• Title/Summary/Keyword: Self-Optimization

검색결과 353건 처리시간 0.025초

Approach of Self-mixing Interferometry Based on Particle Swarm Optimization for Absolute Distance Estimation

  • Li, Li;Li, Xingfei;Kou, Ke;Wu, Tengfei
    • Journal of the Optical Society of Korea
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    • 제19권1호
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    • pp.95-101
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    • 2015
  • To accurately extract absolute distance information from a self-mixing interferometry (SMI) signal, in this paper we propose an approach based on a particle swarm optimization (PSO) algorithm instead of frequency estimation for absolute distance. The algorithm is utilized to search for the global minimum of the fitness function that is established from the self-mixing signal to find out the actual distance. A resolution superior to $25{\mu}m$ in the range from 3 to 20 cm is obtained by experimental measurement, and the results demonstrate the superiority of the proposed approach in comparison with interpolated FFT. The influence of different external feedback strength parameters and different inertia weights in the algorithm is discussed as well.

LTE 네트워크에서 SON ANR 기술 분석 (Analysis of Automatic Neighbor Relation Technology in Self Organization Networks of LTE)

  • 안호준;양모찬
    • 한국전자통신학회논문지
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    • 제14권5호
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    • pp.893-900
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    • 2019
  • 본 논문에서는 LTE 네트워크에서 SON(: Self Organization Networks) 기술 분석을 다룬다. SON은 이전 셀룰러 시스템인 UMTS, GSM과 비교되는 LTE 만의 차별적인 기능이고, 무선 라디오가 변화하는 환경에서 비용 효율적으로 최고의 성능을 도출하는 도구이다. 또한, SON은 운영자가 네트워크의 설정들을 자동화하는 기능이 있으며, 중앙 집중적 계획이 가능하여 수작업에 대한 요구를 감소시켰다. SON은 크게 Self-Configuration, Self-Optimization, Self-Healing의 3가지 범주로 나누어진다. 각각의 큰 범주는 세부적인 기술 내용을 가지고 있고 각 범주의 기술들이 모두 모여서 SON이라는 기술을 완성시키게 된다. 본 논문에서는 각 3가지 범주에서 Self-Configuration의 기술 중 ANR에 대해서 집중적으로 분석하였다.

인공위성 카메라 주반사경의 위상최적화 (Topology Optimization of the Primary Mirror of a Multi-Spectral Camera)

  • 박강수;장수영;이응식;윤성기
    • 대한기계학회논문집A
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    • 제26권6호
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    • pp.1194-1202
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    • 2002
  • A study on the topology optimization of a multi-spectral camera for space-use is presented. The optimization is carried out under self-weight and polishing pressure loading. A multi-spectral camera for space-use experiences degradation of optical image in the space, which can not be detected on the optical test bench on the earth. An optical surface deformation of a primary mirror, which is a principal component of the camera system, is an important factor affecting the optical performance of the whole camera system. In this study, topology optimization of the primary mirror of the camera is presented. As an objective function, a measure of Strehl ratio is used. Total mass of the primary mirror is given as a constraint to the optimization problem. The sensitivities of the objective function and constraint are calculated by direct differentiation method. Optimization procedure is carried out by an optimality criteria method. For the light-weight primary mirror design, a three dimensional model is treated. As a preliminary example, topology optimization considering a self-weight loading is treated. In the second example, the polishing pressure is also included as a loading in the topology optimization of the mirror. Results of the optimized design topology for the mirror with various mass constraints are presented.

인공위성 카메라 주반사경의 위상 최적화 (Topology Optimization of the Primary Mirror of a Multi-Spectral Camera)

  • 박강수;장수영;이응식;윤성기
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2001년도 춘계학술대회논문집A
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    • pp.920-925
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    • 2001
  • A study on the topology optimization of a multi-spectral camera for space-use is presented. A multi-spectral camera for space-use experiences degradation of optical image in the space, which can not be detected on the optical test bench on the earth. An optical surface deformation of a primary mirror, which is a principal component of the camera system, under the self-weight loading is an important factor affecting the optical performance of the whole camera system. In this study, topology optimization of the primary mirror of the camera is presented. Total mass of the primary mirror is given as a constraint to the optimization problem. The sensitivities of the objective function and constraint are calculated by direct differentiation method. Optimization procedure is carried out by an optimality criterion method using the sensitivities of the objective function and the constraint. As a preliminary example, topology optimization considering a self-weight loading is treated. For practical use, the polishing pressure is included as a loading in the topology optimization of the primary mirror. Results of the optimized design topology for the primary mirror with varying mass ratios are presented.

