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Elimensi Journal of Electrical Engineering
Published by Cattleya Darmaya Fortuna
ISSN : -     EISSN : 29872928     DOI : https://doi.org/10.54209/elimensi.v3i03
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering
Articles published in cover key areas in electrical engineering such as : Electrical power and energy: Transmission and distribution, high voltage, electrical energy conversion, power electronics and drive. Telecomunication and Signal Processing: Antenna and wave propagation, network and systems, Modulation and signal processing, Radar and sonar, Radar imaging; Radio, multimedia content, Routing protocols, Wireless communications, Signal Processing, Image Processing, Voice Processing. Control automation and Robotic: Robotics, Automation, Pattern Recognition, Biosignal Engineering, Control Theory, Applied Control, System Design, Optimization, Process Control, Sensor, Machine Learning.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 30 Documents
 1   2   3 
Self-Supervised Multimodal Biosignal Processing for Early Detection of Cardiac Arrhythmias Using Wearable Silaen, Murni
Journal of Electrical Engineering Vol. 3 No. 02 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i02.404

Abstract

This study proposes a multimodal self-supervised framework for early detection of cardiac arrhythmias based on wearables combining 1-lead ECG, PPG, and IMU. The core method includes contrastive pretraining + masked reconstruction on synchronized windows and adaptive fusion weighted by Signal Quality Index (SQI) and aleatoric uncertainty, complemented by domain adaptation for invariant representation across devices and populations. The unlabeled corpus for pretraining contains 2,400 hours of free-living data from 820 participants (three different devices), while fine-tuning and clinical testing used 1,100 hours of labeled data (n=210; paroxysmal AF, PVC/PAC, SVT, episodic brady/tachycardia). In subject-wise testing, the model achieved Se 92.8%, Sp 97.1%, F1 90.3%, AUROC 0.972 for AF; F1 83.6% for PVC/PAC; and Se 88.9% for SVT. At episode-level evaluation (≥30 s), AF sensitivity was 94.6% with false alarms per hour (FPh) of 0.28 and a median time-to-detection of 22 s. Robustness increased at high activity (ECE 0.032, NLL −27%), leave-device-out generalization remained strong (AUROC 0.957), and the on-device implementation met resource limits (~68 ms/window on an edge-class MCU, <2.3 MB memory). These results demonstrate that signal quality/uncertainty-aware multimodal SSL can suppress false alarms without sacrificing sensitivity, enabling reliable and label-efficient home monitoring for wearable-based arrhythmia screening.
Adaptive Resilient Control of Grid-Forming Converters for High-Renewable Transmission Networks khalifa, Rio
Journal of Electrical Engineering Vol. 3 No. 02 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i02.405

Abstract

High renewable penetration makes the transmission grid inertia-light and vulnerable to frequency/voltage deviations, interconverter resonances, and current limits during disturbances. This paper proposes an Adaptive-Resilient Grid-Forming Converter (AR-GFC) that integrates online grid strength estimation (eSCR), multi-scale tuning—slow-adaptation for virtual inertia/damping/droop and fastadaptation for impedance reshaping—as well as angle-aware current limiting and post-fault resynchronization. Stability verification is performed through passivity/sector-bounded certification and ISS Lyapunov, while multi-GFC coordination utilizes virtual Δf/ΔV sharing. EMT and HIL evaluations under SCR 6→1.7, load step, and FRT 1-φ/3-φ (120–200 ms) scenarios show that AR-GFC reduces ROCOF by ~35–40%, increases nadir frequency by +0.1–0.2 Hz, accelerates settling by 30–35%, and reduces voltage overshoot by 2530%. At FRT, current violations are limited to ≤20 ms around ≈100% without internal angle loss; phase margin increases by +20–25°, the 4–6 Hz resonance peak is eliminated, and power sharing errors shrink to |ΔP|, |ΔQ| ≈3–4% while reducing I²t by ~20% and curtailment by 12 18%. These results confirm that AR-GFC maintains robust and fault tolerant grid-forming properties, making it suitable for adoption in high renewable penetration transmission networks. 
Reinforcement Learning-Based Model Predictive Controller for Mobile Robots in Dynamic Environments with Safety Constraints Sulistina, Olivia
Journal of Electrical Engineering Vol. 3 No. 02 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i02.408

