Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Leveraging Green IoT to Enhance Energy-Saving Efficiency in Fairness-Oriented Residential Photovoltaic Charging Stations

2025-09-12T21:52:40+00:00

As 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.

ANFIS-Controlled High Step Up DC DC Converter for Fuel Cell Systems with Enhanced Efficiency Against Load Variation

2025-08-22T00:34:47+00:00

The 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. To overcome this issue, 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 fuel cell (FC) power systems. The proposed work encompasses the modeling, analytical design, and structural development of the converter and its intelligent control mechanism, supported by comprehensive simulation results. The converter structure incorporates a clamp unit, a VMC (Voltage Multiplier Cell), and cascaded QBC (Quadratic Boost Converter) stages for achieving ultra-high voltage gain, enabling a substantial voltage gain of up to 9.65 times, effectively boosting the voltage from 45 V to 400 V. The system's performance was evaluated under three distinct scenarios: (1) varying input voltage with constant load power, (2) constant input voltage and load power, and (3) simultaneous variation in both input voltage and load demand. The ANFIS controller effectively maintains a stable output voltage of 400 V with a maximum deviation of only ±3.5%. In addition, the proposed converter achieves a peak efficiency of 87% under varying load conditions, demonstrating its suitability for fuel cell-based energy systems

Comparative Evaluation of BM25–FAISS and Small-LLM–GPT in Retrieval-Augmented Generation Concept Map Assessment

2025-12-22T04:38:07+00:00

The development of Large Language Models (LLMs) has opened up new opportunities in the development of automated concept map-based assessment systems. One promising approach is Retrieval-Augmented Generation (RAG), which combines search capabilities to find relevant information with generation to produce more meaningful context-based assessments. This study compares two search methods, namely BM25 based on keyword matching and FAISS based on vector representation, as well as two generative models, namely Small-LLM and GPT, in the task of concept map proposition assessment in the relational database domain. The results show that the FAISS–GPT combination provides the best performance with a Macro-F1 score of 0.338, a QWK score of 0.146, and the lowest error with an MAE of 0.973 and an RMSE of 1.321, indicating a slight but noticeable improvement in agreement with expert scores compared to other configurations. Additionally, this combination also displayed an Explanation Relevance Score (ERS) of 0.79, demonstrating GPT's ability to generate more relevant, consistent, and human-like explanations. In contrast, Small-LLM had lower accuracy despite excelling in computational time efficiency, making it a viable option for resource-constrained systems. Overall, the results of this study confirm that the integration of dense retrieval FAISS and large GPT language models can improve the quality of concept-based automatic assessment in terms of accuracy, consistency, and semantic relevance, thereby potentially strengthening concept-based learning systems in digital education.

Speed Synchronization of Multi-Conveyor System Using Bidirectional Interaction Topologies

2025-08-26T08:30:09+00:00

Long production lines composed of multiple stand-alone mode controllers often face challenges when speed synchronization is required, as each setpoint must be manually adjusted one by one. This study proposes a leader-follower multi-conveyor system using distributed cooperative control, enabling all follower conveyors to synchronize their speeds with that of a designated leader. In this setup, the leader is equipped with the ability to command all followers to align their speeds to its own, which is governed by a fuzzy logic controller (FLC). Each follower operates in one of two modes: a stand-alone FLC mode or a synchronization mode using cooperative control. The cooperative control mechanism relies on speed information shared among neighboring conveyors, as defined by the system topology. Two types of bidirectional interaction topologies are explored in this work: The Bidirectional Coordinated Conveyor Topology (BCCT) and the Bidirectional Leader Coordinated Conveyor Topology (BLCCT). The proposed control scheme was implemented on a miniature multi-conveyor system, yielding RMSE values of 30.88 RPM for BCCT and 43.87 RPM for BLCCT.

A very short-term global solar irradiance forecasting of photovoltaic generation systems using backpropagation neural network

2025-08-22T00:25:30+00:00

Solar power plants, which are a form of renewable energy, are highly dependent on the variability of solar radiation intensity. To ensure the stability and efficiency of the system, an accurate and very short-term solar radiation intensity forecasting model is required. The objective of this study is to develop a global solar irradiance forecasting model that predicts one-hour-ahead data using a backpropagation neural network (BPNN). The data used are the results of meteorological variable measurements, including solar irradiance, air temperature, humidity, panel output power, and clarity index, collected from the solar power generation system at Surabaya State University. Training and testing were conducted using a trial-and-error approach. Performance evaluation was conducted using the MSE, RMSE, MAPE, and R² metrics. Simulation results showed that the network configuration with 15 input neurons, 25 hidden layer neurons, and 1 output neuron trained with 2000 epochs provided the best performance, with an R² value of 0.98, an average MAPE of 5.89%, the smallest RMSE of 0.04, and the smallest MSE of 0.00027. This model is capable of capturing the temporal patterns of historical data and has proven effective in predicting very short-term solar irradiance.

