Response-based method for determining the extreme behaviour of floating offshore platforms

The accurate prediction of extreme excursion and mooring force of floating offshore structures due to multi-variete environmental conditions which requires the joint probability analysis of environmental conditions for the worst case situation is still impractical as the processing of large amount of met-ocean data is required. On the other hand, the simplified multiple design criteria (e.g. the N-year wave with associated winds and currents) recommended by API known as traditional method does lead neither to the N-year platform response nor to the N-year mooring force. Therefore, in order to reduce the level of conservatism as well as uncertainties involved in the traditional method the response-based method can be used as a reliable alternative approach. In this paper this method is described. In order to perform the calculations faster using large databases of sea states, Artificial Neural Networks (ANN) is designed and employed. In the paper the response-based method is applied to a 200,000 tdw FPSO and the results are discussed.     

假晴东方鲀人工育苗研究

假晴东方鲀人工育苗获得４１．８ｍｍ种苗３５１０００尾，每立方水体出 苗８６０尾。越冬成活率９１．６％，增重率６１．８％。利用室内水泥池及养虾土池 养成商品鱼，分别获得１９６９ ｇ／ｍ３和 ２０８ ｋｇ／亩的收成。     

黑鲷三倍体的人工诱导研究

分别于1989年3—6月、1990年3—5月在天津和青岛两地采用冷、热休克法对黑鲷进行三倍体的人工诱导研究。结果表明,冷休克法诱导三倍体的效果好于热休克法,其最适诱导条件为卵子在受精后5min,温度为3—4℃的海水中处理10—20min,三倍体诱导率最高可达50.35％;正交试验证明,三倍体率受冷、热休克法的三因素影响程度的顺序由大到小依次为,处理时间、处理时刻、处理温度;黑鲷染色体数目:2n=48,3n=72,它们的染色体组型也同时获得。     

空间智能:地理信息科学的新进展

在总结多年来研究GIS智能计算的理论与实践基础上,提出地理信息科学发展的新方向:空间智能.空间智能强调发现与应用空间模式,以增强GIS处理复杂数据和解决复杂问题的能力.空间智能主要的技术体系由空间分析、空间优化和空间模拟三大模块构成,其技术基础包括空间统计与索引、智能代理、高级启发式,以及数学规划等系列智能技术.由于空间智能融合了机器学习、统计分析和人工智能等多个学科理论,面向解决实际工程需求中大量存在的复杂时空问题,因此理论上具有广阔的发展空间,实践上也有重大的应用需求.随着空间智能体系的完善和技术的进一步成熟,它将在实际应用中具有巨大的价值.     

基于蚁群智能的遥感影像分类新方法

智能式遥感分类是遥感研究的新热点.提出了一种基于蚁群智能规则挖掘(ant-miner)的遥感影像分类新方法.遥感数据各波段之间存在较强的相关性,这种相关性往往会导致分类产生误差.而ant-miner算法中的信息素是基于规则整体性能的,信息素的动态更新能有效地处理相关性较强的数据,所提供的正反馈信息能纠正启发式函数缺陷所造成的错误.因此,蚁群智能算法应用于遥感分类具有一定的优势.将该方法用于广州市地区的遥感影像,取得了较好的分类结果.并与See5.0决策树方法及最大似然方法(MLH)进行了对比研究,实验结果表明,蚁群智能算法分类精度比后两者的分类精度更高.     

含矿预测的人工神经网络方法

运用神经网络模型的一典型模型——“反向传播”模型的改进形式,处理矿产资源统计预测问题,得出与数量化理论Ⅱ处理极为相似的结果.     

The almost constant-speed two-body problem with resistance

The almost constant-speed motion of a mass acted upon by a Newtonian attraction and a resisting force is treated. The equation of orbit is derived for a specific type of resistance which covers the familiar case of Danby's drag(=r ^–2) whilst the vector invariants are obtained by direct operation on the vector form of the equation of motion.     

Aftershocks series monitoring of the September 18, 2004 M = 4.6 earthquake at the western Pyrenees: A case of reservoir-triggered seismicity?

On September 18, 2004, a 4.6 mbLg earthquake was widely felt in the region around Pamplona, at the western Pyrenees. Preliminary locations reported an epicenter less than 20 km ESE of Pamplona and close to the Itoiz reservoir, which started impounding in January 2004. The area apparently lacks of significant seismic activity in recent times. After the main shock, which was preceded by series of foreshocks reaching magnitudes of 3.3 mbLg, a dense temporal network of 13 seismic stations was deployed there to monitor the aftershocks series and to constrain the hypocentral pattern. Aftershock determinations obtained with a double-difference algorithm define a narrow epicentral zone of less than 10 km², ESE–WNW oriented. The events are mainly concentrated between 3 and 9 km depth. Focal solutions were computed for the main event and 12 aftershocks including the highest secondary one of 3.8 mbLg. They show mainly normal faulting with some strike-slip component and one of the nodal planes oriented NW–SE and dipping to the NE. Cross-correlation techniques applied to detect and associate events with similar waveforms, provided up to 33 families relating the 67% of the 326 relocated aftershocks. Families show event clusters grouped by periods and migrating from NW to SE. Interestingly, the narrow epicentral zone inferred here is located less than 4 km away from the 111-m high Itoiz dam. These hypocentral results, and the correlation observed between fluctuations of the reservoir water level and the seismic activity, favour the explanation of this foreshock–aftershock series as a rapid response case of reservoir-triggered seismicity, burst by the first impoundment of the Itoiz reservoir. The region is folded and affected by shallow dipping thrusts, and the Itoiz reservoir is located on the hangingwall of a low angle southward verging thrust, which might be a case sensible to water level fluctuations. However, continued seismic monitoring in the coming years is mandatory in this area to infer more reliable seismotectonic and hazard assessments.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.