Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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A qualitative analysis of technical and compositional constraints in FPV drone videography for vertical social media content video
No full textThe increasing dominance of vertical video formats on mobile-first platforms such as Instagram Reels and TikTok presents new challenges for First Person View (FPV) drone videography, especially when capturing natural landscapes. This study aims to investigate the technical and compositional constraints of FPV drones in producing vertical video content for social media. Using a qualitative case study approach, data were collected through field observation, interviews with 10 professional FPV pilots, and a supplementary online survey. The findings reveal key limitations including fixed horizontal camera orientation, lack of gimbal-based stabilization, and control systems ergonomically optimized for horizontal framing. These constraints hinder smooth movement, reduce composition flexibility, and increase the difficulty of capturing high-quality vertical footage. Although some pilots attempt workarounds, such as custom camera mounts and post-production cropping, these solutions are often labor-intensive and compromise video quality. This study contributes practical insights for drone designers and content creators seeking to adapt FPV technology for vertical video production and offers a foundation for future research into hardware innovation and workflow adaptation in response to evolving social media trends
Artistic exploration of national song arrangement through twelve-tone techniques for guitar ensemble: impact on higher education music learning
Full text linkThis study addresses a pedagogical challenge in higher education, namely the lack of instructional materials for guitar ensemble courses. This issue has been identified in the Music Department of ISI Yogyakarta, where students encounter difficulties engaging with classical Western music materials due to unfamiliarity, particularly among those without prior training in formal music education or vocational music programs. To mitigate this challenge, arrangements of Indonesian national songs are introduced as a strategic approach, leveraging their familiarity to enhance student engagement and motivation in ensemble practice. This study arranges Bangun Pemudi Pemuda, a national song selected for its rhythmic and melodic qualities that lend themselves well to classical guitar ensemble performance. The arrangement employs four distinct voices, mirroring choral harmonization. A key innovation in this arrangement is the incorporation of the dodecaphonic scale, designed to introduce students to modern arrangement techniques, specifically the twelve-tone series. This research adopts an artistic research methodology, encompassing the arrangement process and its implementation in guitar ensemble classes. The qualitative dimension is examined through focus group discussions (FGDs), literature reviews, and classroom observations, fostering dialogue and interaction. This study aims to develop instructional materials that support students in cultivating classical guitar proficiency, ensuring that the applied techniques contribute to their technical advancement while utilizing national songs as an accessible entry point for learning
Teachers’ Motivation in Pursuing Their Teaching Careers: Voices from Four In-Service EFL Teachers
No full textThis study listed factors that affect motivation among Indonesian English as Foreign Language (EFL) in-service teachers using the Maslow Hierarchy of Needs theory located at an urban secondary school in North Sumatera, Indonesia. Four Indonesian EFL in-service teachers had participated in this qualitative study, with interviews and observations used as primary methods in data collect ion. In this study, researchers found that fulfillment of educational background, feelings of love towards children, passion for becoming teachers, family inspiration, and religious reasons were motivational factors for teachers. From these findings, it can be concluded that teachers were self-motivated (intrinsic) and were not influenced by external factors. However, factor that negatively affected the motivation of teachers was working environment, mainly at school (extrinsic) level. These factors include having a big group of students in a classroom and extracurricular activities which resulted in students being tired, which could cause teachers to lack enthusiasm. Therefore, teachers require professional development to address self-actualization needs to excel in their competence
Hand Keypoint-Based CNN for SIBI Sign Language Recognition
Full text linkSIBI is less widely adopted, and the lack of an efficient recognition system limits its accessibility. SIBI gestures often involve subtle hand movements and complex finger configurations, requiring precise feature extraction and classification techniques. This study addresses these issues using a Hand Keypoint-based Convolutional Neural Network (HK-CNN) for SIBI classification. The research utilizes Kinect 2.0 for precise data collection, enabling accurate hand keypoint detection and preprocessing. The optimal data acquisition distance between 50 and 60 cm from the camera is considered to obtain clear and detailed images. The methodology includes four key stages: data collection, preprocessing (keypoint extraction and image filtering), classification using HK-CNN with ResNet-50, EfficientNet, and InceptionV3, and performance evaluation. Experimental results demonstrate that EfficientNet achieves the highest accuracy of 99.1% in the 60:40 data split scenario, with superior precision and recall, making it ideal for real-time applications. ResNet-50 also performs well with 99.3% accuracy in the 20:80 split but requires longer computation time, while InceptionV3 is less efficient for real-time applications. Compared to traditional CNN methods, HK-CNN significantly enhances accuracy and efficiency. In conclusion, this study provides a robust and adaptable solution for SIBI recognition, facilitating inclusivity in education, public services, and workplace communication. Future research should expand dataset diversity and explore dynamic gesture recognition for further improvements
