• Journal of KIISE:Information Networking
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• v.35 no.1
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• pp.46-55
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• 2008

As a mobility support for IP have studied, Internet Engineering Task Force(IETF) standardized the Mobile IPv6(MIPv6) protocol. When a mobile node moves between subnets, MIPv6 maintains connectivity to network and supports seamless communication, and these processes are called a Handover. Whenever the mobile node moves between subnets, the Handover is performed. The mobile node can not communicate during the Handover. This period is Galled Handover latency. To reduce this latency, mipshop working group standardizes Fast Handovers for Mobile IPv6(FMIPv6), but latency which the mobile node registers its new care-of address to a home agent and a correspondent node is still long. To solve this problem, we propose a scheme that the mobile node registers the new care-of address to the home agent and initiates Return Routability procedure in advance during layer 2 handover, based on FMIPv6 and IEEE 802.11. We analyze MIPv6, FMIPv6 and the proposed scheme in term of packet transmission cost during the Handover. Compared to MIPv6 the proposed scheme gains 79% improvement, while it gains 31% improvement compared to FMIPv6.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.69-78
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• 2017

Shannon of the 5G smartphone and Fourier of the signal processing meet in the sampling theorem (2 times the highest frequency 1). In this paper, the initial Shannon Theorem finds the Shannon capacity at the point-to-point, but the 5G shows on the Relay channel that the technology has evolved into Multi Point MIMO. Fourier transforms are signal processing with fixed parameters. We analyzed the performance by proposing a 2N-1 multivariate Fourier-Jacket transform in the multimedia age. In this study, the authors tackle this signal processing complexity issue by proposing a Jacket-based fast method for reducing the precoding/decoding complexity in terms of time computation. Jacket transforms have shown to find applications in signal processing and coding theory. Jacket transforms are defined to be $n{\times}n$ matrices $A=(a_{jk})$ over a field F with the property $AA^{\dot{+}}=nl_n$, where $A^{\dot{+}}$ is the transpose matrix of the element-wise inverse of A, that is, $A^{\dot{+}}=(a^{-1}_{kj})$, which generalise Hadamard transforms and centre weighted Hadamard transforms. In particular, exploiting the Jacket transform properties, the authors propose a new eigenvalue decomposition (EVD) method with application in precoding and decoding of distributive multi-input multi-output channels in relay-based DF cooperative wireless networks in which the transmission is based on using single-symbol decodable space-time block codes. The authors show that the proposed Jacket-based method of EVD has significant reduction in its computational time as compared to the conventional-based EVD method. Performance in terms of computational time reduction is evaluated quantitatively through mathematical analysis and numerical results.

• Korean Journal of Construction Engineering and Management
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• v.11 no.4
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• pp.22-31
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• 2010

Because the estimated cost at early stage has great influence on decisions of project owner, the importance of early cost estimation is increasing. However, it depends on experience and knowledge of the estimator mainly due to shortage of information. Those tendency developed into case-based reasoning(CBR) method which solves new problems by adapting previous solution to similar past problems. The performance of CBR model is affected by attribute weight, so that its accurate determination is necessary. Previous research utilizes mathematical method or subjective judgement of estimator. In order to improve the problem of previous research, this suggests CBR schematic cost estimation method using genetic algorithm to determine attribute weight. The cost model employs nearest neighbor retrieval for selecting past case. And it estimates the cost of new cases based on cost information of extracted cases. As the result of validation for 17 testing cases, 3.57% of error rate is calculated. This rate is superior to accuracy rate proposed by AACE and the method to determine attribute weight using multiple regression analysis and feature counting. The CBR cost estimation method improve the accuracy by introducing genetic algorithm for attribute weight. Moreover, this makes user understand the problem-solving process easier than other artificial intelligence method, and find solution within short time through case retrieval algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.17-23
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• 2010

In the mobile cloud computing, the mobile node should request and receive the services while being connected. In PMIPv6, all packets sent by mobile nodes or correspondent nodes are transferred through the local mobility anchor. This unnecessary detour still results in high delivery latency and significant processing cost. Several PMIPv6 route optimization schemes have been proposed to solve this issue. However, they also suffer from the high signaling costs and handover latency when determining the optimized path. We propose the route optimization handover scheme which adopts the prediction algorithm in PFMIPv6. In the proposed scheme, the new mobile access gateway establishes the bi-directional tunnel with the correspondent node's MAG using the context message when the mobile node's handover is imminent. This tunnel may eliminate the need of separate route optimization procedure. Hence, the proposed scheme can reduce the signaling cost than other conventional schemes do. Analytical performance evaluation is preformed to show the effectiveness of the proposed scheme. The result shows that our scheme is more effective than other schemes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.12
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• pp.73-82
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• 2008

There are several approaches for reducing the ROB(Reorder Buffer) complexity in processors. The one technique that makes the simplest ROB ports relies on a distributed implementation that spreads the centralized ROB structure across the functional units(FUs). Each distributed buffers are decided on the size of them by workload of the functional units. The performance of the processor depends on the size of distributed ROB. However, most of previous works have depended on the simulation results to decide the optimsize of distributed ROB. In this Paper, we use an analytical model based on the M/M/1 Queuing theory to determine the optimum size of each distributed ROB. Our schemes are evaluated by using the simulation performed by the CPU2000 benchmarks. We are able to choose the optimum size of distributed ROB showing the 99.2% performance compared with existing superscalar processors. We can save 82% hardware resources in ports and reduce more than 30% of delay when ROB and distributed ROB proposed in this paper are designed by HDL.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.10
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• pp.1109-1114
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• 2012

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.535-543
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• 2014

In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.47-52
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• 2021

최근 지문을 바탕으로 답을 추론하는 연구들이 많이 이루어지고 있으며, 대표적으로 기계 독해 연구가 존재하고 관련 데이터 셋 또한 여러 가지가 공개되어 있다. 그러나 한국의 대학수학능력시험 국어 영역과 같은 복잡한 구조의 문제에 대한 고차원적인 문제 해결 능력을 요구하는 데이터 셋은 거의 존재하지 않는다. 이로 인해 고차원적인 독해 문제를 해결하기 위한 연구가 활발히 이루어지고 있지 않으며, 인공지능 모델의 독해 능력에 대한 성능 향상이 제한적이다. 기존의 입력 구조가 단조로운 독해 문제에 대한 모델로는 복잡한 구조의 독해 문제에 적용하기가 쉽지 않으며, 이를 해결하기 위해서는 새로운 모델 훈련 방법이 필요하다. 이에 복잡한 구조의 고차원적인 독해 문제에도 대응이 가능하도록 하는 모델 훈련 방법을 제안하고자 한다. 더불어 3가지의 데이터 증강 기법을 제안함으로써 고차원 독해 문제 데이터 셋의 부족 문제 또한 해소하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.319-326
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• 2015

Recently in the fields o f stochastic optimal control ( SOC) and reinforcemnet l earning (RL), there have been a great deal of research efforts for the problem of finding data-based sub-optimal control policies. The conventional theory for finding optimal controllers via the value-function-based dynamic programming was established for solving the stochastic optimal control problems with solid theoretical background. However, they can be successfully applied only to extremely simple cases. Hence, the data-based modern approach, which tries to find sub-optimal solutions utilizing relevant data such as the state-transition and reward signals instead of rigorous mathematical analyses, is particularly attractive to practical applications. In this paper, we consider a couple of methods combining the modern SOC strategies and approximate inference together with machine-learning-based data treatment methods. Also, we apply the resultant methods to a variety of application domains including financial engineering, and observe their performance.