Search results for: test data compression (TDC)

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. Govindarajan, R. M. Chandrasekaran

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.

Keywords: Back propagation, classification accuracy, textmining, time complexity.

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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: A. Abdullah, N. Faghih Mirzaei, S. Hany Haron

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Neighbourhood environment walkability on reported physical activity (PA) levels of students of Universiti Sains Malaysia (USM) in Malaysia. Compared with previous generations, today’s young people spend less time playing outdoors and have lower participation rates in PA. Research suggests that negative perceptions of neighbourhood walkability may be a potential barrier to adolescents’ PA. The sample consisted of 200 USM students (to 24 years old) who live outside of the main campus and engage in PA in sport halls and sport fields of USM. The data were analysed using the t-test, binary logistic regression, and discriminant analysis techniques. The present study found that youth PA was affected by neighbourhood environment walkability factors, including neighbourhood infrastructures, neighbourhood safety (crime), and recreation facilities, as well as street characteristics and neighbourhood design variables such as facades of sidewalks, roadside trees, green spaces, and aesthetics. The finding also illustrated that active students were influenced by street connectivity, neighbourhood infrastructures, recreation facilities, facades of sidewalks, and aesthetics, whereas students in the less active group were affected by access to destinations, neighbourhood safety (crime), and roadside trees and green spaces for their PAs. These results report which factors of built environments have more effect on youth PA and they message to the public to create more awareness about the benefits of PA on youth health.

Keywords: Fear of crime, neighbourhood built environment, physical activities, street characteristics design.

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Authors: Oluwaseyi Jaiyeoba, Vetria Byrd

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Systemic lupus erythematosus (aka lupus) is a chronic disease known for its chameleon-like ability to mimic symptoms of other diseases rendering it hard to detect, diagnose and treat. The heterogeneous nature of the disease generates disparate data that are often multifaceted and multi-dimensional. Musculoskeletal manifestation of lupus is one of the most common clinical manifestations of lupus. This research links disparate literature on the treatment of lupus as it affects the musculoskeletal system using the discoveries from literature-based research articles available on the PubMed database. Several Natural Language Processing (NPL) tools exist to connect disjointed but related literature, such as Connected Papers, Bitola, and Gopalakrishnan. Literature-based discovery (LBD) has been used to bridge unconnected disciplines based on text mining procedures. The technical/medical literature consists of many technical/medical concepts, each having its  sub-literature. This approach has been used to link Parkinson’s, Raynaud, and Multiple Sclerosis treatment within works of literature.  Literature-based discovery methods can connect two or more related but disjointed literature concepts to produce a novel and plausible approach to solving a research problem. Data visualization techniques with the help of natural language processing tools are used to visually represent the result of literature-based discoveries. Literature search results can be voluminous, but Data visualization processes can provide insight and detect subtle patterns in large data. These insights and patterns can lead to discoveries that would have otherwise been hidden from disjointed literature. In this research, literature data are mined and combined with visualization techniques for heterogeneous data to discover viable treatments reported in the literature for lupus expression in the musculoskeletal system. This research answers the question of using literature-based discovery to identify potential treatments for a multifaceted disease like lupus. A three-pronged methodology is used in this research: text mining, natural language processing, and data visualization. These three research-related fields are employed to identify patterns in lupus-related data that, when visually represented, could aid research in the treatment of lupus. This work introduces a method for visually representing interconnections of various lupus-related literature. The methodology outlined in this work is the first step toward literature-based research and treatment planning for the musculoskeletal manifestation of lupus. The results also outline the interconnection of complex, disparate data associated with the manifestation of lupus in the musculoskeletal system. The societal impact of this work is broad. Advances in this work will improve the quality of life for millions of persons in the workforce currently diagnosed and silently living with a musculoskeletal disease associated with lupus.

Keywords: Systemic lupus erythematosus, LBD, Data Visualization, musculoskeletal system, treatment.

