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To Exploiting Lower Tidal Flats for Expending Living Space of China

Chen Jiyu

Strategic Study of CAE 2000, Volume 2, Issue 3,   Pages 27-31

Abstract:

It was predicted that the population of China will increase by 2〜3 hundred millions by the middle of next century, but the arable land will decrease by 18 million ha. This means that there will be a shortage of living space for 4〜5 hundred million populations, which will aggravate the situations of population, resources and environment. To expend living space is difficult duo to closing off afforested mountain for protection on upstream and recovering lakes from onec reclaimed farmland. However, the tidal flats along the coasts can provide a large amount of land resources. Historically, there were over 100 000 km2 obtained from land progression and reclamation. In recent 50 years, China has acquired 11 000～12 000 km2 new land with enhancing reclamation. This paper suggests making 10 000 — 15 000 km2 living space for 20～30 millions population to get employment before the year 2050. The reclaimation to lower tidal flats is the key and difficult point in the next 50 years. According to the sediment shortage from sea or river between artificial landmaking and natural landmaking, the artificial impelling siltation and bio-impelling siltation engineerings are necessary to make up for sediment shortage of natural land-making. Besides, it is also necessary to improve environmental monitor and prediction precision and to piake innovations on engineering technology and so on.

Keywords： tidal flats     living space     reclamation     artificial coastline     engineering impelling siltation     bioimpelling siltation    

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A data-driven method for estimating the target position of low-frequency sound sources in shallow seas Research Articles

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 1020-1030 doi: 10.1631/FITEE.2000181

Abstract: Estimating the target position of low-frequency sound sources in a environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed (C-RNN) model that compresses the signal received by a into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the , and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a environment.

Keywords： 矢量水听器；浅海；低频；位置估计；循环神经网络    

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Title Author Date Type Operation

To Exploiting Lower Tidal Flats for Expending Living Space of China

Chen Jiyu

Journal Article

A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang,robin_sun@qut.edu.cn,jiaxinming_123@163.com

Journal Article