• Title/Summary/Keyword: 통계적 시뮬레이션

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A Study on the Estimation of Probabilistic Repair.Reinforcement Cycles from Rating Curve of Steel Girder Bridges (강재 교량의 노후화에 따른 확률적 보수.보강 주기 추정에 관한 연구)

  • Kim, Hyun-Bae;Kim, Yong-Su
    • Korean Journal of Construction Engineering and Management
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    • v.10 no.2
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    • pp.102-110
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    • 2009
  • The cost for maintenance of bridge structures such as repair or reinforcement is increasing. In addition, the efforts for inspection of bridge structures is becoming more important since the proper repair or reinforcement should be performed to save the maintenance cost and ensure the safety for public infrastructure. Therefore, it is studied on this paper to estimate the repair or reinforcement cycles using probabilistic approach for the steel-box girders of bridge superstructure. In addition, a computer simulation program is uniquely developed based on probabilistic approach to calculate the cycles derived from the function of age of bridge and performance rating curve which were previously studied. In order to ensure the reliability of results and appropriateness of the model, statistical analyses were performed. Also, the results were compared and proved to be similar with ones from previous statistical data related to the repair or reinforcement cycles. The results from this study is expected to be useful for the determination of proper time to repair or reinforce the bridge structure and raise the safetyness of bridge structure in advance.

A Comparison of Three Fixed-Length Sequence Generators of Synthetic Self-Similar Network Traffic (Synthetic Self-Similar 네트워크 Traffic의 세 가지 고정길이 Sequence 생성기에 대한 비교)

  • Jeong, Hae-Duck J.;Lee, Jong-Suk R.
    • The KIPS Transactions:PartC
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    • v.10C no.7
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    • pp.899-914
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    • 2003
  • It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern telecommunication networks than Poisson Processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. Three generators of pseudo-random self-similar sequences, based on the FFT〔20〕, RMD〔12〕 and SRA methods〔5, 10〕, are compared and analysed in this paper. Properties of these generators were experimentally studied in the sense of their statistical accuracy and times required to produce sequences of a given (long) length. While all three generators show similar levels of accuracy of the output data (in the sense of relative accuracy of the Horst parameter), the RMD- and SRA-based generators appear to be much faster than the generator based on FFT. Our results also show that a robust method for comparative studies of self-similarity in pseudo-random sequences is needed.

A Study on Attitude Estimation of UAV Using Image Processing (영상 처리를 이용한 UAV의 자세 추정에 관한 연구)

  • Paul, Quiroz;Hyeon, Ju-Ha;Moon, Yong-Ho;Ha, Seok-Wun
    • Journal of Convergence for Information Technology
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    • v.7 no.5
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    • pp.137-148
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    • 2017
  • Recently, researchers are actively addressed to utilize Unmanned Aerial Vehicles(UAV) for military and industry applications. One of these applications is to trace the preceding flight when it is necessary to track the route of the suspicious reconnaissance aircraft in secret, and it is necessary to estimate the attitude of the target flight such as Roll, Yaw, and Pitch angles in each instant. In this paper, we propose a method for estimating in real time the attitude of a target aircraft using the video information that is provide by an external camera of a following aircraft. Various image processing methods such as color space division, template matching, and statistical methods such as linear regression were applied to detect and estimate key points and Euler angles. As a result of comparing the X-plane flight data with the estimated flight data through the simulation experiment, it is shown that the proposed method can be an effective method to estimate the flight attitude information of the previous flight.

Algorithmic Generation of Self-Similar Network Traffic Based on SRA (SRA 알고리즘을 이용한 Self-Similar 네트워크 Traffic의 생성)

  • Jeong HaeDuck J.;Lee JongSuk R.
    • The KIPS Transactions:PartC
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    • v.12C no.2 s.98
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    • pp.281-288
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    • 2005
  • It is generally accepted that self-similar (or fractal) Processes may provide better models for teletraffic in modem computer networks than Poisson processes. f this is not taken into account, it can lead to inaccurate conclusions about performance of computer networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A generator of pseudo-random self similar sequences, based on the SRA (successive random addition) method, is implemented and analysed in this paper. Properties of this generator were experimentally studied in the sense of its statistical accuracy and the time required to produce sequences of a given (long) length. This generator shows acceptable level of accuracy of the output data (in the sense of relative accuracy of the Hurst parameter) and is fast. The theoretical algorithmic complexity is O(n).

A Comparison Study on Statistical Modeling Methods (통계모델링 방법의 비교 연구)

  • Noh, Yoojeong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.5
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    • pp.645-652
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    • 2016
  • The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.

