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GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement. ALGORITHMS 2021. [DOI: 10.3390/a14120349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.
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López-Rubio E, Molina-Cabello MA, Luque-Baena RM, Domínguez E. Foreground Detection by Competitive Learning for Varying Input Distributions. Int J Neural Syst 2018; 28:1750056. [DOI: 10.1142/s0129065717500563] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.
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Affiliation(s)
- Ezequiel López-Rubio
- Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain
| | - Miguel A. Molina-Cabello
- Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain
| | - Rafael Marcos Luque-Baena
- Department of Computer Systems and Telematics Engineering, University of Extremadura, Calle Sta. Teresa Jornet, 38, 06800 Mérida (Badajoz), Spain
| | - Enrique Domínguez
- Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain
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Abstract
This paper presents a review of research reported on simulated annealing (SA). Different cooling/annealing schedules are summarized. Variants of SA are delineated. Recent applications of SA in engineering are reviewed.
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Affiliation(s)
- Nazmul Siddique
- School of Computing and Intelligent Systems, Ulster University, Northland Road, Londonderry, BT48 7JL, United Kingdom
| | - Hojjat Adeli
- College of Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210 USA
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Peng H, Wang J, Shi P, Pérez-Jiménez MJ, Riscos-Núñez A. An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems. Int J Neural Syst 2016; 26:1650004. [DOI: 10.1142/s0129065716500040] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object’s evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.
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Affiliation(s)
- Hong Peng
- Center for Radio Administration and Technology Development, School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical and Electronic Information Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Peng Shi
- College of Automation, Harbin Engineering University, Harbin 150001, P. R. China
- College of Engineering and Science, Victoria University, Melbourne VIC 8001, Australia
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia
| | - Mario J. Pérez-Jiménez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
| | - Agustín Riscos-Núñez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
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Abstract
In a recent article the authors reviewed the principles of harmony search and journal articles on harmony search algorithm (HSA). This article presents a review of applications of HSA including structural design, hydrologic model design, water distribution network design, water pump switching problem, transmission network expansion planning problem, job shop scheduling problem, university timetable and rosterering problem, training neural networks, clustering, combined heat and power economic dispatch problem, economic load dispatch problem, and economic and emission dispatch problem.
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Affiliation(s)
- Nazmul Siddique
- School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL, UK
| | - Hojjat Adeli
- College of Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA
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Abstract
Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.
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Affiliation(s)
- Nazmul Siddique
- School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL, UK
| | - Hojjat Adeli
- College of Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA
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