基于广播星历和神经网络的GPS卫星坐标计算方法

通过在开普勒轨道模型中加入神经网络算法,得到一种全新的卫星坐标计算的混合模型:基于广播星历和神经网络混合模型,从而提高了利用广播星历计算卫星坐标的精度.计算结果表明:混合模型较传统的开普勒轨道模型在X、Y、Z3个方向上卫星坐标计算的精度提高了约10％.     

Delaunay三角网关键技术探讨

利用计算机技术,基于实际测量数据,利用逐点插入法,在不建立格网索引的情况下,提出一种高效的Delaunay三角网构建方法,与建立格网索引法搜索点所在的三角形相比,具有较高的执行效率.     

三亚三美湾和鹿回头湾人工沙滩设计

通过分析三亚三美湾和鹿回头湾不同的海岸地貌、沉积物类型、水动力条件和岸线形态,结合岸滩演变和水动力数值模型,因地制宜提出不同的人工沙滩设计方法、平面布置方案,选取了粒径合适的回填沙,确定了合理的防波(潜)堤修筑位置和高程。人工沙滩工程完成后滩面形态、岸线走向和景观稳定,达到了改善海岸环境的预期效果。     

基于地脉动噪声的呼伦贝尔地区监测能力研究

基于地脉动噪声计算方法,以呼伦贝尔市地震台网8个台站连续波形资料为基础,对地震观测台网监测能力进行评估,同时开展台网优化和科学布局研究。通过累计的历史噪声数据,用来评估台站观测环境的变迁,及时掌握地震监测能力的变化。该成果将为地震速报、重点地震危险区震情跟踪、强震后现场应急提供科学参考。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

水污染物浓度的神经网络预测模式及效果检验

污染物浓度预测是环境保护的重要内容,将神经网络用于水中有机污染物浓度的预测并对效果进行检验.结果表明,预测值与观测值符合较好.     

节理网络分形在隧道超前地质预报中的应用

分形几何是20世纪70年代以来发展起来的研究非线性现象的理论和方法,这一理论的诞生为揭示隐藏于混乱复杂现象的精细结构和定量地描述它们提供了理论基础。将分形的方法引入到隧道的超前地质预报,通过岩体结构面网络的分维值来预测岩体的质量。结合工程实例可以看出,岩体结构具有良好的分形特征,其分形描述可作为评价结构岩体质量的标准之一。分形方法是评价岩体性质的一个有效的方法,具有广泛的应用前景。     

General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet, an efficient and highly generalized deep learning neural network for P- and S-wave phase picking. The software organically combines multiple modules including application terminal interface, docker container, data visualization, SSH protocol data transmission and other auxiliary modules. Characterized by a series of technologically powerful functions, the software is highly convenient for all users. To obtain the P- and S-wave picks, one only needs to prepare three-component seismic data as input and customize some parameters in the interface. In particular, the software can automatically identify complex waveforms (i.e. continuous or truncated waves) and support multiple types of input data such as SAC, MSEED, NumPy array, etc. A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software. The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data, thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.     

STUDY ON MIXED MODEL OF NEURAL NETWORK FOR FARMLAND FLOOD/DROUGHT PREDICTION

The paper concerns a flood/drought prediction model involving the continuation of time seriesof a predictand and the physical factors influencing the change of predictand.Attempt is made toconstruct the model by the neural network scheme for the nonlinear mapping relation based onmulti-input and single output.The model is found of steadily higher predictive accuracy by testingthe output from one and multiple stepwise predictions against observations and comparing theresults to those from a traditional statistical model.     

长江流域大洪水有序网络结构及其预测研究

根据1827年以来的统计资料进行分析,长江流域大洪水展示出极为显著的有序性.运用翁文波信息预测理论,构建长江大洪水二维平面和三维立体信息有序网络结构并进行综合分析和预测,结果表明：2014、2020、2030、2036、2051与2058年前后的汛期,长江流域将有可能发生大洪水.