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31,291 Article Results

Integrating large language models for context-aware decision making in autonomous mobile robots

10.11591/ijra.v15i3.pp607-620
Vishnu Kumar Mishra , Megha Mishra , Talasila Ram Kumar , Yenna Geetha Reddy , Gundla Rajesh , Battula Phijik , Bandla Srinivasa Rao
The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Volume: 15
Issue: 3
Page: 607-620
Publish at: 2026-09-01

Remote controlled agricultural robot for spraying liquid type pesticides

10.11591/ijra.v15i3.pp553-560
Kalagotla Chenchireddy , Vadthya Jagan , M. Aruna Bharathi , Malaji Sushama , Varghese Jegathesan , Shabbier Ahmed Sydu
In modern agriculture, the use of automation and robotics is becoming increasingly essential to enhance efficiency, reduce labor, and ensure the safe handling of hazardous materials. This paper presents the design and development of a remote-controlled agricultural robot specifically intended for spraying liquid-type pesticides using radio frequency communication. The system comprises a mobile robot platform equipped with a pesticide tank, a spraying mechanism powered by a DC pump, and a set of drive motors for navigation. The robot is remotely controlled through an RF transmitter and receiver pair, allowing the operator to manually guide the robot across agricultural fields without direct contact with pesticides. The RF module transmits commands such as movement directions and spray activation, which are interpreted by an Arduino microcontroller onboard the robot. The system aims to reduce human exposure to harmful chemicals, minimize labor efforts, and increase precision in pesticide application. This solution is cost-effective, user-friendly, and adaptable for small to medium-sized farms, offering a practical step toward smart farming.
Volume: 15
Issue: 3
Page: 553-560
Publish at: 2026-09-01

Optimization of tensile strength and modulus elasticity from recycled mask polymer blends

10.11591/ijaas.v15.i3.pp894-901
Indah Widiastuti , Slamet Jatmiko , Budi Harjanto , Arif Bagus Panuntun
This research aims to analyze the effect of differences in the proportion of recycled polypropylene (r-PP), compatibilizer, and type of mask on the tensile strength and modulus of elasticity of recycled mask product materials. The research design used in this research uses the Taguchi orthogonal L4 experimental method, which uses a quantitative approach, namely by manipulating certain conditions and providing control over the test samples. This research focused on the effect of varying the proportion of r-PP composition, maleic anhydride polypropylene (MAPP) compatibilizer, as well as varying the type of mask waste used. In this research, the composition of r-PP used was 10% and 30%. Meanwhile, the MAPP composition used is 0% and 6%. In this study, tensile testing was carried out in accordance with the American Society for Testing and Materials (ASTM) D 638 standard type V in the form of a dogbone. The results of the research show that differences in the proportion of r-PP in the composition of the polymer mixture have an effect on the tensile strength and modulus of elasticity of the recycled mask product material.
Volume: 15
Issue: 3
Page: 894-901
Publish at: 2026-09-01

Textile industry innovation: systematic review of key trends and particularities

10.11591/ijra.v15i3.pp720-736
Sebastián Cardona-Acevedo , Alejandro Arango-Correa , Diana Carolina Rios Echeverri , Alejandro Valencia-Arias , Jhon Edward Aguirre Cuervo
Innovation in the textile industry is a key strategic factor, influenced by geographic disparities, structural challenges, and rapid technological change. However, fragmented knowledge makes it difficult to fully understand the phenomenon. This study aimed to analyse how various types of innovation appear and interact in the global textile sector. A systematic literature review was carried out following PRISMA 2020 guidelines, using Scopus and Web of Science databases. From an initial pool of 94 articles, 19 met the inclusion criteria. Findings reveal that beyond specific advancements like automation or smart textiles, structural tensions hinder the integrated adoption of technological, organisational, and sustainability innovations. The diversity of analytical approaches shows there is no single, unified path to innovation in this sector. Instead, multiple innovation trajectories coexist, influenced by local conditions and unequal institutional capacities. In addition, knowledge gaps between developed and emerging regions, as well as the lack of focus on early stages of the supply chain, highlight the need to rethink research priorities. Ultimately, innovation in the textile industry must be understood as a comprehensive process that brings together technology, organisational change, and sustainability, requiring a holistic approach to improve competitiveness and ensure long-term transformation across the sector.
Volume: 15
Issue: 3
Page: 720-736
Publish at: 2026-09-01

