Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Not a member yet
424 research outputs found
Sort by
ANFIS-Controlled High Step Up DC DC Converter for Fuel Cell Systems with Enhanced Efficiency Against Load Variation
No full textThe primary challenge in utilizing Fuel Cell (FC) systems lies in their inherently low and fluctuating output voltage, which contrasts with the requirements of a Direct Current (DC) bus network that demands a stable and relatively high voltage level. Ensuring consistent voltage regulation in the DC bus network is essential for reliable system performance. An interface converter is required to elevate and stabilize the voltage output under dynamic operating conditions. This paper introduces a high step-up DC–DC converter integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control scheme for enhancing the performance of FC power systems. The proposed work encompasses the modeling, analytical design, and structural development of the converter and its intelligent control mechanism. The proposed high step-up converter exhibits a novel structural configuration that integrates a clamp unit, a Multiplier Cell (MC), and cascaded Quadratic Boost Converter (QBC) stages. The contribution of this converter topology lies in its ability to enhance the reliability of fuel cell–based renewable energy systems, achieve high voltage amplification, ensure optimal efficiency, and maintain dynamic stability. This topology is specifically developed to attain an ultra-high voltage conversion ratio, achieving a significant voltage gain of up to 9.65 times, thereby effectively increasing the input voltage from 45 V to 400 V. The ANFIS controller effectively maintains a stable output voltage of 400 V with a maximum deviation of only ±3.5%. The proposed converter achieves a peak efficiency of 87% under varying load conditions, demonstrating its suitability for fuel cell-based energy systems
Hybridization of PSO-SSA for Photovoltaic System MPPT Under Dynamic Irradiance and Temperature
No full textMaximum Power Point Tracking (MPPT) has become an important area of research to optimize the power generated by photovoltaic (PV) systems, particularly under various configurations such as series and parallel. Conventional methods, including Perturb and Observe (P&O) and Incremental Conductance (InC), often fail under dynamic or partial shading conditions, while metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Salp Swarm Algorithm (SSA) provide global optimization but still suffer from slow convergence and power oscillations. This study proposes a hybrid MPPT approach by combining PSO and SSA to overcome these limitations. The algorithm was implemented in MATLAB/Simulink and tested under 96 scenarios covering series and parallel configurations with irradiance and temperature variations that change both suddenly (< 1 s) and gradually (> 1 s). Simulation results demonstrate that the hybrid PSO–SSA consistently achieves faster convergence compared to standalone PSO or SSA, with an average convergence time of 0.286 s in the series configuration (25–36% faster) and 0.282–0.284 s in parallel configuration, while achieving comparable power output to PSO. Overall, the proposed hybrid PSO–SSA algorithm provides a faster, more adaptive, and robust MPPT strategy under realistic PV operating conditions, contributing to reducing energy losses in fluctuating environments
Performance Analysis of Position Estimation in a Quarter-Car Suspension System Using Kalman-Bucy as a State Observer
No full textThis study explores the implementation of the Kalman-Bucy observer for state estimation in a quarter-car suspension system operating under various real-world conditions. The research focuses on evaluating the observer’s performance in the presence of road surface disturbances, such as speed bumps, humps, and potholes, combined with stochastic noise and parameter variations. To test its robustness, the system is subjected to Gaussian white noise with an intensity of 10% in both the process and measurement signals. A sensitivity analysis is also carried out by varying the vehicle mass between 400 kilograms under unloaded conditions and 600 kilograms when fully loaded, thereby simulating different passenger and cargo scenarios. Simulation results demonstrate that the Kalman-Bucy observer consistently provides accurate and stable estimations of vehicle position, even in noisy and dynamically changing environments. The observer achieves a Root Mean Square Error (RMSE) of 3.3885 × 10⁻⁵ m, indicating near-perfect estimation accuracy. When integrated into a PID control framework, the proposed observer significantly improves system performance by reducing rise time from 9.76 s to 0.16 s, decreasing undershoot from −0.22 m to −0.15 m, and maintaining a similar settling time of approximately 25 s. Overall, the Kalman-Bucy observer proves to be a reliable and efficient method for state estimation and control enhancement in active suspension systems, showing strong potential for real-world automotive applications
Integrating Meteorological and PV Data for Short-Term Solar Irradiance Forecasting Using BPNN
