퍼지 클러스터링 기반 신경회로망과 SVM 패턴 분류기 설계에 관한 연구 : 검은색 폐플라스틱 분류를 중심으로 = A study on design of fuzzy clustering-based neural networks and support vector machine pattern classifier : focused on black plastics sorting

다국어 초록 (Multilingual Abstract)

Lately, the amount of waste plastics including black plastics is getting more and more increasing. According as lots of plastics are widely used in various industrial fields. Under these circumstances, necessity for recycling of limited useful resources is getting more and more important gradually and research related to plastic sorting system is being largely required for plastic recycling. Plastic sorting system constructed currently by Near Infrared Ray(NIR) is being exploited to classify colored plastics besides black plastic. However, the classification of black plastics still remains a challenging issue, because of the absorption of infrared rays of NIR spectrometer for black plastics. Design methodology to identify black plastics in introduced. ATR FT-IR, Raman, and LIBS spectroscopies are used to carry out qualitative as well as quantitative analysis and also comparative studies for black plastics. For ATR FT-IR spectrometer, the spectra data of black plastics can be measured through the contact of interval gap between the spectrometer and plastic. Its measurement speed is faster compared to NIR spectrometer. ATR FT-IR spectrometer which is the contact type of interval gap, has difficulty in the on-line application. As the contactless type of interval gap, Raman spectrometer can measure the samples quickly, but its ensuing effect leads to the difficulty of data extraction due to lots of noises as well as the difficulty of application to on-line system. Therefore, LIBS spectrometer which is the contactless type, is used to effectively extract spectra data being applied in the on-line system. But, whenever the spectra data are measured in the same sample through spectrometer, the position of peak points of the characteristic spectra data are partially changed or shifted. Design methodology which takes into consideration for the changed or shifted spectra data are introduce in this study. The design method of determining input variables corresponding to data peak points based on the chemical characteristic lead to more reasonable and effective technique for improving the performance of FRBFNN and SVM classifiers. Moreover, in order to improve the identification performance, intelligent computing algorithms such as Principal Component Analysis(PCA), Fuzzy Transform(FT), Fuzzy Radial Basis Function Neural Networks(FRBFNN), Support vector machine classifiers(SVM) and Particle Swarm Optimization(PSO) are considered to analyze and classify some types of black plastics. In the preprocessing step for classifying some black plastics, the characteristic peak points are extracted and region corresponding to each characteristic peak point is taken into consideration. Here, as the preprocessing techniques, PCA and Fuzzy Transform algorithms are used for the dimension reduction of data. And FRBFNN and SVM are exploited as intelligent classifiers. FRBFNN classifier is considered as the powerful tool with the synthesis technologies of fuzzy theory and neural networks for the identification of black plastics. SVM classifier is used for comparative studies with FRBFNN classifier. In conclusion, the design methodology related to preprocessing techniques based FRBFNN classifier is demonstrated as competitive and preferred network architecture, as well as superb performance.

목차 (Table of Contents)

• Ⅰ. 서 론 1
• 1. 연구 배경 및 필요성 1
• 2. 연구 내용 및 절차 2
• Ⅱ. 검은색 플라스틱 분류를 위한 데이터 전처리 7
• 1. 분광장비의 특성 및 구조 7
• 1) ATR FT-IR 분광장비의 특성 및 구조 8
• 2) Raman 분광장비의 특성 및 구조 10
• 3) LIBS 분광장비의 특성 및 구조 12
• 2. 분광기법을 이용한 데이터의 전처리과정 13
• 1) 분광장비를 통해 획득된 데이터의 분석 14
• 2) 화학적 특징 Peak점 선정 및 영역 추출에 대한 방법 21
• 3. 전처리 알고리즘을 이용한 데이터 축소 및 특징 추출 33
• 1) PCA를 이용한 데이터 축소 및 특징 추출 33
• 2) Fuzzy Transform을 이용한 데이터 축소 및 특징 추출 35
• Ⅲ. 재질분류를 위한 분류기의 구조 및 설계 37
• 1. 제안하는 FRBFNN의 구조 37
• 2. Support Vector Machine의 구조 42
• 3. Particle Swarm Optimization을 이용한 파라미터 최적화 44
• Ⅳ. 실험 연구 및 결과 고찰 45
• 1. 실험의 전체 개요 45
• 2. 플라스틱 재질별 분류 실험 결과 및 고찰 50
• 1) 생활계 폐플라스틱 데이터를 이용한 분류기의 결과 고찰 50
• 2) 폐소형 가전 폐플라스틱 데이터를 이용한 분류기의 결과 고찰 80
• Ⅴ. 결 론 88
• 참 고 문 헌 90
• ABSTRACT 94