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Motivation AI and Its Application to Smart Systems and Industrial Innovations

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 930

Special Issue Editor

Prof. Dr. Dong Won Kim

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Guest Editor
Department of Digital Electronics, Inha Technical College, 100 Inha-ro, Hagik 1(il)-dong, Nam-gu, Incheon, Republic of Korea
Interests: intelligent humanoid robot, autonomous multi-mobile robot system, and AI and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of artificial intelligence (AI). In recent decades, automated and intelligent systems have emerged, opening new research directions that are still evolving due to new challenges and technological advances in the field.

Topics

The scope of this Special Issue encompasses the application of artificial intelligence techniques and algorithms to design and solve existing problems of smart systems. These techniques include the following:

  • Computer vision for smart systems;
  • Natural language interfaces for smart systems;
  • Knowledge-based smart systems;
  • Agent-based smart systems;
  • Fuzzy logic- or deep learning-based smart systems;
  • Artificial neural networks for smart systems;
  • Ontology-based smart systems;
  • Human–robot interaction for smart systems;
  • Smart systems in sensing and perception for robotic systems;
  • Bio-inspired and neural approaches to sensing, representation, and action for robotic systems;
  • Smart systems in machine vision for robotic systems.

Prof. Dr. Dong Won Kim
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • intelligent modeling
  • application
  • control system
  • robotics system
  • knowledge-based system
  • artificial model forecasting

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Published Papers (2 papers)

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Research

23 pages, 2620 KiB  
Article
A Novel Overload Control Algorithm for Distributed Control Systems to Enhance Reliability in Industrial Automation
by Taikyeong Jeong
Appl. Sci. 2025, 15(10), 5766; https://doi.org/10.3390/app15105766 - 21 May 2025
Viewed by 216
Abstract
This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve [...] Read more.
This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system’s effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing—domains where real-time optimization and operational reliability are essential. Full article
(This article belongs to the Special Issue Motivation AI and Its Application to Smart Systems and Industrial Innovations)
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17 pages, 1086 KiB  
Article
Optimized Controller Design Using Hybrid Real-Time Model Identification with LSTM-Based Adaptive Control
by Yeon-Jeong Park and Joon-Ho Cho
Appl. Sci. 2025, 15(4), 2138; https://doi.org/10.3390/app15042138 - 18 Feb 2025
Viewed by 493
Abstract
Most of the processes with various dynamic characteristics can be reduced to the Second Order Plus Time Delay (SOPTD) model by using the model reduction method. We propose a novel hybrid approach that combines Long Short-Term Memory (LSTM)-based real-time model identification with Genetic [...] Read more.
Most of the processes with various dynamic characteristics can be reduced to the Second Order Plus Time Delay (SOPTD) model by using the model reduction method. We propose a novel hybrid approach that combines Long Short-Term Memory (LSTM)-based real-time model identification with Genetic Algorithms to enhance the Smith predictor control structure. This method compensates for the delay time of the SOPTD model while minimizing the Integral Time Absolute Error performance index. Our approach integrates an optimally adaptive Proportional–Integral–Derivative (PID) controller design algorithm that estimates the coefficients of the SOPTD model in the Smith Predictor control structure and adjusts the PID controller parameters dynamically. The method is improved through a combination of numerical calculation, Genetic Algorithms, and LSTM networks, showing approximately 15% better performance compared to conventional methods. The system demonstrates significant improvements in both performance metrics and resource utilization, including a 40% reduction in execution time and enhanced resource efficiency. Simulation results show that the proposed scheme exhibits improved adaptability to disturbances and process variations, with faster response times and reduced overshoots compared to traditional methods. The steady-state response of the higher-order model and the reduced model shows perfect matching for the unit feedback input. Full article
(This article belongs to the Special Issue Motivation AI and Its Application to Smart Systems and Industrial Innovations)
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