@article {753,
	title = {How Much Difference in Earthquake Risk among China{\textquoteright}s Areas: A Study based on Pricing a Seismic Catastrophe Bond},
	journal = {Journal of Digital Information Management},
	volume = {21},
	year = {2023},
	month = {03/2023},
	pages = {18-23},
	type = {Research},
	chapter = {18},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">Firstly, this paper gives a data aggregation algorithm based on learning automata to solve the problem that the existing data aggregation algorithm can{\^a}{\texteuro}{\texttrademark}t solve, the uneven energy cost, and the existing algorithm can{\^a}{\texteuro}{\texttrademark}t change the gathering path dynamically existing the overhead environment. In the proposed method, nodes can change its gathering path to adjust the overhead environment. All the nodes of WSN equipped with a learning automata. These leaning automata learn all the gathering path of the nodes. In the process of transmit information two kinds of data are transmitted, including data packet, knowledge packet .When the information of the nodes changes, according to the feedback of the nods, the learning automata gives the reward or punish to the current gathering path, which help to find the best gathering path. Secondly, this paper improved the wavelet data compression algorithm, which was brought out as the correlation between different data. The algorithm do not reduce much of the data relate to the original data. After the wavelet data compression, Huffman coding compression algorithm will improve the data compression ratio.</span></p>
},
	doi = {https://doi.org/10.6025/jdim/2023/21/1/18-23},
	url = {http://www.dline.info/download.php?sn=3683},
	author = {Yong Zhang and Wei Liu}
}