Abstract

This paper proposes a Reinforcement Learning-based Model Predictive Controller (RL-MPC) for mobile robots operating in dynamic environments with stringent safety constraints. The key challenges addressed include model and perception uncertainty, moving obstacles, and real-time computational requirements. The proposed framework combines three key components: first, a learned dynamics model with uncertainty estimation to enhance the robustness of the system in uncertain environments; second, a risk-aware MPC that uses chance constraints and Conditional Value-at-Risk (CVaR) to enforce safety by ensuring that violation probabilities remain below a predefined threshold; and third, a Control Barrier Function (CBF) that acts as a safety layer, projecting actions to stay within a predefined safe set. The policy learning, utilizing Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC), is integrated with reward shaping and safety shielding to ensure that the robot prioritizes safety while achieving performance. Additionally, a sim-to-real strategy with domain randomization is employed to improve robustness when transitioning from simulation to real-world applications. The framework is evaluated in three different scenarios: solid static obstacles, moving obstacles, and multi-agent traffic. The results demonstrate that RL-MPC reduces the safety violation rate to ≤2%, a significant improvement compared to 2.8–12.3% in the baseline. Moreover, RL-MPC increases the minimum distance between the robot and obstacles to approximately 0.2 meters in dynamic scenarios and achieves a success rate of 95–99%, without significantly increasing the path length or energy consumption. Although the computational overhead increases by approximately 3–5 ms compared to classical MPC, the system still meets the 20 ms per cycle requirement, making it suitable for real-time applications. The ablation study confirms the dominant role of the CBF and risk-based constraints in preventing near-collisions, highlighting their crucial contribution to the system's safety. Overall, the RL-MPC framework provides a favorable trade-off between safety, efficiency, and implementation feasibility, offering a promising solution for online autonomous operations in dynamic environments.
Real-Time Hand Gesture Recognition from EMG Biosignals Using Interpretable Deep Learning for Adaptive Prosthesis Tina, Trifena
Journal of Electrical Engineering Vol. 3 No. 03 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i03.412

Abstract

Surface electromyogram (sEMG)-based hand gesture recognition has the potential to improve natural prosthetic control, but its performance often suffers from domain shift (electrode drift, fatigue, cross-day variability) and limited model interpretability. This study proposes an interpretable and adaptive deep learning framework that combines two representation streams (time and time-frequency) with multi-head attention and attribution consistency regularization to generate stable and clinician-auditable relevance maps. Robustness across sessions and subjects is enhanced through self-paced pretraining (temporal contraction), few-shot calibration, and domain alignment (DANN). Uncertainty estimation and calibration (MC-Dropout + temperature scaling) trigger confidence-gated control as a safety safeguard. Evaluation across three scenarios shows within-session accuracy of 96.8% (macro-F1 96.1%), cross-session accuracy of 91.7% (macro-F1 90.5%, ECE ≈ 3.6%), and cross-subject accuracy of 86.4%. Edge optimization (INT8 + structured pruning) reduces inference latency from 92 ms (FP32) to ~52–66 ms with only a ~2% accuracy reduction, and power consumption is ~5–6 W, meeting real-time response requirements. Reliability diagrams confirm the calibrated probabilities, while ablation analysis demonstrates significant contributions of attention, loss attribution, and DANN to performance and stability. Overall, this framework bridges the lab-clinic gap by providing an accurate, explainable, adaptive, efficient, and safe solution for real-time hand prosthesis control, while opening up research directions for longitudinal and continuous control based on user co-adaptation.
MPC-Bayesian Process Control Optimization for Continuous Chemical Plant with Load Variability and Time Delay Hartono, Rudi
Journal of Electrical Engineering Vol. 3 No. 03 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i03.413

Abstract

This research proposes a Bayesian MPC framework for continuous chemical plant operations facing load variability and non-stationary time delays. The process model is built in a grey-box manner and supplemented with Gaussian Process-based residual learning to capture the model-plant mismatch and its uncertainties. Delays are modeled as time-varying variables through probabilistic estimation (multiple-model/particle-based), which are then integrated into a delay-aware predictor so that state propagation considers the delay distribution over the horizon. State estimation is performed using Unscented Kalman Filter (UKF) or Ensemble Kalman Filter (EnKF), while control decisions are derived from economic MPC with chance constraints to ensure that the risk of constraint violations remains below a predefined threshold. To make it suitable for real-time application on industrial platforms (cycle time ~2 s), we employ adaptive move-blocking, warm-start, and real-time iteration. Evaluation against three benchmarks—coordinated PID, deterministic MPC, and robust MPC—shows consistent performance improvement in scenarios with ±30% throughput change and non-stationary delays ranging from 2 to 8 minutes. Quantitatively, the proposed approach reduces daily economic costs by approximately 6.4% compared to deterministic MPC, decreases energy consumption, reduces off-spec rate to approximately 1.1%, minimizes constraint violations to ≈2 occurrences per 24 hours, and shortens settling time for grade changes to approximately 19 minutes. Ablation studies confirm the complementary contributions of residual learning, delay-aware predictors, and chance constraints to risk and cost reduction. These results confirm the readiness of implementing Bayesian MPC in modern DCS/SCADA for more reliable and cost-effective plant-wide operations.
Design of a Teleoperation Haptic Control System with Network Delay Compensation Using Passivity Based Control and Observers Anggara, Riswan
Journal of Electrical Engineering Vol. 2 No. 03 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i03.415

