• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1869_1870
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• 2009

본 논문에서는 다항식 기반 Type-2 Fuzzy Neural Networks(T2FNN)를 설계하고 이를 패턴분류 문제에 적용하여 그 성능을 분석한다. T2FNN은 Fuzzy C-Means(FCM)을 Type-2 Fuzzy C-Means로 확장시킨 것이라 할 수 있으며, Input layer, Fuzzyification layer, Inference layer, Deffuzification layer의 4층 네트워크로 구성된다. interval Type-1 퍼지 집합인 후반부의 연결가중치는 Gradient Descent Method를 이용하여 학습한다. 제안된 RBF 신경회로망은 모의데이터와 패턴인식 성능 평가에 많이 사용되는 machine learning 데이터에 적용하여 패턴 분류기로서의 성능을 평가받는다.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.167-168
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• 2007

본 논문에서는 얼굴, 지문 등의 생체특징을 안전하게 은닉하고 효과적으로 은닉정보를 추출할 수 있는 웨이블렛 기반 워터마킹 기법을 제안한다. 제안된 방법은 웨이블렛을 이용하여 워터마크 삽입위치를 결정하고 웨이블렛 변환된 영상과 배경영상간의 차와 삽입위치 주변의 영상에 분산값을 이용해 퍼지 함수를 이용하여 적응적 가중치 값을 결정한다. 은닉된 워터마크 데이터는 워터마크가 삽입된 영상에 웨이블렛 변환을 적용하여 효과적으로 생체특징을 추출한다. 제안된 방법의 타당성을 검증하기 위하여 워터마크 데이터인 생체특징의 은닉 전과 후의 특성분석과 워터마크 알고리즘이 생체 인식시스템에 미치는 영향을 평가하였다. 실험한 결과 제안된 방법은 효과적으로 생체정보를 은닉하고 생체인식률의 저하 없이 효과적으로 생체정보를 보호할 수 있음을 확인 할 수 있었다.

• Journal of Global Scholars of Marketing Science
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• v.4
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• pp.173-196
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• 1999

현대의 급변하는 환경 하에서 다양한 고객의 욕구를 총족시켜야 하는 기업으로서는 기업성장에 관련된 전략을 끊임없이 수립하여야 한다. 특히 기존 시장 및 새로운 시장에 대해 지속적으로 소비자의 욕구 및 기업의 목적 등에 어느 정도 기여하는 가를 반드시 평가하여야 한다. 효과적인 시장의 선정은 기업의 목표를 달성하는 데 필요한 시장을 유지하게 할 뿐 아니라 기업의 가용자원을 사업과정에서 할당할 수 있게 해준다. 본 연구에서는 기업의 시장 평가와 선정을 위한 모형을 구축하였다. 기존 시장선정모형에서는 시장 성장률, 판매 수익, 현금 흐름 등과 같은 속성들의 단일 목표에 대한 최적해를 구하고자 하였다. 그러나 기업의 의사결정과정은 여러 상충하는 목적들을 동시에 고려하는 경우가 대부분이기 때문에 이러한 상황에 적합한 다목표 지향적인 수리모형 구축의 필요성이 제시되었다. 또한 제공되는 데이터의 불명확성과 여러 목적들을 동시에 고려할 경우 발생할 수 있는 의사결정자의 열망수준과 그 만족정도를 반영하기 위해 본 연구에서는 퍼지집합을 적용한 3 유형의 다목적계획모형을 제시하였다. 최소연산자 모형, 가중치 다목적계획 모형 및 선제우선순위 다목적계획모형의 구축 후, 설례를 통해 그 적용가능성을 알아보았다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.377-380
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• 2012

본 연구에서는 학습데이터의 빈도요소를 반영하도록 수정된 구조의 FMM 신경망을 소개하고, 이로부터 패턴 분류를 위한 지식 표현을 생성하는 방법론을 제안한다. 하이퍼박스 멤버쉽함수는 5종류의 퍼지 분할을 기반으로 설정한 구간에 대하여 소속정도를 반영하여 결정하며, 각 차원별로 특징범위의 폭과 빈도 요소로부터 가중치 값이 학습된다. 본 연구에서는 제안된 이론을 수화인식 문제를 대상으로 고찰하였다. 인식 시스템의 구성은 특징추출을 위하여 3차원으로 확장된 구조의 CNN 모델을 사용하였으며, 수화패턴 데이터의 표현은 모션 히스토리 볼륨(Motion History Volume) 구조를 기반으로 하였다. 6종류의 수화패턴 동영상으로부터 27개 특징요소를 추출하고 이를 사용한 FMM 신경망의 학습과정과 지식의 추출 과정을 실험으로 보이고 그 유용성을 고찰한다.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.47-67
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• 2014

