Search results for: business data type

Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: Analysis of optimization, artificial intelligence-based optimization, optimization for learning and data analysis, global optimization.

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Authors: M. Govindarajan, R. M.Chandrasekaran

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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: Ginhsuan Li, Chiuyun Hung, Desheng Chen, Yiwen Wang

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Application-Specific Instruction (ASI ) set Processors (ASIP) have become an important design choice for embedded systems due to runtime flexibility, which cannot be provided by custom ASIC solutions. One major bottleneck in maximizing ASIP performance is the limitation on the data bandwidth between the General Purpose Register File (GPRF) and ASIs. This paper presents the Implicit Registers (IRs) to provide the desirable data bandwidth. An ASI Input/Output model is proposed to formulate the overheads of the additional data transfer between the GPRF and IRs, therefore, an IRs allocation algorithm is used to achieve the better performance by minimizing the number of extra data transfer instructions. The experiment results show an up to 3.33x speedup compared to the results without using IRs.

Keywords: Application-Specific Instruction-set Processors, data bandwidth, configurable processor, implicit register.

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Authors: Hiroyuki Ohsaki, Makoto Imase

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In Grid computing, a data transfer protocol called GridFTP has been widely used for efficiently transferring a large volume of data. Currently, two versions of GridFTP protocols, GridFTP version 1 (GridFTP v1) and GridFTP version 2 (GridFTP v2), have been proposed in the GGF. GridFTP v2 supports several advanced features such as data streaming, dynamic resource allocation, and checksum transfer, by defining a transfer mode called X-block mode. However, in the literature, effectiveness of GridFTP v2 has not been fully investigated. In this paper, we therefore quantitatively evaluate performance of GridFTP v1 and GridFTP v2 using mathematical analysis and simulation experiments. We reveal the performance limitation of GridFTP v1, and quantitatively show effectiveness of GridFTP v2. Through several numerical examples, we show that by utilizing the data streaming feature, the average file transfer time of GridFTP v2 is significantly smaller than that of GridFTP v1.

Keywords: Grid Computing, GridFTP, Performance Evaluation, Queuing Theory.

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Authors: K. Indra Gandhi

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Wireless Sensor Networks (WSNs) have always been characterized for application-specific sensing, relaying and collection of information for further analysis. However, software development was not considered as a separate entity in this process of data collection which has posed severe limitations on the software development for WSN. Software development for WSN is a complex process since the components involved are data-driven, network-driven and application-driven in nature. This implies that there is a tremendous need for the separation of concern from the software development perspective. A layered approach for developing data acquisition design based on Model Driven Development (MDD) has been proposed as the sensed data collection process itself varies depending upon the application taken into consideration. This work focuses on the layered view of the data acquisition process so as to ease the software point of development. A metamodel has been proposed that enables reusability and realization of the software development as an adaptable component for WSN systems. Further, observing users perception indicates that proposed model helps in improving the programmer's productivity by realizing the collaborative system involved.

Keywords: Model-driven development, wireless sensor networks, data acquisition, separation of concern, layered design.

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Authors: Ahmad R. Zamani, Luigi Sanguigno, Angelo R. Maligno

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A 3D woven composite, designed for automotive applications, is studied using Abaqus Finite Element (FE) software suite. Python scripts were developed to build FE models of the woven composite in Complete Abaqus Environment (CAE). They can read TexGen or WiseTex files and automatically generate consistent meshes of the fabric and the matrix. A user menu is provided to help define parameters for the FE models, such as type and size of the elements in fabric and matrix as well as the type of matrix-fabric interaction. Node-to-node constraints were imposed to guarantee periodicity of the deformed shapes at the boundaries of the representative volume element of the composite. Tensile loads in three axes and biaxial loads in x-y directions have been applied at different Fibre Volume Fractions (FVFs). A simple damage model was implemented via an Abaqus user material (UMAT) subroutine. Existing tools for homogenization were also used, including voxel mesh generation from TexGen as well as Abaqus Micromechanics plugin. Linear relations between homogenised elastic properties and the FVFs are given. The FE models of composite exhibited balanced behaviour with respect to warp and weft directions in terms of both stiffness and strength.

