• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.141-147
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• 2009

To initialize and maintain self-organizing networks such as mobile ad hoc networks, address allocation protocol is essentially required. However, centralized approaches that pervasively used in traditional networks are not recommended in this kind of networks since they cannot handle with mobility efficiently. In addition, previous distributed approaches suffer from inefficiency with control overhead caused by duplicated address detection and management of available address pool. In this paper, we propose a new dynamic address allocation scheme, which is based on adaptive partition. An available address is managed in distributed way by multiple agents and partitioned adaptively according to current network environments. Finally, simulation results reveal that a proposed scheme is superior to previous approach in term of address acquisition delay under diverse simulation scenarios.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.1-10
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• 2005

A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization (MDO) problems by CFD-CSD coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. An MDO system using high fidelity simulation codes, Navier-Stokes solver and NASTRAN, has been developed and applied to a regional-jet wing design. Because the optimization system becomes very computationally expensive, only brief exploration of the design space has been performed. However, data mining result demonstrates that design knowledge can produce a good design even from the brief design exploration.

• ETRI Journal
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• v.39 no.6
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• pp.771-781
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• 2017

For efficient spectral utilization of satellite channels, a shared band transmission technique is introduced in this paper. A satellite transmits multiple received signals from a gateway and terminal in the common frequency band by superimposing the signals. To improve the power efficiency as well as the spectral efficiency, a travelling wave tube amplifier in the satellite should operate near the saturation level. This causes a nonlinear distortion of the superimposed transmit signal. Without mitigating this nonlinear effect, the self-interference cannot be properly cancelled and the desired signal cannot be demodulated. Therefore, an adaptive compensation scheme for nonlinearity is herein proposed with the proper operation scenario. It is shown through simulations that the proposed shared band transmission approach with nonlinear compensation and self-interference cancellation can achieve an acceptable system performance in nonlinear satellite channels.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.325-327
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• 2005

In this paper, we propose the design of active queue management (AQM) control system using the self-recurrent wavelet neural network (SRWNN). By regulating the queue length close to reference value, AQM can control the congestions in TCP network. The SRWNN is designed to perform as a feedback controller for TCP dynamics. The parameters of network are tunes to minimize the difference between the queue length of TCP dynamic model and the output of SRWNN using gradient-descent method. We evaluate the performances of the proposed AQM approach through computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1110-1116
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• 2010

This paper present an effective complementary filtering method using encoder and gyro sensors for the self-localization(including heading and velocity) of indoor mobile robot. The main idea of the proposed approach is to find the pros and cons of each sensor through a various maneuvering tests and to design of an adaptive complementary filter that works for the entire maneuvering phases. The proposed method is applied to an indoor mobile robot and the performances are verified through extensive experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.370-372
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• 2004

소프트웨어의 외부 환경이 동적으로 변화하고 복잡해지면서 소프트웨어가 예상하지 못한 외부 환경의 변화에 직면하였을 때 변화를 감지하고 대안을 선택하여 지속적인 서비스를 제공할 필요성이 증가하고 있다. 이를 위해 외부 환경의 변화를 감지하고 변화에 적응할 수 있는 적응형 소프트웨어가 나오게 되었다. 그러나 적응형 소프트웨어를 개발하고자 할 때 기존의 요구사항 분석 방법은 소프트웨어의 외부 환경의 변화에 대한 고려가 부족하다. 본 논문에서는 적응형 소프트웨어의 외부 환경의 변화와 그러한 변화에 의해 가변적으로 나타나는 요구사항을 분석하기 위한 문맥 기반 요구사항 분석 방법을 제시한다.

• Research in Community and Public Health Nursing
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• v.32 no.3
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• pp.254-267
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• 2021

Purpose: The purpose of this study was to identify the effects of brain-wave biofeedback training nursing intervention (NFT) upon enhancing self-regulation response in adolescence with emotional behavior problems in school. Methods: A quasi-experimental design was conducted. The participants were assigned to the experimental group (n=24) or the control group (n=24). The experimental group additionally received NFT. The NFT was conducted 10 sessions for 30 minutes per session with the band reward and inhibit training which matched their Quantitative Electroencephalography (QEEG), participant's demand and chief complaint. Data were collected with QEEG and heart rate variability (HRV) in physiological response, self-efficacy in cognitive response, depression in emotional response, impulsivity and delay gratification in behavioral response of self-regulation. Results: The general characteristics and the pre-test scores of two groups were all homogeneous. The experimental group was reported to be significantly higher in QEEG homeostasis, HRV homeostasis, self-efficacy, and delay gratification than the control group. The experimental group was reported to be significantly lower in depression and impulsivity. Conclusion: The results indicate that NFT using brain cognitive neuroscience approach is effective in enhancing self-regulation response. Therefore, this nursing intervention using brain cognitive neuroscience approach can be applied as an effective self-regulation nursing intervention for adolescents with emotional behavior problems in communities for adaptive life.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.