This study examined elements which could evaluate a container terminal logistics system from the viewpoint of supply chain management. This study derived the elements of a container terminal logistics system such as flexibility, reliability, responsiveness, and information sharing and 16 evaluation sub-items in the aspect of a supply chain. In the result of analysis, the weight between SCM elements of a container terminal logistics system was the highest in reliability(0.282), followed by flexibility(0.273), responsiveness(0.224), and information sharing(0.221). The conversion weight was calculated by combining the weight of elements of a container terminal logistics system and the weight of evaluation sub-items. The highest weight which was considered as the most important factor to evaluate a container terminal logistics system was work planning(berth, yard) of flexibility(0.081), followed by accurate fulfillment of container work schedule(ship, yard) and the optimum distribution and arrangement of equipment(QC, TC, YT)(0.079), stable works without damage of containers and ships(0.071), and preventive maintenance of equipment and operators' skill(0.070).

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.307-316
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• 2016

A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.100-109
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• 2019

Maintainability indicates the extent to which maintenance can be done easily and quickly. The consideration of maintainability is crucial to reduce the operation and support costs of weapon systems, but if the maintainability is evaluated after the prototype production is done and necessitates design changes, it may increase the cost and delay the schedule. The evaluation should verify whether maintenance work can be performed, and support the designers in developing a design to improve maintainability. In previous studies, the maintainability index was calculated using the graph theory at the early design phase, but evaluation accuracy appeared to be limited. Analyzing the methods of evaluating the maintainability using fuzzy logic and 3D modeling indicate that the design of a system with good maintainability should be done in an integrated manner during the whole system life cycle. This paper proposes a method to evaluate maintainability using SysML-based modeling and simulation technique and fuzzy logic. The physical design structure with maintainability attributes was modeled using SysML 'bdd' diagram, and the maintainability was represented by an AHP matrix for maintainability attributes. We then calculated the maintainability using AHP-based weighting calculation and fuzzy logic through the use of SysML 'par' diagram that incorporated MATLAB. The proposed maintainability model can be managed efficiently and consistently, and the state of system design and maintainability can be analyzed quantitatively, thereby improving design by early identifying the items with low maintainability.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.897-907
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• 2013

Climate change is expected to worsen the depletion of streamflow in urban watershed. In this study, we therefore considered the treated wastewater (TWW) use as an adaptation strategy and devised a framework to identify prioritized areas for TWW use. An integrated framework that includes hydrological factors as well as social and environmental components were employed to determine the criteria for decision making. Fuzzy theory was employed to consider the uncertainties in the climate change scenarios and the weights of the performance value. All alternatives were evaluated using the fuzzy TOPSIS method. In addition, statistical method and decision making methods under complete uncertainty were used for robust decision making. As a result, ranking the alternatives using the fuzzy TOPSIS method and robust approach such as maximin, maximax, Hurwicz and equal likelihood criterion mitigated the level of uncertainty and ambiguity in each alternative. The finding of this study can be helpful in prioritizing water resource management projects considering various climate change scenarios.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.637-644
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• 2011

Being digitalized, the music can be easily purchased and delivered to the users. However, there is still some difficulty to find the music which fits to someone's taste using traditional music information search based on musician, genre, tittle, album title and so on. In order to reduce the difficulty, the contents-based or the emotion-based music retrieval has been proposed and developed. In this paper, we propose new method to determine the importance of MPEG-7 low-level audio descriptors which are multi-dimensional vectors for the emotion-based music retrieval. We measured the mutual similarities of musics which represent a pair of emotions expressed by opposite meaning in terms of each multi-dimensional descriptor. Then rough approximation, and inter- and intra similarity ratio from the similarity relation are used for determining the importance of a descriptor, respectively. The set of weights based on the importance decides the aggregated similarity measure, by which emotion-based music retrieval can be achieved. The proposed method shows better result than previous method in terms of the average number of satisfactory musics in the experiment emotion-based retrieval based on content-based search.