Advanced Systems Engineering: Theory and Applications, 2nd Volume

Topic Information

Dear Colleagues,

This topic is focused in the most recent developments in Advanced Systems Engineering, and the development of related subsystems and components.

Advanced Systems Engineering consists of solutions (and respective development) that increasingly rely on intelligent components and subsystems to deliver an improved performance for different and complex applications of engineering systems. In this context, the correct understanding of the interaction and connection between subsystems is crucial for making products that are more efficient and reliable and, most of all, successfully designed for specific successful engineering applications. More than ever, these systems belong to a new generation of more integrated and complex products with more dedicated and sophisticated applications in several domains, bringing together critical knowledge about design, materials, energy, sustainability and reliability.

The topics to be considered in this context include, but are not limited to, the following:

• Aerospace Technology and Astronautics
• Agricultural Processes
• Applied Mechanics
• Automotive Engineering
• Biotechnological and Environmental Systems
• Biotechnology
• Biomechanics
• Cyber–Physical Systems
• Control Theory and Architectures
• Control Technology
• Decision Theory and Algorithms
• Dynamical Systems
• Discrete-Event Systems
• Distributed and Networked Control
• Economic Models
• Engine Technology
• Engineering Design
• Engineering Thermodynamics, Heat and Mass Transfer
• Fault-Tolerant Control
• Fluid Mechanics
• Fuzzy and Neuro-Fuzzy Systems
• Genetic Algorithms and Nonlinear Control
• Hardware for Control Systems
• Image Processing and Computer Vision
• Industrial Automation
• Industrial Networking
• Instrumentation, Sensors and Actuators
• Machinery and Machine Elements
• Manufacturing Engineering
• Manufacturing Systems and Scheduling
• Marketing and Entrepreneurship
• Marine Control
• Materials Engineering
• Mechanical Systems Design
• Mechanical Structures and Stress Analysis
• Mechanical Vibrations
• Mechatronics Design
• Mechatronics Modelling, Simulation and Identification
• Medical Devices
• MEMS
• Model-Based Design and Development
• Modeling and Identification
• Nanotechnology and Microengineering
• Neural Networks
• Open Innovation
• Power Systems
• Precision Engineering, Instrumentation, Measurement
• Process Control
• Real-Time Systems Architectures
• Rehabilitation Devices
• Reliable Systems
• Remote and Virtual Laboratories
• Renewable Energy Systems
• Requirements Analysis
• Robust Control
• Robotics
• Synergy between EU research, innovation and development funds
• Social and Industrial entrepreneurship
• Sustainability Successful Practices
• Theoretical and Applied Mechanics
• Transportation Systems
• Tribology and Surface Technology
• Web Remote Control
• Wellbeing
• Wireless Applications and Systems

Dr. Katarzyna Antosz
Dr. Jose Machado
Dr. Erika Ottaviano
Dr. Pierluigi Rea
Dr. Camelia Avram
Dr. Vijaya Kumar Manupati
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Metals metals	2.9	4.4	2011	15 Days	CHF 2600	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit
Systems systems	1.9	3.3	2013	16.8 Days	CHF 2400	Submit
Inventions inventions	3.4	5.4	2016	17.4 Days	CHF 1800	Submit

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Published Papers (4 papers)

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All Applied Sciences Electronics Energies Metals Sensors Systems Inventions

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19 pages, 8948 KiB  

Open AccessArticle

Offline Identification of a Laboratory Incubator

by Süleyman Mantar and Ersen Yılmaz

Appl. Sci. 2024, 14(8), 3466; https://doi.org/10.3390/app14083466 - 19 Apr 2024

Viewed by 400

Abstract

Laboratory incubators are used to maintain and cultivate microbial and cell cultures. In order to ensure suitable growing conditions and to avoid cell injuries and fast rise and settling times, minimum overshoot and undershoot performance indexes should be considered in the controller design [...] Read more.

