Search Results (4,874)

The performance analysis of a 10 kW_p rooftop grid connected solar photovoltaic (PV) system located in Sagolband, Imphal, India has been studied for 5 years. The key technical parameters such as array yield ($Y_A$), reference yield ($Y_R$), final yield ($Y_F$), capacity utilization factor (CUF), PV system efficiency (${\eta }_{Sys}$), and performance ratio (PR) were used to investigate its performance. In this study, the experimentally measured results of the system’s performance for the five years (i.e., July 2018 to June 2023) were compared with the predicted results, which were obtained using PVsyst V7.3.0 software. The measured energy generation in 5 years (including 40 days OFF due to inverter failure on 17 June 2019 because of a surge, which was resolved on 27 July 2019) was 58,911.3 kWh as compared to the predicted 77,769 kWh. The measured daily average energy yield was 3.2 kWh/kW_p as compared to the predicted 4.2 kWh/kW_p. It can be seen that there was a large difference between the real and predicted values, which may be due to inverter downtime, local environmental variables (e.g., lower-than-expected solar irradiation and temperature impacts), and the possible degradation of photovoltaic modules over time. The measured daily average PR of the system was 70.71%, and the maximum occurred in the months of October, November, December, and January, which was almost similar to the predicted result. The measured daily average CUF of the system was 13.36%, and the maximum occurred in the months of March, April, and May. The measured daily average system efficiency was 11.31%. Moreover, the actual payback was 4 years and 10 months, indicating strong financial viability despite the system’s estimated lifespan of 25 years. This study highlights the importance of regular maintenance, fault detection, and better predictive modelling for more accurate energy projections, and also offers an understanding of real-world performance fluctuations. Full article

Adaptive educational systems are essential for addressing the diverse learning needs of students by dynamically adjusting instructional content and user interfaces (UI) based on real-time performance. Traditional adaptive learning environments often rely on static fuzzy logic rules, which lack the flexibility to evolve with learners’ changing behaviors. To address this limitation, this paper presents an adaptive UI system for educational software in Java programming, integrating fuzzy logic and reinforcement learning (RL) to personalize learning experiences. The system consists of two main modules: (a) the Fuzzy Inference Module, which classifies learners into Fast, Moderate, or Slow categories based on triangular membership functions, and (b) the Reinforcement Learning Optimization Module, which dynamically adjusts the fuzzy membership function thresholds to enhance personalization over time. By refining the timing and necessity of UI modifications, the system optimizes hints, difficulty levels, and structured guidance, ensuring interventions are neither premature nor delayed. The system was evaluated in educational software for Java programming, with 100 postgraduate students. The evaluation, based on learning efficiency, engagement, and usability metrics, demonstrated promising results, particularly for slow and moderate learners, confirming that reinforcement learning-driven fuzzy weight adjustments significantly improve adaptive UI effectiveness. Full article

