Search results for: Data Mining

Authors: Abigail Qian Zhou

Abstract:

In today's China, the well-educated middle class, with stable jobs and above-average income, are the driving force behind its Internet society. Through the analysis of data from the 2015 Chinese General Social Survey and 50 interviewees, this study investigates the current situation of this group’s specific internet usage. The findings of this study demonstrate that daily life among the members of this socioeconomic group is closely tied to the Internet. For Chinese middle class, the Internet is used to socialize and entertain self and others. It is also used to search for and share information as well as to build their identities. The empirical results of this study will provide a reference, supported by factual data, for enterprises seeking to target the Chinese middle class through online marketing efforts.

Keywords: China, internet use, middle class, network behavior, online marketing.

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Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell

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Native Language Identification is one of the growing subfields in Natural Language Processing (NLP). The task of Native Language Identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL) and then the trained models are evaluated on a different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and Logistic Regression. Results show that content-based features are more accurate and robust than content independent ones when tested within corpus and across corpus.

Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML.

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Authors: Yanling Li, Linying Ji, Zita Oravecz, Timothy R. Brick, Michael D. Hunter, Sy-Miin Chow

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Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals’ ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

Keywords: Dynamic modeling, missing data, multiple imputation, physiological measures.

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Authors: Lin, Huifen, Fang, Yueh-chiu

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This study explores perceptions of English as a Foreign Language (EFL) learners on using computer mediated communication technology in their learner of English. The data consists of observations of both synchronous and asynchronous communication participants engaged in for over a period of 4 months, which included online, and offline communication protocols, open-ended interviews and reflection papers composed by participants. Content analysis of interview data and the written documents listed above, as well as, member check and triangulation techniques are the major data analysis strategies. The findings suggest that participants generally do not benefit from computer-mediated communication in terms of its effect in learning a foreign language. Participants regarded the nature of CMC as artificial, or pseudo communication that did not aid their authentic communicational skills in English. The results of this study sheds lights on insufficient and inconclusive findings, which most quantitative CMC studies previously generated.

Keywords: computer-mediated communication, EFL, writing

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Authors: Aref Aasi, Sadegh Mehdi Aghaei, Balaji Panchapakesan

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This work aims to evaluate the free and forced vibration of a beam with two end joints subjected to a concentrated moving mass and a load using the Euler-Bernoulli method. The natural frequency is calculated for different locations of the concentrated mass and load on the beam. The analytical results are verified by the experimental data. The variations of natural frequency as a function of the location of the mass, the effect of the forced frequency on the vibrational amplitude, and the displacement amplitude versus time are investigated. It is discovered that as the concentrated mass moves toward the center of the beam, the natural frequency of the beam and the relative error between experimental and analytical data decreases. There is a close resemblance between analytical data and experimental observations.

Keywords: Euler-Bernoulli beam, natural frequency, forced vibration, experimental setup.

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Authors: Shyh-Ming Chern

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Cubic equations of state (EoS), popular due to their simple mathematical form, ease of use, semi-theoretical nature and reasonable accuracy, are normally fitted to vapor-liquid equilibrium P-v-T data. As a result, they often show poor accuracy in the region near and above the critical point. In this study, the performance of the renowned Peng-Robinson (PR) and Patel-Teja (PT) EoS’s around the critical area has been examined against the P-v-T data of water. Both of them display large deviations at critical point. For instance, PR-EoS exhibits discrepancies as high as 47% for the specific volume, 28% for the enthalpy departure and 43% for the entropy departure at critical point. It is shown that incorporating P-v-T data of the supercritical region into the retuning of a cubic EoS can improve its performance at and above the critical point dramatically. Adopting a retuned acentric factor of 0.5491 instead of its genuine value of 0.344 for water in PR-EoS and a new F of 0.8854 instead of its original value of 0.6898 for water in PT-EoS reduces the discrepancies to about one third or less.

Keywords: Equation of state, EoS, supercritical water, SCW.

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Authors: Gyoutae Park, Woosuk Kim, Sangguk Ahn, Seungmo Kim, Minjun Kim, Jinhan Lee, Youngdo Jo, Jongsam Moon, Hiesik Kim

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In this research, we designed a remote system to test parameters of gas detectors such as gas concentration and initial response time. This testing system is available to measure two gas instruments simultaneously. First of all, we assembled an experimental jig with a square structure. Those parts are included with a glass flask, two high-quality cameras, and two Ethernet modems for transmitting data. This remote gas detector testing system extracts numerals from videos with continually various gas concentrations while LCDs show photographs from cameras. Extracted numeral data are received to a laptop computer through Ethernet modem. And then, the numerical data with gas concentrations and the measured initial response speeds are recorded and graphed. Our remote testing system will be diversely applied on gas detector’s test and will be certificated in domestic and international countries.