Laboratory incubators are used to maintain and cultivate microbial and cell cultures. In order to ensure suitable growing conditions and to avoid cell injuries and fast rise and settling times, minimum overshoot and undershoot performance indexes should be considered in the controller design for incubators. Therefore, it is important to build proper models to evaluate the performance of the controllers before implementation. In this study, we propose an approach to build a model for a laboratory incubator. In this approach, the incubator is considered a linear time-invariant single-input, single-output system. Four different model structures, namely auto-regressive exogenous, auto-regressive moving average exogenous, output error and Box–Jenkins, are applied for modeling the system. The parameters of the model structures are estimated by using prediction error methods. The performances of the model structures are evaluated in terms of mean squared error, mean absolute error and goodness of fit. Additionally, residue analysis including auto-correlation and cross-correlation plots is provided. Experiments are carried out in two scenarios. In the first scenario, the identification dataset is collected from the unit-step response, while in the second scenario, it is collected from the pseudorandom binary sequence response. The experimental study shows that the Box–Jenkins model achieves an over 90% fit percentage for the first scenario and an over 95% fit percentage for the second scenario. Based on the experimental results, it is concluded that the Box–Jenkins model can be used as a successful model for laboratory incubators. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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31 pages, 2284 KiB  

Open AccessSystematic Review

Risks of Drone Use in Light of Literature Studies

by Agnieszka A. Tubis, Honorata Poturaj, Klaudia Dereń and Arkadiusz Żurek

Sensors 2024, 24(4), 1205; https://doi.org/10.3390/s24041205 - 13 Feb 2024

Viewed by 1030

Abstract

This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning [...] Read more.

This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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24 pages, 946 KiB  

Open AccessArticle

A Systematic Model to Improve Productivity in a Transformer Manufacturing Company: A Simulation Case Study

by Yung-Tsan Jou, Ming-Chang Lin, Riana Magdalena Silitonga, Shao-Yang Lu and Ni-Ying Hsu

Appl. Sci. 2024, 14(2), 519; https://doi.org/10.3390/app14020519 - 07 Jan 2024

Viewed by 928

Abstract

The global economy’s slow recovery has led to an increased need for transformers in organizations in recent years. An optimal strategy for production line optimization is to enhance the allocation of staff at each workstation and increase the amount of operational equipment. The [...] Read more.

The global economy’s slow recovery has led to an increased need for transformers in organizations in recent years. An optimal strategy for production line optimization is to enhance the allocation of staff at each workstation and increase the amount of operational equipment. The focus of this study is the investigation of the transformer production line. This study carried out a comprehensive examination of manufacturing area one, manufacturing area two, and manufacturing area three, respectively. The findings revealed that the case factory requires enhancements in the allocation of its workers. The simulation approach allows for the implementation of multi-scenario evaluation and adjustment, ensuring optimal utilization of resources in the enhanced production line, hence enhancing production efficiency and total productivity. Implementing both rotational shifts and night shifts in manufacturing area one enhances the overall production efficiency of the manufacturing area. By redistributing the workforce in area two, it proved feasible to manage the production capacity of a manufacturing area and maintain the operation of the gas-phase drying furnace. With regard to the final aspect, it is imperative to enhance the processing time of preprocessing goods in order to guarantee a consistent supply of the appropriate quantity of products. This will effectively minimize production line delays and enhance overall production efficiency. These enhancement strategies aid the manufacturing company in optimizing resource allocation to enhance production efficiency and productivity. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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25 pages, 1850 KiB  

Open AccessArticle

Multi-Objective PSO for Control-Loop Tuning of DFIG Wind Turbines with Chopper Protection and Reactive-Current Injection

by Milton E. B. Aguilar, Denis V. Coury, Romeu Reginatto, Renato M. Monaro, Paulo Thiago de Godoy and Tales G. Jahn

Energies 2024, 17(1), 28; https://doi.org/10.3390/en17010028 - 20 Dec 2023

Viewed by 458

Abstract

The control systems for the variable-speed wind turbine based on the Doubly Fed Induction Generator (DFIG) pose some tuning challenges. The performance and stability of DFIG wind turbines during faults in power grids are directly related to their controller settings. This work investigates [...] Read more.

The control systems for the variable-speed wind turbine based on the Doubly Fed Induction Generator (DFIG) pose some tuning challenges. The performance and stability of DFIG wind turbines during faults in power grids are directly related to their controller settings. This work investigates how incorporating protection via a braking-Chopper controller connected to the DC link (DC Chopper) and a reactive-current injection during the PI-tuning process affects the performance of DFIG wind turbines during electrical faults. For the tuning process, the Multi-Objective-Particle-Swarm-Optimization (MOPSO) algorithm was used. Thus, two different approaches adopting this methodology were investigated, considering sequential and simultaneous tuning. The results showed that sequential tuning presented a better performance in relation to the reactive-current injection and lower amplitude deviations of the electrical quantities during and after the fault. On the other hand, simultaneous tuning reached damping of the mechanical oscillations faster and presented better performance of the protection system. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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