Digital pulse-width modulation (DPWM)-based controls are characterized by a non-negligible phase delay due to analog-to-digital (ADC) conversion, sampling time, carrier shape, and algorithm computation time. These delays degrade the performance in closed-loop systems, where the bandwidth must be reduced to avoid instability issues due to the reduced closed-loop phase margin. To mitigate these delays, approaches such as utilizing low-latency ADCs or increasing the sampling frequency have been employed. However, these methods are often costly and do not address the fundamental delay issues inherent to DPWMs. In this paper, a novel zero phase-delay DPWM architecture is proposed. This enhanced architecture seamlessly integrates pulse width and frequency modulation to create a programmable derivative action, capable of effectively recovering the DPWM delay. The proposed architecture employs a reliable and straightforward organization, suitable for implementation in commercial field programmable gate array (FPGA). Furthermore, this architecture inherently generates a trigger signal that can be used in numerous power electronic applications to capture the average value in piecewise linear inductor currents. The validity of the proposed architecture is substantiated through simulations and experimental tests. The final implementation is shared in an open-source resource. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal mining, posing difficulties in handling large-scale geographic data. This paper proposes NextMove, a novel POI recommendation model that integrates four key modules to address these issues. Specifically, the Probabilistic User Preference Generation Module first employs Latent Dirichlet Allocation (LDA) and a user preference network to model user personalized interests dynamically by capturing latent geographical topics. Secondly, the Self-Attention-based Arrival Time Prediction Module utilizes a Multi-Head Attention Mechanism to extract time-varying features, improving the precision of arrival time estimation. Thirdly, the Transformer-based Trajectory Representation Module encodes sequential dependencies in user behavior, effectively capturing contextual relationships and long-range dependencies for accurate future location forecasting. Finally, the Next Location Feature-Aggregation Module integrates the extracted representation features through an FC-based nonlinear fusion mechanism to generate the final POI recommendation. Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed NextMove over state-of-the-art methods. These results validate the effectiveness of NextMove in modeling dynamic user preferences, enhancing arrival time prediction, and improving POI recommendation accuracy. Full article

Accurate fault diagnosis in analog circuits faces significant challenges owing to the inherent complexity of fault data patterns and the limited feature representation capabilities of conventional methodologies. Addressing the limitations of current convolutional neural networks (CNN) in handling heterogeneous fault characteristics, this study presents an efficient channel attention-enhanced multi-input CNN framework (ECA-MI-CNN) with dual-domain feature fusion, demonstrating three key innovations. First, the proposed framework addresses multi-domain feature extraction through parallel CNN branches specifically designed for processing time-domain and frequency-domain features, effectively preserving their distinct characteristic information. Second, the incorporation of an efficient channel attention (ECA) module between convolutional layers enables adaptive feature response recalibration, significantly enhancing discriminative feature learning while maintaining computational efficiency. Third, a hierarchical fusion strategy systematically integrates time-frequency domain features through concatenation and fully connected layer transformations prior to classification. Comprehensive simulation experiments conducted on Butterworth low-pass filters and two-stage quad op-amp dual second-order low-pass filters demonstrate the framework’s superior diagnostic capabilities. Real-world validation on Butterworth low-pass filters further reveals substantial performance advantages over existing methods, establishing an effective solution for complex fault pattern recognition in electronic systems. Full article

In this article, we introduce a novel approach to numerical integration based on a modified composite diagonal (CD) method, which is a variation of the semi-implicit Euler–Cromer method. This approach enables the finite-difference scheme to maintain the dynamic regime of the solution while adjusting the integration time step. This makes it possible to implement variable-step integration. We present a variable-step MCD (VS-MCD) version with a simple and stable Hairer step size controller. We show that the VS-MCD method is capable of changing the dynamics of the solution by changing the symmetry coefficient (reflecting the ratio between two internal steps within the composition step), which is useful for tuning the dynamics of the obtained discrete model, with no influence of the appropriate step size. We illustrate the practical application of the developed method by constructing a direct chaotic communication system based on the Sprott Case S chaotic oscillator, demonstrating high values in the largest Lyapunov exponent (LLE). The tolerance parameter of the step size controller is used as the modulation parameter to insert a message into the chaotic time series. Through numerical experiments, we show that the proposed modulation scheme has competitive robustness to noise and return map attacks in comparison with those of modulation methods based on fixed-step solvers. It can also be combined with them to achieve an extended key space. Full article

