• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.55-61
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• 1997

This paper proposes a MSHMM(Multi-Section Hidden Markov Model) recognition method based on Fuzzy Concept, as a method on the speech recognition of speaker-independent. In this recognition method, training data are divided into several section and multi-observation sequences given proper probabilities by fuzzy rule according to order of short distance from MSVQ codebook per each section are obtained. Thereafter, the HMM per each section using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. In this paper, other experiments to compare with the results of these experiments are implemented by the various conventional recognition methods(DP, MSVQ, DMS, general HMM) under the same data. Through results of all-round experiment, it is proved that the proposed MSHMM based on fuzzy concept is superior to DP method, MSVQ method, DMS model and general HMM model in recognition rate and computational time, and does not decreases recognition rate as 92.91% in spite of increment of speaker number.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.61-68
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• 1998

It has become important to evaluate the qualitive reliability and condition assessment of existing structural systems in order to establish a rational program for repair and maintenance. Since most of if existing structural system may suffer from defect corrosion and damage, it is necessary to account for their effects in fuzzy reliability analysis, In this paper, an attempt is made to develope a reliability analysis for damaged structural systems using failure possibility theory. Damage state is specified in terms of linguistic valiables using natural language information and numerical information, which are defined by fuzzy sets. Using a subjective condition index of failure possibility and information of the damage state is introduced into the calculation of failure probability. The subjective condition index of quantitative and qualitative analysis method is newly proposed based on the fuzzy set operations, namely logical product, drastic product, logical sum and drastic sum

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.68-74
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• 1991

This paper proposes a HMM model based on fuzzy, as a method on the speech recognition of speaker-independent. In this recognition method, multi-observation sequences which give proper probabilities by fuzzy rule according to order of short distance from VQ codebook are obtained. Thereafter, the HMM model using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. The vocabularies for recognition experiment are 146 DDD are names, and the feature parameter is 10S0thT LPC cepstrum coefficients. Besides the speech recognition experiments of proposed model, for comparison with it, we perform the experiments by DP, MSVQ and general HMM under same condition and data. Through the experiment results, it is proved that HMM model using fuzzy proposed in this paper is superior to DP method, MSVQ and general HMM model in recognition rate and computational time.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2653-2655
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• 2001

In this paper, we proposed a method of modeling a system using Fuzzy Equalization(FE) and Genetic Algorithm(GA). The initial model is constructed using FE. The antecedent parameters and the rules in fuzzy logic are tuned by GA. The proposed system minimizes the modeling error and the size of structure. The process of building membership functions using PDF(Probability Density Function) and GA tunes the antecedent parameter and rules for minimizing the error and structure. The usefulness of proposed method is demonstrated by applying to Box-Jenkins furnace data.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.199-207
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• 2023

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise- free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.128-133
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• 1998

This paper aims to present some ideas for implementation of Uncertain Relational Databases (URD) which are extensions of classical relational databases. Our system firstly is based on possibility distribution and probability theory to represent and manipulate fuzzy and probabilistic information, secondly adopts flexible mechanisms that allow the management of uncertain data through the resources provided by both available relational database management systems and front-end interfaces, and lastly chooses dynamic SQL to enhance versatility and adjustability of systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.1-3
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• 2009

Recently, Carlsson, Full\acute{e}$r and Majlender [1] presented the concept of possibilitic correlation representing an average degree of interaction between marginal distribution of a joint possibility distribution as compared to their respective dispersions. They also formulated the weak and strong forms of the possibilistic Cauchy-Schwarz inequality. In this paper, we define a new probability measure. Then the weak and strong forms of the Cauchy-Schwarz inequality are immediate consequence of probabilistic Cauchy-Schwarz inequality with respect to the new probability measure.

• Journal for History of Mathematics
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• v.25 no.1
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• pp.1-13
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• 2012

Possibility theory is a kind of mathematics of uncertainty for handling incomplete information. In this paper, we discuss vagueness and randomness as some causes of uncertainty and we introduce the possibility theory as a way of dealing with uncertainty, comparing it with the probability theory.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.9-15
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• 2009

This paper proposes a cancellation algorithm of impulse noise using a rank-order method. The proposed method is a fast and simple algorithm that is composed of two parts. The first part involves noise detection using a fuzzy technique, where an image is divided into RGB color channels. Then every pixel in each color channel is investigated and assigned a probability indicating its chances of being a noise pixel. At this time, the rank order method using a noise-detection mask is utilized for accurate noise detection. Thereafter, the second part involves noise-cancellation, where each noise-pixel value in an image is replaced in proportion to its fuzzy probability. Through the experiments, both the conventional and proposed methods were simulated and compared. As a result, it is shown that proposed method is able to detect noisy pixels more accurately, and produce resulting images with high PSNR values.