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Self-Organizing Network에서 기계학습 연구동향-II (Research Status on Machine Learning for Self-Organizing Network-II)

  • 권동승;나지현
    • 전자통신동향분석
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    • 제35권4호
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    • pp.115-134
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    • 2020
  • Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.

진화론적 알고리즘에 의한 퍼지 다항식 뉴론 기반 고급 자기구성 퍼지 다항식 뉴럴 네트워크 구조 설계 (Design of Advanced Self-Organizing Fuzzy Polynomial Neural Networks Based on FPN by Evolutionary Algorithms)

  • 박호성;오성권;안태천
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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    • pp.322-324
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    • 2005
  • In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

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CFD를 이용한 트림 최적화 연구 (A Study on Trim Optimization by using CFD Analysis)

  • 김인철;윤지현;정영준
    • 대한조선학회 특별논문집
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    • 대한조선학회 2015년도 특별논문집
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    • pp.41-45
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    • 2015
  • In this study reviewed the validity of the estimated optimum trim by the numerical analysis. For this purpose, the numerical analysis of the trim optimization for 6500TEU container carrier and capesize bulk carrier were carried out using Star-CCM+, which results were compared with the results of model tests. The reliability of results of the numerical analysis was confirmed via comparing the resistance determined by the numerical analysis and model test. The performance of self-propulsion at each trim conditions were estimated using the calculated resistance by numerical analysis. The BHP at each trim condition were calculated by estimated performance of self-propulsion, which trend of results were confirmed similar trend of result of model test.

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3GPP LTE 시스템에서 기지국 구성 자동 설정 동작을 위한 하드 핸드오버 알고리즘 (Hard Handover Algorithm for Self Optimization in 3GPP LTE System)

  • 이두원;현광민;김동회
    • 한국통신학회논문지
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    • 제35권3A호
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    • pp.217-224
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    • 2010
  • 본 논문에서는 3GPP의 LTE시스템에서 자동화기술 중의 하나인 기지국 구성 자동 설정 (Self-Optimization)을 위한 하드 핸드오버 알고리즘을 제안한다. 제안하는 알고리즘은 먼저 주변 셀로부터의 수신신호세기와 X2인터페이스를 통해 eNB(evolved Node-B)간의 정보 교환으로 수집된 후보 목표 셀들의 셀 부하 정보를 이용하여, 최적의 목표 셀을 선택하는 혼합형 목표 셀 선택방식과 핸드오버 성능에 영향을 주는 다양한 환경 요소들의 비용함수들에 의해서 최적의 핸드오버 히스테리시스(Hysteresis) 값을 선택하는 다중 요소 기반 능동 히스테리시스 방식으로 구성되어 있다. 본 논문에서 제안하는 알고리즘은 핸드오버 성능에 영향을 주는 요소들에 대한 정보를 바탕으로 LTE시스템에서의 기지국 운용 자동 최적화을 위한 최적화된 목표 셀과 히스테리시스 값을 선택하는 동작을 수행함으로써 핸드오버의 가장 중요한 성능인 핸드오버 실패율과 부하균형 측면에서 우수한 성능을 얻게 한다.

Cost effective optimal mix proportioning of high strength self compacting concrete using response surface methodology

  • Khan, Asaduzzaman;Do, Jeongyun;Kim, Dookie
    • Computers and Concrete
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    • 제17권5호
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    • pp.629-638
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    • 2016
  • Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.

퍼지다항식 뉴론 기반의 유전론적 최적 자기구성 퍼지 다항식 뉴럴네트워크 (Genetically Opimized Self-Organizing Fuzzy Polynomial Neural Networks Based on Fuzzy Polynomial Neurons)

  • 박호성;이동윤;오성권
    • 대한전기학회논문지:시스템및제어부문D
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    • 제53권8호
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    • pp.551-560
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    • 2004
  • In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.