Keywords: Gas leakage detector, inspection instrument, extracting numerals, concentration.

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Authors: B. K. Gill, Himani Sharma, V. K. Rattan

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Experimental data of refractive index, excess molar volume and viscosity of binary mixture of morpholine with cumene over the whole composition range at 298.15 K, 303.15 K, 308.15 K and normal atmospheric pressure have been measured. The experimental data were used to compute the density, deviation in molar refraction, deviation in viscosity and excess Gibbs free energy of activation as a function of composition. The experimental viscosity data have been correlated with empirical equations like Grunberg- Nissan, Herric correlation and three body McAllister’s equation. The excess thermodynamic properties were fitted to Redlich-Kister polynomial equation. The variation of these properties with composition and temperature of the binary mixtures are discussed in terms of intermolecular interactions.

Keywords: Cumene, excess Gibbs free energy, excess molar volume, morpholine.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: M. Pandi, K. Premalatha

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The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.

Keywords: Spread out, simplex, multi-minima, fitness function, optimization, search area, monocyte, solution, genomes.

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Authors: Shao-lun Zeng, Yu-long Ren

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Benchmarking cleaner production performance is an effective way of pollution control and emission reduction in coal-fired power industry. A benchmarking method using two-stage super-efficiency data envelopment analysis for coal-fired power plants is proposed – firstly, to improve the cleaner production performance of DEA-inefficient or weakly DEA-efficient plants, then to select the benchmark from performance-improved power plants. An empirical study is carried out with the survey data of 24 coal-fired power plants. The result shows that in the first stage the performance of 16 plants is DEA-efficient and that of 8 plants is relatively inefficient. The target values for improving DEA-inefficient plants are acquired by projection analysis. The efficient performance of 24 power plants and the benchmarking plant is achieved in the second stage. The two-stage benchmarking method is practical to select the optimal benchmark in the cleaner production of coal-fired power industry and will continuously improve plants- cleaner production performance.

Keywords: benchmarking, cleaner production performance, coal-fired power plant, super-efficiency data envelopment analysis

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Authors: Engin Yesil, Leon Urbas

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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: Tajul Urus, S., Molla, A., Teoh, S.Y.

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A number of studies highlighted problems related to ERP systems, yet, most of these studies focus on the problems during the project and implementation stages but not during the postimplementation use process. Problems encountered in the process of using ERP would hinder the effective exploitation and the extended and continued use of ERP systems and their value to organisations. This paper investigates the different types of problems users (operational, supervisory and managerial) faced in using ERP and how 'feral system' is used as the coping mechanism. The paper adopts a qualitative method and uses data collected from two cases and 26 interviews, to inductively develop a casual network model of ERP usage problem and its coping mechanism. This model classified post ERP usage problems as data quality, system quality, interface and infrastructure. The model is also categorised the different coping mechanism through use of 'feral system' inclusive of feral information system, feral data and feral use of technology.

Keywords: Case Studies, Coping Mechanism, Post Implementation ERP system, Usage Problem

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Authors: Alina Trifan, António J. R. Neves

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Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: Bayer images, CFA, losseless compression, image coding standards.

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Authors: Wipada Chiawchan

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This study found that most corporate personnel are using social media to communicate with colleagues to make the process of working more efficient. Complete satisfaction occurred on the use of security within the University’s computer network. The social network usage for communication, collaboration, entertainment and demonstrating concerns accounted for fifty percent of variance to predict interpersonal relationships of corporate personnel. This evaluation on the effectiveness of social networking involved 213 corporate personnel’s. The data was collected by questionnaires. This data was analyzed by using percentage, mean, and standard deviation. The results from the analysis and the effectiveness of using online social networks were derived from the attitude of private users and safety data within the security system. The results showed that the effectiveness on the use of an online social network for corporate personnel of Suan Sunandha Rajabhat University was specifically at a good level, and the overall effects of each aspect was (Ẋ=3.11).

Keywords: Behaviors, Social Media, Social Network.

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Authors: Chao-Linag Hsieh, Zheng-Wei Ye, Chen-Kang Huang, Yeun-Chung Lee, Chih-Hong Sun, Tzai-Hung Wen, Jehn-Yih Juang, Joe-Air Jiang

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Air pollution and climate warming become more and more intensified in many areas, especially in urban areas. Environmental parameters are critical information to air pollution and weather monitoring. Thus, it is necessary to develop a suitable air pollution and weather monitoring system for urban areas. In this study, a vehicle monitoring system (VMS) based on the IoT technique is developed. Cars are selected as the research tool because it can reach a greater number of streets to collect data. The VMS can monitor different environmental parameters, including ambient temperature and humidity, and air quality parameters, including PM2.5, NO₂, CO, and O₃. The VMS can provide other information, including GPS signals and the vibration information through driving a car on the street. Different sensor modules are used to measure the parameters and collect the measured data and transmit them to a cloud server through the LoRa protocol. A user interface is used to show the sensing data storing at the cloud server. To examine the performance of the system, a researcher drove a Nissan x-trail 1998 to the area close to the Da’an District office in Taipei to collect monitoring data. The collected data are instantly shown on the user interface. The four kinds of information are provided by the interface: GPS positions, weather parameters, vehicle information, and air quality information. With the VMS, users can obtain the information regarding air quality and weather conditions when they drive their car to an urban area. Also, government agencies can make decisions on traffic planning based on the information provided by the proposed VMS.

