Design and analysis of a performance monitoring system for a seed metering device based on pulse width recognition

To realize real-time and accurate performance monitoring of large- and medium-sized seed metering devices, a performance monitoring system was designed for seed metering devices based on LED visible photoelectric sensing technology and a pulse width recognition algorithm. Through an analysis of the of sensing component pointing characteristics and seed motion characteristics, the layout of the sensing components and critical photoelectric sensing system components was optimized. Single-grain seed metering devices were employed as monitoring objects, and the pulse width thresholds for Ekangmian-10 cotton seeds and Zhengdan-958 corn seeds were determined through pulse width threshold calibration experiments employed at different seed metering plate rotational speeds. According to the seeding quantity monitoring experiments, when the seed metering plate rotational speed ranged from 28.31~35.71 rev/min, the accuracy reached 98.41% for Ekangmian-10 cotton seeds. When the seed metering plate rotational speed ranged from 13.78~19.39 rev/min, the seeding quantity monitoring accuracy reached 98.19% for Zhengdan-958 corn seeds. Performance monitoring experiments revealed that the qualified seeding quantity monitoring accuracy of cotton precision seed metering devices, missed seeding quantity monitoring accuracy, and reseeding quantity monitoring accuracy could reach 98.75%, 94.06%, and 91.30%, respectively, within a seeding speed range of 8~9 km/h. This system meets the requirements of real-time performance monitoring of large- and medium-sized precision seed metering devices, which helps to improve the operational performance of seeding machines.

Comparison of Heart Rate Monitoring Accuracy between Chest Strap and Vest during Physical Training and Implications on Training Decisions

Heart rate (HR) and heart rate variability (HRV) based physiological metrics such as Excess Post-exercise Oxygen Consumption (EPOC), Energy Expenditure (EE), and Training Impulse (TRIMP) are widely utilized in coaching to monitor and optimize an athlete’s training load. Chest straps, and recently also dry electrodes integrated to special sports vests, are used to monitor HR during sports. Mechanical design, placement of electrodes, and ergonomics of the sensor affect the measured signal quality and artefacts. To evaluate the impact of the sensor mechanical design on the accuracy of the HR/HRV and further on to estimation of EPOC, EE, and TRIMP, we recorded HR and HRV from a chest strap and a vest with the same ECG sensor during supervised exercise protocol. A 3-lead clinical Holter ECG was used as a reference. Twenty-five healthy subjects (six females) participated. Mean absolute percentage error (MAPE) for HR was 0.76% with chest strap and 3.32% with vest. MAPE was 1.70% vs. 6.73% for EE, 0.38% vs. 8.99% for TRIMP and 3.90% vs. 54.15% for EPOC with chest strap and vest, respectively. Results suggest superior accuracy of chest strap over vest for HR and physiological metrics monitoring during sports.

Wireless communication network security intelligent monitoring system based on machine learning

Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

Monitoring method for leakage current of transmission line insulators under typical environmental conditions

There are many factors affecting insulator leakage current of transmission line, resulting in low accuracy of leakage current monitoring. This paper designs a monitoring method for insulator leakage current of transmission line under typical environmental conditions. The influence of different factors on the accuracy of leakage current monitoring is analyzed. Based on the fuzzy mathematics theory, the insulator operating characteristic quantity is analyzed, and the uncertain factors in the insulator operating characteristic quantity are calculated, so as to realize the insulator leakage current monitoring of transmission line under typical environmental conditions. The example analysis shows that the leakage current monitoring accuracy of the method studied in this paper is high in the case of no arc, local arc, adjacent flashover, pollution and saturated environment, which proves the effectiveness of the method studied.

Effects of self-scoring their math problem solutions on primary school students’ monitoring and regulation

Preparing students to become self-regulated learners has become an important goal of primary education. Therefore, it is important to investigate how we can improve self-monitoring and self-regulation accuracy in primary school students. Focusing on mathematics problems, we investigated whether and how (1) high- and low-performing students differed in their monitoring accuracy (i.e., extent to which students’ monitoring judgments match their actual performance) and regulation accuracy (i.e., extent to which students’ regulation judgments regarding the need for further instruction/practice match their actual need), (2) self-scoring improved students’ monitoring and regulation accuracy, (3) high- and low-performing students differed in their monitoring and regulation accuracy after self-scoring, and (4) students’ monitoring and regulation judgments are related. On two days, students of 9 − 10 years old from 34 classes solved multiplication and division problems and made monitoring and regulation judgments after each problem type. Next, they self-scored their answers and again made monitoring and regulation judgments. On the multiplication problems, high-performing students made more accurate monitoring and regulation judgments before and after self-scoring than low-performing students. On the division problems, high-performing students made more accurate monitoring judgments before self-scoring than low-performing students, but after self-scoring this difference was no longer present. Self-scoring improved students’ monitoring and regulation accuracy, except for low- and high-performing students’ regulation accuracy on division problems. Students’ monitoring and regulation judgments were related. Our findings suggest that self-scoring may be a suitable tool to foster primary school students’ monitoring accuracy and that this translates to some extent into more accurate regulation decisions.

Design and Research of Remote Monitoring System for Sports Injury Rehabilitation Training

In order to study the sports injuries that often occur in athletes’ training and competition and solve the problems of low monitoring accuracy of injury mode data and large difference of resistance signal waveforms in the traditional monitoring system, this paper proposes the application of wireless sensor network in monitoring process. The accuracy of data monitoring with 9 different degree injury modes set by 1–9 squares in the traditional system is lower, while the accuracy of sports injury rehabilitation monitoring based on wireless sensor network is higher, which can be maintained above 90%. The experimental results show that the monitoring system has high monitoring accuracy of damage mode data and small difference of resistance signal waveform. It is basically consistent with the actual waveform.