Automatic recognition of image of abnormal situation in scenic spots based on Internet of things

Lin, Yigang

doi:10.1016/j.imavis.2020.103908

Cited by 6 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When students make some actions, there are differences in the characteristic images in the camera of students in a series of actions. A real-time image recognition system can exactly cover the height difference in the image (Rahman, 2021; Chen and Yang, 2021; Lin, 2020). When the positioning camera successfully captures a student beyond the preset height, it will make the judgment that the student is answering questions.…”

Section: Scheme Designmentioning

confidence: 99%

Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication

2022

LHT

View full text Add to dashboard Cite

PurposeThe digital media recording and broadcasting classroom using Internet real-time intelligent image positioning and opinion monitoring in communication teaching is researched and analyzed.Design/methodology/approachFirst, spatial grid positioning and monitoring and image intelligent recognition technologies were used to extract and analyze teaching images by mastering Internet of Things (IoT) technology and establishing an intelligent image positioning and opinion monitoring digital media recording and broadcasting system framework. Next, a positioning node algorithm was utilized to measure the image distance, and then a moving node location model under the IoT was established. In addition, a radial basis function (RBF) neural network was used to realize the signal transmission function. The experimental data of the adopted RBF based on the optimization of the adaptive cuckoo search (ACS-RBF) neural network, particle swarm algorithm neural network, and method of least squares optimization were compared and analyzed. In addition, a more efficient RBF neural network was adopted. Finally, the digital media recording and broadcasting classroom scheme of real-time intelligent image positioning and opinion monitoring was designed. In addition, the application environment of digital media actual teacher teaching was detected, and recording and broadcasting pictures were analyzed and researched.FindingsThe actual value, predicted value, and the number of predicted samples of the ACS-RBF model were all better than those of the two other neural networks. According to the analysis and comparison of the sampling optimization Monte Carlo localization (SOMCL), Monte Carlo, and genetic algorithm optimization-based Monte Carlo positioning algorithms, the SOMCL algorithm showed better robustness, and its positioning efficiency was superior to that of the two other algorithms. In addition, the SOMCL algorithm greatly reduced the positioning and monitoring energy consumption.Originality/valueThe application of real-time intelligent image positioning and monitoring technology in actual communication teaching was realized in the study.

show abstract

Section: Scheme Designmentioning

confidence: 99%

Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication

2022

LHT

View full text Add to dashboard Cite

show abstract

“…Compared with other proposed models and state-of-the-art methods, XgBoost provides excellent accuracy, precision, F1-score and MCC. Lin [22] proposed an automatic image algorithm for tourism scene anomalies. On the basis of traditional image segmentation technology, the dynamic characteristics of continuous frames were added according to the neighbourhood correlation characteristics of Markov random fields and reconstruct the Gibbs energy function.…”

Section: Introductionmentioning

confidence: 99%

In-depth basic data detection device based on Internet of Things technology

Xie,

Zhang,

Cheng

et al. 2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Due to the limited computing power of the perception layer of the Internet of Things (IoT), the ability to analyse and process the collected complex object information data is insufficient, and it is also difficult to complete the storage of a large amount of collected data. Through convolutional neural network-simple recurrent unit (CNN-SRU) deep learning, we preprocess a large amount of complex data in the perception layer. The data collected by the perception layer are first transmitted to the CNN for simple category screening and analysis, and then they reach the SRU link, which is updated and optimised again, to improve the integrity and accuracy of IoT information collection. The results show that the accuracy of gated recurrent unit (GRU), long–short-term memory (LSTM) and SRU algorithms shows a downward trend under the three error evaluation standards of root mean squared error (RMSE), mean absolute error (MAE) and relative error (RE), from 0.034 to 0.015, 0.028 to 0.012 and 0.024 to 0.013, respectively; in terms of training time. The SRU algorithm is increased by 54.52%; the maximum SRU in terms of data storage is increased to 33.22%; and the maximum SRU reduction in data mining energy consumption is 11.45%. This meets the requirements of IoT applications in big data mining.

show abstract

“…At present, there are more than 1,500 forest fire video monitoring points, more than 670 forest fire risk monitoring stations, more than 110 combustible material information collection stations, and more than 17,000 artificial observation platforms for fire-fighting teams. The application of more than 10,000 IoT-related technologies in forest fire prevention mainly includes three aspects: one is positioning, the other is video monitoring, and the third is abnormal temperature sensing [ 18 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

Equivalence Assessment Method of Forest Tourism Safety Based on Internet of Things Application

Gao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the continuous development of the society, China's economy and technology have been greatly improved, and network technology has also been widely used in life. In China's forestry management, the use of Internet of Things technology has gradually formed a model, which has greatly helped the economic benefits of forests. In addition, with the rapid development of the tourism industry, the number of tourists has increased sharply, the tourism infrastructure and tourism management are relatively lagging behind, and tourism safety accidents have occurred from time to time. However, the application of IoT technology in forestry is still in its infancy, with a small scope of application and low technical level. Aiming at the uniqueness of forest management, this paper proposes the development direction and application planning of IoT in forest resource supervision and service, forest fire prevention and control and service, ecological environment monitoring, and forest tourism supervision and service. In addition, this paper also discusses the acquisition technology of geological disasters, air quality, meteorological conditions, passenger flow conditions, and traffic flow mainly involved in tourism safety from the macroperspective of the Internet of Things. At the same time, the specific application of these technologies in tourist attractions is discussed to provide some technical reference for the realization of scientific and safe tourism management.

show abstract

Automatic recognition of image of abnormal situation in scenic spots based on Internet of things

Cited by 6 publications

References 30 publications

Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication

Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication

In-depth basic data detection device based on Internet of Things technology

Equivalence Assessment Method of Forest Tourism Safety Based on Internet of Things Application

Contact Info

Product

Resources

About