Students' perspectives on the effectiveness of ChatGPT in virtual learning

10.11591/ijaas.v15.i3.pp921-931
Isaac Asampana , Henry Akwetey Matey , Ben T. Ocra , Jones Yeboah Nyame
This research investigates how students perceive chat generative pre-trained transformer (ChatGPT) as a learning aid, examining its effects on engagement, usability, and educational achievement. Using the technology acceptance model (TAM) as the conceptual framework, a 24-item survey was designed and administered through simple random sampling. Results reveal that students largely view ChatGPT positively, describing it as an accessible and beneficial tool that promotes flexible and independent learning. The findings confirm TAM predictions, showing that perceived ease of use and favorable attitudes significantly influence adoption. By combining insights from both distance and on-campus learners, the study enriches understanding of how generative artificial intelligence enhances cognitive development and learning adaptability. Overall, the research reflects cautious optimism about ChatGPT’s pedagogical value, acknowledging both its educational benefits and challenges while underscoring the essential role of usability and learner attitudes in fostering sustained artificial intelligence utilization in higher education.
Volume: 15
Issue: 3
Page: 921-931
Publish at: 2026-09-01

Flight supervision and pilot training standards: a study of international aviation regulations

10.11591/ijaas.v15.i3.pp1203-1213
Heni Puspita , Ade Gafar Abdullah , Agus Setiawan , Isma Widiaty , Ike Yuni Wulandari , Johanes Adi Prihantono , Erlian Supriyanto
This paper analyzes variations in pilot training and licensing policies across four major organizations: the International Civil Aviation Organization (ICAO), the Federal Aviation Administration (FAA), the European Union Aviation Safety Agency (EASA), and the Directorate General of Civil Aviation (DGCA) of Indonesia. The main issue here is the impact of contradictory training pathway requirements, flight hours, simulator requirements, language proficiency requirements, and instructor qualification requirements on aviation safety, license transferability, and the construction of the pilot training system in Indonesia. This study employs a qualitative research design, combining a systematic literature review with regulatory comparative analysis. The findings demonstrate that the prescriptive and competency-based model is most prevalent in EASA, where simulation and multi-crew requirements are widely applied; whereas in the FAA, the emphasis is more on total flight experience and rules related to 1,500 hours; and in the Civil Aviation Safety Regulations (CASR), there is minimal implementation but overall consistent with ICAO minimum. The paper finds that the country must progressively harmonize with modern ICAO, EASA, and FAA standards of good practice, prioritizing competency-based training (CBT), fatigue risk management, simulator capacity, and instructor professionalization to enhance safety performance and international recognition.
Volume: 15
Issue: 3
Page: 1203-1213
Publish at: 2026-09-01

Comparation analysis of SSA and GWO algorithms for maximum power point tracking in standalone solar modules

10.11591/ijpeds.v17.i3.pp1962-1973
Indhana Sudiharto , Mochammad Machmud Rifadil , Muhammad Affid Febriansyah
Solar photovoltaic (PV) systems experience continuous output fluctuations due to changes in solar irradiance and operating temperature, reducing the effectiveness of power extraction. To improve energy harvesting capability, an adaptive maximum power point tracking (MPPT) method is required. This study evaluates the performance of the salp swarm algorithm (SSA) and grey wolf optimization (GWO) for MPPT control in a 100 Wp standalone PV system employing a single-ended primary inductor converter (SEPIC). The analysis focuses on tracking speed, efficiency, and stability under varying environmental conditions. Simulations were carried out in the ALTAIR PSIM Professional 2022.1.0.8 platform with irradiance levels ranging from 200-1000 W/m² and temperatures between 35-55 °C, including dynamic irradiance transitions. The obtained results show that SSA achieved a higher average tracking efficiency of 97.46% with a convergence time of 0.2276 s, while GWO produced 87.94% efficiency and a 0.2512 s convergence time. In addition, SSA demonstrated more stable tracking behavior and lower oscillation during low-irradiance operation. These results indicate that SSA provides better MPPT performance for compact standalone PV applications operating under fluctuating environmental conditions. Future work will involve hardware-based validation and real-time implementation.
Volume: 17
Issue: 3
Page: 1962-1973
Publish at: 2026-09-01

