珠母贝人工育苗技术的初步研究

2002年3月1日于海南陵水县黎安港湛江海洋大学珍珠试验站 ,在珠母贝年龄偏小、性腺发育尚未十分饱满的情况下 ,选取雄性2只、雌性10只 ,通过人工催产的方法 ,促使2雄8雌排放精卵 ,受精率为86.36 % ,受精后20h胚胎发育为直线绞合幼虫。在水温23.5～29.7℃ ,盐度30.6,pH8.1的条件下 ,幼虫经66d的培育 ,共出池3mm±0.3mm的珠母贝苗126.8万只 ,初步取得了珠母贝人工育苗试验的成功。     

人工神经网络在风暴潮增水预报中的应用

本文根据1967～1982年问影响湛江站增水的热带气旋资料，选取了其中对湛江站增水影响比较显著的因子，运用人工神经网络中的BP算法，建立了湛江站风暴潮增水预报的人工神经网络模型，并使用贝叶斯优化算法提高了BP网络模型的预报能力，结果表明所建BP网络模型的训练拟合效果和预报效果都比较好。表明该网络模型可以用于湛江站风暴潮增水的预报。同时本文也为其它的港口、码头等近岸海洋工程风暴潮增水的预报提供了一个有效可行的方案。     

基于BP神经网络的天然气水合物相平衡计算及预测

针对天然气水合物相平衡问题,文中提出用基于带动量因子的BP神经网络进行计算和预测。首先用遗传算法优化确定BP神经网络的结构和参数,得到最优化结构的神经网络;其次结合Levenberg-Marquart优化算法,建立天然气水合物相平衡计算及预测的神经网络模型;最后以实验测定的(CH4 CO2 H2S)三元酸性天然气水合物体系的平衡数据为训练和预测样本进行了计算。计算表明,预测结果与实验数据有良好的一致性,而且由于BP神经网络作为所谓的“纯粹”的算法不需要热力学模型,这对于相平衡计算是非常方便的,所以是研究天然气水合物相平衡计算及预测的一种新的有效方法。     

Predicting the capability-polar-plots for dynamic positioning systems for offshore platforms using artificial neural networks

As the capability of polar plots becomes better understood, improved dynamic positioning (DP) systems are possible as the control algorithms greatly depend on the accuracy of the aerodynamic and hydrodynamic models. The measurements and estimation of the environmental disturbances have an important role in the optimal design and selection of a DP system for offshore platforms. The main objective of this work is to present a new method of predicting the Capability-Polar-Plots for offshore platforms using the combination of the artificial neural networks (NNs) and the capability polar plots program (CPPP). The estimated results from a case study for a scientific drilling vessel are presented. A trained artificial NN is designed in this work and is able to predict the maximum wind speed at which the DP thrusters are able to maintain the offshore platform in a station-keeping mode in the field site. This prediction for the maximum wind speed will be a helpful tool for DP operators in managing station-keeping for offshore platforms in an emergency situation where the automation of the DP systems is disabled. It is obvious from the obtained results that the developed technique has potential for the estimation of the capability-polar-plots for offshore platforms. This tool would be suitable for DP operators to predict the maximum wind speed and direction in a very short period of time.     

南黄海和东海“人工水母”投放试验

1984—1986年,作者在南黄海和东海投放大量“人工水母”测量底层流。本文介绍了“人工水母”的投放、回收和漂流概况,根据这一实验结果,参考有关黄、东海海流研究的成果,绘出了调查试验海区的底层流模式。     

Features of geonomic prognostic functions for the maximum possible earthquake

The recent artificial intelligence techniques are commonly applied to solving problems in which multidimensional statistical analyses of various quantities and their modelling prognostic functions can be used. This paper attempts to summarize the characteristics of the prognostic functions applied in the determination of the maximum possible earthquake. Geonomic quantities are reviewed and categorized with respect to their influence upon the estimation of the maximum possible earthquake.     

杜氏鰤人工苗养殖生物学特性及养殖技术研究

报道杜氏鰤(Seriola dumerili)人工苗养殖生物学特性及养殖技术研究结果。在海区水温6-30．5℃。海水盐度14．33，溶解氧6．5-7．3mg／L，pH7．5-8．1，水流为15-35cm／s的网箱中，杜氏鰤人工苗叉长40-50mm，体质量1．5-1．7g，经208d养殖，叉长达到330-388mm，体质量达到950-1400g，饲料系数为11，成活率为71．7％；养殖720d，体质量达4．5-6．5k，饲料系数为8；养殖l080d，体质量达7．5-11．5kg，饲料系数为7；养殖l440d，体质量达13．，15．5k异，饲料系数为6；养殖l800d，体质量达15．5-18．5kg，饲料系数为5。第4年性腺发育成熟，4-5月份，海上网箱人工催产，平均每尾雌鱼可产卵1．8kg左右。     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

A new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.