To improve the efficiency of harvesting cherry tomato and reduce its breakage rate, we design a harvesting robot base on image recognition and modular control. After image acquisition with IOT technology, the binary processing and expansion and corrosion processing of original image can effectively increase the fruit recognition rate. In addition, the use of fuzzy control technology processes the response error of manipulator. We test the performance of cherry tomato harvesting robot through harvesting experiment. The experimental results show that the harvesting efficiency significantly improves and the degree of crushing cherry tomato greatly reduces after using the cherry tomato harvesting robot.

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The Design and Realization of Cherry Tomato Harvesting Robot Based on IOT

In recent years, wireless communication technology has developed rapidly and the speed of wireless communication data transmission is faster and faster, as well as remote control delay has greatly reduced, which is almost negligible, so the remote control is gradually being widely used in various fields. This paper achieves the  motion control of picking manipulator and obstacle avoidance system with the advantages of IOT remote control technology and PLC real-time control system. Experiments show that the designed picking manipulator motion control and obstacle avoidance system not only have strong obstacle avoidance ability but also have smooth path optimization with high reliability and stability.

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Design and Research of Picking Manipulator Obstacle Avoidance System Based on IOT

The key and necessary condition for accurate picking are to identify and locate the target fruit accurately. This paper focuses on the development of picking robot, mainly through two aspects: software and hardware. Moreover, it mainly consists of the binocular vision system and the camera calibration model. Based on these two core components, it can complete the function of identification and positioning. The results show that it can accurately locate the target location in the aspect of recognition function. What is more, its error is no more than 8mm, and its picking rate is over 96%. It shows high precision and efficiency, which plays a decisive role in the realization of picking automation.

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Research on Picking Identification and Positioning System Based on IOT

In order to forecast the malfunctional state of main power plant on ships through predicting the trend of unsteadily changed thermal performance parameters in main power plant system, this paper established AR model for thermal performance parameters using time series analysis method, by which to predict the change trend analysis of thermal performance parameters, and comparing the predicted values and the measured values. The actual application case of predicting a main engine’s exhaust temperature has been validated by using of the AR model, and results showed that: AR model can accurately predict the change trend of smoke temperature, and the prediction precision is higher, the average relative error was 0.25%.

Prediction of Ship Main Engine Exhaust Gas Temperature Using AR Model

The specialty setting and employment guidance in colleges and universities are usually worked out based on empiricism. Data mining technology is rarely used to guide it. In recent years, under the background of Belt and Road Initiative, many schools have offered foreign the majors related to foreign trade English. What is the setting of these majors and the employment satisfaction of graduates? Both have an impact on social and economic development. Access to this information in a timely manner becomes the key. In this paper, the rough set theory, a data mining technology, is used to solve this problem. First of all, this paper collects the employment statistics of foreign trade English graduates from Guangxi Talent International College. Secondly, based on the rough set theory, the quantitative measurement method of college students' employment satisfaction research is put forward, and the intelligent data analysis model based on rough set is established. The experimental results show that the new method is effective and can provide some references for school employment guidance and the training of foreign trade English majors. Finally, the resolution matrix of rough set is used to reduce the attributes of the evaluation data and reduce the unnecessary evaluation indexes. The experimental results show that the evaluation method after the statute is more efficient.

An Analysis of Employment Satisfaction of Business English Majors under the background of Belt and Road Initiative: An Intelligent Data Analysis Method based on Rough Set Attribute Specification

Samples gotten from lubricating oil used in the marine diesel engine cylinder piston ring were analyzed with the spectrum analyzer to get the value of wearing elements. The time series autoregressive (AR) prediction model was established with monitoring data to predict the wearing data. Actual results showed that, AR model can handle non-linear dynamic data, reflect the nonlinear relationship among the various elements, and the prediction accuracy was high and with a strong practicability.

Zhu Zong-ming

Papers

Research on Wearing Prediction of the Cylinder Surface Based on Time Series Autoregressive (AR) Model