Keywords: Vehicle, monitoring system, LoRa, smart city.

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Authors: Vijay Shankar

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Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate estimation is essential for hydrological studies. In past, various estimation methods have been developed for different climatological data, and the accuracy of these methods varies with climatic conditions. Reference crop evapotranspiration (ET0) is a key variable in procedures established for estimating evapotranspiration rates of agricultural crops. Values of ET0 are used with crop coefficients for many aspects of irrigation and water resources planning and management. Numerous methods are used for estimating ET0. As per internationally accepted procedures outlined in the United Nations Food and Agriculture Organization-s Irrigation and Drainage Paper No. 56(FAO-56), use of Penman-Monteith equation is recommended for computing ET0 from ground based climatological observations. In the present study, seven methods have been selected for performance evaluation. User friendly software has been developed using programming language visual basic. The visual basic has ability to create graphical environment using less coding. For given data availability the developed software estimates reference evapotranspiration for any given area and period for which data is available. The accuracy of the software has been checked by the examples given in FAO-56.The developed software is a user friendly tool for estimating ET0 under different data availability and climatic conditions.

Keywords: Crop coefficient, Crop evapotranspiration, Field moisture, Irrigation Scheduling.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

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Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data.

Keywords: Gradient descent method, jacobian matrix.Levenberg-Marquardt algorithm, quadratic error surfaces,

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Authors: S. Priyanto, E.P Friandi

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Yogyakarta, as the capital city of Yogyakarta Province, has important roles in various sectors that require good provision of public transportation system. Ideally, a good transportation system should be able to accommodate the amount of travel demand. This research attempts to develop a trip generation model to predict the number of public transport passenger in Yogyakarta city. The model is built by using multiple linear regression analysis, which establishes relationship between trip number and socioeconomic attributes. The data consist of primary and secondary data. Primary data was collected by conducting household surveys which randomly selected. The resulted model is further applied to evaluate the existing TransJogja, a new Bus Rapid Transit system serves Yogyakarta and surrounding cities, shelters.

Keywords: Multiple linear regression, shelter evaluation, travel demand, trip generation.

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Authors: G. Lavanya Devi, Allam Appa Rao, A. Damodaram, GR Sridhar, G. Jaya Suma

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Cluster analysis divides data into groups that are meaningful, useful, or both. Analysis of biological data is creating a new generation of epidemiologic, prognostic, diagnostic and treatment modalities. Clustering of protein sequences is one of the current research topics in the field of computer science. Linear relation is valuable in rule discovery for a given data, such as if value X goes up 1, value Y will go down 3", etc. The classical linear regression models the linear relation of two sequences perfectly. However, if we need to cluster a large repository of protein sequences into groups where sequences have strong linear relationship with each other, it is prohibitively expensive to compare sequences one by one. In this paper, we propose a new technique named General Regression Model Technique Clustering Algorithm (GRMTCA) to benignly handle the problem of linear sequences clustering. GRMT gives a measure, GR*, to tell the degree of linearity of multiple sequences without having to compare each pair of them.

Keywords: Clustering, General Regression Model, Protein Sequences, Similarity Measure.

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Authors: Chetneti Srisa-an

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In Knowledge and Data Engineering field, relational database is the best repository to store data in a real world. It has been using around the world more than eight decades. Normalization is the most important process for the analysis and design of relational databases. It aims at creating a set of relational tables with minimum data redundancy that preserve consistency and facilitate correct insertion, deletion, and modification. Normalization is a major task in the design of relational databases. Despite its importance, very few algorithms have been developed to be used in the design of commercial automatic normalization tools. It is also rare technique to do it automatically rather manually. Moreover, for a large and complex database as of now, it make even harder to do it manually. This paper presents a new complete automated relational database normalization method. It produces the directed graph and spanning tree, first. It then proceeds with generating the 2NF, 3NF and also BCNF normal forms. The benefit of this new algorithm is that it can cope with a large set of complex function dependencies.

Keywords: Relational Database, Functional Dependency, Automatic Normalization, Primary Key, Spanning tree.

