Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

灾害孤岛潜势风险分群与降雨警戒值探讨

台湾本身地貌特性以及近年来强降雨事件发生频繁,促使山区地带发生复合式灾害,如崩塌、泥石流与山洪等,因此灾害孤岛效应有必要仿效泥石流以及洪水灾害,制定出参考警戒值以及警戒线,将有助于防灾决策、救难资源投入以及灾害风险判释。本文针对台湾发生过灾害孤岛效应的114个村进行分析,且着重于首次发生灾害的台风降雨事件。首先通过群集分析并依照各样本的6个灾因指标进行分类,群集结果显示可分为8个群,并再以象限分布描述各群的潜势相对风险。采用2004—2015年台风降雨事件的观测雨量数据,并利用距离反比权重法,得出各村的累积降雨量(R)与最大时雨量(I)。整合各群集样本雨量数据后,使用台湾水土保持局使用的RTI模式概念,计算出各群集之RTImin、RTI30、RTI50和RTI70,以绘制低风险区(0～30%)、中风险区(30～70%)和高风险区(70%～100%),其中着重探讨灾害孤岛事件的下限值RTImin,以便可得知最易发生灾害的群集。结果显示,各群的潜势风险反映RTImin的效果良好,如潜势风险越高则其RTImin值越低,即雨量驱动灾害发生的条件较低。群集中,第1与2群为低风险群、第3、5与6群为中风险群、第4、7与8群为高风险群,仅第5群无法透过RTImin验证潜势风险关系,故本文挑选的6项灾因指标,为辨别灾害孤岛效应潜势等级的重要因子。     

Vibration analysis and identification of breathing cracks in beams subjected to single or multiple moving mass using online sequential extreme learning machine

In this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (DSEs) and translational accelerations in beam structures under moving mass were used as forward problem and application of an emergent learning algorithm called the online sequential extreme learning machine algorithm as inverse problem to predict crack depths and locations. To demonstrate the potential of the proposed vibration analysis over existing ones, two validation studies have been done. To evaluate the proposed method to identify breathing cracks, two examples, namely, clamped–clamped beam and two span continuous beams have been studied. Also, the effect of the discrepancy in stiffness between the finite-element model and the actual tested dynamic system has been investigated. Another examination has been performed in which moving mass with different speeds are utilized. Also, the effect of multi mass passing through the beam has been studied. The obtained results indicated that the proposed method could identify the breathing cracks existence and severity in the beam under moving mass using DSE and accelerations, which may be noisy or noise free.     

基于PSO-ELM模型的潜油柱塞泵冲次优化方法

为改进油井后期泵效低下、能耗高等缺陷，提出了一种基于PSO-ELM模型的潜油柱塞泵冲次优化方法.采用粒子群（PSO）算法与极限学习机（ELM）相结合的方式来实现动液面软测量建模；根据动液面及潜油柱塞泵工作电流变化，以油井运行经济性最优为目的建立目标函数得到潜油柱塞泵冲次，解决了在油井生产时不能准确调节抽油机冲次问题；最后以目标函数关系建立模糊控制器模型，根据输入参数调整潜油柱塞泵冲次.实验结果表明，建立的软测量模型预测动液面精度高，模糊控制器能够更加合理地调整抽油机冲次，最终达到智能调整冲次大小、提高油井采油率及节能的目的.     

平原地区城市湖泊防洪与景观功能协调研究——以枝江市金湖为例

为了使城市湖泊尽可能滞蓄雨洪、发挥景观功能、打造良好的居民"亲水空间",以枝江市金湖为例,根据金湖主要泄洪渠道的实际情况,利用Mike 21模型模拟计算汛限水位值,按照景观水位确定大、小洪水工况下合理的水位。在此基础上,提出传统的静态景观水位与改进的动态景观水位两种方案。结果表明:以50年一遇、30年一遇为代表的大洪水工况下合理景观水位为40.7 m,以20年一遇、10年一遇为代表的小洪水工况合理景观水位为41.3 m;静态方案采用50年一遇洪水计算结果为标准执行,将40.7 m作为金湖固定的景观水位;动态方案水位可在41.3~40.7 m之间波动;金湖适宜采用动态景观水位运行方案,水位可在40.7~41.3 m之间波动;根据短期预报及历史监测资料分析后合理调度,将41.3 m作为金湖常水位,确定预测洪水大小后调整水位迎战洪水。据此,可在充分利用水资源的同时有效协调景观高水位需求与防洪之间的矛盾。     

不同降雨入渗条件下预应力锚索加固边坡稳定性分析

为了研究不同降雨条件下边坡安全系数及预应力锚索轴力变化规律,以省道S303沿线某边坡为研究对象,考虑土体饱和-非饱和渗流与非饱和强度理论,基于有限元分析,对不同降雨工况下的孔隙水压分布、锚索轴力及采用强度折减法计算的边坡安全系数变化规律进行了研究。结果表明: 降雨量一定时,降雨入渗范围随雨强增大而减小,暂态饱和区域随雨强增加而增大; 边坡在加固前后的安全系数均随降雨量增加而不断减小,采用预应力锚索加固能有效提高边坡安全系数,并减小降雨对边坡稳定性的影响; 随着降雨量增加,各锚索轴力均增大,当降雨量一定时,雨强越小,对应的锚索轴力提升幅度越大,靠近底部的锚索轴力对降雨最为敏感。     

降雨入渗及地震作用下边坡稳定性研究

边坡滑坡给社会经济和人民生命安全带来了巨大威胁，降雨和地震是边坡滑坡的两大诱因。以云南省某矿山边坡作为研究对象，取样后在室内进行岩石力学实验，得到后期工作所需的岩石力学参数，利用霍克布朗准则推导出岩体的力学参数，运用理论计算和数值模拟分析降雨和地震对边坡稳定性影响，结果表明：1）在降雨入渗作用下，边坡出现滑坡主要是静水压力造成的；2）在地震作用下，当地震峰值加速度为0.1g时，有效塑性区主要位于边坡中下部，当增大地震峰值加速度，其范围向坡脚移动，当加速度为0.3g时，塑性区出现贯通趋势。