In order to improve the environmental quality of atmosphere, Fine Particles monitoring work is of great significance. The Internet of things transmission technology and image processing technology, two advancing technologies, are used to monitoring Fine Particles data. Use Huawei MC509 wireless data transmission module of CDMA2000 1Xev-DO network structure and four direction of edge detection method, then by Yeelink platform to implement the collected a large number of particle image access management, data storage and data presented to the server computer at any time. Through the analysis of experimental results, there correlation coefficient is 0.9693 between the number of particles detected by the system and the official Fine Particles density. Because of the highly linear positive correlation, Fine Particles monitoring method has important practical reference value.
In this paper,we study the convergence rates of error variance estimates under (?)-mixingerror in linear models.Under the weak confinement for mixing rates,our results are consis-tent with corresponding results in the independent case.
The application of deep learning algorithms for detecting anomalies in data is becoming increasingly prevalent, and accurate identification of these anomalies is of utmost importance. In this paper, the dimensionality of the data is augmented by employing a Gaussian kernel function during the data preprocessing stage, which allowed for the extraction of more potential features. To address the issue of poor prediction accuracy associated with random forest algorithms when faced with limited samples and features, adaptive algorithms based on random forest classifiers were utilized to propose a convergent anomaly detection model. Additionally, a comparative analysis of different combinations of water quality parameters is conducted to determine the optimal combination of indices.
Recently, the formation control of multiple autonomous mobile robots (AMRs) have gained significant attention, and autonomous mobile robots (AMRs) have applied to all aspects of our life. Multi-agent reinforcement learning is used to solve the autonomously sequential decision-making problem of agents in a common environment with competition or cooperation. Therefore, we present a utility function and a reward function to achieve formation control with collision avoidance for a rigid AMRs system, and build a simulation environment to meet environmental requirements based on MPE.
With the development and application of computer theory, electronic information technology, economics and other disciplines, more and more models are being used to discuss electricity consumption factors. Thesis based on actual data of China, this paper discusses the influence of economic growth, electricity price, urbanization level, industrial structure, science and technology investment level and opening degree on electric power consumption level of our country, and analyses the influence of grey correlation analysis method. To drive electricity consumption, the first is to optimize and upgrade the industrial structure, and the second is to further improve the level of urbanization. The research results of this paper have certain reference value for relevant managers and government departments to formulate policies.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)