Cost-aware global frontier matching for ROS 2 multi-robot exploration

10.11591/ijra.v15i3.pp589-596
Chu Van Cuong , Tran Tuan Anh
Multi-robot frontier exploration supports warehouse mapping and inspection robotics, but geometric assignment can produce overlapping motion and inefficient target pairing. This paper presents a ROS 2 frontier-allocation layer that combines a weighted frontier cost with global one-to-one matching. The study isolates global matching from sequential assignment while keeping the cost formulation and ROS 2 execution stack fixed. Three policies are evaluated on two indoor maps, four team sizes, and three seeds. Across 72 completed main-policy runs, global matching gives the lowest mean completion time, travelled distance, path overlap, and assignment conflict. Relative to sequential cost-based assignment, it reduces completion time by 24.3%, travelled distance by 14.2%, and path overlap by 65.8%, with lower final coverage under the same stopping rule. The results support a coordination-efficiency benefit in the tested ROS 2 simulations; broader claims require larger, heterogeneous, dynamic, and physical deployments.
Volume: 15
Issue: 3
Page: 589-596
Publish at: 2026-09-01

Hybrid machine learning for predictive spare parts replacement in medical equipment

10.11591/ijaas.v15.i3.pp990-997
Fabian Halley Pata Alban Dattu , Syed Tarmizi Syed Shazali , Shirley Jonathan Tanjong , Abdul Rani Achmed Abdullah , Nurlaila Rosli
Stability of medical equipment is critical for uninterrupted healthcare services, yet hospital computerized maintenance management systems (CMMS) records are often incomplete, heterogeneous, and partially labeled for spare parts usage. In the analyzed CMMS dataset, spare-part-related work orders accounted for approximately 65.1% of maintenance cases, indicating that spare-part readiness is a major contributor to maintenance response time and potential equipment downtime. This study proposes a semi‑supervised hybrid random forest-neural network (RF-NN) model to predict whether a work order will require spare‑parts replacement and to support proactive inventory planning. Retrospective CMMS work‑order records from six hospitals (N = 295) were pre‑processed using leakage‑safe pipelines (categorical one‑hot encoding, text feature extraction from fault descriptions, and standardization of numeric fields). Semi‑supervised self‑training was used to exploit unlabeled or weakly labeled records through iterative pseudo‑labelling. Models were evaluated using stratified 10‑fold cross‑validation and reported as mean ± SD. The hybrid RF-NN achieved Accuracy = 0.986 ± 0.018, precision = 0.980 ± 0.026, recall = 1.000 ± 0.000, and F1‑score = 0.990 ± 0.013, outperforming RF, NN, and Markov baselines. These results demonstrate the practical advantage of combining semi‑supervised learning with hybrid modelling to reduce missed spare‑parts cases, minimize downtime risk, and strengthen CMMS‑ready procurement decision support.
Volume: 15
Issue: 3
Page: 990-997
Publish at: 2026-09-01

Design of a novel single-source 15-level inverter with self-balancing capacitors and sensorless PWM control

