• 콘크리트학회논문집
• /
• 제17권6호
• /
• pp.1045-1051
• /
• 2005

For critical structures and application, where a given reliability must be met, it is necessary to account for uncertainties and variability in material properties, structural parameters affecting the corrosion process, in addition to the statistical and decision uncertainties. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters and the fuzziness of the corrosion time is determined by the fuzzy arithmetic of interval arithmetic and extension principle

• International Journal of Control, Automation, and Systems
• /
• 제5권3호
• /
• pp.307-316
• /
• 2007

In real system application, the interacting multiple model (IMM) based algorithm operates with the following problems: it requires less computing resources as well as a good performance with respect to the various target maneuvering, it requires a robust performance with respect to the time-varying measurement noise, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an adaptive fuzzy interacting multiple model (AFIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as the inputs of the fuzzy decision maker whose widths are adjusted, is proposed. To verify the performance of the proposed algorithm, a radar target tracking is performed. Simulation results show that the proposed AFIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권7호
• /
• pp.2377-2389
• /
• 2022

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.

• Computers and Concrete
• /
• 제19권2호
• /
• pp.133-142
• /
• 2017

Two-stage concrete (TSC), also known as pre-placed aggregate concrete, is characterized by its unique placement technique, whereby the coarse aggregate is first placed in the formwork, then injected with a special grout. Despite its superior sustainability and technical features, TSC has remained a basic concrete technology without much use of modern chemical admixtures, new binders, fiber reinforcement or other emerging additions. In the present study, an experimental database for TSC was built. Different types of cementitious binders (single, binary, and ternary) comprising ordinary portland cement, fly ash, silica fume, and metakaolin were used to produce the various TSC mixtures. Different dosages of steel fibres having different lengths were also incorporated to enhance the mechanical properties of TSC. The database thus created was used to develop fuzzy logic models as predictive tools for the grout flowability and mechanical properties of TSC mixtures. The performance of the developed models was evaluated using statistical parameters and error analyses. The results indicate that the fuzzy logic models thus developed can be powerful tools for predicting the TSC grout flowability and mechanical properties and a useful aid for the design of TSC mixtures.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.6-8
• /
• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권8호
• /
• pp.2178-2198
• /
• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• 한국항만경제학회지
• /
• 제31권1호
• /
• pp.85-110
• /
• 2015

본 논문에서는 아시아 컨테이너항만들 간의 클러스터링 추세를 분석하기 위해서 퍼지(평균지수변환)DEA모형과 교차효율성모형에 대해서 이론적으로 설명하고, 아시아 38개 컨테이너항만들의 12년간 자료를 4개의 투입요소(선석길이, 수심, 총면적, 크레인 수), 1개의 산출요소(컨테이너화물처리량)를 이용하여 국내항만(부산, 인천, 광양항)들이 어떤 항만들과 클러스터링 해야만 하는지에 대한 측정방법을 실증적으로 보여 주고 분석하였다. 실증분석의 주요한 결과는 다음과 같다. 첫째, 퍼지(평균지수변환)DEA모형에 의한 클러스터링 추세분석에서 국내항만들은 클러스터링을 통해서 효율성을 증대[부산항(56.29%), 인천항(57.96%), 광양항(66.80%)]시 킬 수 있는 것으로 나타났다. 둘째, 원자료를 이용한 교차효율성 모형을 이용한 클러스터링분석에서는 부산항(홍콩, 코오베, 마닐라, 싱가포르, 카오슝, 림찬방, 방콕항), 인천항(아카바, 담만, 카라치, 모하메드 빈 오아심, 다바오), 광양항(담만, 요코하마, 나고야, 킬롱, 카오슝, 방콕항)과 각각 클러스터링을 해야만 하는 것으로 나타났다. 셋째, 퍼지(평균지수변환)DEA모형에 교차효율성 모형을 접목시킨 모형에서는 부산항은 71.38%, 인천항은 103.89%, 광양항은 168.55% 증가가 이루어 졌다. 넷째, 효율성 순위를 검정한 윌콕슨부호순위검정에서는, 세 가지 모형사이의 효율성 순위에 대해서는 약 66%-67% 수준에서 순위에 차이가 없는 것으로 나타났다. 본 논문이 갖는 정책적인 함의는 첫째, 항만정책입안자들이 본 연구에서 사용한 두 가지 모형과 접목시킨 모형을 항만의 클러스터링 정책에 도입하여 해당항만이 발전할 수 있는 전략을 수립하고 이행해 나가야만 한다는 점이다. 둘째, 본 논문의 실증분석결과 국내항만들의 참조항만, 클러스터링항만들로서 나타난 아시아항만들에 대하여, 그들 항만들의 항만개발, 운영에 대한 내용을 정밀하게 분석하고 도입하여 실시하는 것이 필요하다.

• International Journal of Control, Automation, and Systems
• /
• 제3권2호
• /
• pp.183-194
• /
• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• 한국지능시스템학회논문지
• /
• 제18권1호
• /
• pp.41-47
• /
• 2008

클러스터 기반 퍼지 모델트리에서 훈련 데이터의 과잉 적응은 검중 데이터의 성능을 저하시키는 문제점을 가지고 있다. 이러한 문제점을 해결하기 위한 방법으로 본 논문에서는 상호 노드간의 정보를 고려하는 방법을 제안하고자 한다. 제안된 방법은 우선 입력과 출력변수의 속성을 고려한 퍼지 클러스터링에 의해 중심벡터를 계산한 후, 중심벡터들과 입력 속성간의 소속도를 이용하여 구간 분할된 영역별로 각각의 선형모델을 구축한다. 예측 단계에서는 입력된 데이터가 잎노드에 도달하기까지 경유하게 되는 노드들의 중심벡터들과 입력 데이터간의 거리값에 따른 소속도를 계산한 후 최종적으로 각 노드의 선형모델들과 계산된 소속도를 이용하여 출력값을 예측하게 된다. 제안된 방법의 우수성을 보이기 위해 다양한 벤치마크 데이터를 대상을 실험한 결과, 기존의 클러스터 기반 퍼지 모델트리보다 향상된 성능을 보임을 알 수 있었다.

• 한국지능시스템학회논문지
• /
• 제19권6호
• /
• pp.865-871
• /
• 2009

본 논문은 이산 시간 비선형 시스템을 이산 시간 T-S 퍼지 모델에 의해 표현되는 새로운 궤환 선형화에 대해서 논한다. T-S fuzzy 모델의 국부적인 선형 모델들은 각각 가제어 표준형으로 변환되어지고, 그것들의 T-S 퍼지 결합은 궤환 선형화 가능한 T-S fuzzy 모델이 된다. 이 모델을 토대로 비선형 상태 궤환 선형 입력이 결정된다. 비선형 상태 변환은 가제어 표준형에 대한 선형 상태 변환으로부터 추론된다. 본 논문에서 제안하는 방법은 충분한 수학적 배경이 요구되는 고전적인 궤환 선형화 기법과 비교하여 수학적으로 보다 직관적이고 이해하기 쉽다. 본 논문의 궤환 선형화 조건은 고전적인 궤환 선형화와 비교하여 더 완화되었다. 이것은 고전적인 선형화방식 보다 더 큰 범주의 비선형 시스템이 선형화가 가능해진다는 것을 의미 한다.