Search results for: Data Integration

Authors: Adil AL-Rammahi

Abstract:

When we prefer to make the data secure from various attacks and fore integrity of data, we must encrypt the data before it is transmitted or stored. This paper introduces a new effective and lossless image encryption algorithm using a natural logarithmic function. The new algorithm encrypts an image through a three stage process. In the first stage, a reference natural logarithmic function is generated as the foundation for the encryption image. The image numeral matrix is then analyzed to five integer numbers, and then the numbers’ positions are transformed to matrices. The advantages of this method is useful for efficiently encrypting a variety of digital images, such as binary images, gray images, and RGB images without any quality loss. The principles of the presented scheme could be applied to provide complexity and then security for a variety of data systems such as image and others.

Keywords: Linear Systems, Image Encryption, Calculus.

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Authors: Andra Costache, Rădiţa Alexe

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The analysis of the spatial disparities of social marginalization is a requirement in the present-day socio-economic and political context of Romania, an East-European state, member of the European Union since 2007, at present faced with the imperatives of the growth of its territorial cohesion. The main objective of this article is to develop a methodology for the assessment of social marginalization, in order to understand the intensity of the marginalization phenomenon at different spatial scales. The article proposes a social marginalization index (SMI), calculated through the integration of ten indicators relevant for the two components of social marginalization: the material component and the symbolical component. The results highlighted a strong connection between the total degree of social marginalization and the dependence on social benefits, unemployment rate, non-inclusion in the compulsory education, criminality rate, and the type of pension insurance.

Keywords: Romania, social marginalization index, territorial disparities.

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Authors: P. Parvizi, S. Mohammadi

Abstract:

an intelligent BRT system is necessary when communities looking for new ways to use high capacity rapid transit at a reduced cost.This paper will describe the intelligent control system that works with Datacenter. With the help of GPS system, the data center can monitor the situation of each bus and bus station. Through RFID technology, bus station and traffic light can transfer data with bus and by Wimax communication technology all of parts can talk together; data center learns all information about the location of bus, the arrival of bus in each station and the number of passengers in station and bus.Finally, the paper presents the case study of those theories in Tehran BRT.

Keywords: TehranBRT, RFID, Intelligent Transportation

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Authors: Danielle Morin, Jennifer D.E.Thomas, Raafat G. Saade

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While computers are known to facilitate lower levels of learning, such as rote memorization of facts, measurable through electronically administered and graded multiple-choice questions, yes/no, and true/false answers, the imparting and measurement of higher-level cognitive skills is more vexing. These require more open-ended delivery and answers, and may be more problematic in an entirely virtual environment, notwithstanding the advances in technologies such as wikis, blogs, discussion boards, etc. As with the integration of all technology, merit is based more on the instructional design of the course than on the technology employed in, and of, itself. With this in mind, this study examined the perceptions of online students in an introductory Computer Information Systems course regarding the fostering of various higher-order thinking and team-building skills as a result of the activities, resources and technologies (ART) used in the course.

Keywords: Critical thinking, deep learning, distance learning, elearning, online learning, virtual environments.

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Authors: Tirtha S. Das, Ayan K. Sau, Subir K. Sarkar

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Digital watermarking is a way to provide the facility of secure multimedia data communication besides its copyright protection approach. The Spread Spectrum modulation principle is widely used in digital watermarking to satisfy the robustness of multimedia signals against various signal-processing operations. Several SS watermarking algorithms have been proposed for multimedia signals but very few works have discussed on the issues responsible for secure data communication and its robustness improvement. The current paper has critically analyzed few such factors namely properties of spreading codes, proper signal decomposition suitable for data embedding, security provided by the key, successive bit cancellation method applied at decoder which have greater impact on the detection reliability, secure communication of significant signal under camouflage of insignificant signals etc. Based on the analysis, robust SS watermarking scheme for secure data communication is proposed in wavelet domain and improvement in secure communication and robustness performance is reported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data.

