• 응용통계연구
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• 제27권5호
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• pp.693-703
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• 2014

실험영역을 벗어나는 다중 외삽점들에 관한 실험설계를 기획하는 경우 실험자는 종종 어느 외삽점에 더 많은 노력을 집중하여야 하는지 주어진 모형이 있다하더라도, 고민하는 경우가 있다. 본 연구에서는 이러한 상황에 관한 실험설계 문제를 다루었다. 첫 번째는 주어진 모형이 실험영역을 벗어나더라도 모형이 타당한 경우 다중 외삽점에 관한 실험설계고 다른 하나는 그렇지 않은 경우이다. 첫 번째인 경우는 비교적 기존 문헌에서 알려진 방법들이 적용될 수 있으나 그렇지 않은 경우 즉, 모형의 타당성이 의심되는 경우는 다른 실험설계기준을 제시하여야 한다, 본 연구는 이와 관련 다양한 하이브리드 방법을 제시하여 다중 외삽점에서의 문제가 어떻게 모형 불확실성하에서 전개되어야 하는지 다루어 보았다, 이를 위해 서치알고리즘의 하나인 유전알고리즘을 적용하였다. 왜냐하면 전통적인 교환알고리즘의 복잡성보다는 유전알고리즘의 효율성이 더 뛰어났다고 보기 때문이다.

• ETRI Journal
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• 제37권4호
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• pp.717-726
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• 2015

In this paper, we study an asymmetric two-way relaying network where two source nodes intend to exchange information with the help of multiple relay nodes. A hybrid time-division broadcast relaying scheme with joint relay selection (RS) and power allocation (PA) is proposed to realize energy-efficient transmission. Our scheme is based on the asymmetric level of the two source nodes' target signal-to-noise ratio indexes to minimize the total power consumed by the relay nodes. An optimization model with joint RS and PA is studied here to guarantee hybrid relaying transmissions. Next, with the aid of our proposed intelligent optimization algorithm, which combines a genetic algorithm and a simulated annealing algorithm, the formulated optimization model can be effectively solved. Theoretical analyses and numerical results verify that our proposed hybrid relaying scheme can substantially reduce the total power consumption of relays under a traffic asymmetric scenario; meanwhile, the proposed intelligent optimization algorithm can eventually converge to a better solution.

• 전기학회논문지
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• 제56권2호
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• pp.396-406
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• 2007

In this paper, we proposed development of improved model reduction and design of hierarchical controller using reduction model. The model reduction is considered that it is the transient response and the steady-state response through the use of nyquist curve. The hierarchical controller selected tuning of PID controller to ensure specified gain and phase margin and hybrid smith-predictor fuzzy controller using reduction model. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• 제어로봇시스템학회논문지
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• 제9권2호
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• pp.159-167
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• 2003

This study suggests the hybrid fault diagnosis method of signed digraph (SDG) and partial least squares (PLS). SDG offers a simple and graphical representation for the causal relationships between process variables. The proposed method is based on SDG to utilize the advantage that the model building needs less information than other methods and can be performed automatically. PLS model is built on local cause-effect relationships of each variable in SDG. In addition to the current values of cause variables, the past values of cause and effect variables are inputted to PLS model to represent the Process armies. The measured value and predicted one by dynamic PLS are compared to diagnose the fault. The diagnosis example of CSTR shows the proposed method improves diagnosis resolution and facilitates diagnosis of masked multiple-fault.

• 한국공작기계학회논문집
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• 제18권1호
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• 한국정보통신학회논문지
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• 제18권1호
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• pp.57-62
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• 2014

다수의 중계기가 균등하게 분포된 무선 소거 네트워크의 용량 스케일링 법칙을 분석함으로써 인프라 구조 사용시 이득을 보인다. 가정하는 네트워크 하에서 소거 확률을 적절히 모델링함에 근거하여, 혼합 소거 네트워크에서 취득 가능한 네트워크 용량을 보인다. 보다 구체적으로, 지수 감쇠 모델 및 다항 감쇠 모델 이렇게 두 가지 물리적 모델을 사용한다. 중계기 도움이 없는 다중 홉 전송, 중계기 도움을 받는 다중 홉 전송 이렇게 두 가지 존재하는 기술을 사용하여 취득 용량을 분석한다. 유도된 용량 스케일링 법칙은 두 가지 물리적 모델 모두에 대해 노드 수 및 중계기의 수에 의존함을 확인한다.

• 에너지공학
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• 제26권2호
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• pp.99-120
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• 2017

한국지역난방공사 SS지사에서는 시설용량 전기 99MW, 열 98Gcal/h 규모의 열병합(Combined Heat & Power) 발전소를 구역전기사업으로 운영하고 있다. 이 지역은 경기불황과 수요감소로 하절기 6~9월 사이에 잉여열 처리문제가 발생하여 발전기를 가동하기 곤란한 상황이므로 경제성 있는 에너지 신사업모델 개발이 절실하다. 본 연구에서는 이곳의 실제 운영자료를 기반으로 신재생 에너지 하이브리드 시스템을 도입하여 최적화 운영모델을 개발하고자 한다. 특히 신재생에너지 중에서도 입지제약이 작고 열과 전기를 동시에 생산할 수 있는 연료전지(Fuel Cell)발전과 대표적인 신재생에너지인 태양광(Photovoltaic)발전과 심야발전시 전력을 저장하여 주간에 전력을 방출 할 수 있는 ESS(Energy Storage System)의 조합을 검토하였다. 이에 따른 최적화 모델 선정은 HOMER(Hybrid Optimization of Multiple Energy Resources) 프로그램을 활용하였다. 경제성 분석을 수행한 결과, 순 현재비용(NPC) 측면에서는 기존의 99MW 열병합발전이 가장 경제적이지만 신재생에너지를 사용하여 발생되는 탄소배출권 거래와 REC(Renewable Energy Certificate) 거래를 포함한 측면에서는 99MW의 CHP와 5MW의 연료전지, 521kW의 태양광을 하이브리드 시켜서 전력과 열을 공급하는 것이 99MW의 CHP 열병합발전만으로 전력과 열을 공급하는 것보다 최대 2,475억원 경제적인 것으로 나타났다. 구역전기사업에서 최적화 공정모델로 연료전지와 신재생에너지 하이브리드 시스템을 도입함으로써 경제성을 개선시킬 수 있는 결과를 확인하였다.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.110-115
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• 2004

The hybrid fault diagnosis method based on a combination of the signed digraph (SDG) and the partial least-squares (PLS) has the advantage of improving the diagnosis resolution, accuracy and reliability, compared to those of previous qualitative methods, and of enhancing the ability to diagnose multiple fault. In this study, the method is applied for the multiple fault diagnosis of the Tennessee Eastman challenge process, which is a realistic industrial process for evaluating process contol and monitoring methods. The process is decomposed using the local qualitative relationships of each measured variable. Dynamic PLS (DPLS) model is built to estimate each measured variable, which is then compared with the estimated value in order to diagnose the fault. Through case studies of 15 single faults and 44 double faults, the proposed method demonstrated a good diagnosis capability compared with previous statistical methods.

• Computers and Concrete
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• 제22권4호
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.