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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: Vahid Mazloum, Mansour Sahebozamani, Amirhossein Barati, Nouzar Nakhaee, Pouya Rabiei

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Non-specific chronic low back pain (NSCLBP) is a common condition with no exact diagnosis and mechanism for its occurrence. Recently, different therapeutic exercises have taken into account to manage NSCLBP. So, the aim of this study has mainly been placed on comparing the effects of Pilates and Mackenzie exercises on quality of life (QOL) lumbar spine position sense (LSPS) in patients with NSCLBP. In this randomized clinical trial, 47 patients with NSCLBP were voluntarily divided into three groups of Pilates (n=16) (with mean age 37.1 ± 9.5 years, height 168.9 ± 7.4 cm, body mass 76.1 ± 5.9 k), McKenzie (n=15) (with mean age 42.7 ± 8.1 years, height 165.7 ± 6.8, body mass 74.1 ± 4.8 kg) and control (n=16) (with mean age 39.3 ± 9.8 years, height 168.1 ± 8.1 cm, body mass 74.2 ± 5.8 kg). Primary outcome included QOL and secondary was LSPS. Both variables were assessed by the WHOQOL-BREF questionnaires and electrogoniameter, respectively. The measurements were performed at baseline, following a 6-week intervention, and after a 4-week follow-up. The ANCOVA test at P < 0.05 was administrated to analyze the collected data using SPSS software. There was a statistically significant difference between experimental groups and the control group to improve QOL. But, no difference was seen regarding the effects of two exercises on LSPS (p < 0.05). Both Pilates and Mackenzie exercises demonstrated improvement in QOL after 6-week intervention and a 4-week follow-up while none of them considerably affected LSPS. Further studies are required to establish a supporting evidence for the effectiveness of two exercises on NSCLBP.

Keywords: Pilates, Mackenzie, proprioception, low back pain, physical health.

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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: Heeyeul Kwon, Yongtae Park

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Despite the highly touted benefits, emerging technologies have unleashed pervasive concerns regarding unintended and unforeseen social impacts. Thus, those wishing to create safe and socially acceptable products need to identify such side effects and mitigate them prior to the market proliferation. Various methodologies in the field of technology assessment (TA), namely Delphi, impact assessment, and scenario planning, have been widely incorporated in such a circumstance. However, literatures face a major limitation in terms of sole reliance on participatory workshop activities. They unfortunately missed out the availability of a massive untapped data source of futuristic information flooding through the Internet. This research thus seeks to gain insights into utilization of futuristic data, future-oriented documents from the Internet, as a supplementary method to generate social impact scenarios whilst capturing perspectives of experts from a wide variety of disciplines. To this end, network analysis is conducted based on the social keywords extracted from the futuristic documents by text mining, which is then used as a guide to produce a comprehensive set of detailed scenarios. Our proposed approach facilitates harmonized depictions of possible hazardous consequences of emerging technologies and thereby makes decision makers more aware of, and responsive to, broad qualitative uncertainties.

Keywords: Emerging technologies, futuristic data, scenario, text mining.

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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: Climstein Mike, Walsh Joe, John Best, Heazlewood Ian Timothy, Burke Stephen, Kettunen Jyrki, Adams Kent, DeBeliso Mark

