• 한국구조물진단유지관리공학회 논문집
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• 제7권3호
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• pp.251-262
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• 2003

교량의 건설계획단계에서 LCC을 고려한 의사결정이나 공용중인 교량의 체계적인 유지관리 전략을 수립하기 위해서도 최소한의 점검결과만으로 노후화를 예측할 수 있는 LCP가 필요하다. 본 연구에서는 국내외 연구결과를 토대로 무리함수 $y=\sqrt{y^2_0-at}$로 표현되는 LCP를 제안하여 국내외연구결과에서 적용한 D/B에 적용한 결과 상관계수가 0.99이상으로 노후화 경향을 표현할 수 있었으며, 전국에 분포되어 있는 슬래브교량을 대상으로 정밀점검 및 정밀안전진단의 BMS를 Fuzzy Logic을 이용하여 정량적 평가하여 회귀분석을 실시한 결과 0.81의 상관계수를 갖는 노후화 예측모델을 도출할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.231-239
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• 2014

It presents the concepts of ordinary smooth interior and ordinary smooth closure of an ordinary subset and their structural properties. It also introduces the notion of ordinary smooth (open) preserving mapping and addresses some their properties. In addition, it develops the notions of ordinary smooth compactness, ordinary smooth almost compactness, and ordinary near compactness and discusses them in the general framework of ordinary smooth topological spaces.

• 전자공학회논문지B
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• 제32B권11호
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• Journal of applied mathematics & informatics
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• 제39권5_6호
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• pp.769-783
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• 2021

Demirci ((1999) Vague groups. J. Math. Anal. Appl. 230, 142-156) introduced the concept of vague groups as one of uncertain reasoning structures where indistinguishable operators separate points. In this paper, we consider vague groups in which an indistinguishable operator does not need to separate points because it seems more appropriate to handle ambiguous situations. For our purposes we generalize or redefine some notions such as: vague closed subset, vague subgroup, vague kernel and vague injectiveness. Consequently we generalize most of the known results and obtain some new additional fundamental properties of vague groups, some of which are similar to ones of ordinary groups.

• Annals of Fuzzy Mathematics and Informatics
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• 제16권3호
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• pp.333-336
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• 2018

One of the well known results in general topology says that every compact subset of a Hausdorff space is closed. This result in soft topology is not true in general as demonstrated throughout this note. We begin this investigation by showing that [Theorem 3.34, p.p.23] which proposed by Varol and $Ayg{\ddot{u}}n$ [7] is invalid in general, by giving a counterexample. Then we derive under what condition this result can be generalized in soft topology. Finally, we evidence that [Example 3.22, p.p. 20] which introduced in [7] is false, and we make a correction for this example to satisfy a condition of soft Hausdorffness.

• 지능정보연구
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• 제20권4호
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• pp.43-58
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• 2014

최근 정보기술의 발전으로 복잡하고 방대한 양의 주가 데이터에 대한 실시간 분석이 가능해지면서 인공지능 기법을 활용해 주식 시장의 등락을 예측하고, 이를 기반으로 매매 거래를 수행하는 트레이딩 시스템에 대한 세간의 관심이 높아지고 있다. 본 연구는 이러한 트레이딩 시스템의 시장 예측 알고리즘으로 활용될 수 있는 새로운 주식 시장 등락 예측 모형을 제시한다. 본 연구의 제안 모형은 ${\pi}$-퍼지 논리를 이용해 모든 입력변수의 차원을 low, medium, high로 퍼지변환한 입력값을 대상으로 Support Vector Machine(SVM)을 적용하여 익일 시장의 등락을 예측하도록 설계되었다. 그런데 이 경우 입력변수의 수가 3배로 늘어나기 때문에, 적절한 입력변수의 선택이 요구된다. 이에 본 연구에서는 유전자 알고리즘을 활용하여 입력변수 선택 집합을 최적화하도록 하였으며, 동시에 ${\pi}$-퍼지 논리 및 SVM에 적용되는 조절 파라미터들의 값도 함께 최적화 하도록 하였다. 모형의 성능을 검증하기 위해, 본 연구에서는 지난 2004년부터 2013년까지의 10년치 국내 주식시장 데이터를 기반으로 한 KOSPI 200 지수의 등락 예측에 제안모형을 적용해 보았다. 이 때, 비교모형으로 로지스틱 회귀모형, 다중판별분석, 의사결정나무, 인공신경망, SVM, 퍼지SVM 등도 함께 적용시켜 성과를 정밀하게 검증해 보고자 하였다. 그 결과, 제안모형이 예측 정확도는 물론 투자수익률(Return on Investment) 측면에서도 다른 모든 비교모형들에 비해 월등히 우수한 성능을 보임을 확인할 수 있었다.

• 전기학회논문지
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• 제59권2호
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Advances in aircraft and spacecraft science
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• 제10권6호
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• pp.495-519
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• 2023

The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.