Narrow Results

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

With the development of computer technology, its application in athletes’ motion capture is more and more extensive, which can be used to design suitable sensors by the Surendra algorithm. In recent years, more and more scholars have begun to use motion capture technology to study human motion posture, analyze and study human motion posture data and apply it to people’s work, study and life. Motion capture technology has also become a key technology in the field of human motion posture research and is playing an increasingly important role. In this paper, a three-dimensional skeletal model of the athlete’s body is first established based on dynamics, and the athlete’s movement characteristics are simulated by this model. Then, the athlete’s movement posture is judged to determine the appropriate form of movement expression. Then, the improved Surendra algorithm is used to detect the movement movements.

Explosive power is one of the key basic indicators for improving the best athletic performance of athletes. The exploration of explosive power has always been an important topic in the field of physical education and research. Artificial intelligence technology has been widely used in the field of sports. Based on the explosive power data collected by the tension pressure sensor and speed acquisition sensor, multi-sensor data fusion technology is introduced to comprehensively analyze the key factors affecting the explosive power of athletes. By analyzing the theoretical basis of data fusion, the algorithm structure of multi-source data fusion is described from multiple levels and the mathematical model of simple multi-Bayesian data fusion algorithm is optimized and updated. Based on the characteristics of motion sensor data, a scheme to improve the optimization process of the fusion algorithm is proposed to further improve the anti-interference and accuracy of data fusion. Through the simulation performance test, this research has good application value. Through simulation performance testing, we have verified the effectiveness of the system and found that it has a good application value in athlete explosive power analysis. This system can accurately evaluate the explosive power level of athletes, provide scientific training guidance for coaches and help athletes improve their sports performance.

MEMS research has been carried out through industry-university (Tohoku) collaboration for practical applications. Sophisticated devices such as electrostatically levitated rotational gyroscope, MEMS relay for wafer level packaging, array MEMS including multi-probe data storage and multi-column electron beam lithography system, small diameter fiber optic pressure sensor and SiC micro structure or glass press molding, have been developed. Electrical feedthroughs in glass play important role in the wafer level packaging and array MEMS. Materials such as conductive polymer for recording media, carbon nanotube for electron field emitter, SiC for harsh environment are used in these MEMS because of their unique features.

This paper reviews our work on the development of microwave carbon nanotube resonator sensors for gas detection. The sensor consists of a radio frequency resonator coated with a layer of carbon nanotubes. Upon exposure to gasses, the resonant frequency of the sensor shifts to indicate the presence of gasses. Our experimental results demonstrate that the microwave carbon nanotube resonator sensor achieves a sensitivity of 4000 Hz/ppm upon exposure to ammonia and the resonant frequency is recovered when ammonia is evacuated. The sensing mechanism is dependent on electron transfer from the ammonia to the nanotubes. This sensor platform has great potential for wireless sensing network applications.

Visualization is primarily utilized as a training method to enhance athletic movement quality, increase concentration power, and minimize competition stress on the player while building firm confidence. Physical literacy (PL) provides a valuable lens for analyzing physical activity (PA) movement in more significant social and affective learning processes. This paper presents an Interactive Visualization positioning in physical education (IVPPE) to deal with the signal fluctuations and positioning techniques in visualizing Deep Neural Network (DNN). To ensure the success of their game, athletes are always looking for new ways to improve their health and performance. Using sensors to keep tabs on training and recovery has become more popular among athletes. Currently, sports teams are using sensors to track both the players’ internal and external workloads. It illustrates the multilayer localizer (MLL) based on transfer learning to improve the positioning accuracy and physical literacy positioning model (PLPM) as a health determinant. A variety of data augmentation techniques are used to combat signal fluctuations. As a result, the combined effects of motivation-promoting physical activity-based visualization improve the accuracy ratio to 96.7%, prediction ratio to 96.2%, efficiency ratio to 96.8%, and reduce the error rate to 18.7%, stress level (52.8%) compared to other conventional models and have a positive impact on the localizer and positioning, making a difference in physical activity (PA) levels.