Background: Post-COVID-19 syndrome (PCC) is characterized by autonomic nervous system (ANS) dysregulation. Reduced heart rate variability (HRV) serves as a biomarker for ANS function. Few studies have assessed HRV modulations over treatment in PCC patients. This study evaluates the effects of a multimodal immersive virtual reality intervention—integrating cognitive training, physical exercise, and mindfulness practices—on HRV parameters. Methods: Eighteen PCC adults were assigned to reduced (16 sessions) and extended (24 sessions) training. HRV was assessed using an electrocardiogram weight scale at baseline, in the mid-term, and at the end of the intervention. Time-domain and frequency-domain HRV measures were extracted. Results: No significant group-by-time interactions were found. However, certain time-domain HRV parameters showed significant changes over time. Unexpectedly, HRV decreased from baseline to mid-intervention in both groups, with recovery by the end of the intervention. No significant changes were observed in frequency-domain measures. Conclusions: The temporary reduction in HRV suggested that the initial cognitive and physical demands may have temporarily induced physiological stress. The subsequent restoration of HRV suggested adaptation and increased resilience. The absence of enhanced HRV with extended training suggests that session intensity may be more influential than the number of sessions in modulating HRV among PCC patients. Full article

Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion of multiple articulated robots used in automated systems under limited computing resources. The proposed framework consists of two modules: (1) a federated learning module for the cooperative training of multiple joint robots on a part-picking task and (2) an articulated robot control module to balance the efficiency of limited resources. The proposed framework is applied to cases with different numbers of joint robots, and its performance is evaluated in terms of training completion time, resource share ratio, network traffic, and completion time of a picking task. Under the devised framework, the experiment demonstrates object recognition by three joint robots with an accuracy of approximately 80% at a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. As a result, this study contributes to the expansion of federated learning use for articulated robot control in limited environments, such as small and medium-sized enterprises. Full article

In the presence of external interference, multimodal target classification plays a crucial role. Traditional single-modal classification systems are limited by the singularity of data representation and their sensitivity to environmental conditions, making it challenging to meet the robustness requirements for target classification under external disturbances. This paper addresses the inadequacies of single-modal target classification by proposing a target classification algorithm based on audiovisual fusion. The innovative contributions of this work are as follows. (1) To resolve the issue of the lack of correlation between audio signals and image signals, we introduce a method that converts audio signals into spectrograms and fuses them with target images. The advantage of this method is that the spectrogram can fully utilize the effective information in the audio, ensuring stability, while also effectively addressing the challenge of fusing one-dimensional time series audio signals with two-dimensional discrete image signals. (2) We propose a convolutional extraction and modal fusion network framework that incorporates an attention mechanism module during the fusion process, ensuring the stability and robustness of the fused data for audiovisual target classification. Validation was conducted on both a custom dataset and the YouTube-8M dataset. The experimental results indicate that the proposed method demonstrates improvements in accuracy of 2.9%, 2.4%, 1.2%, and 0.9% compared to other multimodal fusion target classification methods on the custom dataset. This demonstrates the effectiveness of the proposed multimodal fusion recognition approach and fully validates the theoretical rationale behind our method. Full article

This paper presents two innovative wireless network designs for the automation system of the Sof-Algeen water station in Zintan, addressing the challenge of connecting field instruments—such as pressure switches, solenoid valves, and differential pressure sensors—over distances of up to 4 km. Due to high costs, limited flexibility, and scalability concerns, traditional hardwired solutions are impractical for such distances. A comprehensive analysis of various Industrial Internet of Things (IIoT) network designs determined that the IEEE 802.11 standard and Phoenix Contact’s Trusted Wireless technology best meet the project’s requirements for long-distance connectivity, real-time data acquisition, system compatibility, and compliance with national telecommunications regulations. This study proposes optimal network designs using the IEEE 802.11 standard and a hybrid mesh and star network for Trusted Wireless, and evaluates these technologies based on performance, reliability, and infrastructure compatibility using simulation. The network designs were validated using the Radio Mobile tool, considering the water station’s specific terrain and wireless module parameters. The findings indicate distinct differences in structure, operation, and cost-effectiveness between the two proposed solutions, highlighting the benefits of each in achieving optimal link feasibility for robust water station automation. Full article