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting and tracking of physical items in combination with Internet of Things (IoT) developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm decision-making process does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyze on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue and environmental impact. Evolutionary Computing (EC) can be very effective in finding the optimal combination of sets of some objects and finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and EC in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management and its uptake has become a continuing trend.

Keywords: Big data, evolutionary computing, cloud, precision technologies

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Authors: Yong-Yoon Cho, Jong-Bae Moon, Young-Chul Kim

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Developers need to evaluate software's performance to make software efficient. This paper suggests a performance evaluation system for embedded software. The suggested system consists of code analyzer, testing agents, data analyzer, and report viewer. The code analyzer inserts additional code dependent on target system into source code and compiles the source code. The testing agents execute performance test. The data analyzer translates raw-level results data to class-level APIs for reporting viewer. The report viewer offers users graphical report views by using the APIs. We hope that the suggested tool will be useful for embedded-related software development,because developers can easily and intuitively analyze software's performance and resource utilization.

Keywords: Embedded Software, Performance EvaluationSystem, Testing Agents, Report Generator

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Authors: Haode Huo, Jingjing He, Rui Kang, Xuefei Guan

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This paper presents a study of Lamb wave damage diagnosis of composite delamination using instantaneous phase data. Numerical experiments are performed using the finite element method. Different sizes of delamination damages are modeled using finite element package ABAQUS. Lamb wave excitation and responses data are obtained using a pitch-catch configuration. Empirical mode decomposition is employed to extract the intrinsic mode functions (IMF). Hilbert–Huang Transform is applied to each of the resulting IMFs to obtain the instantaneous phase information. The baseline data for healthy plates are also generated using the same procedure. The size of delamination is correlated with the instantaneous phase change for damage diagnosis. It is observed that the unwrapped instantaneous phase of shows a consistent behavior with the increasing delamination size.

Keywords: Delamination, lamb wave, finite element method, EMD, instantaneous phase.

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Authors: Aparajita Goswami, Mihir K Purkait

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In the present study Schwertmannite (an iron oxide hydroxide) is selected as an adsorbent for defluoridation of water. The adsorbent was prepared by wet chemical process and was characterized by SEM, XRD and BET. The fluoride adsorption efficiency of the prepared adsorbent was determined with respect to contact time, initial fluoride concentration, adsorbent dose and pH of the solution. The batch adsorption data revealed that the fluoride adsorption efficiency was highly influenced by the studied factors. Equilibrium was attained within one hour of contact time indicating fast kinetics and the adsorption data followed pseudo second order kinetic model. Equilibrium isotherm data fitted to both Langmuir and Freundlich isotherm models for a concentration range of 5-30 mg/L. The adsorption system followed Langmuir isotherm model with maximum adsorption capacity of 11.3 mg/g. The high adsorption capacity of Schwertmannite points towards the potential of this adsorbent for fluoride removal from aqueous medium.

Keywords: Adsorption, fluoride, isotherm study, kinetics, schwertmannite.

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: Degree, initial cluster center, k-means, minimum spanning tree.

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Authors: Yeqing Guan, Fen Yang

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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: A. Baith Mohamed, Ayman Abdel-Hamid, Kareem Youssri Mohamed

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Current proposals for E-passport or ID-Card is similar to a regular passport with the addition of tiny contactless integrated circuit (computer chip) inserted in the back cover, which will act as a secure storage device of the same data visually displayed on the photo page of the passport. In addition, it will include a digital photograph that will enable biometric comparison, through the use of facial recognition technology at international borders. Moreover, the e-passport will have a new interface, incorporating additional antifraud and security features. However, its problems are reliability, security and privacy. Privacy is a serious issue since there is no encryption between the readers and the E-passport. However, security issues such as authentication, data protection and control techniques cannot be embedded in one process. In this paper, design and prototype implementation of an improved E-passport reader is presented. The passport holder is authenticated online by using GSM network. The GSM network is the main interface between identification center and the e-passport reader. The communication data is protected between server and e-passport reader by using AES to encrypt data for protection will transferring through GSM network. Performance measurements indicate a 19% improvement in encryption cycles versus previously reported results.

Keywords: RFID "Radio Frequency Identification", EPC"Electronic Product Code", ICAO "International Civil Aviation Organization", IFF "Identify Friend or Foe"

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Authors: Ayudhia P. Gusti, Semin

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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: Monika Chuchro, Maciej Dwornik

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The extraction of meaningful information from image could be an alternative method for time series analysis. In this paper, we propose a graphical analysis of time series grouped into table with adjusted colour scale for numerical values. The advantages of this method are also discussed. The proposed method is easy to understand and is flexible to implement the standard methods of pattern recognition and verification, especially for noisy environmental data.

Keywords: graphical analysis, time series, seasonality, noisy environmental data

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