• 대한산업공학회지
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• 제27권3호
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• pp.239-249
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• 2001

Competitive coevolution models, often called host-parasite models, are searching models that imitate the biological coevolution that is a series of reciprocal changes in two competing species. The models are known to be an effective method of solving complex and dynamic problems such as game problems, neural network design problems and constraint satisfaction problems. However, previous models consider only ectoparasites that live on the outside of the host when designing the models, not considering endoparasites that live on the inside of the host. This has a limitation to exploiting some information. In this paper, we develop an artificial adaptation model simulating the process in which hosts coevolve with both ectoparasites and endoparasites. In the model, the endoparasites play important roles as follows. By means of them, we can keep the history on results of previous competition between hosts and parasites, and use endogeneous fitness, not exogeneous. Extensive experiments are carried out to show the coevolution phenomenon and to verify the performance of the proposed model. Nim game problems and neural network problems are used as test-bed problems. The results are reported in this paper.

• 한국통신학회논문지
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• 제38C권2호
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• pp.213-218
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• 2013

은닉 마코프 모델 (hidden Markov Model, HMM) 기반 음성 합성 시스템에서 파라미터 적응을 위해 널리 쓰이는 기법으로 최대 공산 선형 회귀 (maximum likelihood linear regression, MLLR)이 있다. 이전 연구에서 우리는 각 MLLR 파라미터를 인자화된 MLLR (Factored MLLR, FMLLR) 형태로 확장하는 형태를 제안하였다. FMLLR 파라미터를 기존의 EM 알고리즘 형태로 구하는 기법 역시 제안하였고, 이를 통해 보완 정보를 활용하여 적응 학습을 수행할 수 있게 하였다. 본 논문에서는, FMLLR 기법을 스펙트럼 파라미터에 사용하는 것뿐 아니라 피치에도 적용하여 그 성능을 향상시키는 것에 대한 탐구를 수행하였다. 감정 음성을 생성하는 여러 실험을 통해, 우리는 제안하는 기법이 피치 및 스펙트럼에 대해 효과적으로 작용하는 것을 확인하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.678-680
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• 2023

본 논문은 정확하면서도 효율적인 한국어 문장 분류 기법에 대해서 논의한다. 최근 자연어처리 분야에서 사전 학습된 언어 모델(Pre-trained Language Models, PLM)은 미세조정(fine-tuning)을 통해 문장 분류 하위 작업(downstream task)에서 성공적인 결과를 보여주고 있다. 하지만, 이러한 미세조정은 하위 작업이 바뀔 때마다 사전 학습된 언어 모델의 전체 매개변수(model parameters)를 학습해야 한다는 단점을 갖고 있다. 본 논문에서는 이러한 문제를 해결할 수 있도록 도메인 적응기(domain adapter)를 활용한 한국어 문장 분류 프레임워크인 DAKS(Domain Adaptation-based Korean Sentence classification framework)를 제안한다. 해당 프레임워크는 학습되는 매개변수의 규모를 크게 줄임으로써 효율적인 성능을 보였다. 또한 문장 분류를 위한 특징(feature)으로써 한국어 사전학습 모델(KLUE-RoBERTa)의 다양한 은닉 계층 별 은닉 상태(hidden states)를 활용하였을 때 결과를 비교 분석하고 가장 적합한 은닉 계층을 제시한다.

• 한국강구조학회 논문집
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• 제15권4호통권65호
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• pp.413-422
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• 2003

구조 설계를 위해 초기 부재를 가정할 때나 건축 실무에서 개산 견적을 계산할 때 기술자의 직관이나 비슷한 조건의 기존 설계 평균값을 사용하고 있으나 설계 조건은 모두 다르기 때문에 큰 오차가 발생할 수밖에 없다. 이러한 문제점을 해결하기 위해서 확률론적인 절차가 내재되어 있어 불확실성을 다룰 수 있는 인공 신경 회로망의 이용하여 와렌 트러스를 설계하므로써 적용성을 평가하였다. 제안된 신경망 설계변수값와 구조설계 단계에 따라 다양한 와렌 트러스를 설계하여 MIDAS 프로그램 설계결과의 10% 오차 이내로 근사 설계를 하므로써 모델의 타당성을 검증하였다. 제안된 모델은 약간의 오차를 포함하지만 적은 시간과 노력으로 신뢰할 수 있는 설계 결과를 얻을 수 있으며, 부재 테이블을 사용하는 비선형 관계의 구조설계에도 적용 가능한 특성을 가지고 있다.

