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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 14 Documents
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Search results for , issue "Vol 2, No 3 (2024)" : 14 Documents clear
Ensuring Safety in Human-Robot Cooperation: Key Issues and Future Challenges Sharkawy, Abdel-Nasser; Mahmoud, Khaled H.; Abdel-Jaber, Gamal T.
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.154

Abstract

Human-robot cooperation (HRC) is becoming increasingly essential in many different sectors such as industry, healthcare, agriculture, and education. This cooperation between robot and human has many advantages such as increasing and boosting productivity and efficiency, executing the task easily, effectively, and in a fast time, and minimizing the efforts and time. Therefore, ensuring safety issues during this cooperation are critical and must be considered to avoid or minimize any risk or danger whether for the robot, human, or environment. Risks may be such as accidents or system failures. In this paper, an overview of the safety issues of human-robot cooperation is discussed. The main key challenges in robotics safety are outlined and presented such as collision detection and avoidance, adapting to unpredictable human behaviors, and implementing effective risk mitigation strategies. The difference between industrial robots and cobots is illustrated. Their features and safety issues are also provided. The problem of collision detection or avoidance between the robot and environment is defined and discussed in detail. The result of this paper can be a guideline or framework to future researchers during the design and the development of their safety methods in human-robot cooperation tasks. In addition, it shapes future research directions in safety measures.
Integration of Renewable Energy, Microgrids, and EV Charging Infrastructure: Challenges and Solutions Prianka, Yingking Mitra; Sharma, Anik; Biswas, Chanchal
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.142

Abstract

As global efforts toward sustainable energy transition and electric vehicle (EV) adoption accelerate, the seamless integration of renewable energy sources (RES), microgrids, and EV charging infrastructure is becoming increasingly critical. This review examines recent advancements in the integration of solar and wind power with microgrids and EV charging infrastructure, focusing on energy management techniques, grid stability solutions, and the development of charging infrastructure. The study emphasizes the difficulties relating to energy management techniques, grid stability, intermittency and variability of renewable energy, and the development of charging infrastructure. Microgrids are critically examined for their ability to enhance energy security and resilience by integrating distributed energy resources (DERs) and optimizing power generation and usage. The contribution of microgrids to improving energy security and resilience is thoroughly examined, along with how they allow distributed energy resources (DERs) to maximize power generation and consumption. Additionally, this review assesses how energy storage systems (ESS) and bidirectional vehicle-to-grid (V2G) technology affect peak load reduction and energy balance. The integration of these systems is made easier by a number of smart grid technologies, power electronics solutions, and communication protocols that are covered. The assessment also discusses the standards, policy frameworks, and future lines of inquiry that will be needed to hasten the establishment of a reliable and scalable network of electric vehicle charging stations coupled with microgrids and renewable energy sources. The results of this research offer valuable perspectives for creating sustainable energy strategies that facilitate the swift expansion of electric vehicle adoption, all the while reducing ecological footprints and augmenting grid stability.
Enhanced Compressive Strength Prediction of Slump Concrete Using a Hybrid Support Vector Regression-Genetic Algorithm Model Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.223

Abstract

This paper proposes a hybrid predictive model that integrates Support Vector Regression (SVR) with a Genetic Algorithm (GA) to estimate the compressive strength of concrete using slump test data, thereby offering an alternative to conventional, resource-intensive laboratory testing. The employed dataset encapsulates the nonlinear relationship between concrete slump and compressive strength. Given the sensitivity of SVR to hyperparameter selection, its standalone application yielded suboptimal predictive performance. To mitigate this, GA was utilized for hyperparameter optimization, selected for its effectiveness in global search and handling complex parameter spaces compared to traditional optimization techniques. The SVR-GA model was systematically evaluated against established machine learning algorithms, including Decision Tree, Neural Network, Naïve Bayes, and K-Nearest Neighbors, chosen based on their prevalence and diverse methodological characteristics. Performance evaluation incorporated robust validation methods to prevent overfitting and ensure generalizability. The results indicate that the proposed model delivers rapid and accurate predictions, suitable for practical, on-site application, with the potential to significantly reduce time and costs associated with traditional testing. Limitations related to dataset specificity and model generalizability are acknowledged. Future research directions include extending the framework to additional concrete properties and the development of real-time predictive systems. A schematic representation of the SVR-GA integration is included.
MayNet: A Neural Network Ensemble Approach Based on May's Theorem for Improved Classification Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.213

Abstract

In this study, we explored the possibility of applying May's Theorem to neural networks and proposed a new unified network architecture called MayNet. MayNet achieves category prediction by integrating multiple neural network "voters" and uses majority voting to determine the final classification result. Experimental results show that MayNet outperforms traditional single neural networks on CIFAR-10 and MedMNIST datasets and has high robustness. The paper compares the performance of MayNet with popular convolutional neural networks (such as ResNet18) on various datasets and demonstrates its superior performance. May's Theorem provides a solid theoretical foundation for the majority voting mechanism in neural network ensembles, ensuring improved decision accuracy through collective judgments of independent voters. MayNet’s architecture innovatively integrates multiple independently trained convolutional neural networks as voters, leveraging majority voting to combine their outputs effectively. This design enhances classification accuracy, robustness, and generalization ability.

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