Mathematical modeling of adaptive lighting system based on solar panels and digital communication for microalgae synthesis

2025-08-19T13:10:06+00:00

Microalgae are promising photosynthetic microorganisms with wide applications in biofuel production, environmental remediation, and pharmaceutical industries. Optimizing their synthesis process requires precise control of environmental parameters—especially light intensity, which is critical for effective photosynthesis. This study presents a mathematical model of an adaptive lighting system integrated with solar panels and Internet of Things (IoT) technology for microalgae cultivation in controlled environments. The system is designed to dynamically adjust LED illumination based on temperature and pH feedback, using a rule-based control logic.

The methodology includes system modeling using ordinary differential equations, algorithm development for environmental feedback control, and numerical simulation in MATLAB. The model comprises four major subsystems: solar energy input, PWM-based LED control, environmental sensing, and communication delay modeling via a first-order system. A battery energy balance model ensures the sustainability of energy usage over time.

Simulation results demonstrate that the proposed adaptive lighting system effectively responds to dynamic environmental changes while maintaining energy efficiency. Compared to static lighting systems, the model shows a reduction in unnecessary power consumption, ensuring light intensity is delivered only when environmental parameters indicate suboptimal conditions. The inclusion of communication delay modeling further reflects realistic network behavior without degrading control performance.

This research contributes a foundational mathematical framework for future optimization and control of photobioreactor systems. The integration of adaptive lighting, renewable energy, and IoT provides a scalable approach for enhancing sustainability in microalgae synthesis.

Website Quality Evaluation of OKE Garden Using WebQual, Marketing Mix, and Importance-Performance Analysis

2025-08-01T03:53:50+00:00

The rapid advancement of digital technologies has significantly impacted various service sectors, including the garden landscape industry. In response to this development, OKE Garden has implemented a website-based e-commerce platform aimed at improving service accessibility and operational efficiency. This study seeks to evaluate the usability and service quality of this digital platform from the user’s perspective by adopting the Technology Acceptance Model (TAM) as an analytical framework. Within this framework, Perceived Ease of Use (PEOU) is assessed using WebQual 4.0 indicators, while Perceived Usefulness (PU) is measured through the four elements of the marketing mix, namely Product, Price, Place, and Promotion. To analyze the alignment between user expectations and actual service performance, the Importance-Performance Analysis (IPA) method was utilized. Data were obtained from 57 respondents in the Greater Jakarta area (Jabodetabek), primarily first-time users who had previously interacted with the OKE Garden website. Prior to analysis, the data underwent validity and reliability testing to ensure robustness. The findings show that users rated the importance of website attributes higher than their actual performance, indicating a gap that highlights areas requiring improvement. Several key indicators were identified, including ease of navigation, clarity of information, data security, and pricing strategy, which were categorized in Quadrant I (high importance, low performance), indicating areas that require immediate attention. Overall, the results suggest that while digital technology adoption has taken place, user acceptance remains suboptimal. Therefore, a more comprehensive enhancement of usability and service quality is necessary to meet user expectations and improve overall satisfaction.

YOLOv9-Assisted Vision System for Health Assessment in Poultry Using Deep Neural Networks

2025-08-04T03:49:19+00:00

Poultry farming represents one of the fastest growing sectors in global food production, yet disease outbreaks, high mortality, and labor shortages continue to threaten its sustainability. Conventional health monitoring methods based on visual inspection are time-consuming, subjective, and inadequate for early anomaly detection. In response, computer vision and deep learning have emerged as transformative tools for livestock management. While prior implementations of the YOLO object detection family, such as YOLOv5 and YOLOv8, have achieved notable success, their performance often deteriorates in dense flocks, low-light conditions, and occlusion-prone environments. This study introduces a YOLOv9-assisted vision framework tailored for poultry health assessment in commercial farm settings. The system integrates smart cameras with edge computing to enable real-time detection of behavioral and physiological anomalies without dependence on high-bandwidth or cloud-based resources. A dataset of 903 annotated poultry images, categorized into healthy and sick classes, was employed for model development. The trained model achieved 88.7% precision, 97% recall, an F1-score of 0.82, and a mAP@0.5 of 0.88, demonstrating robustness under variable illumination, bird occlusion, and high-density environments. Comparative evaluation confirmed that YOLOv9 provides a superior balance of accuracy, generalization, and computational efficiency relative to YOLOv8–YOLOv11, supporting practical deployment on edge devices. Limitations include the binary scope of health classification and reliance on a single dataset. Future directions involve extending the framework to multi-class disease recognition, cross-dataset validation, behavior-based temporal modeling, and multimodal fusion, advancing predictive analytics and welfare-oriented poultry farming.