The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm (DBFLA), to address these challenges. The DBFLA enhances the performance of machine learning models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods, by fine-tuning their parameters to improve fault detection rates. It effectively identifies critical faults such as module mismatches, open circuits, and short circuits. The research demonstrates that DBFLA significantly improves the performance of conventional machine learning techniques by forming a stacking classifier, achieving an individual meta-learner accuracy of approximately 98.75% on real PV datasets. This approach not only accommodates new operating modes and an expanded range of fault conditions but also enhances the reliability of fault detection schemes. The primary contribution of DBFLA lies in its ability to balance exploration and exploitation efficiently, resulting in superior classification accuracy compared to existing optimization techniques. By combining real and simulated datasets, the proposed hybrid method showcases its potential to substantially improve the precision and speed of PV fault detection models. Future work will focus on integrating these advanced models into real-time PV monitoring systems, aiming to reduce detection times and further enhance the reliability and operational efficiency of PV systems. Full article

In modern electronic reconnaissance systems, digital receivers play an important role in receiving a variety of complex signals, in which signal-to-time extraction is a key issue, but traditional methods often rely on the signal envelope, which is easily affected by the value of the threshold setting and the signal-to-noise ratio (SNR) of the signal. In fact, the pulse envelope front has a large derivative, which leads the envelope differentiation to show sharp peaks. In this paper, a time of arrival (TOA) extraction method based on first-order envelope differentiation of the signal is proposed. The method realizes the normalized extraction of different modulated signals by estimating the location where the sharp peaks appear, and it is not easily affected by the threshold setting. The processing flow of the digital receiver is as follows: the signal is first processed by digital channelization, and, after channelization, it passes through the signal detection module; then, after envelope differentiation, the useful signal is filtered out according to the result, and, finally, the pulse descriptor word consisting of the pulse arrival time, pulse width, signal frequency, and signal amplitude is formed, which is convenient for the subsequent processing. The experimental results verify the effectiveness and reliability of the signal arrival time extraction method. Full article

This study presents a comprehensive solution for precise and timely pest monitoring in field environments through the development of an advanced rice pest detection system based on the YOLO-RMD model. Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system integrates three innovative components: (1) a novel Receptive Field Attention Convolution module enhancing feature extraction in complex backgrounds; (2) a Mixed Local Channel Attention module balances local and global features to improve detection precision for small targets in dense foliage; (3) an enhanced multi-scale detection architecture incorporating Dynamic Head with an additional detection head, enabling simultaneous improvement in multi-scale pest detection capability and coverage. The experimental results demonstrate a 3% accuracy improvement over YOLOv8n, achieving 98.2% mean Average Precision at 50% across seven common rice pests while maintaining real-time processing capabilities. This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions. Full article

The developments in building information modeling (BIM) technology provide a new approach for remote real-time visualized bridge health monitoring and structural damage detection, but so far, there are scarcely any application cases of a BIM-based SHM system for butterfly arch bridges around the world. This paper reviewed the recent progress on the butterfly arch bridge and its requirements for the integration between SHM and BIM. Based on an actual project in southwest China, work on the spatial mechanical properties, the analysis of monitoring requirements, and the design of functional modules of SHM are elaborately conducted. Subsequently, the lightweight BIM is established and integrated into the web client-side of the SHM system with the skeleton-template method, CATIA platform, and sensor data. With the implementation of user-defined virtual sensor parameter linkage, the design of the specific databases is accomplished in the SQL server environment. Based on one actual incident that saw an overweight/oversize vehicle (with the weight of 80 t, 2015) pass over the arch bridge, the fuzzy relation synthesis and data cleaning method were improved to compare the standard deviation with the threshold value of the correlation degree, and a method is adopted to evaluate the structural operation behavior of the bridge and the service condition of the BIM-based SHM system after the ultra-limit accident. The study results evince the validity and efficiency of the BIM-based SHM system, which could lay a foundation for the visualized assessment and early warning system of long-span bridges. Full article