• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1849-1850
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• 2008

Type-2 퍼지 집합은 Type-1 퍼지 집합의 확장으로 Type-1 퍼지 집합으로는 다루기 힘든 언어적인 불확실성을 다루기 위해 고안되었다. 대표적인 퍼지 논리 시스템(Fuzzy Logic System; FLS)으론 Mamdani FLS 모델과 TSK FLS모델이 있다. 본 논문에서는 Interval Type-2 TSK FLS를 구성한다. FLS 구성을 위한 전반부는 가우시안 형태의 Type-2 멤버쉽 함수를 사용하며, 전.후반부 파라미터들은 오류역전파 알고리즘을 통한 학습으로 결정한다. 본 논문에서는 Type-1 TSK FLS와 Interval Type-2 TSK FLS를 설계하고 가스로 공정 데이터에 적용하여 성능을 비교 분석한다. 또한 노이즈를 추가한 데이터들을 통하여 노이즈에 대한 성능도 비교 분석한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1986-1991
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• 2007

In this paper, we discussed, intelligent soft filter for MR(magnetoresistive) sensor. Most navigation systems today use some type of compass to determine heading direction. Using the earth's magnetic field, electronic compass based on MR(magnetoresistive) sensors can electrically resolve better then 0.1 degree rotation. Intelligent methode for soft building a one degree compass using MR(magnetoresistive) sensors will also be discussed. Compensation techniques are shown to correct for compass tilt angels and nearby ferrous material disturbances. we proved the fuzzy logic that based on the way the ham deals with inexact information is useful for MR sensors.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.251-252
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• 2008

Type-2 퍼지 집합은 Type-1 퍼지 집합에서는 다루기 어려운 언어적인 불확실성을 더욱 효과적으로 다룰 수 있다. TSK 퍼지 로직 시스템(TSK Fuzzy Logic Systems; TSK FLS)은 후반부를 1차 및 2차 함수식으로 나타내며 Mamdani 모델과 함께 가장 널리 사용되는 모델이다. 본 연구의 Interval Type-2 TSK FLS은 전반부에서 Type-2 퍼지 집합을 이용하고 후반부는 계수가 Type-1 퍼지집합인 1차식을 사용한다. 또한 전반부는 가우시안 형태의 Type-2 멤버쉽 함수를 사용하며, 오류역전파 학습알고리즘을 사용하여 파라미터들을 최적화 한다. 또한 학습에 앞서 PSO(Particle Swarm Optimization) 알고리즘을 사용하여 최적 학습률을 찾아 모델의 학습능력을 보다 효율적으로 한다. 본 논문에서는 Type-1과 Type-2 FLS의 성능을 가스로 공정 데이터를 적용하여 두 모델의 성능을 비교하고 노이즈를 추가한 데이터를 이용하여 노이즈에 대한 성능도 비교 분석한다.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.87-95
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• 2015

In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets). We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes. The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity. The emergence and construction of granular models of type-2 and type-n (in general) is discussed. It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.60-65
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• 2004

Induction heating system is time varying system around curie point. So, it has many troubles which are system shut down and change the load impedance. In this paper has been designed the parallel resonant inverter which controlling the constant power and tracking the load resonant frequency with PLL is possible, in order to minimize switching losses and solve it's many troubles. The current full-bridge type parallel resonant inverter of an induction heating system was composed of IGBT in switching device. For regulating the output power of an induction heating system, the Fuzzy logic controller is used. The Fuzzy controller makes the control signal for a stable power regulating control and when reference is changed, it is superior to adaptability. It has been evaluated a stable behavior for a noise with switching and a load disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.220-223
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• 2000

타입-2 퍼지집합을 이용하여 퍼지논리시스템(Fuzzy Logic System : FLS)을 구현하기 위한 연구들이 R. I John, N. Karnik, J. Mendel 등에 의해 현재 진행되고 있다. 타입-2 집합을 이용한 타입-2 FLS은 기존의 타입-1 FLS보다 제어규칙이나 소속함순가 가지고 있는 불확실성을 표현하는데 있어서 더 효과적이다. 그러나, 타입-2 FLS 역시 타입-1 FLS이 가지고 있는 문제점인 설계시 전문가에게 의존하여 시간과 비용이 많이 소요되고, 제어기의 구성요소들을 효율적으로 생성하기가 어렵다는 문제점을 더욱 심각하게 가지고 있다. 또한, 그 문제점을 해결하기 위한 연구들도 아직 미진한 상태이다. 본 논문에서는 타입-2 FLS의 설계를 위해 유전자 알고리즘을 사용하는 방법을 제안한다. 타입-2 FLS를 설계하기 위해서는 소속함수와 제어규칙을 생성하여야 한다. 본 논문에서는 유전자 알고리즘을 사용하여 타입-2 퍼지제어규칙과 소속함수를 설계하는 방법을 제안한다. 먼저, 유전자 알고리즘에서 사용할 수 있는 유전자의 형태로 타입-2 퍼지제어규칙과 소속함수를 표현하기 위한 인코딩방법을 제안하고, 각각의 염색체를 진화시키기 위한 교차 연산자와 돌연변이 연산자를 정의한다. 그리고, 제안된 방법을 함수근사문제에 적용하여 유효성과 성능을 평가, 검증한다.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2265-2267
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• 1998

This paper deals with study of Fast Speaker Adaptation Type Speech Recognition, and to analyze speech signal efficiently in time domain and time-frequency domain, utilizes SCONN[1] with Speech Signal Process suffices for Fast Speaker Adaptation Type Speech Recognition, and examined Speech Recognition to investigate adaptation of system, which has speech data input after speaker dependent recognition test.

• Smart Structures and Systems
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• v.23 no.2
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• pp.155-171
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• 2019

In this study, Semi-active Tuned Mass Dampers (STMDs) are employed in order to cover the prevailing uncertainties and promote the efficiency of the Tuned Mass Dampers (TMDs) to mitigate undesirable structural vibrations. The damping ratio is determined using type-1 and type-2 Fuzzy Logic Controllers (T1 and T2 FLC) based on the response of the structure. In order to increase the efficiency of the FLC, the output membership functions are optimized using genetic algorithm. The results show that the proposed FLC can reduce the sensitivity of STMD to excitation records. The obtained results indicate the best operation for T1 FLC among the other control systems when the uncertainties are neglected. According to the irrefutable uncertainties, three supplies for these uncertainties such as time delay, sensors measurement noises and the differences between real and software model, are investigated. Considering these uncertainties, the efficiencies of T1 FLC, ground-hook velocity-based, displacement-based and TMD reduce significantly. The reduction rates for these algorithms are 12.66%, 26.43%, 20.98% and 21.77%, respectively. However, due to nonlinear behavior and considering a range of uncertainties in membership functions, T2 FLC with 7.2% reduction has robust performance against uncertainties compared to other controlling systems. Therefore, it can be used in actual applications more confidently.