Abstract: Aiming at the problem of large errors in the positioning algorithm of underwater wireless sensor networks, an underwater DV-Hop positioning algorithm optimized based on the squirrel algorithm is proposed. The estimated position of the unknown nodes in the DV-Hop algorithm is optimized by the squirrel algorithm, and optimization is carried out with the squirrel algorithm, the number of hops between nodes is optimized using the hop count adjustment factor, the beacon nodes that will lead to a large error are removed by the use of the covariance degree, and the average hopping distance of beacon nodes is optimized using the weighted processing method, and the average value of the improved average hopping distance is taken to be the average hopping distance of each unknown node, to Improve the localization accuracy of DV-Hop algorithm. The simulation results show that the improved algorithm improves the positioning accuracy by 34.02% and 9.75% compared with the traditional 3DDV-Hop and the hopping distance optimized 3DDV-Hop, respectively. Read More

Abstract: This paper explores the innovative use of coffee grounds as electrode materials in capacitors, addressing the need for sustainable and efficient energy storage solutions. The study begins with an examination of the properties of coffee grounds, focusing on their chemical composition and structural characteristics that make them suitable for electrode applications. Various preparation methods are discussed, highlighting techniques to optimize the physical and electrochemical properties of coffee grounds to enhance their performance as electrode materials. The research delves into the practical applications of coffee grounds in capacitor electrodes, providing insights into their potential to replace conventional materials. This includes an analysis of their electrical conductivity, stability, and energy storage capacity, emphasizing the environmental benefits and cost-effectiveness of using a waste product like coffee grounds. The results demonstrate the feasibility of coffee grounds as a viable alternative in the development of sustainable capacitor technologies. The study concludes by considering the future perspectives and potential advancements in this field, suggesting areas for further research to overcome current limitations and improve the performance of coffee ground-based electrode materials. This work contributes to the broader effort of integrating waste materials into high-value applications, promoting environmental sustainability and resource efficiency in energy storage systems. Read More

Abstract: There are some weak defects on the surface of aviation clamps. Because they are very weak, it is difficult to identify them efficiently and accurately by the existing visual detecting algorithms, and the existing methods have high arithmetic power requirements for vision detecting systems. So, this work proposes a detecting algorithm for a weak feature defect detection of aviation clamps (YOLO-OGS). Firstly, in order to improve the ability of convolutional operations of extract features and decrease the model's GFLOPs, the Multidimensional dynamic convolutional ODConv is added to the backbone network of YOLOv8. Then, in order to reduce the complexity of the model while increasing the effectiveness of feature fusion at various levels by keeping more of the hidden connections in the channels, the GhostSlimFPN paradigm network structure, which contains GSConv convolution and slim-neck structure, is introduced in the neck network. Finally, the Shuffle Attention module is used to widen the image's sensory field and enhance the details of weak flaws. Based on the aviation clamp defect data set, the comparative analysis results of YOLO-OGS and YOLOv8s algorithms show that YOLO-OGS decreases the GFLOPs by 14.4% and increases precision, recall, mAP@0.5, and GFLOPs by 4%, 6.4%, and 3.4%, respectively. And compared with the other existing mainstream networks YOLOv6, YOLOv8s, YOLOv8n, YOLOv5n, YOLOv5s, YOLOv7, YOLOv3-tiny. 8.3%, 3.4%, 4.4%, 15.4%, 8%, 13.4%, and 12% improvement in mAP@0.5. Read More

Abstract: The performance of the water cooling system of a new type of burner plays an important role in the safe and stable operation of the whole equipment. The material properties of steel, such as yield strength and modulus of elasticity, deteriorate under high-temperature conditions, leading to a decrease in the structural load-bearing capacity, which will bring about potential safety hazards when the burner is in operation. In this paper, the coupled thermal-electrical model and fluid temperature field model of graphite electrode and electrode holder of the burner are established, and the water-cooled heat dissipation simulation of the electrode holder of the burner is carried out by taking into account the effects of radiant heat dissipation, air convection, electric current and fluid flow on the temperature of the burner when it is in operation. The simulation results show that: After the inlet flow rate of 42.54L/min liquid water cooling, the temperature of the collet is reduced to the safe temperature, and the wedge-shaped clamping block is below the maximum continuous working temperature of the material, and the temperature continues to decrease with the increase of inlet flow rate. In summary, the minimum inlet flow rate of the water cooling system for the burn-through device is 42.54 L/min. Read More

Abstract: Electrical control systems are a key component of modern industry and daily life. From power generation to smart homes, from industrial automation to smart cities, electrical control systems are everywhere. With the continuous development of intelligent technology and digital transformation, electrical control systems play an increasingly important role in multiple industries. This paper analyzes the current status and future development trends of this field by exploring the integration and innovation of digital and intelligent technologies in electrical control systems. Combined with industry development cases, this paper proposes the future technical development direction of electrical control systems and explores the application of smart grids, industrial automation, and building energy management. Finally, the article evaluates the actual application effect of digital intelligent technology in electrical control systems and proposes the possibilities and challenges of future technological innovation. Read More

Abstract: The integration of digital design and manufacturing technologies has revolutionized sports equipment production, improving efficiency, precision, and customization. This study explores the role of CAD, CAE, and 3D printing in enhancing design accuracy and reducing development cycles, while highlighting intelligent manufacturing’s impact on cost reduction and resource optimization. Personalized equipment production, driven by 3D printing and consumer data, is examined alongside simulation technologies for performance testing and iterative optimization. Furthermore, real-time data feedback and lifecycle management systems demonstrate the potential for sustainable and adaptive designs. These advancements redefine industry standards, fostering innovation, sustainability, and user-centric development in sports equipment manufacturing. Read More

Abstract: The high-precision intelligent control technology for hydraulic power machinery is of great significance in improving the automation level of the equipment manufacturing industry. This study addresses issues such as low precision and poor reliability in traditional hydraulic control systems by developing a high-precision control technology solution based on intelligent algorithms. The research adopts a control strategy combining adaptive PID and fuzzy neural networks, establishing a real-time monitoring and fault diagnosis system to achieve intelligent control of the system. Engineering applications show that this technology significantly improves equipment control precision and system stability, reduces equipment failure rates and operational costs, and enhances production efficiency and product quality, providing good economic benefits and application value for promotion. Read More

Abstract: To achieve precise tracking and aiming in ground-based laser strikes against drones, this paper proposes a high-speed target tracking algorithm adapted for UAVs (Unmanned Aerial Vehicle) in aerospace backgrounds. The algorithm integrates the KCF algorithm with a custom simple template matching algorithm and optimizes the custom template matching method using NEON. On the Nvidia Orin NX, this approach significantly improves tracking real-time performance compared to using KCF alone. Applying this tracking strategy to a custom dataset and parts of the ANTI-UAV dataset yielded good tracking results. Read More

Abstract: This study is dedicated to solving the control hysteresis problem caused by the slow response speed of traditional fixed-parameter machining and control systems, and investigates a method aimed at combining deep learning and PID controllers to achieve intelligent optimization of the feed rate for ternary impeller milling. A two-stage step-by-step prediction of the spindle power is used to take advantage of the LSTM prediction optimization solving algorithm's fast response to time series data processing, and the PID controller adjusts the feed multiplication rate by proportional, integral and differential control strategies according to the model output to achieve constant power stable cutting with pre-adjustment of feed multiplication rate for CNC milling process. The experimental results show that the intelligent optimization scheme of feed multiplication for ternary impeller milling based on deep learning and PID controller has high effect. Through continuous learning and intelligent adjustment, the machining efficiency is significantly improved, while the machining quality and stability are ensured. Read More