基于概率神经网络的地铁车站易损性分析

基于深度学习方法提出了一种地震响应概率模型，并基于此模型推导了地铁车站结构极限状态超越概率的计算公式，以评价结构的地震易损性。首先采用主成分分析对地震强度指标进行正交化和降维；为了克服传统地震响应概率模型中地震动强度指标与结构地震响应指标服从对数空间线性分布假设的局限性，基于BP神经网络建立趋势模型以预测结构的地震响应；基于概率神经网络建立误差模型以描述基于统计的趋势模型与基于物理机制的数值模型之间的误差，以拓展残差的齐次方差正态分布假设的严格限制。最后，以上海典型2层三跨地铁车站为算例，计算得到其易损性曲线。结果表明，基于深度学习建立的趋势模型较好地模拟了地铁车站结构地震响应随地震强度指标第一主成分的非线性变化特征；建立的误差模型准确地描述了趋势模型预测值的残差随地震强度指标第一主成分的方差非齐次特征。     

基于Sinc插值与相关谱的纵横波速度比扫描方法

在多分量地震技术中，准确地求取出纵、横波速比（γ）是多分量资料处理、反演、解释各环节中的一个重要步骤。由于地下同一层位具有相同的波阻抗比，因此该层位的反射纵波和反射转换波相互关联，纵横波速比与时间比存在相互转化关系。基于Sinc插值算法与相关系数谱分析的纵横波速比扫描方法是通过设置一系列γ，利用Sinc插值算法将纵波拉伸后与转换波进行互相关，当互相关系数达到最大时即可求出相应的γ。理论数据和实际资料的测试，验证了该方法的有效性、准确性和实用性。     

Seismites(地震岩)等术语的概念与名称

seismites,按其字面意思理应翻译成地震岩,即与地震有成因联系或受发震断裂改造、变形的岩石。按发震时地质材料的固结程度与力学性质,地震岩可分为震积岩(地震时尚未固结成岩的含水沙土层,即软沉积物)和脆性断层岩(地震时已经固结成岩的沉积岩、岩浆岩和变质岩)。脆性断层岩包括断层角砾岩、碎裂岩、超碎裂岩、假熔岩和断层泥等。发育由沙土液化形成的软沉积物变形构造的沉积岩,统称为“沙土液化岩”(liquefactites),但是长期以来一直被误译为seismites。大量的研究表明,并不是所有的沙土液化和软沉积物变形都是由地震造成的,也并不是所有地震都能导致沙土液化。只有那些所在区域存在重要发震断层、真正由地震引起的区域性的沙土液化造成的软沉积物发育变形构造的岩层才能称之为震积岩(seismic liquefactites)。震积岩不应再翻译成seismites。零星出现的软沉积物变形构造与沙土液化构造(例如:砂涌丘)不足以作为古地震的证据。     

利用反预报方法从截面预言近滴线丰质子核的结合能

在Origin等数据处理软件中,当实验数据较少时,自由参数的不同初始化设置会导致较大的结果差异,这为物理结果的确定带来较大不确定性.通过最小二乘法分析了345MeV/u ~(78) Kr+~9Be反应中产生的丰质子同位素的截面和结合能,并得到线性回归方程.通过回归方程,利用结合能预报部分丰质子核素的截面,以及通过实验截面对近质子滴线核素的结合能进行反预报测量.这对于近质子滴线的丰质子核素实验测量具有较好的借鉴意义.     

线性回归分析与能源需求预测

结合实例介绍了回归模型方法在能源预测中的应用，具体地讨论了最简单、最基本的直线回归模型结构及参数估计方法，对于其他一些曲线回归模型则可通过变量代换转化为直线回归模型。     

Robustness of alternative non‐linearity tests for SETAR models

In recent years there has been a growing interest in exploiting potential forecast gains from the non‐linear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type non‐linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non‐linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non‐linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR‐type non‐linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

动态校正的PI型广义预测控制器及其在反应釜系统中的仿真研究

提出了一种新型的广义预测控制器。由于在控制器的设计中,进行了预测误差的动态校正,并且控制器采用了比例积分型(PI)目标函数,因此该控制器对未建模动态具有很强的鲁棒性。应用低阶的模型对一个强放热的化学反应釜模型进行的仿真研究检验了该方法的有效性。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

The controlling factors and distribution prediction of H2S formation in marine carbonate gas reservoir, China

Generally, there are some anhydrites in carbonate reservoir, as H2S is also familiar in carbonate oil and gas reservoirs. Nowadays, natural gas with high H2S concentration is usually considered as TSR origin, so there is close relationship between H2S and anhydrite. On the contrary, some carbonate rocks with anhydrite do not contain H2S. Recently, researches show that H2S is only a necessary condition of H2S formation. The reservoir porosity, sulfate ion content within formation water, reservoir temperature, oil/gas and water interface, hydrocarbon and some elements of reservoir rock have great controlling effects on the TSR occurrence. TSR deoxidizes hydrocarbon into the acidic gas such as H2S and CO2, and the H2S formation is controlled by TSR occurrence, so the relationship among reaction room, the contact chance of sulfate ion and hydrocarbon, the reservoir temperature has great influence on the TSR reaction. H2S has relatively active chemical quality, so it is still controlled by the content of heavy metal ion. Good conditions of TSR reaction and H2S preservation are the prerequisite of H2S distribu- tion prediction. This paper builds a predictive model based on the characteristic of natural gas reservoir with high H2S-bearing. In the porosity reservoir with anhydrite, the formation water is rich in sulfate and poor in heavy metal ion. Oil and gas fill and accumulate in the gas reservoir with good preservation conditions, and they suffered high temperature later, which indicates the profitable area of natural gas with high H2S-bearing.     

神经网络在可靠性分析中的应用

应用人工神经网络对舰炮发生故障的发射炮弹数进行了预测。借助前馈神经网络对非线性函数的逼近能力，对发生故障的发射炮弹数进行了单步预测，并与使用灰色数列预测的结果进行了对比，验证了人工神经网络进行可靠性预测的可行性。