No full textSolar power plants are highly dependent on solar radiation intensity, which fluctuates due to changes in atmospheric conditions. To maintain system stability and efficiency, an accurate short-term solar radiation prediction model is essential. This study developed a model for forecasting global solar radiation one hour ahead using the Backpropagation Neural Network (BPNN) method. The dataset was obtained from a photovoltaic (PV) system at Building A8 of Surabaya State University, recorded over four days (June 14-17, 2025) at two-minute intervals. Five input variables were used: clearness index, solar radiation, air temperature, air humidity, and PV output power, resulting in a total of 3,020 data samples. The model was trained through a trial-and-error process by varying the number of neurons, hidden layers, and epochs to determine the optimal configuration. The forecast capability of the model was assessed through four statistical indicators: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The best performance was achieved with a network architecture of 15 input neurons representing input variables resulting from data transformation using the sliding window method, one hidden with 25 neurons, and a single unit in the output layer trained for 2000 epochs, resulting in R2 = 0.98, MAPE = 5.89%, and MSE = 0.00027. The novelty of this research lies in the integration of meteorological data with actual PV power output as model input, enabling the network to capture more realistic nonlinear temporal relationships. The proposed short-term forecasting model provides a practical approach to predicting solar radiation based on historical data and can support efficient energy management and photovoltaic system performance analysis
From Digital Literacy to Public Trust: The Strategic Role of E-Government Service Quality
No full textThe transformation of public services in the digital era necessitates a synergistic alignment between e-governance practices and the digital competencies of the community to ensure services that are both high in quality and satisfactory to users. This study investigates the effect of e-governance and digital literacy on public satisfaction, with digital service quality serving as a mediating variable. The research focuses on the utilization of the S-Kepuharjo village digital service platform. Employing a quantitative approach, data were collected through a survey of 385 respondents and analyzed using Structural Equation Modeling (SEM) with AMOS software. The findings reveal that e-governance has a significant impact on satisfaction, both directly and indirectly via service quality. On the other hand, digital literacy does not directly influence satisfaction but exerts a significant indirect effect when mediated by digital service quality. The study confirms that service quality acts as a critical intermediary linking governance to user satisfaction. These results highlight that the success of village-level digital transformation is largely determined by the responsiveness and effectiveness of digital services. Accordingly, enhancing the inclusiveness, accessibility, and user-oriented nature of these services is essential for fostering public satisfaction and engagement in the digital landscape
Enhancing Plant Recommendation through IoT-integrated LLM Systems
No full textOver the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a wide range of industries. Alongside these developments, the agricultural sector stands to benefit significantly from the integration of technology. A significant challenge encountered by farmers is selecting the appropriate crop to plant. The selection of crops is influenced by various factors. Despite advancements in agricultural technology, a considerable gap remains in the integration of IoT with large language models (LLM) for delivering context-specific and data-driven plant recommendation. This study evaluates the reliability of plant recommendations produced by Internet of Things (IoT) devices utilizing the Llama 3.2 model. The model leverages real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations from the base model and a fine-tuned model were compared using precision, recall and F1-score metrics, and were further assessed against established agricultural literature on plant compatibility and growth requirements through human evaluation. The results show substantial performance improvements. The proposed approach achieved an AUC value 59% higher than that of the base model. Precision increased by 40%, recall improved by 105%, and the F1 score rose by 80% compared to the base model
Mathematical Modeling of an Adaptive Lighting System Based on Solar Panels and Digital Communication for Microalgae Synthesis
No full textMicroalgae are promising photosynthetic microorganisms widely used in biofuel, pharmaceutical, and environmental applications. Their cultivation efficiency is highly influenced by light intensity, temperature, and pH. This study presents a mathematical model of an adaptive lighting system powered by solar energy and controlled through digital communication for sustainable microalgae synthesis. The system dynamically regulates LED illumination using real-time environmental feedback from temperature and pH sensors integrated into an IoT network. The model combines first-order ordinary differential equations (ODEs) to describe solar input, LED power consumption, environmental response, and communication delay. Numerical simulations performed in MATLAB show that the adaptive control algorithm maintains optimal illumination while minimizing unnecessary energy use. Compared to conventional static lighting, the proposed model achieves a 35% reduction in energy consumption and improved environmental stability despite communication latency. The study provides a foundational framework for developing intelligent, energy-efficient photobioreactor systems that align with the Sustainable Development Goals (SDG 7 and SDG 13). Future work may extend the model toward real-time, predictive, and machine-learning-based control for field-scale implementation