Keywords: Spread spectrum modulation, spreading code, signaldecomposition, security, successive bit cancellation

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Authors: Manas Ranjan Kabat, Rajib Mall, Chita Ranjan Tripathy

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A scalable QoS aware multicast deployment in DiffServ networks has become an important research dimension in recent years. Although multicasting and differentiated services are two complementary technologies, the integration of the two technologies is a non-trivial task due to architectural conflicts between them. A popular solution proposed is to extend the functionality of the DiffServ components to support multicasting. In this paper, we propose an algorithm to construct an efficient QoSdriven multicast tree, taking into account the available bandwidth per service class. We also present an efficient way to provision the limited available bandwidth for supporting heterogeneous users. The proposed mechanism is evaluated using simulated tests. The simulated result reveals that our algorithm can effectively minimize the bandwidth use and transmission cost

Keywords: Differentiated Services, multicasting, QoSheterogeneity, DSCP

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Authors: Ion-Cosmin Dita, Vasile Gui, Franz Quint, Marius Otesteanu

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In automatic manufacturing and assembling of mechanical, electrical and electronic parts one needs to reliably identify the position of components and to extract the information of these components. Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today’s usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper compares two methods for estimating the angle of orientation of Data Matrix Codes: one method based on the Hough Transform and the other based on the Mean Shift Algorithm. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.

Keywords: Industrial data matrix code, Hough transform, mean shift.

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Authors: Chee Siong Teh, Chee Peng Lim

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This paper proposes a novel architecture for developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be incorporated into a computerized system and, at the same time, to preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.

Keywords: Interactive evolutionary computation, multivariate data projection, pattern classification, topographic map.

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Authors: Jiho Park, Yong-Gyu Lee, Gilwon Yoon

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Data security in u-Health system can be an important issue because wireless network is vulnerable to hacking. However, it is not easy to implement a proper security algorithm in an embedded u-health monitoring because of hardware constraints such as low performance, power consumption and limited memory size and etc. To secure data that contain personal and biosignal information, we implemented several security algorithms such as Blowfish, data encryption standard (DES), advanced encryption standard (AES) and Rivest Cipher 4 (RC4) for our u-Health monitoring system and the results were successful. Under the same experimental conditions, we compared these algorithms. RC4 had the fastest execution time. Memory usage was the most efficient for DES. However, considering performance and safety capability, however, we concluded that AES was the most appropriate algorithm for a personal u-Health monitoring system.

Keywords: biosignal, data encryption, security measures, u-health

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Authors: Kristina Georgoulakis

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A new blind symbol by symbol equalizer is proposed. The operation of the proposed equalizer is based on the geometric properties of the two dimensional data constellation. An unsupervised clustering technique is used to locate the clusters formed by the received data. The symmetric properties of the clusters labels are subsequently utilized in order to label the clusters. Following this step, the received data are compared to clusters and decisions are made on a symbol by symbol basis, by assigning to each data the label of the nearest cluster. The operation of the equalizer is investigated both in linear and nonlinear channels. The performance of the proposed equalizer is compared to the performance of a CMAbased blind equalizer.

Keywords: Blind equalization, channel equalization, cluster based equalisers

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Authors: Vivek Bharti, Inderpreet Kaur, Saurabh Verma, Renu Gangwar, Hari Kumar Singh

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This work presents a dual-band Coplanar Waveguide (CPW) fed rectangular patch antenna designed for applications in Industrial, Scientific, and Medical (ISM) Band sensing. Fabricated on a cost-effective FR-4 substrate with specific dimensions, the antenna incorporates a rectangular slot and a copper patch, optimized through simulation to achieve desired characteristics, particularly focusing on the reflection coefficient. A unique feature is introduced through the integration of copper cells forming a Partially Reflecting Surface (PRS) to feed the antenna. Dual-band functionality is achieved, covering frequencies of 1.28 GHz-1.3 GHz and 4.9 GHz-5.2 GHz, catering to diverse communication needs within these frequency ranges. The addition of a superstrate enhances the antenna’s gain, resulting in 4.5 dBi at 1.28 GHz-1.3 GHz and 6.5 dBi at 4.9 GHz-5.2 GHz, with an efficiency of 58%.

Keywords: ISM, Coplanar waveguide fed, superstrate, circularly polarized.

