Abstract The paper explores the application of AI-driven computer vision to enhance formative assessment in classroom settings. It emphasizes how technologies such as face detection, face recognition, human pose estimation, and facial expression analysis can objectively evaluate student... The paper explores the application of AI-driven computer vision to enhance formative assessment in classroom settings. It emphasizes how technologies such as face detection, face recognition, human pose estimation, and facial expression analysis can objectively evaluate student engagement, attendance, and emotional states. The authors propose a multi column convolutional neural network architecture combined with sliding window fusion techniques to improve object and scene recognition accuracy. Experimental results on datasets like MNIST, MIT, and SUN397 demonstrate the model’s superior performance, achieving reduced error rates and enhanced generalization. The study concludes that integrating AI and computer vision into teaching evaluation provides richer, real time data for educators, supports pedagogical improvements, and enables more interactive and responsive classroom environments. Despite promising outcomes, challenges remain in data validity and system design, underscoring the need for further research to refine video based evaluation frameworks. Read More Read Less

Abstract The paper investigates the modular architecture of distributed electrical control systems to enhance their performance, reliability, and scalability. Unlike traditional centralized control systems, distributed systems divide control tasks among independent modules such as data acquisition, communication, and... The paper investigates the modular architecture of distributed electrical control systems to enhance their performance, reliability, and scalability. Unlike traditional centralized control systems, distributed systems divide control tasks among independent modules such as data acquisition, communication, and distributed control offering greater flexibility for complex industrial applications. The author reviews prior work in modular design techniques, communication technologies (e.g., Ethernet and wireless networks), and data processing methods, highlighting ongoing challenges in real time performance and system reliability. The study proposes a distributed design framework that integrates modularization strategies with optimized communication protocols and data transmission approaches. It also explores the application of intelligent algorithms including Support Vector Machines, neural networks, fuzzy logic, and data mining for system classification and pattern recognition. Experimental validation is conducted in real world engineering contexts, demonstrating improved voltage regulation, system stability, and classification accuracy. Results from iterative testing show effective convergence and enhanced model performance (e.g., R² = 0.7237 in biomass estimation tasks). The paper concludes that thoughtful module design significantly boosts system effectiveness and provides practical guidance for engineering implementations. However, the author acknowledges remaining challenges and calls for further refinement of classification algorithms and experimental methodologies to ensure robustness and adaptability across diverse industrial environments. Read More Read Less

Abstract The paper “Error Correction Technology for Welding Robots Based on Three-Dimensional Visual Localization” by Ruichun Gu presents an innovative approach to improving welding accuracy by integrating 3D visual localization with welding robots. Traditional welding methods often suffer... The paper “Error Correction Technology for Welding Robots Based on Three-Dimensional Visual Localization” by Ruichun Gu presents an innovative approach to improving welding accuracy by integrating 3D visual localization with welding robots. Traditional welding methods often suffer from seam deviations that compromise product quality. To address this, the study leverages 3D visual technologies such as laser scanning, structured light projection, and multi-sensor fusion to capture precise geometric data (position, shape, size) of weld seams in real time. This data enables immediate error detection and correction during the welding process. The research incorporates machine learning algorithms, particularly Support Vector Machines (SVMs), along with geometric features such as curvature and normal vectors, to classify and refine weld-seam data from 3D point clouds. Experimental results demonstrate high system accuracy, with a relative error of only 0.38% in seam distance measurements and endpoint positioning errors of 2.5-4 mm— well within industrial tolerances. The study validates the method on crane main beams using CO‚ shielded robotic welding. Findings confirm that 3D visual localization significantly enhances welding precision, reduces defects, and improves production efficiency. The paper concludes that combining advanced visual sensing with intelligent algorithms offers a promising pathway for next generation welding automation, with potential for further integration of deep learning and real time adaptive control systems. Read More Read Less