Improved Trajectory Tracking for Nonholonomic Mobile Robots Via Dynamic Weight Adjustment in Type-2 Fuzzy Model Predictive Control
Full text linkThis paper presents an advanced methodology for trajectory control of non-holographic mobile robots. It addresses the challenges of dynamic environments and system uncertainty by proposing a fuzzy model predictive control (FMPC) system that combines Type-2 fuzzy logic (F2MPC) with model predictive control (MPC) to enhance tracking accuracy and adaptability. A Takagi-Sugeno (T-S) fuzzy model changes the MPC weighting matrices in real-time based on speed and distance errors, while the Type-2 fuzzy system handles uncertainties better than Type-1 systems. Tests using circular and wavy trajectories show that the Type-2 Fuzzy MPC (F2MPC) works better than traditional methods, achieving fewer tracking errors (Integral Squared Error of 0.0011), faster convergence (in 1.2 seconds), and using 65% less energy for movement than conventional MPC. Robustness tests show the controller's stability under disturbances, with minimal deviation and quick recovery. The results highlight the F2MPC's precision, efficiency, and adaptability, making it a promising solution for complex robotic navigation tasks. The study found that Type-2 fuzzy logic and predictive control improve trajectory tracking, paving the path for real-world applications and computational optimisations
A Systematic Review of Inverse Kinematics Methods for Fixed-Base Serial Manipulators: Analytical, Numerical, and Machine Learning Methods
Full text linkInverse kinematics is essential for precision tasks in fixed-base serial robots, such as surgical robotics or high-speed manufacturing, where delays or errors can have critical consequences. Current inverse kinematic methods face a fundamental trade-off: analytical solutions are fast but limited to spherical-wrist manipulators, while numerical and AI-based approaches sacrifice speed for generality. Despite prior reviews comparing performance metrics, no study provides a unified quantitative framework to guide method selection based on robot structure or application requirements. This systematic review addresses this lack of (1) quantitatively contrasting (response time, accuracy) analytical, numerical, and AI-based methods using studies in fields such as industrial robotics, medicine, and collaborative spaces and (2) identifying optimal hybrid strategies for real-time applications such as path planning. Using PRISMA, we analyzed 47 peer-reviewed articles from Scopus/Web of Science between 2019-2024, excluding algorithms for continuous, parallel, or mobile robots to focus solely on fixed-base serial architectures; selecting topics like ’inverse kinematics and serial robots and analytical or numeric or machine learning methods’. The review reveals that 32% of the analyzed methods are numerical, while 30% are AI-based approaches, reflecting the growing interest in data-driven solutions for IK problems; this scenario highlights the implementation of these methods given the limitations of analytical methods. Moreover, 56% of the nonanalytical approaches achieve an accuracy better than 0.01 mm; and about 70% of these approaches have response times exceeding 20 ms or don´t evaluate the metric, highlighting a critical bottleneck for real-time use. We conclude that hybrid IK methods, combined with standardized validation protocols, are essential for critical applications like robotic surgery. Future work must address benchmarking gaps, especially in AI-based IK, to enable reliable adoption in industry
Efficient Vision-Guided Robotic System for Fastening Assembly Using YOLOv8 and Ellipse Detection in Industrial Settings
Full text linkThe assembly of fastening components traditionally relies on labour-intensive human-machine collaboration, which incurs high costs. Existing methods often assume fixed positions or use markers for guidance, requiring extra effort to place and maintain them. This study aims to develop an intelligent control system for a vision-equipped robotic arm to autonomously assemble fastening components in industrial settings, enhancing flexibility and reducing labour costs. The system integrates object detection with edge and ellipse detection, alongside filtering techniques, to accurately locate the centres of the fastening components.  The key contribution is the system's ability to perform autonomous assembly without predefined positions, enhancing flexibility in varied environments. YOLOv8 is employed to detect the bolt and nut, followed by edge and ellipse detection to pinpoint centre coordinates. A depth camera and kinematic calculations enable accurate 3D positioning for pick-and-place tasks. Experimental results demonstrate the system’s high effectiveness, with less than 1% of targets undetected. Based on experiments conducted in randomly arranged conditions, the system demonstrated high effectiveness, achieving over 99% detection accuracy. It achieved an 87% average success rate for picking fastening components ranging from sizes M6 to M18, and a 90% success rate for precise placement. Additionally, the system demonstrated robustness across various component sizes, with a minor increase in orientation errors for smaller components, attributed to depth estimation challenges. Future work could explore alternative depth data collection methods to improve accuracy. These results confirm the reliability of the system in flexible assembly tasks, demonstrating its potential to reduce costs by minimising manual involvement in industrial settings