Keywords: 3D woven composite, meso-scale finite element modelling, homogenisation of elastic material properties, Abaqus Python scripting.

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Authors: Roza Dastres, Mohsen Soori

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In order to electronically exchange information between network users in the web of data, different software such as outlook is presented. So, the traffic of users on a site or even the floors of a building can be decreased as a result of applying a secure and reliable data sharing software. It is essential to provide a fast, secure and reliable network system in the data sharing webs to create an advanced communication systems in the users of network. In the present research work, different encoding methods and algorithms in data sharing systems is studied in order to increase security of data sharing systems by preventing the access of hackers to the transferred data. To increase security in the networks, the possibility of textual conversation between customers of a local network is studied. Application of the encryption and decryption algorithms is studied in order to increase security in networks by preventing hackers from infiltrating. As a result, a reliable and secure communication system between members of a network can be provided by preventing additional traffic in the website environment in order to increase speed, accuracy and security in the network and web systems of data sharing.

Keywords: Secure Socket Layer, Security of networks.

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Authors: Zenab Naseem, Sadia Imran

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One of the key solutions to the climate change crisis is to develop renewable energy resources, such as solar and wind power and biogas. This paper explores the socioeconomic and environmental viability of solar energy, based on a case study of the Soan Valley Development Program. Under this project, local farmers were provided solar water pumps at subsidized rates. These have been functional for the last seven years and have gained popularity among the local communities. The study measures the economic viability of using solar energy in agriculture, based on data from 36 households, of which 12 households each use diesel, electric and solar water pumps. Our findings are based on the net present value of each technology type. We also carry out a qualitative assessment of the social impact of solar water pumps relative to diesel and electric pumps. Finally, we conduct an environmental impact assessment, using the lifecycle assessment approach. All three analyses indicate that solar energy is a viable alternative to diesel and electricity.

Keywords: Alternative energy sources, pollution control adoption and costs, solar energy pumps, sustainable development.

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behavior of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: Central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities.

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: Human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing.

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Authors: Renáta Iváncsy, Sándor Juhász

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Web usage mining has become a popular research area, as a huge amount of data is available online. These data can be used for several purposes, such as web personalization, web structure enhancement, web navigation prediction etc. However, the raw log files are not directly usable; they have to be preprocessed in order to transform them into a suitable format for different data mining tasks. One of the key issues in the preprocessing phase is to identify web users. Identifying users based on web log files is not a straightforward problem, thus various methods have been developed. There are several difficulties that have to be overcome, such as client side caching, changing and shared IP addresses and so on. This paper presents three different methods for identifying web users. Two of them are the most commonly used methods in web log mining systems, whereas the third on is our novel approach that uses a complex cookie-based method to identify web users. Furthermore we also take steps towards identifying the individuals behind the impersonal web users. To demonstrate the efficiency of the new method we developed an implementation called Web Activity Tracking (WAT) system that aims at a more precise distinction of web users based on log data. We present some statistical analysis created by the WAT on real data about the behavior of the Hungarian web users and a comprehensive analysis and comparison of the three methods

Keywords: Data preparation, Tracking individuals, Web useridentification, Web usage mining

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Authors: Mohammadreza Amini, Omid Moradtalab, Ebadollah Zohrevandi

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In real-time networks a large number of application programs are relying on video data and heterogeneous data transmission techniques. The aim of this research is presenting a method for end-to-end vouch quality service in surface applicationlayer for sending video data in comparison form in wireless heterogeneous networks. This method tries to improve the video sending over the wireless heterogeneous networks with used techniques in surface layer, link and application. The offered method is showing a considerable improvement in quality observing by user. In addition to this, other specifications such as shortage of data load that had require to resending and limited the relation period length to require time for second data sending, help to be used the offered method in the wireless devices that have a limited energy. The presented method and the achieved improvement is simulated and presented in the NS-2 software.

