Search Results (3)

The global adoption of solar-powered streetlights has grown significantly, driven by their cost-effectiveness and potential to reduce dependence on fossil fuels associated with conventional street lighting. Battery storage represents a substantial portion of the total capital cost in solar-powered streetlight systems. Therefore, selecting an efficient charge regulator is crucial to protect battery lifespan and reduce energy losses. In this context, the choice of an appropriate charge regulator plays a vital role in enhancing system reliability and overall performance. This study presents a practical approach for evaluating three commercially available 6 A-rated Pulse Width Modulation (PWM) solar charge regulators intended for recharging lead-acid batteries in a proposed 12 V off-grid solar photovoltaic (PV) street lighting system. The regulators were evaluated concurrently in separate circuits, each experiencing similar meteorological conditions, including similar temperature and solar irradiance. The measured data for each regulator were acquired using LabVIEW-based virtual instruments. The performance comparison was conducted using the Linear Regression Algorithm (LRA) to support decision-making. Based on the analysis, the most suitable PWM charge regulator was identified as the one offering the best charging performance due to low internal losses. Hence, solar battery charge regulators with identical load current ratings do not necessarily deliver equivalent charge/discharge performance. Full article

In our previous study, we proposed a vision-based ranging algorithm (LRA) that utilized a monocular camera with four lasers (MC4L) for indoor positioning in dark environments. The LRA achieved a positioning error within 2.4 cm using a logarithmic regression algorithm to establish a linear relationship between the illuminated area and real distance. However, it cannot distinguish between obstacles and walls. Hence, it results in severe errors in complex environments. To address this limitation, we developed an LBP-CNNs model that combines local binary patterns (LBPs) and self-attention mechanisms. The model effectively identifies obstacles based on the laser reflectivity of different material surfaces. It reduces positioning errors to 1.27 cm and achieves an obstacle recognition accuracy of 92.3%. In this paper, we further enhance LBP-CNNs by combining it with fast Fourier transform (FFT) to create an LBP-FFT-CNNs model that significantly improves the recognition accuracy of obstacles with similar textures to 96.3% and reduces positioning errors to 0.91 cm. In addition, an inertial measurement unit (IMU) is integrated into the MC4L device (MC4L-IMU) to design an inertial-based indoor positioning algorithm. Experimental results show that the LBP-FFT-CNNs model achieves the highest determination coefficient (R² = 0.9949), outperforming LRA (R² = 0.9867) and LBP-CNN (R² = 0.9934). In addition, all models show strong stability, and the prediction standard index (PSI) values are always below 0.02. To evaluate model robustness and MC4L-IMU work reliably under different conditions, the experiments were conducted in a controlled indoor environment with different obstacle materials and lighting conditions. Full article

This study investigated the use of the linear regression algorithm (LRA) for estimating the cost of tungsten inert gas (TIG) welding of dissimilar metals, specifically, stainless steel 304 and aluminum 2024. Various cost analysis parameters, including weld time, power cost rate, labor cost, filler rod cost, and shielding gas cost, were considered. LRA was employed to develop a predictive model for welding costs based on these parameters. The model was then used to optimize welding parameters by identifying the combination that minimized overall welding costs. The results demonstrate that LRA effectively identified a more cost-effective parameter set compared to traditional methods. This highlights the potential of LRA in enhancing the cost-effectiveness of TIG welding processes. The findings have practical implications for field engineers and researchers, enabling the identification of optimal parameters for cost estimation and significant cost savings. Full article