• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.394-394
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• 2000

This Paper proposes a new nonlinear partial least square method that extends the linear PLS. Proposed nonlinear PLS uses self-organizing feature map as PLS outer relation and multilayer neural network as PLS inner regression method.

• ETRI Journal
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• 제46권3호
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제3B권3호
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• pp.128-132
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• 2003

This paper presents the optimum design of a Linear Induction Motor (LIM) using Genetic algorithm, Niching Genetic algorithm and Neural Network. The design variables are optimized by different optimization methods and the results are discussed.

• 한국융합학회논문지
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• 제5권4호
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• pp.163-169
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• 2014

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2010년도 춘계 학술논문 발표대회 1부
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• pp.131-133
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• 2010

As linear scheduling method has been used since 1929, Empire State Building linear schedule, it is being applied in various fields such as construction and manufacturing. When addressing concurrent critical path occurring on linear schedule of construction, the empirical researches stress the resource management which should be applied for optimizing work flow, flexible work productivity and continuos resource allocation. However, work relationships has been usually overlooked for making the linear schedule from existing network schedule. Therefore, this research analyze the previous researches related to linear scheduling model and then propose the method that can be applied for adopting the relationships of network schedule to the linear schedule.

• 한국통신학회논문지
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• 제41권6호
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• pp.672-677
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• 2016

인터넷의 발전은 네트워크 기술과 응용의 확장적 배치와 더불어 성공적으로 고무되어 왔다. 하지만, 요즘에는 인터넷의 사용은 심각한 트래픽 과부하를 야기 시킨다. 따라서, 우리는 효율적인 자원 할당을 위해 네트워크 가상화의 지속적인 연구와 발전이 필요하다. 본 논문은 Piecewise Linear 비용함수를 이용한 비용 최소화 가상 네트워크 매핑 알고리즘을 제안 한다. 노드 매핑에는 선형 프로그래밍을 이용한 알고리즘과 D-VINE을 이용하였고, 링크 매핑에는 선형 프로그래밍 솔루션을 기반으로 최단 경로 알고리즘을 이용하였다. 이와 같은 방법으로 네트워크상에서 Linear와 Tree 구조로 형성된 VN request의 도착률에 따른 평균 비용을 ViNEYard와 비교 분석하였다. 시뮬레이션 구현을 통해 우리의 알고리즘이 ViNEYard 을 사용할 때 보다 발생하는 평균 비용이 낮음을 확인할 수 있었다.

• 한국건축시공학회지
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• 제10권4호
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• pp.31-39
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• 2010

선형 공정계획 방법은 1929년 엠파이어 스테이트 빌딩에서 그래픽한 용도로 사용되면서 현재는 다양한 작업 공간, 현장 작업과 조립 작업에 적용되고 있다. 선형 공정계획 상에 동시적인 크리티칼 패스가 발생하면 자원 관리는 작업흐름의 최적화 문제로 연결되어 유연한 작업생산성과 지속적인 자원의 할당을 하기 위해 적용되고 있다. 그러나 선형 공정계획 모델 연구에서 간과하고 있는 선형 공정계획 모델의 작업 관계성을 고려하는 것이 필요하다. 이에 본 연구는 선형 공정계획 모델에 관한 기존 연구를 분석하여 네트워크 공정표의 관계성을 선형 공정표에 적용할 수 있는 방법을 제시한다. 네트워크 공정표를 선형공정표로 변환 시에 발생하는 작업의 관계성을 고찰하고 건축물의 물리적 층수 변화와 같이 작업공간의 변화에 따라 선형 공정표에 반영되어야 할 선형 공정표상의 액티비티의 이동 문제를 고찰하여 네트워크 공정표를 선형 공정표로 호환할 수 있는 시스템 개발을 위한 기초연구를 제공하는 것이 본 연구의 목적이다.

• 전기전자학회논문지
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• 제18권4호
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• pp.517-522
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• 2014

본 논문은 임의의 네트워크 시간 지연이 존재하는 선형 다개체 시스템의 일치문제를 다룬다. 다개체 시스템의 상태일치를 위한 충분조건은 선형행렬방정식을 이용하여 제공된다. 또한, 제안한 충분조건아래에서 임의의 크기를 갖는 네트워크 지연이 존재하는 경우에도 일치에 도달할 수 있음을 증명한다. 마지막으로 제안한 결과의 유효성을 증명하기 위하여 수치 예제를 제공한다.

• 한국경영과학회지
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• 제20권3호
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.