• Title/Summary/Keyword: network integration

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Novel Trusted Hierarchy Construction for RFID Sensor-Based MANETs Using ECCs

  • Kumar, Adarsh;Gopal, Krishna;Aggarwal, Alok
    • ETRI Journal
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    • v.37 no.1
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    • pp.186-196
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    • 2015
  • In resource-constrained, low-cost, radio-frequency identification (RFID) sensor-based mobile ad hoc networks (MANETs), ensuring security without performance degradation is a major challenge. This paper introduces a novel combination of steps in lightweight protocol integration to provide a secure network for RFID sensor-based MANETs using error-correcting codes (ECCs). The proposed scheme chooses a quasi-cyclic ECC. Key pairs are generated using the ECC for establishing a secure message communication. Probability analysis shows that code-based identification; key generation; and authentication and trust management schemes protect the network from Sybil, eclipse, and de-synchronization attacks. A lightweight model for the proposed sequence of steps is designed and analyzed using an Alloy analyzer. Results show that selection processes with ten nodes and five subgroup controllers identify attacks in only a few milliseconds. Margrave policy analysis shows that there is no conflict among the roles of network members.

An Integrated Approach for Position Estimation using RSSI in Wireless Sensor Network

  • Pu, Chuan-Chin;Chung, Wan-Young
    • Journal of Ubiquitous Convergence Technology
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    • v.2 no.2
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    • pp.78-87
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    • 2008
  • Received signal strength indicator (RSSI) is used as one of the ranging techniques to locate dynamic sensor nodes in wireless sensor network. Before it can be used for position estimation, RSSI values must be converted to distances using path loss model. These distances among sensor nodes are combined using trilateration method to find position. This paper presents an idea which attempts to integrate both path loss model and trilateration as one algorithm without going through RSSI-distance conversion. This means it is not simply formulas combination but a whole new model was developed. Several advantages were found after integration: it is able to reduce processing load, and ensure that all values do not exceed the maximum range of 16-bit signed or unsigned numbers due to antilog operation in path loss model. The results also show that this method is able to reduce estimation error while inaccurate environmental parameters are used for RSSI-distance conversion.

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Understanding Disease Susceptibility through Population Genomics

  • Han, Seonggyun;Lee, Junnam;Kim, Sangsoo
    • Genomics & Informatics
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    • v.10 no.4
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    • pp.234-238
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    • 2012
  • Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.

A comparative Study of ARIMA and Neural Network Model;Case study in Korea Corporate Bond Yields

  • Kim, Steven H.;Noh, Hyunju
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.10a
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    • pp.19-22
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    • 1996
  • A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

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Damage localization in plate-like structure using built-in PZT sensor network

  • Liu, Xinglong;Zhou, Chengxu;Jiang, Zhongwei
    • Smart Structures and Systems
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    • v.9 no.1
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    • pp.21-33
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    • 2012
  • In this study, a Lamb-wave based damage detection approach is proposed for damage localization in plate. A sensor network consisting of three PZT wafer type actuators/sensors is used to generate and detect Lamb waves. To minimize the complication resulted from the multimode and dispersive characteristics of Lamb waves, the fundamental symmetric Lamb mode, $S_0$ is selectively generated through designing the excitation frequency of the narrowband input signal. A damage localization algorithm based upon the configuration of the PZT sensor network is developed. Time-frequency analysis method is applied to purify the raw signal and extract damage features. Experimental result obtained from aluminum plate verified the proposed damage localization approach.

Measure Correlation Analysis of Network Flow Based On Symmetric Uncertainty

  • Dong, Shi;Ding, Wei;Chen, Liang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.6
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    • pp.1649-1667
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    • 2012
  • In order to improve the accuracy and universality of the flow metric correlation analysis, this paper firstly analyzes the characteristics of Internet flow metrics as random variables, points out the disadvantages of Pearson Correlation Coefficient which is used to measure the correlation between two flow metrics by current researches. Then a method based on Symmetrical Uncertainty is proposed to measure the correlation between two flow metrics, and is extended to measure the correlation among multi-variables. Meanwhile, the simulation and polynomial fitting method are used to reveal the threshold value between different correlation degrees for SU method. The statistical analysis results on the common flow metrics using several traces show that Symmetrical Uncertainty can not only represent the correct aspects of Pearson Correlation Coefficient, but also make up for its shortcomings, thus achieve the purpose of measuring flow metric correlation quantitatively and accurately. On the other hand, reveal the actual relationship among fourteen common flow metrics.

