• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
• /
• pp.28-31
• /
• 1996

A bag is a set-like entity which can contain repeated elements. Fuzzy bags have been studied by Yager, who defined their basic relations and operations. However, his definitions of the basic relations and operations are inconsistent with the corresponding relations and operations for ordinary fuzzy sets. The present paper presents new basic relations and operations of fuzzy bags using a grade sequence for each element of the universal set. Moreover the .alpha.-cut, t-norms, the extension principle, and the composition of fuzzy bag relations are described.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제14권4호
• /
• pp.240-248
• /
• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• 한국지능시스템학회논문지
• /
• 제17권1호
• /
• pp.143-147
• /
• 2007

With a new ordinary norm as an analogy of Krishna and Sarma[5] and Bag and Samanta[1], we will characterize the notions of the convergence of the sequences of fuzzy points, the fuzzy, ${\alpha}$-Cauchy sequence and fuzzy completeness.

• 대한기계학회논문집A
• /
• 제20권1호
• /
• pp.24-34
• /
• 1996

In ordaer to hold an underwater vehicle at a certain depth, buoyancy that acts on the underwater vehicle can be modulated. In this research, buoyancy that could control depth of underwater vehicle is generated by a buoyancy bag. Solenoid valves are operated by pulse with modulation(PWM) method. State equation, in consideration of the volume of buoyancy bag, pressure inside bag, and dynamic of the underwater vehicle, is derived. This system is very unstable, inculdes modelling error and nonlinearity. In depth control system, maintanance of performance is required., anainst vatiation of systerm parameter and operating depth, and designed. Through the computer simulation, performance is comparerd for each controllers.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제15권4호
• /
• pp.217-231
• /
• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• Journal of information and communication convergence engineering
• /
• 제11권3호
• /
• pp.179-184
• /
• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제16권2호
• /
• pp.104-110
• /
• 2016

Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.

• 대한전자공학회논문지TC
• /
• 제43권9호
• /
• pp.135-141
• /
• 2006

교통사고를 예방하기 위해서는 시트조정을 통해서 운전자의 시야를 확보하고, 운전자가 뒤에 오는 자동차를 알아 볼 수 있도록 룸미러의 위치를 조정하는 것이 매우 중요하다. 본 논문에서는 이러한 문제점을 해결하기 위해서, 안전하고 편리한 차량을 목표로 운전자가 차량에 앉게 되면, 자동으로 시트를 조정하여 운전자의 시야를 확보 할 수 있도록 한다. 또한 백미러를 자동 조정해서, 운전자가 안전운전에 도움이 될 수 있는 자동차 시트 자동 조정 알고리즘을 개발하였다. 특히, 교통사고 발생해서, 에어백이 작동 할 때에 본 논문에서는,승객의 몸무게에 따라서 충격완화를 위한 시트 자동조정 알고리즘을 기능을 추가하였다. 뿐만 아니라, 본 논문에서는, 교통사고 발생 시 운전자가 위험지역을 통과할 때 위험지역임을 운전자에게 통지하여 안전한 운전이 되도록 하는 알고리즘을 개발하였으며, 유비쿼터스 환경에서 모의실험을 하였다. 모의실험 결과 지능을 이용한 교통사고 분석 방식이기존의 방식보다 25% 이상 교통사고를 줄일 수 있음을 확인하였다