• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.275-283
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• 2004

The paper describes the integrated design system using MDO and approximation technique. In MDO related research, final target is an integrated and automated MDO framework systems. However, in order to construct the integrated design system, the prerequisite condition is how much save computational cost because of iterative process in optimization design and lots of data information in CAD/CAE integration. Therefore, this paper presents that an efficient approximation method, Adaptive Approximation, is a competent strategy via MDO framework systems.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.3
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• pp.317-325
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• 2015

In an interference environment, adaptive sidelobe blanking(adaptive SLB: ASB) algorithm effectively cancels the high-duty cycle jammer and blocks the sidelobe signals without the auxiliary antenna. The adaptive SLB for the linearly constrained minimum variance (LCMV) is proposed in this paper. In the proposed scheme, the interference covariance matrix is modified to satisfy the direction constraints of LCMV and the normalized output can be obtained to block sidelobe signals. As the LCMV can be represented as a generalized sidelobe canceller(GSC) form, which is the general framework of various adaptive beamforming(ABF) algorithms, the proposed adaptive SLB can be applied to various ABF methods. The performance of the proposed method is verified through simulation and analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.493-505
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• 2003

A new inter-client synchronization framework for multicast media streaming is proposed employing a server-client coordinated adaptive playout control. The proposed adaptive player controls the playback speed of audio and video by adopting the time-scale modification of audio. Based on the overall synchronization status as well as the buffer occupancy level, the playout speed of each client is manipulated within a perceptually tolerable range. Additionally, the server implicitly helps increasing the time available for retransmission while the clients perform an interactive error recovery mechanism with the assistance of playout control. The network-simulator based simulations show that the proposed framework can reduce the playout discontinuity without degrading the media quality, and thus mitigate the client heterogeneity.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1122-1133
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• 2019

We have designed and implemented a new data processing framework called MOHA(Mtc On HAdoop) which can effectively support Many-Task Computing(MTC) applications in a YARN-based Hadoop platform. MTC applications can be composed of a very large number of computational tasks ranging from hundreds of thousands to millions of tasks, and each MTC application may have different resource usage patterns. Therefore, we have implemented MOHA-TaskExecutor(a pilot-job that executes real MTC application tasks)'s Adaptive Parallel Computability which can adaptively execute multiple tasks simultaneously, in order to improve the parallel computability of a YARN container and the overall system throughput. We have implemented multi-threaded version of TaskExecutor which can "independently and dynamically" adjust the number of concurrently running tasks, and in order to find the optimal number of concurrent tasks, we have employed Hill-Climbing algorithm.

• Journal of the Society of Disaster Information
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• v.15 no.3
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• pp.367-379
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• 2019

Purpose: This study aims to propose a framework for climate change risk management by analyzing characteristics of climate-induced disasters. Method: The recent global and domestic trends of loss and damage under natural disaster events and the characteristics of climate-induced disasters were analyzed to design a framework for climate change risk management. Results: In consideration of the uncertainty of climate risk and various spatio-temporal scales of climate disasters, a new framework is suggested for comprehensive climate risk management that includes risk assessment, goal setting, planning, monitoring and evaluation, learning and adjustment. The framework aims at an iterative process that is activated by stakeholder engagement. Conclusion: Pilot studies need to be conducted to revise and polish the framework in the future, and institutional arrangements should be prepared for the effective implementation of the comprehensive climate risk management.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.134-146
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• 2005

Research about QoS in the ad-hoc networks for stable service of various applications has been needed as the expectation about the realization of the ad-hoc networks grows bigger. Existing researches about QoS in the ad-hoc network had the problems which can not guarantee the quantitative services or create the overhead. In this paper, we propose a novel algorithm of QFAN(QoS Framework for Ad-hoc Networks) the framework to resolve such problems and considered application of the proposed algorithm into the ad-hoc networks. Our model can guarantee the minimum bandwidth of the real-time traffic as minimized the overhead. And, disproportionate distribution of bandwidth problem can resolve by the proposed algorithm through the fair share between real-time traffic and best-effort traffic about available bandwidth. We design both the TiRe(Tiny Reservation) and the ADR(Adaptive Drop Rate) control algorithm to apply the proposed QFAN. Using simulation, we confirm fair share of available bandwidth between real-time traffic and best-effort traffic as guarantee minimum required bandwidth of real-time traffic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2396-2413
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• 2018

Trust management is one of the most challenging issues for the highly heterogeneous Internet of Things (IoT). In the context of the IoT, it is difficult to evaluate the node's trustworthiness in the same trust model when a node provides different services. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of IoT environments. With these issues in mind, this paper propose a robust and adaptive trust management system for the IoT that is able to measure the trustworthiness of nodes based on feedbacks collected from participants in a specific context and ensure the availability of trust management services. The main contributions of our system are: 1) Proposing a partly decentralized trust management framework, which improves the resiliency of the trust mechanism; 2) Proposing an adaptive trust evaluation scheme and a three-dimensional context representation makes trust evaluation more accurate and specific; 3) Enhancing the adaptive trust evaluation scheme by incorporating a bad behavior factor in trust estimation, which efficiently distinguishes misleading feedbacks from On-Off attacks. Simulation results show the good performance of the proposed system and especially show effectiveness against On-Off attacks compared to other trust mechanisms.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.87-107
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• 2018

With more than 10 million inhabitants, in particular, Seoul, the capital of Korea, has already experienced a number of severe heat wave. To alleviate the potential impacts of heat wave and the vulnerability to heat wave, policy-makers have generally considered the option of heat wave strategies containing adaptation elements. From the perspective of sustainable planning for adaptation to heat wave, the objective of this study is to identify the elements of vulnerability and assess heat wave-vulnerability at the dong level. This study also performs an exploratory investigation of the spatial pattern of vulnerable areas in Seoul to heat wave by applying exploratory spatial data analysis. Then this study attempts to select areas with the relatively highest and lowest level of adaptive capacity to heat wave based on an framework of climate change vulnerability assessment. In our analysis, the adaptive capacity is the relatively highest for Seongsan-2-dong in Mapo and the relatively lowest for Changsin-3-dong in Jongno. This study sheds additional light on the spatial patterns of heat wave-vulnerability and the relationship between adaptive capacity and heat wave.

• Journal of Drive and Control
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• v.19 no.1
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• pp.51-61
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• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.