Perspective Framework on the Fourth-Generation Mobile Communication Systems

  • Kim, Jin-Young;Kim, Duk-Kyung;Park, Seong-Soo;Lee, Goon-Seop;Ryu, Si-Hoon;Chang, Myung-Rae;Koo, Jun-Mo
    • Journal of Communications and Networks
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    • v.4 no.4
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    • pp.321-335
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    • 2002
  • Emergence of the fourth generation mobile communication system (4G system) is now in its dawn. This article proposes a perspective framework on the 4G system, and discusses various kinds of system aspects and technological requirements in terms of novel service features, spectrum management, radio access technologies, wired access integration, core network, and mobile terminal. The focus of the article is to define the scope and features of the 4G system in an overall system/network viewpoint. From the foreseeable development trends, it is highly expected that whatever emerges in the 4G system will be some kind of constantly evolving and grand recursive concatenation of all the existing system/network developments.

Integrating Operation of Dispersed Generation to Automation Distribution Center for Distribution Network Reconfiguration

  • Park, Joon-Ho;Kim, Jae-Chul;Moon, Seung-Il
    • KIEE International Transactions on Power Engineering
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    • v.2A no.3
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    • pp.102-108
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    • 2002
  • Due to the many attractive aspects of DG in the future power distribution system, distribution automation will be a center hub of integration of the distribution system and resources to satisfy the various needs of customers in a competitive and deregulated environment. In this paper, operation strategies are presented which use network reconfiguration of the automated distribution systems with DG as a real-time operation tool for loss reduction and service restoration from the view of distribution operation. The algorithms and operation strategies of an automated distribution system with DG are introduced to achieve the positive effects of DG in distribution systems. A simple case study shows the effectiveness of the proposed operation strategies.

Modeling & Error Compensation of Walking Navigation System (보행항법장치의 모델링 및 오차 보정)

  • Cho, Seong-Yun;Park, Chan Gook
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.6
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    • pp.221-227
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    • 2002
  • In this paper, the system model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. WNS(Walking Navigation System) is a kind of personal navigation system using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigation performance, computational methods have been investigated. The step is detected using the walking patterns, stride is determined by neural network and azimuth is calculated with gyro output. The neural network filters off unnecessary motions. However, the error compensation method is needed, because the error of navigation information increases with time. In this paper, the accumulated error due to the step detection error, stride error and gyro bias is compensated by the integrating with GPS. Loosely coupled Kalman filter is used for the integration of WNS and GPS. It is shown by simulation that the error is bounded even though GPS signal is blocked.

ECG Pattern Classification Using Back Propagation Neural Network (역전달 신경회로망을 이용한 심전도 신호의 패턴분류에 관한 연구)

  • 이제석;이정환;권혁제;이명호
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.6
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    • pp.67-75
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    • 1993
  • ECG pattern was classified using a back-propagation neural network. An improved feature extractor of ECG is proposed for better classification capability. It is consisted of preprocessing ECG signal by an FIR filter faster than conventional one by a factor of 5. QRS complex recognition by moving-window integration, and peak extraction by quadratic approximation. Since the FIR filter had a periodic frequency spectrum, only one-fifth of usual processing time was required. Also, segmentation of ECG signal followed by quadratic approximation of each segment enabled accurate detection of both P and T waves. When improtant features were extracted and fed into back-propagation neural network for pattern classification, the required number of nodes in hidden and input layers was reduced compared to using raw data as an input, also reducing the necessary time for study. Accurate pattern classification was possible by an appropriate feature selection.

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