Plant diseases and insect pests are common factors affecting plant growth, which is directly harmful to the quality of agricultural production. In order to identify and classify plant diseases and insect pests, in this paper, a detection method based on convolutional neural network (CNN) is proposed. Specifically, this paper first introduces the processes of plant diseases and insect pests data collection, and then the methodology for training detection model based on CNN is described. Finally, a series of comparative experiments are conducted to demonstrate the effectiveness of our model, and experimental results show our model achieves competitive performance on plant diseases and insect pests dataset.

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model, determining automatically the intention of the user to open or close the mosquito net. We used optical flow to extract pressure map features, and they were fed to a 3-dimensional convolutional neural network (3D-CNN) classification model subsequently. We achieved the expected results using a nested cross-validation method to evaluate our model. Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users. This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

With the rapid development of power systems, the switchgear, which is widely used, plays an increasingly important role. Partial discharge is one of the main causes of insulation failure of the switchgear, which is a serious threat to personal and equipment safety. In this paper, the partial discharge of the switchgear is detected based on the ultrasonic method. According to the characteristics of ultrasonic waves generated by partial discharge, a partial discharge detection method for the switchgear based on the characteristics of ultrasonic wave signal is designed. The wavelet threshold denoising algorithm is used to denoise the ultrasonic signal and draw the partial discharge phase distribution map by using the characteristics of low amplitude and wide frequency distribution of the interference signal. Finally, the improved support vector machine algorithm is used to identify and diagnose four types of discharge based on 280 groups of experimental data. By adjusting the kernel function and optimizing the support vector machine algorithm, the final recognition accuracy is more than 90%.

The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.