Research of Neural Network Applied on Seabed Sediment Recognition

• Main Authors: Po-Yi Lee, 李柏儀
• Other Authors: Ruey-Chang Wei
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/95237592433628083312

碩士 === 國立中山大學 === 海下技術研究所 === 88 === Along with advancement of human industrialization, pollution in the ocean is getting worse. Moreover, the overfishing through the years has caused catastrophic damage to the ocean eco-system. In order to avoid exhaustion of fishery resource, many concepts of planned administrative fishery has become popular, and thereamong, ocean ranch draws the most attention. Artificial reef plays a key role in an ocean ranch, which starts with incubating brood fish in the laboratory. Often, the brood fish will grow in the cage near coast till proper size, then be released to the artificial reef. If fish groups do not disperse and multiply, the artificial reef can be considered successful. The success of the artificial reef relies on the stable foundation. Consequently, the composition of seabed sediment under the planned site should be investigated thoroughly before hand. This research introduced a remote investigation method, which an active sonar, depth sounder, was used to emit and collect acoustic signals. By using the signals reflected from the seabed, the sediment composition can be analyzed. However, all acoustic signals are subjected to noise through propagation, and distorted somehow. Therefore, certain signal pre-processing should be applied to the received signal, and representative characteristics can be extracted from it. In this research, the recognition platform was built on artificial neural network (ANN) in this research. Among many network algorithm modes, this research chose the widely used backpropagation learning algorithm to be the main structure in ANN. The goal of this research was to discriminate among three seabed sediments: fine sand, medium sand, and rock. During the signal processing, characteristics were extracted by using peak value selection method. Selected major frequency peaks were fed into the network to train and learn. According to partial error relation between recognition and practical result, weights of the network were adjusted for improving successful ratio. Finally, a reliable acoustic wave signal recognition system was constructed.