Abstract

This study proposes a robust bilateral haptic teleoperation control architecture against time-varying network delays by combining Passivity-Based Control (PBC), passivity observer–controller (PO/PC), energy tank, and passivated observers (disturbance/force observer and passivity-limited Kalman filter). The communication channel is modeled as a packet network with delay, jitter, and packet loss, while transparency and stability are maintained through scattering variables and passivity metric-based adaptive energy regulation. Validation is performed on three network scenarios (light–medium–heavy) through simulation/Hardware-in-the-Loop and WAN emulation tests. Results show that the complete scheme significantly decreases position RMSE and force RMSE compared to the PBC baseline, generally in the range of 25–40%, widens the Z-width by about 35–60%, and improves the MOS of haptic perception. Energy-based stability indicators show Max ΔE ≈ 0 and the tank energy remains positive, confirming the passivity and stability of input–output under delay/jitter/loss variations. Mechanistically, adaptive coupling between the PO/PC and the observer keeps the system stable when conditions deteriorate and restores transparency when conditions improve. These findings confirm the feasibility of a passivity-based approach for haptic teleoperation in real packet networks and open the development direction to multi-DOF, energy-based event-triggered communication, and large-scale user studies.
Performance Analysis of 5G Massive MIMO Networks Using Hybrid Beamforming and Geometric Channel Models Sinaga, Tommy
Journal of Electrical Engineering Vol. 3 No. 03 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i03.416

Abstract

This paper analyzes the performance of a 5G massive MIMO network based on hybrid beamforming (HBF) on geometric channels of 3GPP TR 38.901. The evaluation is carried out through link/system-level simulations at 28 GHz (OFDM 400 MHz), BS 128 antennas, 8 UEs, HBF architecture with N_RF.∈{12,16} and a 3–4 bit phase shifter, covering both UMi street canyon and indoor office (LOS/NLOS) scenarios. Channel estimation and precoder/combiner design follow the framework of compressed/hierarchical beam training and factorization of fully digital solutions; each configuration is evaluated on ≥1000 Monte Carlo drops. Results show that HBF (N_RF=16, 4-bit) achieves ≈90–95% sum spectral efficiency (SE) over fully digital with an average gap of 3–6 bps/Hz, while N_RF=12 reduces SE by an additional ~1–2 bps/Hz but provides the highest energy efficiency (EE). Compared to fully digital, HBF improves EE by ~4–5× due to the RF-chain reduction despite a slight SE decrease. Robustness tests against angle misestimation show SE degradation slopes of approximately 0.5%/° (fully digital), ~1.2%/° (4-bit HBF), and ~1.6%/° (3-bit HBF). The differences between scenarios highlight the sensitivity of HBFs to wider angular spread indoors. Overall, HBFs with ≥4-bit and N_RF selection proportional to the number of user flows provide the best SE-EE compromise, making them feasible for 5G mmWave implementations. Future directions include beam-squint mitigation in broadband, more detailed hardware impairment modeling, and robust machine learning-based HBF design.
Advanced Control Systems Model Predictive Control (MPC), Adaptive Control, Robust Control, Sliding Mode Control, Reinforcement Learning based Control Wanto, Ari
Journal of Electrical Engineering Vol. 2 No. 02 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i02.417