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Authors: Y. N. Phang, E. F. Loh

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The zero inflated models are usually used in modeling count data with excess zeros where the existence of the excess zeros could be structural zeros or zeros which occur by chance. These type of data are commonly found in various disciplines such as finance, insurance, biomedical, econometrical, ecology, and health sciences which involve sex and health dental epidemiology. The most popular zero inflated models used by many researchers are zero inflated Poisson and zero inflated negative binomial models. In addition, zero inflated generalized Poisson and zero inflated double Poisson models are also discussed and found in some literature. Recently zero inflated inverse trinomial model and zero inflated strict arcsine models are advocated and proven to serve as alternative models in modeling overdispersed count data caused by excessive zeros and unobserved heterogeneity. The purpose of this paper is to review some related literature and provide a variety of examples from different disciplines in the application of zero inflated models. Different model selection methods used in model comparison are discussed.

Keywords: Overdispersed count data, model selection methods, likelihood ratio, AIC, BIC.

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focusses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: Auto-ID, Construction Logistic, Fuzzy, Monitoring, RFID, Scheduling.

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Authors: Sherif S. Nafee, Amir K. Al-Ramady, Salem S. Shaheen

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Evaluated nuclear decay data for the 217Po nuclide is presented in the present work. These data include recommended values for the half-life T1/2, α-, β-- and γ-ray emission energies and probabilities. Decay data from 221Rn α and 217Bi β—decays are presented. Q(α) has been updated based on the recent published work of the Atomic Mass Evaluation AME2012. In addition, the logft values were calculated using the Logft program from the ENSDF evaluation package. Moreover, the total internal conversion electrons and the K-shell to L-shell and L-shell to M-shell and to N-shell conversion electrons ratios K/L, L/M and L/N have been calculated using Bricc program. Meanwhile, recommendation values or the multi-polarities have been assigned based on recently measurement yield a better intensity balance at the 254 keV and 264 keV gamma transitions.

Keywords: Atomic Mass Evaluation, Nuclear Data Evaluation, Total Electron Conversion Electrons.

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: Keypoint detection, curve feature, convolutional neural network, press-fit assembly.

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Authors: M. Tahmasebi, R.A. Rahman, M. Mailah, M. Gohari

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The control of sprayer boom undesired vibrations pose a great challenge to investigators due to various disturbances and conditions. Sprayer boom movements lead to reduce of spread efficiency and crop yield. This paper describes the design of a novel control method for an active suspension system applying proportional-integral-derivative (PID) controller with an active force control (AFC) scheme integration of an iterative learning algorithm employed to a sprayer boom. The iterative learning as an intelligent method is principally used as a method to calculate the best value of the estimated inertia of the sprayer boom needed for the AFC loop. Results show that the proposed AFC-based scheme performs much better than the standard PID control technique. Also, this shows that the system is more robust and accurate.

Keywords: Active force control, sprayer boom, active suspension, iterative learning.

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Authors: Yongyoon Suh, Chulhyun Kim, Moon-soo Kim

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As the trend of manufacturing is being dominated depending on services, products and processes are more and more related with sophisticated services. Thus, this research starts with the discussion about integration of the product, process, and service in the innovation process. In particular, this paper sets out some foundations for a theory of service innovation in the field of manufacturing, and proposes the dynamic model of service innovation related to product and process. Two dynamic models of service innovation are suggested to investigate major tendencies and dynamic variations during the innovation cycle: co-innovation and sequential innovation. To structure dynamic models of product, process, and service innovation, the innovation stages in which two models are mainly achieved are identified. The research would encourage manufacturers to formulate strategy and planning for service development with product and process.

Keywords: dynamic model, service innovation, service innovation models, innovation cycle, manufacturing industry.

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Authors: A. Youseef, F. Liu

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The existing information system (IS) developments methods are not met the requirements to resolve the security related IS problems and they fail to provide a successful integration of security and systems engineering during all development process stages. Hence, the security should be considered during the whole software development process and identified with the requirements specification. This paper aims to propose an integrated security and IS engineering approach in all software development process stages by using i* language. This proposed framework categorizes into three separate parts: modelling business environment part, modelling information technology system part and modelling IS security part. The results show that considering security IS goals in the whole system development process can have a positive influence on system implementation and better meet business expectations.

Keywords: Business Process Modelling (BPM), Information System Security, Software Development Process, Requirement Engineering.