• 한국경영과학회지
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• 제28권3호
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• pp.31-47
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• 2003

Quality design in practice highly depends on human designer's intuition and past experiences due to lack of formal knowledge about the relationship among 10 variables. This paper represents an data mining approach for developing quality design support system that integrates Case Based Reasoning (CBR) and Artificial Neural Networks (ANN) to effectively support all the steps in quality design process. CBR stores design cases in a systematic way and retrieve them quickly and accurately. ANN predicts the resulting quality attributes of design alternatives that are generated from CBR's adaptation process. When the predicted attributes fail to meet the target values, quality design simulation starts to further adapt the alternatives to the customer's new orders. To implement the quality design simulation, this paper suggests (1) the data screening method based on ξ-$\delta$ Ball to obtain the robust ANN models from the large production data bases, (2) the procedure of quality design simulation using ANN and (3) model management system that helps users find the appropriate one from the ANN model base. The integration of CBR and ANN provides quality design engineers the way that produces consistent and reliable design solutions in the remarkably reduced time.

• 한국환경복원기술학회지
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• 제18권2호
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• pp.79-88
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• 2015

Preparations need to be made for Korean pine(Pinus koraiensis) in anticipation of climate change because Korean pine is an endemic species of South Korea and the source of timber and pine nut. Therefore, climate change adaptation policy has been established to conduct an impact assessment on the distribution of Korean pine. Our objective was to predict the distribution of Korean pine while taking into account uncertainty and afforestation conditions. We used the 5th forest types map, a forest site map and BIOCLIM variables. The climate scenarios are RCP 4.5 and RCP 8.5 for uncertainty and the climate models are 5 regional climate models (HadGEM3RA, RegCM4, SNURCM, GRIMs, WRF). The base period for this study is 1971 to 2000. The target periods are the mid-21st century (2021-2050) and the end of the 21st century (2071-2100). This study used the MaxEnt model, and 50% of the presences were randomly set as training data. The remaining 50% were used as test data, and 10 cross-validated replicates were run. The selected variables were the annual mean temperature (Bio1), the precipitation of the wettest month (Bio13) and the precipitation of the driest month (Bio14). The test data's ROC curve of Korean pine was 0.689. The distribution of Korean pine in the mid-21st century decreased from 11.9% to 37.8% on RCP 4.5 and RCP 8.5. The area of Korean pine at an artificial plantation occupied from 32.1% to 45.4% on both RCPs. The areas at the end of the 21st century declined by 53.9% on RCP 4.5 and by 86.0% on RCP 8.5. The area of Korean pine at an artificial plantation occupied 23.8% on RCP 4.5 and 7.2% on RCP 8.5. Private forests showed more of a decrease than national forests for all subsequent periods. Our results may contribute to the establishment of climate change adaptation policies for considering various adaptation options.

• 한국통신학회논문지
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• 제34권3B호
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• pp.311-317
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• 2009

기존의 망 이상 상태 탐지 시스템들은 주로 정상 상태의 시스템 사용률 등과 같은 통계 값으로 결정된 임계값을 기반으로 탐지하기 때문에 이상 상태임에도 불구하고 정상 상태와 비슷한 시스템 통계 값을 가지면 탐지하지 못하는 문제점이 있다. 이러한 단점들을 해결하기 위하여 본 논문에서는 인간면역체계의 학습, 적응, 기억 능력등의 특성을 이용하는 인공면역체계 기반의 적응적 망 이상 상태 탐지 모델을 제안한다. 이를 위하여 인간면역 시스템의 수지상 세포 (Dendritic Cell)와 T 세포 사이의 상호 작용을 이용한 탐지 모델을 설계하고 각 구성 요소 및 기능을 정의한다. 중앙 집중 제어 노드는 각 라우터 노드로부터 전달받은 정보를 분석하여 대응 방법을 해당 라우터들에게 전달한다. 또한 라우터 노드는 학습을 통해 얻어진 데이터를 기반으로 이상 상태를 탐지할 뿐만 아니라 중앙 집중 제어 노드로부터 전달받은 정보를 이용하여 이상 상태를 처리한다. 최종적으로 제안된 이상 상태탐지 모델의 타당성을 검증하기 위하여 구성 모듈을 설계하고 flooding 공격에 대한 시뮬레이션을 수행한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 전기학회논문지P
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• 제54권1호
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.