10.11591/ijpeds.v17.i3.pp1950-1961
Taoufiq El Ansari , Ayoub El Gadari , Youssef Ounejjar
Multilevel inverters (MLIs) are widely used in energy-conversion systems because they generate high-quality AC voltages with reduced harmonic distortion. However, existing MLIs often require numerous power devices, multiple DC sources, voltage sensors, or dedicated capacitor-balancing controllers, which increases both hardware cost and control complexity. This paper proposes a single-phase 15-level inverter derived from the Packed U-Cell structure. It employs a single DC source, three capacitors, and a reduced number of power switches. Based on the number of power switches, gate drivers, diodes, capacitors, DC sources, output levels, and total standing voltage per unit (TSVpu), the proposed topology achieves a lower cost function than the compared topologies reported in the literature. The proposed topology achieves a relatively low total standing voltage per unit (TSVpu) of 4.43. Sensorless open-loop SPWM offers inherent capacitor-voltage self-balancing, eliminating the need for voltage sensors or additional balancing loops. MATLAB/Simulink validation at 2 kHz and a modulation index of 1 covers steady-state operation, load transients, nonlinear loading, and DC-source voltage fluctuations. For a 50 Ω–20 mH load, the current THD is 2.20% without an output filter, confirming suitability for energy-conversion systems.
Volume: 17
Issue: 3
Page: 1950-1961
Publish at: 2026-09-01

Deep learning-based intelligent islanding detection for grid-connected photovoltaic systems using convolutional neural networks

10.11591/ijpeds.v17.i3.pp1755-1767
Dondapati Ravi Kishore , T. Vijay Muni , K. Venkata Kishore , V. Suresh , S. Saahithi , Thandava Krishna Sai Pandraju , S. N. Chaitra , B. Logeshwary
The increasing integration of photovoltaic (PV) systems into smart grids requires fast and reliable islanding detection to maintain grid stability and operational safety. Conventional detection methods often face challenges such as delayed response, reduced accuracy, and large non-detection zones under varying operating conditions. This paper proposes an intelligent islanding detection method for grid-connected PV systems using advanced artificial intelligence and deep learning techniques. Electrical parameters including voltage, current, frequency, and power signals are analyzed using signal processing methods and classified through a convolutional neural network (CNN) model developed in MATLAB/Simulink. Simulation results demonstrate that the proposed AI-based approach achieves rapid and accurate detection of islanding events with improved sensitivity and reduced false detections compared to conventional techniques. The proposed framework enhances the reliability, safety, and protection performance of modern photovoltaic power systems integrated with smart grids.
Volume: 17
Issue: 3
Page: 1755-1767
Publish at: 2026-09-01

Design of an integrated forecasting and scheduling model for power plants to balance solar and wind energy variability using real-time weather data

10.11591/ijpeds.v17.i3.pp2112-2126
Syafii Syafii , Novizon Novizon , Imra Nur Izrillah
The integration of variable renewable energy sources such as solar and wind creates challenges for power system stability and operational scheduling due to their intermittent characteristics. This study proposes an integrated forecasting and scheduling framework using real-time weather data for a hybrid renewable power system consisting of photovoltaic, wind, geothermal, and hydropower plants. Solar irradiance and wind speed data were collected using pyranometer and anemometer sensors and modeled using ARIMA for 24-hour-ahead forecasting. Based on AIC and BIC evaluation, ARIMA (2, 1, 2) and ARIMA (1, 1, 1) were selected for solar irradiance and wind speed forecasting, respectively. The forecasting results achieved MAPE values of 18.43% for solar irradiance and 14.12% for wind speed. The forecasted renewable outputs were integrated into a generation scheduling model, where geothermal power operated as a base-load unit and hydropower acted as a balancing source. The proposed scheduling strategy was evaluated through a 24-hour Newton-Raphson load flow simulation. Results showed that system power losses remained below 2% and bus voltage levels were maintained within acceptable limits, demonstrating reliable operation under fluctuating weather conditions.
Volume: 17
Issue: 3
Page: 2112-2126
Publish at: 2026-09-01

Optimization of hybrid GA-PSO-based energy management with Six Sigma penalty in buildings

10.11591/ijpeds.v17.i3.pp2259-2270
K. N. Nurwijayanti , Rustam Asnawi , Handaru Jati , Linda Faridah , Effendi Dodi Arisandi
This study proposes the optimization of a hybrid genetic algorithm-particle swarm optimization (GA-PSO) building energy management combined with Six Sigma for quality control. The main problems include high energy consumption, large carbon emissions, and performance variability. Six Sigma is applied through control limits (UCL/LCL) and process capability index (Cpk) so that the solution is not only efficient but also stable. Using 30 days of operational data, the model evaluates daily energy consumption (kWh) and carbon emissions, then compares the baseline with pure GA, pure PSO, and GA-PSO+Six Sigma. The results show that GA-PSO reduces average energy consumption by 5.1% compared to GA and 3.5% compared to PSO. When combined with Six Sigma, the savings increased to 7.5% compared to GA and 6.6% compared to PSO, while reducing carbon emissions without compromising operational comfort. These findings present a measurable, sustainable, low-carbon building energy management model that is aligned with the decarbonization framework and ISO 50001 best practices.
Volume: 17
Issue: 3
Page: 2259-2270
Publish at: 2026-09-01

