Shapes classification of dust deposition using fuzzy kernel-based approaches

被引：24
|
作者：
Proietti, Andrea [1 ]
Liparulo, Luca [1 ]
Leccese, Fabio [2 ]
Panella, Massimo [1 ]
机构：
[1] Univ Roma La Sapienza, Dept Informat Engn Elect & Telecommun, I-00184 Rome, Italy
[2] Univ Rome Tre, Dept Sci, I-00146 Rome, Italy
来源：
MEASUREMENT | 2016年 / 77卷
关键词：
Dust; Shape analysis; Classification; Fuzzy; Membership functions; ALGORITHM; IDENTIFICATION; SYSTEMS;
D O I：
10.1016/j.measurement.2015.09.025
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Dust deposition and pollution are relevant issues in indoor environments, especially concerning human health and conservation of things and works. In this framework, several tools have been proposed in the last years in order to analyze dust deposition and extract useful information for addressing the phenomenon. In this paper, a novel approach for dust analysis and classification is proposed, employing machine learning and fuzzy logic to set up a simple and actual tool. The proposed approach is tested and compared with other already introduced similar techniques, in order to evaluate its performance. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：344 / 350
页数：7
相关论文
共 50 条
  • [21] Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures
    Liang, Pei
    Lei, Dingfei
    Chin, KwaiSang
    Hu, Junhua
    KNOWLEDGE-BASED SYSTEMS, 2022, 255
  • [22] Active Tracking Using Intelligent Fuzzy Controller And Kernel-Based Algorithm
    Shirzi, Moteaal Asadi
    Hairi-Yazdi, M. R.
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011), 2011, : 1157 - 1163
  • [23] Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures
    Liang, Pei
    Lei, Dingfei
    Chin, KwaiSang
    Hu, Junhua
    Knowledge-Based Systems, 2022, 255
  • [24] Kernel-Based Fuzzy Clustering of Interval Data
    Pimentel, Bruno A.
    da Costa, Anderson F. B. F.
    de Souza, Renata M. C. R.
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011), 2011, : 497 - 501
  • [25] TSK Fuzzy Model Using Kernel-Based Fuzzy C-Means Clustering
    Cai, Qianfeng
    Liu, Wei
    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, 2009, : 308 - 312
  • [26] Kernel-Based Object Tracking Using a Simple Fuzzy Color Histogram
    Villalba Espinosa, Juan
    Gonzalez Linares, Jose Maria
    Ramos Cozar, Julian
    Guil Mata, Nicolas
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2011, PT I, 2011, 6691 : 513 - 519
  • [27] Kernel-based fuzzy competitive learning clustering
    Mizutani, K
    Miyamoto, S
    FUZZ-IEEE 2005: Proceedings of the IEEE International Conference on Fuzzy Systems: BIGGEST LITTLE CONFERENCE IN THE WORLD, 2005, : 636 - 639
  • [28] Efficient Kernel-Based Fuzzy C-Means Clustering For Pest Detection and Classification
    Vinushree, N.
    Hemalatha, B.
    Kaliappan, Vishnu Kumar
    2014 WORLD CONGRESS ON COMPUTING AND COMMUNICATION TECHNOLOGIES (WCCCT 2014), 2014, : 179 - 181
  • [29] Kernel-based methods for hyperspectral image classification
    Camps-Valls, G
    Bruzzone, L
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (06): : 1351 - 1362
  • [30] Classification Using Mass Spectrometry Proteomic Data with Kernel-Based Algorithms
    Liu, Zhenqiu
    Lin, Shili
    ENGINEERING LETTERS, 2006, 13 (03)
12345