Keywords: Heterogeneous wireless networks, adaptation mechanism, multi-level, Handoff, stop mechanism, graceful degrades, application layer.

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Authors: Rohith K Reddy, David Mayerich, Michael Walsh, P Scott Carney, Rohit Bhargava

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Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that provides both chemically and spatially resolved information. The rich chemical content of data may be utilized for computer-aided determinations of structure and pathologic state (cancer diagnosis) in histological tissue sections for prostate cancer. FT-IR spectroscopic imaging of prostate tissue has shown that tissue type (histological) classification can be performed to a high degree of accuracy [1] and cancer diagnosis can be performed with an accuracy of about 80% [2] on a microscopic (≈ 6μm) length scale. In performing these analyses, it has been observed that there is large variability (more than 60%) between spectra from different points on tissue that is expected to consist of the same essential chemical constituents. Spectra at the edges of tissues are characteristically and consistently different from chemically similar tissue in the middle of the same sample. Here, we explain these differences using a rigorous electromagnetic model for light-sample interaction. Spectra from FT-IR spectroscopic imaging of chemically heterogeneous samples are different from bulk spectra of individual chemical constituents of the sample. This is because spectra not only depend on chemistry, but also on the shape of the sample. Using coupled wave analysis, we characterize and quantify the nature of spectral distortions at the edges of tissues. Furthermore, we present a method of performing histological classification of tissue samples. Since the mid-infrared spectrum is typically assumed to be a quantitative measure of chemical composition, classification results can vary widely due to spectral distortions. However, we demonstrate that the selection of localized metrics based on chemical information can make our data robust to the spectral distortions caused by scattering at the tissue boundary.

Keywords: Infrared, Spectroscopy, Imaging, Tissue classification

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Authors: Jinsuk Yang, Choongjae Joo, Kyoungsu Oh

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Existing methods in which the animation data of all frames are stored and reproduced as with vertex animation cannot be used in mobile device environments because these methods use large amounts of the memory. So 3D animation data reduction methods aimed at solving this problem have been extensively studied thus far and we propose a new method as follows. First, we find and remove frames in which motion changes are small out of all animation frames and store only the animation data of remaining frames (involving large motion changes). When playing the animation, the removed frame areas are reconstructed using the interpolation of the remaining frames. Our key contribution is to calculate the accelerations of the joints of individual frames and the standard deviations of the accelerations using the information of joint locations in the relevant 3D model in order to find and delete frames in which motion changes are small. Our methods can reduce data sizes by approximately 50% or more while providing quality which is not much lower compared to original animations. Therefore, our method is expected to be usefully used in mobile device environments or other environments in which memory sizes are limited.

Keywords: Data Reduction, Interpolation, Vertex Animation, 3D Animation.