Energy optimization of an electric vehicle charging station using a hybrid STA-GWO MPPT strategy

10.11591/ijpeds.v17.i3.pp2183-2196
Samia Amrouni , Said Aissou , Rafik Medjoudj , Elyazid Amirouche , Nabil Benyahia , Abdelhakim Belkaid
This paper presents a hybrid electric vehicle charging station powered by both a PV source and the utility grid, incorporating an energy management strategy that prioritizes the utilization of solar energy while exporting surplus power to the grid during periods of low charging demand. To enhance the performance of maximum power point tracking, a hybrid control strategy integrating the grey wolf optimizer (GWO) and the super-twisting algorithm (STA) is proposed. The GWO performs rapid global exploration to accurately identify the maximum power point, whereas the STA ensures precise, robust, and chattering-free tracking under steady-state operating conditions. The proposed system was modeled in MATLAB/Simulink and validated under a dynamic irradiance profile characterized by both abrupt and gradual variations. Simulation results demonstrate a convergence time of 2-3 ms, residual power oscillations below 0.1%, and an average tracking efficiency of 99.38%. Compared with conventional MPPT techniques, the proposed STA-GWO approach significantly suppresses steady-state oscillations, accelerates convergence, and prevents MPP tracking failure under rapid irradiance fluctuations through the global optimization capability of GWO. These findings highlight the effectiveness of the proposed hybrid MPPT strategy in improving the robustness, energy conversion efficiency, and grid integration capability of PV-powered EV charging stations, making it a promising solution for next-generation sustainable charging infrastructure.
Volume: 17
Issue: 3
Page: 2183-2196
Publish at: 2026-09-01

Comparative simulation of fractional-order PD sliding mode and fuzzy logic controllers for a second-order discrete-time nonlinear system

10.11591/ijpeds.v17.i3.pp1822-1830
Ahmed Bennaoui , Salah Benzian , Hamza Sulimani , Aissa Ameur
Tight output regulation in power converters and electric drive systems requires a control strategy that simultaneously minimizes tracking error and maintains smooth actuation-two objectives that are intrinsically in tension for nonlinear, parameter-varying plants. Despite the widespread deployment of fractional-order PD sliding mode control (FOPD-SMC) and Mamdani fuzzy logic control (FLC) in this domain, no prior study has placed them in a direct, metric-identical comparison on a common plant. The present work closes this gap by implementing both controllers on the same second-order discrete-time nonlinear plant-representative of DC-DC converter output dynamics and motor-drive input-output behavior and evaluating them under a composite reference that combines sinusoidally-modulated ramps with step transitions, scored by the integral of squared error (ISE) and integral of absolute error (IAE). FOPD-SMC achieves ISE= 1.639 × 10-2 and IAE= 3.345 × 10-2, outperforming FLC by 87.6% and 50.4%, respectively; the advantage originates from the non-integer memory embedded in the sliding surface via the Gr¨unwald-Letnikov operator and from the explicit decomposition of the control law into nominal-tracking and robustness components. FLC, conversely, produces a chattering-free, continuously varying control signal a structural consequence of smooth Gaussian membership functions and linguistic rule aggregation, at the cost of a mean absolute tracking error twice that of FOPD-SMC. These findings establish a quantitative selection criterion: FOPD-SMC is recommended when tight voltage or current regulation is the primary objective, while FLC is preferred where smooth torque delivery and reduced actuator stress outweigh marginal gains in tracking accuracy.
Volume: 17
Issue: 3
Page: 1822-1830
Publish at: 2026-09-01
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