authors applied the river analysis system model (HEC-RAS) to present flood flow behaviors in the reach of the Mae Sot stream via the downtown of the Mae Sot City as flood extents as the water surface level at every cross-sectional profiles of the stream. Both models of HMS and RAS were tested in 2013 with observed rainfall and inflow-outflow data from the Mae Sot Dam. The result of HMS showed fit to the observed data at the dam and applied at upstream boundary discharge to RAS in order to simulate flood extents and tested in the field, and the result found satisfying. The product of rainfall from IDV was fair while compared with observed data. However, it is an appropriate tool to use in the ungauged catchment to use with flood hydrograph and river analysis models for future efficient flood relief plan and management.

Keywords: Global rainfall, flood forecasting, hydrologic modeling system, river analysis system.

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Authors: Ahasanul Haque, Abdullah Sarwar, Khaliq Ahmad

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Networking is important among students to achieve better understanding. Social networking plays an important role in the education. Realizing its huge potential, various organizations, including institutions of higher learning have moved to the area of social networks to interact with their students especially through Facebook. Therefore, measuring the effectiveness of Facebook as a learning tool has become an area of interest to academicians and researchers. Therefore, this study tried to integrate and propose new theoretical and empirical evidences by linking the western idea of adopting Facebook as an alternative learning platform from a Malaysian perspective. This study, thus, aimed to fill a gap by being among the pioneering research that tries to study the effectiveness of adopting Facebook as a learning platform across other cultural settings, namely Malaysia. Structural equation modeling was employed for data analysis and hypothesis testing. This study finding has provided some insights that would likely affect students’ awareness towards using Facebook as an alternative learning platform in the Malaysian higher learning institutions. At the end, future direction is proposed.

Keywords: Learning Management Tool, Social Networking, Education, Malaysia.

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Authors: Hoda A. Abdel Hafez

Abstract:

Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: Mining Big Data, Big Data, Machine learning, Data Streams, Telecommunication.

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Authors: Mohamed Dbouk, Ihab Sbeity

Abstract:

Spatial and mobile computing evolves. This paper describes a smart modeling platform called “GeoSEMA". This approach tends to model multidimensional GeoSpatial Evolutionary and Mobile Agents. Instead of 3D and location-based issues, there are some other dimensions that may characterize spatial agents, e.g. discrete-continuous time, agent behaviors. GeoSEMA is seen as a devoted design pattern motivating temporal geographic-based applications; it is a firm foundation for multipurpose and multidimensional special-based applications. It deals with multipurpose smart objects (buildings, shapes, missiles, etc.) by stimulating geospatial agents. Formally, GeoSEMA refers to geospatial, spatio-evolutive and mobile space constituents where a conceptual geospatial space model is given in this paper. In addition to modeling and categorizing geospatial agents, the model incorporates the concept of inter-agents event-based protocols. Finally, a rapid software-architecture prototyping GeoSEMA platform is also given. It will be implemented/ validated in the next phase of our work.

Keywords: Location-Trajectory management, GIS, Mobile- Moving Objects/Agents, Multipurpose/Spatiotemporal data, Multi- Agent Systems.

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Authors: Fábio A. S. Mota, Mauro A. S. S. Ravagnani, E. P. Carvalho

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In the present paper the design of plate heat exchangers is formulated as an optimization problem considering two mathematical modelling. The number of plates is the objective function to be minimized, considering implicitly some parameters configuration. Screening is the optimization method used to solve the problem. Thermal and hydraulic constraints are verified, not viable solutions are discarded and the method searches for the convergence to the optimum, case it exists. A case study is presented to test the applicability of the developed algorithm. Results show coherency with the literature.

Keywords: Plate heat exchanger, optimization, modeling, simulation.

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

Abstract:

Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D1 depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. κ − ε and 2D LES turbulence models were consider in this article. 2D CFD2 simulations for one hill was done to check the depth-averaged model in practise.

Keywords: Depth-averaged equations, numerical modeling, CFD

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Authors: Yayun Hsu, Henry Horng-Shing Lu

Abstract:

This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate crosstalk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds, since biological information is inherently included inside the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: Brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning.

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Authors: Arpan Dwivedi, Yogesh Pahariya

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In this paper, optimal design of hybrid standalone power supply system (SAPS) is done for off grid applications in remote areas where transmission of power is difficult. The hybrid SAPS system uses two primary energy sources, wind and solar, and in addition to these diesel generator is also connected to meet the load demand in case of failure of wind and solar system. This paper presents mathematical modeling of 1.4 MW hybrid SAPS system for rural electrification. This paper firstly focuses on mathematical modeling of PV module connected in a string, secondly focuses on modeling of permanent magnet wind turbine generator (PMWTG). The hybrid controller is also designed for selection of power from the source available as per the load demand. The power output of hybrid SAPS system is analyzed for meeting load demands at urban as well as for rural areas.

Keywords: SAPS, DG, PMWTG, rural area, off grid, PV module.

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Authors: Rim Bouhaouel, Naoufel Kraïem, Zuhoor Al Khanjari

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Unified Modeling Language (UML) is considered as one of the widespread modeling language standardized by the Object Management Group (OMG). Therefore, the model driving engineering (MDE) community attempts to provide reuse of UML diagrams, and do not construct it from scratch. The UML model appears according to a specific software development process. The existing method generation models focused on the different techniques of transformation without considering the development process. Our work aims to construct an UML component from fragments of UML diagram basing on an agile method. We define UML fragment as a portion of a UML diagram, which express a business target. To guide the generation of fragments of UML models using an agile process, we need a flexible approach, which adapts to the agile changes and covers all its activities. We use the software product line (SPL) to derive a fragment of process agile method. This paper explains our approach, named RECUP, to generate UML fragments following an agile process, and overviews the different aspects. In this paper, we present the approach and we define the different phases and artifacts.

Keywords: UML, component, fragment, agile, SPL.

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Authors: C. Ardil

Abstract:

This paper presents architectural acoustic modeling to estimate reverberation time in room acoustic design using multiple criteria decision making analysis. First, fundamental decision criteria were determined to evaluate the reverberation time in the room acoustic design problem. Then, the proposed model was applied to a practical decision problem to evaluate and select the optimal room acoustic design model. Finally, the optimal acoustic design of the rooms was analyzed and ranked using a multiple criteria decision making analysis method.

Keywords: Architectural acoustics, room acoustics, architectural acoustic modeling, reverberation time, room acoustic design, multiple criteria decision making analysis, decision analysis, MCDMA

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