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Authors: S. H. J. Liu

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This paper aims to explore the role of peer Corrective Feedback (CF) in improving written productions by English-as-a- foreign-language (EFL) learners who work together via Wikispaces. It attempted to determine the effect of peer CF on form accuracy in English, such as grammar and lexis. Thirty-four EFL learners at the tertiary level were randomly assigned into the experimental (with peer feedback) or the control (without peer feedback) group; each group was subdivided into small groups of two or three. This resulted in six and seven small groups in the experimental and control groups, respectively. In the experimental group, each learner played a role as an assessor (providing feedback to others), as well as an assessee (receiving feedback from others). Each participant was asked to compose his/her written work and revise it based on the feedback. In the control group, on the other hand, learners neither provided nor received feedback but composed and revised their written work on their own. Data collected from learners’ compositions and post-task interviews were analyzed and reported in this study. Following the completeness of three writing tasks, 10 participants were selected and interviewed individually regarding their perception of collaborative learning in the Computer-Mediated Communication (CMC) environment. Language aspects to be analyzed included lexis (e.g., appropriate use of words), verb tenses (e.g., present and past simple), prepositions (e.g., in, on, and between), nouns, and articles (e.g., a/an). Feedback types consisted of CF, affective, suggestive, and didactic. Frequencies of feedback types and the accuracy of the language aspects were calculated. The results first suggested that accurate items were found more in the experimental group than in the control group. Such results entail that those who worked collaboratively outperformed those who worked non-collaboratively on the accuracy of linguistic aspects. Furthermore, the first type of CF (e.g., corrections directly related to linguistic errors) was found to be the most frequently employed type, whereas affective and didactic were the least used by the experimental group. The results further indicated that most participants perceived that peer CF was helpful in improving the language accuracy, and they demonstrated a favorable attitude toward working with others in the CMC environment. Moreover, some participants stated that when they provided feedback to their peers, they tended to pay attention to linguistic errors in their peers’ work but overlook their own errors (e.g., past simple tense) when writing. Finally, L2 or FL teachers or practitioners are encouraged to employ CMC technologies to train their students to give each other feedback in writing to improve the accuracy of the language and to motivate them to attend to the language system.

Keywords: Peer corrective feedback, computer-mediated communication, second or foreign language learning, Wikispaces.

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Oktavian Muhammad Rizki, Appiah Rita, Lastres Oscar, Miller True, Chapman Alec, Tsoukalas Lefteri H.

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The Purdue University Research Reactor-1 (PUR-1) is a 10 kWth pool-type research reactor located at Purdue University’s West Lafayette campus. The reactor was recently upgraded to use entirely digital instrumentation and control systems. However, currently, there is no automated control system to regulate the power in the reactor. We propose a fuzzy logic controller as a form of digital twin to complement the existing digital instrumentation system to monitor and stabilize power control using existing experimental data. This work assesses the feasibility of a power controller based on a Fuzzy Rule-Based System (FRBS) by modelling and simulation with a MATLAB algorithm. The controller uses power error and reactor period as inputs and generates reactivity insertion as output. The reactivity insertion is then converted to control rod height using a logistic function based on information from the recorded experimental reactor control rod data. To test the capability of the proposed fuzzy controller, a point-kinetic reactor model is utilized based on the actual PUR-1 operation conditions and a Monte Carlo N-Particle simulation result of the core to numerically compute the neutronics parameters of reactor behavior. The Point Kinetic Equation (PKE) was employed to model dynamic characteristics of the research reactor since it explains the interactions between the spatial and time varying input and output variables efficiently. The controller is demonstrated computationally using various cases: startup, power maneuver, and shutdown. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the reactor power to follow demand power without compromising nuclear safety measures.

Keywords: Fuzzy logic controller, power controller, reactivity, research reactor.

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Authors: Fan Ye

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Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: Low visibility, RWIS, traffic safety, visibility.

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Authors: T. Suresh Kumar, M. Arthanari, N. Shanthi

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Nowadays, the Web has become one of the most pervasive platforms for information change and retrieval. It collects the suitable and perfectly fitting information from websites that one requires. Data mining is the form of extracting data’s available in the internet. Web mining is one of the elements of data mining Technique, which relates to various research communities such as information recovery, folder managing system and simulated intellects. In this Paper we have discussed the concepts of Web mining. We contain generally focused on one of the categories of Web mining, specifically the Web Content Mining and its various farm duties. The mining tools are imperative to scanning the many images, text, and HTML documents and then, the result is used by the various search engines. We conclude by presenting a comparative table of these tools based on some pertinent criteria.

Keywords: Data Mining, Web Mining, Web Content Mining, Mining Tools, Information retrieval.