AP-Initiated Flow Redirection Mechanism for AP Load Balancing in WLAN Environments (무선랜 환경에서 AP 로드 밸런싱을 위한 AP-개시 플로우 리다이렉션 메커니즘)

  • Kim, Mi-Hui;Chae, Ki-Joon
    • Journal of Internet Computing and Services
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    • v.10 no.2
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    • pp.65-73
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    • 2009
  • IEEE802.11 Wireless LAN(WLAN) is being widely used in public space such as airport, and increases the networking boundary in campus and enterprise, and it has lastly attracted considerable attention for mesh network and converged network with other 3G mobile communication networks. In WLAN, load balancing among Access Points(AP) is an important issue for efficient resource management or supporting the Quality of Service(QoS) of traffic, but most researches focused on the AP selection in network entry or roaming of Stations(STA). In this paper, we propose an AP-Initiated Flow Redirection(FR) for AP load balancing by monitoring AP's availability in the true sense. When the AP's resource becomes almost saturated, that is used more than a specific threshold, the AP queries the roaming possible neighbor APs about their availability and calculates the distribution of traffic load with statistical methods such as entropy or chi-square. Finally, the AP decides flows and new APs for redirection and performs it. Our simulation results show that our FR mechanism increases the performance in the various views.

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Statistical Modeling of Joint Distribution Functions for Reliability Analysis (신뢰성 해석을 위한 결합분포함수의 통계모델링)

  • Noh, Yoojeong;Lee, Sangjin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.5
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    • pp.2603-2609
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    • 2014
  • Reliability analysis of mechanical systems requires statistical modeling of input random variables such as distribution function types and statistical parameters that affect the performance of the mechanical systems. Some random variables are correlated, but considered as independent variables or wrong assumptions on input random variables have been used. In this paper, joint distributions were modeled using copulas and Bayesian method from limited number of data. To verify the proposed method, statistical simulation tests were carried out for various number of samples and correlation coefficients. As a result, the Bayesian method selected the most probable copula types among candidate copulas even though the candidate copula shapes are similar for low correlations or the number of data is limited. The most probable copulas also yielded similar reliabilities with the true reliability obtained from a true copula, so that it can be concluded that the Bayesian method provides accurate statistical modeling for the reliability analysis.

A Flexible Line-Fitting ICM Approach for Takbon Image Restoration (유연한 선부합 ICM 방식에 의한 탁본영상복원)

  • Hwang, Jae-Ho
    • The KIPS Transactions:PartB
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    • v.13B no.5 s.108
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    • pp.525-532
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    • 2006
  • This paper proposes a new class of image restoration on the Ising modeled binary 'Takbon' image by the flexible line-fitting ICM(Iterated conditional modes) method. Basically 'Takbon' image need be divided into two extreme regions, information and background one due to its stroke combinations. The main idea is the line process, comparing with the conventional ICM approaches which were based on partially rectangular structured point process. For calculating geometrical mechanism, we have defined line-fitting functions at each current pixel array which form the set of linear lines with gradients and lengths. By applying the Bayes' decision to this set, the region of the current pixel is decided as one of the binary levels. In this case, their statistical reiteration for distinct tracking between intra and extra region offers a criterion to decide the attachment at each step. Finally simulations using the binary 'Takbon' image are provided to demonstrate the effectiveness of our new algorithm

Fast Self-Similar Network Traffic Generation Based on FGN and Daubechies Wavelets (FGN과 Daubechies Wavelets을 이용한 빠른 Self-Similar 네트워크 Traffic의 생성)

  • Jeong, Hae-Duck;Lee, Jong-Suk
    • The KIPS Transactions:PartC
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    • v.11C no.5
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    • pp.621-632
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    • 2004
  • Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).

Design of Multiple Fuzzy Prediction System based on Interval Type-2 TSK Fuzzy Logic System (Interval Type-2 TSK 퍼지논리시스템 기반 다중 퍼지 예측시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.3
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    • pp.447-454
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    • 2010
  • This paper presents multiple fuzzy prediction systems based on an Interval type-2 TSK fuzzy Logic System so that the uncertainty and the hidden characteristics of nonlinear data can be reflected more effectively to improve prediction quality. In proposed method, multiple fuzzy systems are adopted to handle the nonlinear characteristics of data, and each of multiple system is constructed by using interval type-2 TSK fuzzy logic because it can deal with the uncertainty and the characteristics of data better than type-1 TSK fuzzy logic and other methods. For input of each system, the first-order difference transformation method are used because the difference data generated from it can provide more stable statistical information to each system than the original data. Finally, computer simulations are performed to show the effectiveness of the proposed method for two typical time series examples.