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Recently, the health of retired National Football League players, particularly lineman has been investigated. A number of studies have reported increased cardiometabolic risk, premature ardiovascular disease and incidence of type 2 diabetes. Rugby union players have somatotypes very similar to National Football league players which suggest that rugby players may have similar health risks. The International Golden Oldies World Rugby Festival (GORF) provided a unique opportunity to investigate the demographics of veteran rugby players. METHODOLOGIES: A cross-sectional, observational study was completed using an online web-based questionnaire that consisted of medical history and physiological measures. Data analysis was completed using a one sample t-test (<50yrs versus >50yrs) and Chi-square test. RESULTS: A total of 216 veteran rugby competitors (response rate = 6.8%) representing 10 countries, aged 35-72 yrs (mean 51.2, S.D. ±8.0), participated in the online survey. As a group, the incidence of current smokers was low at 8.8% (avg 72.4 cigs/wk) whilst the percentage consuming alcohol was high (93.1% (avg 11.2 drinks/wk). Competitors reported the following top six chronic diseases/disorders; hypertension (18.6%), arthritis (OA/RA, 11.5%), asthma (9.3%), hyperlipidemia (8.2%), diabetes (all types, 7.5%) and gout (6%), there were significant differences between groups with regard to cancer (all types) and migraines. When compared to the Australian general population (Australian Bureau of Statistics data, n=18,000), GORF competitors had a Climstein Mike, Walsh Joe (corresponding author) and Burke Stephen School of Exercise Science, Australian Catholic University, 25A Barker Road, Strathfield, Sydney, NSW, 2016, Australia (e-mail: [email protected], [email protected], [email protected]). John Best is with Orthosports, 160 Belmore Rd., Randwick, Sydney,NSW 2031, Australia (e-mail: [email protected]). Heazlewood, Ian Timothy is with School of Environmental and Life Sciences, Faculty Education, Health and Science, Charles Darwin University, Precinct Yellow Building 2, Charles Darwin University, NT 0909, Australia (e-mail: [email protected]). Kettunen Jyrki Arcada University of Applied Sciences, Jan-Magnus Janssonin aukio 1, FI-00550, Helsinki, Finland (e-mail: [email protected]). Adams Kent is with California State University Monterey Bay, Kinesiology Department, 100 Campus Center, Seaside, CA., 93955, USA (email: [email protected]). DeBeliso Mark is with Department of Physical Education and Human Performance, Southern Utah University, 351 West University Blvd, Cedar City, Utah, USA (e-mail: [email protected]). significantly lower incidence of anxiety (p<0.01), arthritis (p<0.06), depression (p<.01) however, a significantly higher incidence of diabetes (p<0.03) and hypertension (p<0.01). The GORF competitors also reported taking the following prescribed medications; antihypertensive (13%), hypolipidemics (8%), non-steroidal anti-inflammatory (6%), and anticoagulants (4%). Significant differences between groups were observed in antihypertensives, anticoagulants and hypolipidemics. There were significant (p<0.05) differences between groups (<50yrs versus >50yrs) with regard to height (180 vs 177cm), weight (97.6 vs 93.1Kg-s), BMI (30 vs 29.7kg/m2) and waist circumference (85.7 vs 93.1cm) however, there were no differences in subsequent parameters of systolic blood pressure, diastolic blood pressure, total cholesterol, HDL-C, LDL-C, triglycerides-C or fasting plasma glucose. CONCLUSIONS: This represents the first collection of demographics on this cohort. GORF participants demonstrated increased cardiometabolic risk with regard to the incidence of hypercholesterolemia, hypertension and type 2 diabetes. Preventative strategies should be developed to reduce this risk with education of these risks for future participants.

Keywords: Masters athlete, rugby union, risk factors, chronic disease.

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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: Mohammad Taghi Darvishi, Mohammad Najafi

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By means of the extended homoclinic test approach (shortly EHTA) with the aid of a symbolic computation system such as Maple, some complexiton type solutions for the (3+1)-dimensional Jimbo-Miwa equation are presented.

Keywords: Jimbo-Miwa equation, painleve analysis, Hirota's bilinear form, computerized symbolic computation.

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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: Roberto Sabatini, Alessandro Gardi, Mark A. Richardson

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The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser-based Light Detection and Ranging (LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LIDAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in low-level flying platforms such as helicopters and small-to-medium size Unmanned Aircraft (UA). The Laser Obstacle Avoidance Marconi (LOAM) system is one of such systems, which was jointly developed and tested by SELEX-ES and the Italian Air Force Research and Flight Test Centre. The system was originally conceived for military rotorcraft platforms and, in this paper, we briefly review the previous work and discuss in more details some of the key development activities required for integration of LOAM on UA platforms. The main hardware and software design features of this LOAM variant are presented, including a brief description of the system interfaces and sensor characteristics, together with the system performance models and data processing algorithms for obstacle detection, classification and avoidance. In particular, the paper focuses on the algorithm proposed for optimal avoidance trajectory generation in UA applications.

Keywords: LIDAR, Low-Level Flight, Nap-of-the-Earth Flight, Near Infra-Red, Obstacle Avoidance, Obstacle Detection, Obstacle Warning System, Sense and Avoid, Trajectory Optimisation, Unmanned Aircraft.