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Authors: Martin Juhas, Bohuslava Juhasova, Igor Halenar, Andrej Elias

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This article deals with the possibility of increasing efficiency, reliability and safety of the system for teledosimetric data collection management and their evaluation as a part of complex study for activity “Research of data collection, their measurement and evaluation with mobile and autonomous units” within project “Research of monitoring and evaluation of non-standard conditions in the area of nuclear power plants”. Possible weaknesses in existing system are identified. A study of available cluster solutions with possibility of their deploying to analysed system is presented

Keywords: Teledosimetric data, efficiency, reliability, safety, cluster solution.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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

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

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Authors: Gökhan Silahtaroğlu

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Clustering large populations is an important problem when the data contain noise and different shapes. A good clustering algorithm or approach should be efficient enough to detect clusters sensitively. Besides space complexity, time complexity also gains importance as the size grows. Using hierarchies we developed a new algorithm to split attributes according to the values they have and choosing the dimension for splitting so as to divide the database roughly into equal parts as much as possible. At each node we calculate some certain descriptive statistical features of the data which reside and by pruning we generate the natural clusters with a complexity of O(n).

Keywords: Clustering, tree, split, pruning, entropy, gini.

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, Apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: Behavior, data mining technique, Apriori algorithm.

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Authors: T. Thulare, M. Herselman, A. Botha

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Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.

Keywords: Data integrity, data integrity challenges, hospital information systems, South Africa.

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Authors: H. Bevrani, A. G. Tikdari, T. Hiyama

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Optimal load shedding (LS) design as an emergency plan is one of the main control challenges posed by emerging new uncertainties and numerous distributed generators including renewable energy sources in a modern power system. This paper presents an overview of the key issues and new challenges on optimal LS synthesis concerning the integration of wind turbine units into the power systems. Following a brief survey on the existing LS methods, the impact of power fluctuation produced by wind powers on system frequency and voltage performance is presented. The most LS schemas proposed so far used voltage or frequency parameter via under-frequency or under-voltage LS schemes. Here, the necessity of considering both voltage and frequency indices to achieve a more effective and comprehensive LS strategy is emphasized. Then it is clarified that this problem will be more dominated in the presence of wind turbines.

Keywords: Load shedding, emergency control, voltage, frequency, wind turbine.

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Authors: Sean Paulsen, Michael Casey

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In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: R. Mirabdollah Yani, E. Darabi

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Responses of the dynamical systems are highly affected by the natural frequencies and it has a huge impact on design and operation of high-rise and high-speed elevators. In the present paper, the variational iteration method (VIM) is employed to investigate better understanding the dynamics of elevator cable as a single-degree-of-freedom (SDOF) swing system. Comparisons made among the results of the proposed closed-form analytical solution, the traditional numerical iterative time integration solution, and the linearized governing equations confirm the accuracy and efficiency of the proposed approach. Furthermore, based on the results of the proposed closed-form solution, the linearization errors in calculating the natural frequencies in different cases are discussed.

Keywords: variational iteration method (VIM), cable vibration, closed-form solution

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

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This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A theory analysis is given in the paper to prove the consistent convergence property, and a simulation study is also done to compare the proposed method with the former shrinkage ones. The experiment results show that this method has advantages in improving classification efficiency, precision and robustness.

Keywords: classification, functional data, feature extraction, K-Lseperability, wavelet.

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Authors: Phu-Cuong Nguyen, Seung-Eock Kim

Abstract:

This paper presents nonlinear elastic dynamic analysis of 3-D semi-rigid steel frames including geometric and connection nonlinearities. The geometric nonlinearity is considered by using stability functions and updating geometric stiffness matrix. The nonlinear behavior of the steel beam-to-column connection is considered by using a zero-length independent connection element comprising of six translational and rotational springs. The nonlinear dynamic equilibrium equations are solved by the Newmark numerical integration method. The nonlinear time-history analysis results are compared with those of previous studies and commercial SAP2000 software to verify the accuracy and efficiency of the proposed procedure.

Keywords: Geometric nonlinearity, nonlinear time-historyanalysis, semi-rigid connection, stability functions.

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Authors: M. Pandi, K. Premalatha

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A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.

Keywords: DNA, Microarray, genomics, Cuckoo Search, Differential Evolution, Gene expression data, Clustering.

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