Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
The development of Internet technology provides a lot of convenience for the promotion of smart agriculture. At present, smart agriculture has gradually realized unmanned and automatic management, which can realize monitoring, supervision, and realtime image monitoring. However, the data in smart agriculture system cannot be guaranteed to be complete and vulnerable to attack. Based on this, this paper studies and analyzes the application of edge computing and blockchain in smart agriculture systems. Based on the simple analysis of the development of smart agriculture, the edge computing framework and the advantages of blockchain are used to build the framework system of smart agriculture. The classical architecture of edge computing and the confidentiality of blockchain are used to realize the analysis and storage of data. In view of the shortcomings of crop image overlap detection, it is proposed to detect the overlapping area and determine the feature points to analyze the image based on the edge computing and hash algorithm. In terms of data integrity, based on the advantages of blockchain, an edge data detection method based on short signature is proposed, and experiments are designed to analyze the accuracy and effectiveness of the algorithm. The simulation results show that the image mosaic algorithm can extract the contour information of the image and realize the fast image matching. The edge data integrity calculation based on short signature can meet the requirements and shorten the response time....
Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared. Ten faults in the area were selected to analyze the prediction effect, and a comparative experiment was done with model structure parameters and training sets. The experimental results indicate that the training parameters have a significant influence on the training time and testing accuracy of the model, while structural parameters and training sets affect the actual prediction effect of the model. By comparison, the fault results predicted by the convolutional neural network are in good agreement with the manual interpretation, and the accuracy of the model is more than 85%, which proves that this method has certain feasibility and provides a new way to shorten the fault interpretation period and improve the interpretation accuracy....
Economic growth is accelerating in the entire world due to new inventions and advancements in the field of science and technology. The technology is progressing at a rapid pace and resulting in the infrastructure and technical progression of each nation. The utilization rate of land in the world has also increased substantially where the cultivated land is used for the development of industries and buildings. This land occupancy is alarming for the environment, and it is impacting the ecosystem of the earth in many adverse ways. In this study, the impact of land cover changes on the wetland ecosystem water environment has been studied in depth using soft computing techniques. The water environment data of the wetland ecosystem are obtained from real-world scenario and are mined using a particle swarm K-means clustering approach based on chaos search. The water environment assessment approach is used to analyze the influence of land cover changes on the water environment of the wetland ecosystem. The findings of this research work reveal that the cultivated land in the wetland is steadily decreasing, and the fertile forestland is steadily decreasing in the early stages and gradually increasing in the latter stages. The wetland water quality is gradually degrading over the period of time. The outcomes of the study demonstrate that a nonpoint source of pollution produced by land-use changes is the most important factor, which is affecting water quality. The analytical results’ accuracy is as high as 0.98 when compared to the real-world circumstances....
For the improvement of the traditional evaluation effect of the automobile sound quality, an evaluation model of automobile sound quality is constructed based on BP neural network. The first is to introduce the basic principle of the BP neural network in detail. The second is to use the MGC parameters to establish the vehicle interior sound conversion model. The converted sound characteristic parameters are taken into the WORLD model to synthesize the new sound signals. Furthermore, the wavelet decomposition method is used to remove noise from the synthesized sound signals. Finally, a sound evaluation model based on BP neural network is established. The sound quality of automobiles can be better evaluated by carrying out the ABX test and MOS test in the field of sound conversion. For the newly synthesized sound signal and the target sound signal, it can be seen that the newly synthesized sound signal is more inclined to the target sound signal, and the sound quality is better. In addition, the sound quality is tested through loudness, roughness, sharpness, and level A in the field of sound quality evaluation.Thefinal results show that the quality of newly synthesized sound is better, and the average errors of sound signals meet the sound standard. Therefore, the constructed sound conversion model and the sound evaluation model are feasible and effective....
With the rapid development of Internet technology, artificial intelligence and soft computing are gradually applied in various fields. Through active transformation, many companies use artificial intelligence, software computing, and other technologies to develop sales channels, improve corporate performance, and obtain greater benefits. However, some traditional enterprises have been gradually eliminated by the market because they have not rapidly transformed and improved their intelligence. Urban intelligence is the trend of urban economic development, and the degree of intelligence in a city is closely related to the number and scale of intelligent enterprises. In the context of artificial intelligence and soft computing, this paper expounds the relationship between enterprise performance and the degree of urban intelligence and analyzes the key factors affecting enterprise performance and the impact of enterprise performance on the degree of urban intelligence. The results show that Internet technologies such as artificial intelligence and soft computing can improve the performance of enterprises, thereby improving the level of intelligence in cities....
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