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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: Anca-Diana Bibiri, Mihaela Mocanu, Adrian Turculeț

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In a language the inventory of greetings is dynamic with frequent input and output, although this is hardly noticed by the speakers. In this register, there are a number of constant, conservative elements that survive different language models (among them, the classic formulae: bună ziua! (good afternoon!), bună seara! (good evening!), noapte bună! (good night!), la revedere! (goodbye!) and a number of items that fail to pass the test of time, according to language use at a time (ciao!, pa!, bai!). The source of innovation depends both of internal factors (contraction, conversion, combination of classic formulae of greetings), and of external ones (borrowings and calques). Their use imposes their frequencies at once, namely the elimination of the use of others. This paper presents a sociolinguistic approach of contemporary Romanian greetings, based on prosodic surveys in two research projects: AMPRom, and SoRoEs. Romanian language presents a rich inventory of questions (especially partial interrogatives questions/WH-Q) which are used as greetings, alone or, more commonly accompanying a proper greeting. The representative of the typical formulae is Ce mai faci? (How are you?), which, unlike its English counterpart How do you do?, has not become a stereotype, but retains an obvious emotional impact, while serving as a mark of sociolinguistic group. The analyzed corpus consists of structures containing greetings recorded in the main Romanian cultural (urban) centers. From the methodological point of view, the acoustic analysis of the recorded data is performed using software tools (GoldWave, Praat), identifying intonation patterns related to three sociolinguistics variables: age, sex and level of education. The intonation patterns of the analyzed statements are at the interface between partial questions and typical greetings.

Keywords: acoustic analysis, greetings, Romanian language, sociolinguistics

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Authors: Monika Chuchro, Maciej Dwornik

Abstract:

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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Authors: Chiharu Okuma, Naoki Yamamoto, Jun Murakami

Abstract:

As a method of expanding a higher-order tensor data to tensor products of vectors we have proposed the Third-order Orthogonal Tensor Product Expansion (3OTPE) that did similar expansion as Higher-Order Singular Value Decomposition (HOSVD). In this paper we provide a computation algorithm to improve our previous method, in which SVD is applied to the matrix that constituted by the contraction of original tensor data and one of the expansion vector obtained. The residual of the improved method is smaller than the previous method, truncating the expanding tensor products to the same number of terms. Moreover, the residual is smaller than HOSVD when applying to color image data. It is able to be confirmed that the computing time of improved method is the same as the previous method and considerably better than HOSVD.

Keywords: Singular value decomposition (SVD), higher-orderSVD (HOSVD), outer product expansion, power method.

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Authors: Mahmoud M. S. Albattah

Abstract:

In this paper, to optimize the “Characteristic Straight Line Method" which is used in the soil displacement analysis, a “best estimate" of the geodetic leveling observations has been achieved by taking in account the concept of 'Height systems'. This concept has been discussed in detail and consequently the concept of “height". In landslides dynamic analysis, the soil is considered as a mosaic of rigid blocks. The soil displacement has been monitored and analyzed by using the “Characteristic Straight Line Method". Its characteristic components have been defined constructed from a “best estimate" of the topometric observations. In the measurement of elevation differences, we have used the most modern leveling equipment available. Observational procedures have also been designed to provide the most effective method to acquire data. In addition systematic errors which cannot be sufficiently controlled by instrumentation or observational techniques are minimized by applying appropriate corrections to the observed data: the level collimation correction minimizes the error caused by nonhorizontality of the leveling instrument's line of sight for unequal sight lengths, the refraction correction is modeled to minimize the refraction error caused by temperature (density) variation of air strata, the rod temperature correction accounts for variation in the length of the leveling rod' s Invar/LO-VAR® strip which results from temperature changes, the rod scale correction ensures a uniform scale which conforms to the international length standard and the introduction of the concept of the 'Height systems' where all types of height (orthometric, dynamic, normal, gravity correction, and equipotential surface) have been investigated. The “Characteristic Straight Line Method" is slightly more convenient than the “Characteristic Circle Method". It permits to evaluate a displacement of very small magnitude even when the displacement is of an infinitesimal quantity. The inclination of the landslide is given by the inverse of the distance reference point O to the “Characteristic Straight Line". Its direction is given by the bearing of the normal directed from point O to the Characteristic Straight Line (Fig..6). A “best estimate" of the topometric observations was used to measure the elevation of points carefully selected, before and after the deformation. Gross errors have been eliminated by statistical analyses and by comparing the heights within local neighborhoods. The results of a test using an area where very interesting land surface deformation occurs are reported. Monitoring with different options and qualitative comparison of results based on a sufficient number of check points are presented.

Keywords: Characteristic straight line method, dynamic height, landslides, orthometric height, systematic errors.

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