Special Issue "Discrete Algorithms and Discrete Problems in Machine Intelligence"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editor

Guest Editor
Dr. Wanquan Liu

School of Electrical Engineering and Computing Curtin University Australia
Website | E-Mail
Interests: control systems; signal processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Over the last two decades, pattern recognition and data clustering have become two important research tasks in machine learning and many important results have been achieved with broad applications. Traditional approaches for small or medium sized data include many optimization algorithms. Pattern recognition and data clustering based on big data are becoming more complex and advanced optimization techniques are required. Recently, significant progress has been made in developing advanced machine intelligence techniques based on big data, especially due to rapid progress in optimization theory and the deep learning neural networks. To reflect such progress, this Special Issue is organized with the aim of selecting some high-quality papers about new optimization algorithms and deep learning techniques with broad applications in machine intelligence. Potential topics include, but are not limited to, the following:

  • New optimization theory and new design for deep learning neural networks
  • Robust face recognition with sparse sensing techniques
  • Image understanding-based PDE optimization
  • New clustering and classification techniques
  • Randomized algorithms for machine intelligence.

Dr. Wanquan Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine intelligence
  • deep learning networks
  • discrete optimization

Published Papers (3 papers)

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Research

Open AccessArticle The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
Algorithms 2018, 11(9), 129; https://doi.org/10.3390/a11090129
Received: 21 June 2018 / Revised: 15 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
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Abstract
With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the
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With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification. Full article
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
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Open AccessArticle A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
Algorithms 2018, 11(8), 112; https://doi.org/10.3390/a11080112
Received: 25 June 2018 / Revised: 20 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
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Abstract
In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the
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In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches. Full article
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
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Open AccessArticle A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining
Algorithms 2018, 11(7), 97; https://doi.org/10.3390/a11070097
Received: 8 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 1 July 2018
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Abstract
The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously
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The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection. Full article
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
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