Adaptive EKF-Based Ship Trajectory Estimation with Earth Curvature Modeling and Dynamic Noise Tuning

2025-08-01T05:37:02+00:00

Accurate position estimation is critical for the effectiveness of automatic weapon and navigation systems. Standard Extended Kalman Filter (EKF) models typically adopt flat-Earth assumptions and static noise covariances, which limit their accuracy in operational environments. This study proposes an optimized EKF framework that integrates two complementary approaches. First, ship trajectories are represented in Earth-Centered Earth-Fixed (ECEF) coordinates with a WGS-84 reference to account for Earth’s curvature. Second, process (Q) and measurement (R) covariances are adaptively determined using Joint Likelihood Maximization (JLM) with logarithmic scale exploration, allowing the filter to automatically identify the most accurate configuration. Each Q/R setting is evaluated within the EKF framework using root mean square error (RMSE) derived from radar data logs. The method was tested under short-history scenarios (5 and 10 data points) within an operational range of ±15 km, reflecting conditions commonly encountered in Combat Management Systems (CMS). Results show that while coordinate transformation alone provides only marginal improvements at short ranges, the combination of curvature modelling and adaptive Q/R tuning significantly reduces RMSE, achieving average errors approaching zero with high repeatability as measured by standard deviation. This research demonstrates a novel integration of geometric and statistical optimization in EKF design and highlights its applicability to ship trajectory estimation and defence systems.

Design of 2x1 Microstrip Antenna Array Single Band with Proximity Coupling for Enhanced CCTV Performance

2025-05-05T05:22:09+00:00

The 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 array antenna 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 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.

Performance Analysis of Estimation position a Quarter-Car Suspension System using Kalman-Bucy as a State Observer

2025-08-19T13:06:17+00:00

This 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, speed 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 percent in both the process and measurement signals. Sensitivity analysis is also carried out by varying the vehicle mass between 400 kilograms in unloaded conditions and 600 kilograms when fully loaded, 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 effectively filters out noise and accurately tracks the system’s dynamic response across all test scenarios.

The main contributions of this research include the development of a mathematical model for a quarter-car suspension system that incorporates realistic road disturbance conditions, the formulation and implementation of the Kalman-Bucy filter for continuous-time state estimation in this system, and a thorough evaluation of the filter’s effectiveness under varying noise and disturbance conditions through MATLAB-based simulations.

To further evaluate the practical value of the Kalman-Bucy observer, it is integrated into a PID control framework. The combined PID and Kalman-Bucy setup is then compared with a conventional PID controller that operates using raw measurement signals. The results indicate that incorporating the Kalman-Bucy observer significantly improves control performance by reducing oscillations, improving settling time, and enhancing the system’s ability to reject disturbances. 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.

From Digital Literacy to Public Trust: The Strategic Role of E-Government Service Quality

2025-07-30T05:59:51+00:00

The 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 user satisfaction. 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 the 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 that links 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.

Hybridization of PSO-SSA for Photovoltaic System MPPT Under Dynamic Irradiation and Temperature

2025-08-01T07:06:09+00:00

Maximum 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 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.

Design of a Real-Time User Feedback for Mitigating Spurious SpO₂ Readings in Pulse Oximetry for Outpatient Monitoring

2025-08-20T01:59:19+00:00

Spurious 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, 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.

Enhancing Plant Recommendation through IoT-integrated LLM Systems

2025-03-03T05:33:10+00:00

Over the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a variety of industries. Along with developments over time, 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 will utilize real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations will be compared between base model and fine tune model using precision, recall and f1 metrics and be assessed in relation to established agricultural literature concerning plant compatibility and growth requirements with human evaluation. This research achieved an AUC value that exceeded that of the base model by 10%, Precision exhibited a 25% increase relative to the base model, while recall demonstrated a significant rise of 52% from the base model. The F1 score also improved by 39% compared to the base model.

Weighted ANOVA and Mutual Information for Enhanced Intrusion Detection System

2025-08-19T14:19:02+00:00

The rapid escalation in the sophistication of network attacks has exposed the limitations of traditional Intrusion Detection Systems (IDS). While machine learning has shown great promise in enhancing IDS performance, its success often hinges on the effectiveness of feature selection. Standard feature selection techniques, however, struggle in cybersecurity applications due to the highly imbalanced nature of network traffic datasets. In such settings, minority attack classes, though critical, are often overshadowed by majority classes, leading to reduced detection of rare intrusions. To address this challenge, we propose a hybrid feature selection framework that integrates Analysis of Variance (ANOVA) and Mutual Information (MI) with a novel class-frequency weighting mechanism. This weighting scheme adjusts the relevance score of each feature according to the distribution of classes, ensuring that features associated with rare attacks are more strongly emphasized during the selection process. We evaluate our method on the UNSW-NB15 dataset using a Support Vector Machine classifier. The results show that our approach achieves substantial gains in recall for underrepresented classes while simultaneously reducing feature dimensionality and maintaining efficiency. By improving the visibility of features tied to minority attacks, the proposed framework provides a more balanced and reliable solution for modern IDS. This contribution advances the detection of rare but impactful threats and highlights a scalable pathway for building more resilient cybersecurity defenses.