Leveraging Green IoT to Enhance Energy-Saving Efficiency in Fairness-Oriented Residential Photovoltaic Charging Stations
No full textAs electric vehicles (EVs) continue to gain global popularity, residential photovoltaic (PV) charging stations are becoming more common, providing a sustainable way to charge EVs. However, the intermittent nature of solar energy creates challenges in ensuring consistent and fair charging, making fairness-based charging scheduling essential. To automate this process, residential PV charging stations require a customized Internet of Things (IoT) system. A significant concern is the substantial energy consumption due to the high volume of data transmission within the IoT system. This research aims to enhance energy efficiency by leveraging green IoT strategies suitable for such applications. The study proposes the use of edge computing, optimized data transmission scheduling, and delta compression techniques at the edge to minimize energy use. The results demonstrate that these strategies are effective in achieving energy savings. Energy-saving efficiency on the source side ranges from 1.96% to 7.84%, while on the load side, it ranges from 57.5% to 61.3%. These findings highlight the effectiveness of the proposed strategies in reducing energy consumption, providing an efficient solution for optimizing data transmission in residential PV charging stations. Overall, the strategies contribute to the sustainable operation of electric vehicle charging infrastructure by improving energy efficiency and ensuring fair distribution of charging resources
Design of 2x1 Single-band Microstrip Antenna Array with Proximity Coupling for Enhanced CCTV Performance
No full textThe increasing demand for reliable wireless communication in modern surveillance systems, particularly Closed-Circuit Television (CCTV), requires the development of antennas with high efficiency, wide bandwidth, and stable signal performance. To meet these requirements, this study presents the design and analysis of a 2×1 microstrip antenna array with rectangular patches that use proximity coupling, optimized for operation in the 2.4 GHz ISM band. The antenna was designed and simulated using CST Studio Suite to evaluate its electromagnetic characteristics, while measurements using a Vector Network Analyzer (VNA) were performed to validate the performance of the manufactured prototype. Simulation results show that the antenna achieves a reflection loss of −24.62 dB, a voltage standing wave ratio (VSWR) of 1.12, and a frequency bandwidth of 159 MHz, indicating good impedance matching and wide operational capability. Meanwhile, measurement results showed a reflection loss of −12.59 dB, a VSWR of 1.15, and a frequency bandwidth of 86 MHz. Both simulation and measurement results showed directional radiation patterns, ensuring efficient energy radiation and better signal focus for monitoring coverage. The designed antenna also shows a measured gain of 9.25 dBi, exceeding the simulated gain of 6.99 dBi, confirming improved performance. The difference between simulation and measurement is mainly due to variations in substrate thickness, material tolerance, and environmental factors during testing. Overall, the proximal coupling approach has proven effective in improving coupling efficiency without adding design complexity. This antenna is well-suited for reliable and efficient data transmission in CCTV applications. Furthermore, the findings contribute significantly to advancements in antenna technology, particularly in the domains of wireless communication, IoT, and smart city-based surveillance systems
Design of a Real-Time User Feedback for Mitigating Spurious SpO₂ Readings in Pulse Oximetry for Outpatient Monitoring
No full textSpurious SpO₂ readings—arising from motion artifacts, environmental interference, or device variability—remain a major limitation in wearable pulse oximetry, potentially triggering false alarms or missing hypoxemia during outpatient monitoring. Conventional devices often lack real-time mechanisms to detect and mitigate such errors, with previous reports indicating measurement biases of 11.2 - 24.5% across different models, underscoring the need for improved accuracy and user guidance. To address this gap, we present the design of an IoT-enabled wearable pulse oximeter with real-time user feedback, delivered through a mobile application. The system integrates a pulse oximetry and heart rate sensor (MAX30100) with a carbon monoxide gas sensor (MQ-7) and provides targeted notifications to guide corrective actions such as repositioning the probe, removing nail polish, or moving to fresh air. Validation involved controlled scenario testing (undetected SpO₂, CO >40 ppm, nail polish, and loose contact) and user trials with 15 healthy volunteers from varied academic backgrounds. The prototype demonstrated high accuracy, with low relative errors—0.92% (HR), 0.93% (SpO₂), and 0.015% (CO)—and strong usability, achieving 93.3% compliance with corrective prompts, an average response time of 4.0±0.7 seconds, and a satisfaction score of 4.3/5. Compared with commercial oximeters, the proposed system improved reliability by reducing measurement errors by at least 87% through real-time corrective feedback. Future work will focus on energy-efficient power management and large-scale community-based trials to further validate performance across diverse patient populations