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Authors: Leonor Varandas, Pedro D. Gaspar, Martim L. Aguiar

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One of the biggest concerns in the field of agriculture is around the energy efficiency of robots that will perform agriculture’s activity and their charging methods. In this paper, two different charging methods for agricultural standalone docking stations are shown that will take into account various variants as field size and its irregularities, work’s nature to which the robot will perform, deadlines that have to be respected, among others. Its features also are dependent on the orchard, season, battery type and its technical specifications and cost. First charging base method focuses on wireless charging, presenting more benefits for small field. The second charging base method relies on battery replacement being more suitable for large fields, thus avoiding the robot stop for recharge. Existing many methods to charge a battery, the CC CV was considered the most appropriate for either simplicity or effectiveness. The choice of the battery for agricultural purposes is if most importance. While the most common battery used is Li-ion battery, this study also discusses the use of graphene-based new type of batteries with 45% over capacity to the Li-ion one. A Battery Management Systems (BMS) is applied for battery balancing. All these approaches combined showed to be a promising method to improve a lot of technical agricultural work, not just in terms of plantation and harvesting but also about every technique to prevent harmful events like plagues and weeds or even to reduce crop time and cost.

Keywords: Agricultural mobile robot, charging base methods, battery replacement method, wireless charging method.

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Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw

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This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.

Keywords: Neural network, Load forecast, Time series, wavelettransform.

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Authors: Aqsa Jamil, Hiroshi Tamura, Hiroshi Katsuchi, Jiaqi Wang

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Yield point represents the upper limit of forces which can be applied on a specimen without causing any permanent deformation. After yielding, the behavior of specimen suddenly changes including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of thermography camera. The yield point of specimens was estimated by the help of temperature dip which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing repeatability analysis. The effect of temperature imperfection and light source has been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of thermographic technique.

Keywords: Signal to noise ratio, thermoelastic effect, thermography, yield point.

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: D. Woradechjumroen, H. Li, Y. Yu

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Supermarkets are the most electricity-intensive type of commercial buildings. The unsuitable indoor environment of a supermarket provided by abnormal HVAC operations incurs waste energy consumption in refrigeration systems. This current study briefly describes significantly solid backgrounds and proposes easyto- use analysis terminology for investigating the impact of HVAC operations on refrigeration power consumption using the field-test data obtained from building automation system (BAS). With solid backgrounds and prior knowledge, expected energy interactions between HVAC and refrigeration systems are proposed through Pearson’s correlation analysis (R value) by considering correlations between equipment power consumption and dominantly independent variables (driving force conditions).The R value can be conveniently utilized to evaluate how strong relations between equipment operations and driving force parameters are. The calculated R values obtained from field data are compared to expected ranges of R values computed by energy interaction methodology. The comparisons can separate the operational conditions of equipment into faulty and normal conditions. This analysis can simply investigate the condition of equipment operations or building sensors because equipment could be abnormal conditions due to routine operations or faulty commissioning processes in field tests. With systematically solid and easy-to-use backgrounds of interactions provided in the present article, the procedures can be utilized as a tool to evaluate the proper commissioning and routine operations of HVAC and refrigeration systems to detect simple faults (e.g. sensors and driving force environment of refrigeration systems and equipment set-point) and optimize power consumption in supermarket buildings. Moreover, the analysis will be used to further study the FDD research for supermarkets in future.

Keywords: Energy interaction, HVAC, R-value, Supermarket buildings.

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Authors: Hiba Hasan, Khalid Raza

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Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to reconstruct genetic regulatory network from time series gene expression data. Supercoiled data set from Escherichia coli has been taken to demonstrate the proposed method.

Keywords: Gene expression, gene regulatory networks (GRNs), clustering, data preprocessing, network visualization.

