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Contact Name
Alfian Ma'arif
Contact Email
alfian_maarif@ieee.org
Phone
-
Journal Mail Official
alfian_maarif@ieee.org
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Control Systems and Optimization Letters
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
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.
Articles 2 Documents
Search results for , issue "Vol 4, No 2 (2026)" : 2 Documents clear
Understanding Large Language Models: A Review Wulandari, Annastasya Nabila Elsa; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
A Review Multiphysics Modeling Techniques for PMSM-Based Electric Vehicle Drives Saleh, Md. Abu; Mia, Md. Mehedi Hasan; Uddin, Md. Jasim; Ali, Md. Sumon
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.188

Abstract

The objective of this paper is to review Multiphysics modeling techniques for PMSM-based electric vehicle (EV) drives. Because of its great efficiency, power density, and ability to precisely adjust power, permanent magnet synchronous motors (PMSMs) have emerged as the go-to option for electric vehicle (EV) drives. However, a Multiphysics approach that incorporates mechanical, thermal, electromagnetic, and control system dynamics is necessary to effectively describe PMSM-based EV drives. This paper examines the benefits, drawbacks, and uses of several Multiphysics modelling approaches applied to PMSM-based EV drives. Analytical methods and finite element analysis (FEA) are two examples of electromagnetic modelling techniques that are examined in connection with loss prediction and motor design optimization. While mechanical modelling techniques concentrate on vibration and acoustic noise difficulties, thermal modelling procedures are examined to address heat dissipation and performance reliability. One of the main issues lies in the accurate representation of coupled losses electromagnetic, thermal, and mechanical especially under dynamic operating conditions typical of EVs. To improve the dynamic performance and fault tolerance of PMSM drives, control-oriented modelling techniques are also examined. Co-simulation frameworks that combine these several physical domains are also presented in the review, offering a thorough understanding of practical EV applications. The paper concludes by discussing future research possibilities in Multiphysics modelling for PMSM-based EV drives, with a focus on real-time simulation capabilities, computational efficiency, and artificial intelligence integration.

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