A Comparative Study of PID, FOPID, ISF, SMC, and FLC Controllers for DC Motor Speed Control with Particle Swarm Optimization
Full text linkDirect Current (DC) motors are extensively used in various applications due to their versatile and precise control capabilities. However, they face operational challenges such as speed instability and sensitivity to load variations and external disturbances. This study compares the performance of several advanced control methods—Proportional Integral Derivative (PID), Fractional Order PID (FOPID), Integral State Feedback (ISF), Sliding Mode Control (SMC), and Fuzzy Logic Controller (FLC) for DC motor control. Particle Swarm Optimization (PSO) is employed to optimize the tuning parameters of PID, FOPID, ISF, and SMC controllers, while FLC is implemented without optimization. The simulation results indicate that the PSO-FOPID controller exhibits the best overall performance, characterized by the fastest rise and settling times and the lowest ITSE, despite a minor overshoot. The PSO-PID controller also performs well, with fast response times, although it is less efficient in terms of settling time and ITSE compared to PSO-FOPID. The OBL/HGSO-PID controller, while stable and overshoot-free, has a slower response. The PSO-ISF controller shows the highest stability with the lowest SSE values, making it suitable for applications requiring high stability. The PSO-SMC controller demonstrates good stability but is slightly slower than PSO-ISF. The FLC controller, however, performs the worst, with significant overshoot and long recovery times, making it unsuitable for fast and precise control applications.  The robustness analysis under varying motor parameters further confirms the superiority of the PSO-FOPID controller, which outperforms OBL/HGSO and OBL-MRFO-SA optimizations across both PID and FOPID controllers, making it the most effective solution for applications requiring high precision and rapid response
Design and Implementation of Proportional-Integral Controller for Single Phase Stand-Alone Inverter with an LC-Filter
Full text linkObtaining a sine wave from a DC source using an inverter and a filter is a challenge that requires a suitable design to meet load requirements as operating conditions change. This work aims to develop a suitable design for an LC-type pass-through filter and a suitable design for a conventional controller. A simulation model for the implementation and operation of a single-phase standalone inverter is being developed and designed using Matlab. In this work, the researchers demonstrate the behavior of a simulated system using a single-phase inverter model connected to a 400 V DC power supply. An LC-type filter is also connected to the inverter and the load. Tests are conducted to determine the system's behavior under various conditions. The researchers are interested in changing operating conditions, and the problem of load variations, on the one hand, and transients and the system's return to a steady state, on the other. The researchers propose one method for overcoming system fluctuations using a conventional controller (PI controller). Tests can cover identifying system behavior, and from there, using the controller, an appropriate reference voltage can be set to supply the load. The proposed model consists of a power supply, four IGBT transistor switches to build a single-phase bridge inverter, a filter with an inductor (4.06e-3H) and a capacitor (6.23e-6F), as well as a reference voltage of 200V and 300V, and a load of 55? and 100?. A suitable conventional microcontroller (PIC) is also designed. The feasibility of providing a sine wave with the proposed reference voltage has been verified, proving the feasibility of the model and its potential for future use. Matlab was used to conduct simulation tests of the proposed model, and high performance, accuracy, and quality were obtained at a level suitable for real-time applications
Optimization of Harmonic Elimination in PV-Fed Asymmetric Multilevel Inverters Using Evolutionary Algorithms
Full text linkModern power electronics depend heavily on Multilevel Inverters (MLIs) to drive high-power systems operating in renewable energy systems electric vehicles along with industrial motor drives. MLIs create AC signals of high quality by joining multiple DC voltage sources which leads to minimal harmonic distortion outputs. The Cascaded H-Bridge MLI (CHB-MLI) stands out as a first choice among different topologies of MLI for photovoltaic (PV) applications because it includes modular features with fault tolerance capabilities and excellent multi-DC source integration. To achieve effective operation MLIs need optimized control strategies that reduce harmonics while maintaining highest performance. Using SHE-PWM technology provides an effective technique for harmonic frequency reduction which allows the improvement of waveform integrity. Technical restrictions make the solution of SHE-PWM nonlinear equations exceptionally challenging to implement. The resolution of complex non-linear equations requires implementation of GA combined with PSO and BO for optimal switching angle determination. The research investigates an 11-level asymmetric CHB-MLI using five solar panels where SHE-PWM switching angles are optimized through GA, PSO and BO applications. Simulation tests validate that the implemented algorithms succeed in minimizing Total Harmonic Distortion (THD) and removing fundamental harmonic disturbances. The evaluation demonstrates distinct capabilities of each optimization approach between accuracy rates and computational speed performance. These optimization methods yield practical advantages which boost the performance of multi-level inverters. The researchers who follow should study actual hardware deployments together with combined control approaches to enhance power electronic applications