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Authors: H. Sakamoto, J. Tochimoto, S. Kurosawa, M. Suzuki, S. Oguri

Abstract:

Salt stress adversely affects plant growth at various stages of development including seed germination, seedling establishment, vegetative growth and finally reproduction. Because of their immobile nature, plants have evolved mechanisms to sense and respond to salt stress. Seed dormancy is an adaptive trait that enables seed germination to coincide with favorable environmental conditions. We identified a novel locus of Arabidopsis, designated SHG1 (salt hypersensitive germination 1), whose disruption leads to reduced germination rate under moderate salt stress conditions. SHG1 encodes a transmembrane protein with an ankyrin-repeat motif that has been implicated in diverse cellular processes such as signal transduction. The shg1-disrupted Arabidopsis mutant died at the cotyledon stage when sown on salt-containing medium, although wild-type plants could form true leaves under the same conditions. On the other hand, this mutant showed similar phenotypes to wild-type plants when sown on medium without salt and transferred to salt-containing medium at the vegetative stage. These results suggested that SHG1 played indispensable role in the seed germination and seedling establishment under moderate salt stress conditions. SHG1 may be involved in the release of seed dormancy.

Keywords: Germination, ankyrin repeat, Arabidopsis, salt tolerance.

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

Abstract:

The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: Concrete bridges, deterioration, Markov chains, probability matrix.

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Authors: G. H. B. Dilhari, A. A. D. T. Saparamadu

Abstract:

Sri Lanka is one of the prominent countries for the coir production. The coir is one of the by-products of the coconut and the coir industry is considered to be one of the traditional industries in Sri Lanka. Because of the inherent nature of the coir industry, labourers play a significant role in the coir production process. The study has analyzed the determinants of the income of the household level coir yarn labourers. The study was conducted in the Kumarakanda Grama Niladhari division. Simple random sampling was used to generate a sample of 100 household level coir yarn labourers and structured questionnaire, personal interviews, and discussion were performed to gather the required data. The obtained data were statistically analyzed by using Statistical Package for Social Science (SPSS) software. Mann-Whitney U and Kruskal-Wallis test were performed for mean comparison. The findings revealed that the household level coir yarn industry is dominated by the female workers and it was identified that fewer numbers of workers have engaged in this industry as the main occupation. In addition to that, elderly participation in the industry is higher than the younger participation and most of them have engaged in the industry as a source of extra income. Level of education, the methods of engagement, satisfaction, engagement in the industry by the next generation, support from the government, method of government support, working hours per day, employed as a main job, number of completed units per day, suffering from job related diseases and type of the diseases were related with income level of household level coir yarn laboures. The recommendations as to flourish in future includes, technological transformation for coir yarn production, strengthening the raw material base and regulating the raw material supply, introduction of new technologies, markets and training programmes, the establishment of the labourers’ association, the initiation of micro credit schemes and better consideration about the job oriented diseases.

Keywords: Coir, Income, Sri Lanka.

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Authors: Resmi Nair, Campbell Wilson, Bala Srinivasan

Abstract:

Data warehouse is a dedicated database used for querying and reporting. Queries in this environment show special characteristics such as multidimensionality and aggregation. Exploiting the nature of queries, in this paper we propose a query driven design framework. The proposed framework is general and allows a designer to generate a schema based on a set of queries.

Keywords: Conceptual schema, data warehouse, queries, requirements.

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Authors: Rajeshwari Madli, Santhosh Hebbar, Vishwanath Heddoori, G. V. Prasad

Abstract:

Agriculture is the major occupation in India and forms the backbone of Indian economy in which irrigation plays a crucial role for increasing the quality and quantity of crop yield. In spite of many revolutionary advancements in agriculture, there has not been a dramatic increase in agricultural performance. Lack of irrigation infrastructure and agricultural knowledge are the critical factors influencing agricultural performance. However, by using advanced agricultural equipment, the effect of these factors can be curtailed.  The presented system aims at increasing the yield of crops by using an intelligent irrigation controller that makes use of wireless sensors. Sensors are used to monitor primary parameters such as soil moisture, soil pH, temperature and humidity. Irrigation decisions are taken based on the sensed data and the type of crop being grown. The system provides a mobile application in which farmers can remotely monitor and control the irrigation system. Also, the water pump is protected against damages due to voltage variations and dry running.

Keywords: Android application, Bluetooth, humidity, irrigation, soil moisture, soil pH, temperature, wireless sensors.

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