Abstract

This study proposes a layered hybrid control framework for automation and robotics systems that combines Tube-based Robust MPC (RMPC) as a safety envelope, adaptive parameter estimation to narrow online uncertainty, Sliding Mode Control (SMC) as a high-resilience fail-safe, and Reinforcement Learning (RL) as a performance booster through a warm-start/residual policy scheme that is always constrained by Control Barrier Functions (CBF). The study aims to achieve better tracking performance and computational efficiency without sacrificing safety and constraint compliance. The methodology includes constrained nonlinear plant modeling, design of tube invariance-based RMPC with constraint tightening, RLS/concurrent learning-based adaptive estimator, SMC for infeasibility/model drift conditions, and a CBF-based safety filter formulated as an online small QP. Evaluation is done through ablation studies on multi-domain benchmarks and HIL-style tests, with finite-horizon cost, ITAE, constraint violation rate, and computational latency (average and WCET) metrics. The results show that the RL+CBF configuration reduces the finite-horizon cost by about 15.7% compared to pure RMPC, maintaining a violation rate of ~0.35% (better than RMPC: 0.80%) and significantly below that of unfiltered RL (4.20%). The warm-start RL scheme accelerates the completion time of the MPC solver by ≈15–18% over various prediction horizons and reduces the WCET (26.4 → 23.3 ms), supporting real-time implementation. These findings confirm that the integration of RMPC–Adaptive–SMC–RL(+CBF) effectively bridges model-based optimality, online adaptivity, robust resilience, and learning from data in a single architecture that is safety-certified and feasible for real-world applications demanding high uptime.
Industrial Automation PLC, SCADA, IoT-based monitoring, smart factories, and cyber physical production systems Dharmawati, Riris
Journal of Electrical Engineering Vol. 2 No. 02 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i02.418

Abstract

This study evaluates an integrated PLC–SCADA–IIoT architecture with edge analytics and a closed-loop digital twin to realize a smart factory/CPPS in a brownfield environment. A design–build–measure–learn methodology is applied with interoperability standards (OPC UA/MQTT), network segmentation (optional TSN), and security governance based on IEC 62443. A hybrid (physics-informed + data-driven) predictive model is built and deployed at the edge; the digital twin is synchronized through state estimation (Δt ≈ 100–200 ms) to provide set-point recommendations and maintenance scheduling audited by the PLC safety guard. Factorial tests on combinations of line speed and degradation levels show significant performance improvements (Welch t-test, α=0.05): OEE increases by ~6–12 absolute points, cycle time decreases by 5–8%, energy/unit decreases by ~8%, and scrap decreases by ~35–40% at severe degradation. The increase in determinism is reflected in the decrease in p95 latency (PLC↔Edge 55→18 ms, Edge↔SCADA 85→35 ms). In predictive maintenance, performance improved (AUROC 0.78→0.94; PR-AUC 0.41→0.72; F1 0.56→0.78; RUL-MAE 22.1→9.4 hours) with the false alarm rate decreasing by 0.095→0.038. The safety posture increased from 1.2–2.0 to 3.9–4.5 (scale 0–5). The study’s key contributions are the co-design of a PLC deterministic loop with an AI adaptive loop at the edge, a closed-loop hybrid digital twin, and the measurement of technical–business benefits linked to ROI, providing a practical, incremental adoption path for manufacturers—especially SMEs—towards smart factories.
Automation In Biomedical Or Agricultural Systems Autonomous Surgery Precision Agriculture Assistive Exoskeletons Langga, Sucipto
Journal of Electrical Engineering Vol. 2 No. 02 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i02.420

Abstract

This research proposes a cross-domain autonomy framework for automation in biomedical and agricultural systems—spanning autonomous surgery, precision agriculture, and assistive exoskeletons—by integrating self-supervised multimodal perception, constraint-based planning–control (Model Predictive Control/MPC with safety filter Control Barrier Functions/CBF), and meta-learning-based fast personalization. The architecture is implemented on low-latency edge computing and evaluated through a standardized protocol linking technical metrics, safety, and operational benefits. In surgical tasks, the proposed method reduces path error by ~39% (1.7 mm vs. 2.8 mm), peak force by ~22% (3.2 N vs. 4.1 N), and constraint violations by ~93% (0.04 vs. 0.60 events/min), with latency <50 ms. In precision agriculture, the system improved weed detection mAP to 0.78, decreased geolocation error to 3.9 cm, reduced nozzle drift to 9.6 cm, and saved 34.5% on average input without sacrificing dose uniformity (CV 17.5% vs. 21.0%). In the exoskeleton, CBF-constrained few-shot personalization reduced metabolic cost by −12.4%, improved step symmetry to 0.90 and user comfort to 4.3/5, while reducing constraint violations by 10× (0.02 vs. 0.20 events/min). The key novelty lies in cross-domain transferable “autonomy primitives” with formal safety guarantees, combined with rapid adaptation at low data cost. Results demonstrate that the framework is safe, adaptive, and efficient, ready to accelerate translation from the laboratory to clinical practice, production fields, and rehabilitation.

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