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Algorithms
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Computer Science and Intelligent Control
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Computer Science and Intelligent Control

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 9356

Special Issue Editor

Dr. Jie Zhang

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: advanced process control; process fault detection and diagnosis; neural networks and neuro-fuzzy systems; multivariate statistical process control; optimal control of batch processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 5th International Symposium on Computer Science and Intelligent Control (ISCSIC 2021) aims to bring together researchers in the areas related to computer science and intelligent control in a single platform and present their stimulating research and knowledge transfer ideas in both computer science and intelligent control. This symposium welcomes new research in the areas of computer science and intelligent control, such as distributed systems, computer vision, image and signal processing, intelligent systems, robotics, industrial process control, big data analytics, modeling and optimization, and machine learning and artificial intelligence. However, we also recognize that the future for computer scientists and control engineers is one where they will be working in interdisciplinary teams to solve new, complex, and evolving problems that will require innovative solutions. Therefore, we encourage the participation of other disciplines in this conference where multidisciplinary research and knowledge transfer projects are presented. 

Selected papers will be considered for this special issue after significant extension. We also welcome other scholars in these fields to contribute papers for this special issue, the keywords include but not limit in below.

Dr. Jie Zhang
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 submissions that pass pre-check are 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 1600 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

  • Autonomous systems
  • Autonomy in cyber-physical systems
  • Computation platforms and networks
  • Computational intelligence and natural computing
  • Computer vision, image and signal processing
  • Decision support systems
  • Deep learning
  • Development and implementation of various cyber-physical systems
  • Distributed optimization and distributed learning
  • Distributed, decentralized control
  • Fuzzy logic
  • Genetic programming
  • Heterogeneous networks
  • Human–computer/machine interaction
  • Human motion tracking
  • Intelligent control theories
  • IoT communication and coordination middleware and platforms
  • Machine learning and artificial intelligence
  • Modeling and control of cyber-physical systems
  • Modeling and formal methods
  • Modeling of tightly integrated physical processes
  • Networked and distributed systems
  • Networked control
  • Networking infrastructure and management for cyber-physical systems
  • Networking infrastructure for IoT to address heterogeneity
  • Neural networks
  • Non-functional issues for cyber-physical systems
  • Optimization
  • Pattern recognition
  • Predictive, robust control
  • Process control and stability
  • Scalability of complex networks
  • Scalability and interoperability issues
  • Security, privacy, reliability, and dependability
  • Software engineering
  • Software tools and middleware for cyber-physical system research
  • Supervised and unsupervised learning
  • Transportation and logistics
  • Wireless sensor networks

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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28 pages, 4391 KiB  
Article
Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
by Carlos Pinto, Rui Pinto and Gil Gonçalves
Algorithms 2022, 15(1), 1; https://doi.org/10.3390/a15010001 - 21 Dec 2021
Cited by 8 | Viewed by 4155
Abstract
The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such [...] Read more.
The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA). Full article
(This article belongs to the Special Issue Computer Science and Intelligent Control)
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13 pages, 3046 KiB  
Article
Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems
by Alexander Schaum
Algorithms 2021, 14(11), 330; https://doi.org/10.3390/a14110330 - 11 Nov 2021
Cited by 1 | Viewed by 1881
Abstract
The application of autoencoders in combination with Dynamic Mode Decomposition for control (DMDc) and reduced order observer design as well as Kalman Filter design is discussed for low order state reconstruction of a class of scalar linear diffusion-convection-reaction systems. The general idea and [...] Read more.
The application of autoencoders in combination with Dynamic Mode Decomposition for control (DMDc) and reduced order observer design as well as Kalman Filter design is discussed for low order state reconstruction of a class of scalar linear diffusion-convection-reaction systems. The general idea and conceptual approaches are developed following recent results on machine-learning based identification of the Koopman operator using autoencoders and DMDc for finite-dimensional discrete-time system identification. The resulting linear reduced order model is combined with a classical Kalman Filter for state reconstruction with minimum error covariance as well as a reduced order observer with very low computational and memory demands. The performance of the two schemes is evaluated and compared in terms of the approximated L2 error norm in a numerical simulation study. It turns out, that for the evaluated case study the reduced-order scheme achieves comparable performance with significantly less computational load. Full article
(This article belongs to the Special Issue Computer Science and Intelligent Control)
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15 pages, 922 KiB  
Article
The GM-BP Neural Network Prediction Model for International Competitiveness of Computer Information Service Industry
by Xianhang Xu, Mohd Anuar Arshad, Ubaid Ali and Arshad Mahmood
Algorithms 2021, 14(11), 308; https://doi.org/10.3390/a14110308 - 23 Oct 2021
Cited by 1 | Viewed by 2117
Abstract
The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the [...] Read more.
The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the critical period of global economic transformation and development, the use of mathematical models to predict its international competitiveness will help scientifically evaluate the development level of the industry and accelerate the adaptation to the needs of the fourth industrial revolution. In this article, a prediction model is proposed for the international competitiveness of the computer information service industry. First, we used the Revealed Comparative Advantage (RCA) index to measure the international competitiveness of the computer information service industry. Furthermore, based on the characteristics of the industry and high-quality development theory, we constructed the evaluation indicator system of influencing factors and used the grey relational analysis method to screen key indicators. Then, we combined the Grey model and BP neural network algorithm to construct the GM-BP prediction model. Finally, China is used as an example to predict the international competitiveness of its computer information service industry, and suggestions are made for industrial development. The results show that the grey relational analysis method can genuinely reflect the impact of different aspects on the international competitiveness of China’s computer information service industry and better determine the key indicators of influencing factors. The GM-BP model has minor errors and excellent simulation results and can accurately predict the future status of international competitiveness. The applicability and reliability of the model are reasonable. Full article
(This article belongs to the Special Issue Computer Science and Intelligent Control)
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