Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system,which can be excited of harmful vibration by external loads.It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model.In this paper,an adaptive inverse control method is proposed on the basis of novel rough neural networks (RNN) to control the harmful vibration of the offshore jacket platform,and the offshore jacket platform model is established by dynamic stiffness matrix (DSM) method.Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory,RNN is utilized to identify the predictive inverse model of the offshore jacket platform system.Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads,and to deal with the delay problem caused by signal transmission in the control process.The numerical results show that the constructed novel RNN has advantages such as clear structure,fast training speed and strong error-tolerance ability,and the proposed method based on RNN can effectively control the harmfid vibration of the offshore jacket platform.     

无机盐溶液作用下砂-膨润土竖向隔离屏障材料化学相容性试验研究

土-膨润土竖向隔离屏障广泛应用于城市工业污染场地。化学溶液作用下屏障材料的化学相容性,即渗透系数及其变化程度,是评价防污性能的关键因素。通过柔性壁渗透试验研究重金属铅-锌复合、六价铬作用下砂-膨润土竖向隔离屏障材料渗透系数的变化规律。由于无机盐溶液中铅、锌、钙对膨润土双电层的压缩,铅-锌复合、钙溶液作用下屏障材料试样渗透系数随金属浓度升高而增大。铅-锌复合作用下,金属浓度增至500mmol/L时试样渗透系数增幅达11倍,且无法满足防渗要求。相反,由于铬以阴离子络合形式存在,其对试样渗透系数影响相对小;与未污染状态测定结果相比,渗透系数增幅≤2倍。在膨润土孔隙比基础上,考虑无机盐溶液对膨润土膨胀特性的影响,建立无机盐溶液作用前后砂-膨润土竖向隔离屏障材料渗透系数统一预测方法。     

Geospatial predictive modelling of the Neolithic archaeological sites of Magnesia in Greece

Abstract

Sources of heterogeneous geospatial data such as the elevation, the slope, the aspect, the water network and the current settlements related to the known Neolithic archaeological sites of Magnesia, are used in an attempt to confirm the existence and allow for the prediction of other archaeological sites using predictive modelling theory. Predictive modelling allows the update of the problem solving strategy as soon as new data layers are available. The Dempster–Shafer Theory also commonly referred to as evidential reasoning (ER) is used to compose probability maps of areas of archaeological interest from physiographical and historical data. The advantage of this theory is that the ignorance is quantified and used to compose the probability maps named as belief, plausibility and belief interval for the archaeological sites. The final digital probability maps show that the Neolithic archaeological sites can be detected in the prefecture of Magnesia. This research study forms a methodological tool for the prediction of new archaeological sites in other areas of archaeological interest according to the physiographical and historical characteristics of the archaeological period being examined. It also contributes to the digital earth modelling and archaeological site protection, one of the most critical and challenging global initiatives.     

分布式传感器网络系统的网络预测控制

本文针对分布式传感器网络系统的Fornasini-Marchesini （FM）状态空间模型,对系统的预测控制器设计问题进行研究.特别是针对所考虑的二维FM传感器网络系统,提出了一种新的网络预测控制方案来补偿通信时滞.首先,根据李雅普诺夫稳定性理论,给出了二维系统保持稳定的充分条件;然后利用稳定性条件,提出了一种新的预测控制器设计策略并保证系统的控制性能;最后,通过一个数值实例验证了所设计控制器的有效性.     

Establishing a predictive performance indicator for real‐time hybrid simulation

Real‐time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber‐physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost‐effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single‐degree‐of‐freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.     

Dereverberation of marine reflection seismic data by a spatial combination of predictive deconvolution and velocity filtering

Contamination of seismic reflection records at early times by first-order water reverberations can be especially severe during survey operations over hard and flat sea floors on the continental shelf or in lake environments. A new dereverberation scheme based on two classical techniques — predictive deconvolution and velocity filtering — has been developed to address this problem. The techniques are combined spatially to take advantage of their complementary offset- and time-dependent properties. Stage I of the scheme consists of applying predictive deconvolution at short offset. The data are previously conditioned by a normal moveout correction with the water velocity which restores the periodicity of the reverberations in the offset-time plane and enhances the performance of deconvolution. Stage II of the scheme involves velocity filtering in the common-midpoint domain which is particularly effective at long offset where the moveout difference between primary reflections and reverberations is largest. The dereverberation scheme is well suited for the initial processing of large volumes of data due to the general availability of cost-effective deconvolution and velocity filtering algorithms in seismic processing software packages. Practical implementation issues are illustrated by a field example from the GLIMPCE survey in Lake Superior.Lithoprobe Publication No. 475.     

Predictive optimal control for seismic analysis of non-linear and hysteretic structures

In this paper, an effective active predictive control algorithm is developed for the vibration control of non-linear hysteretic structural systems subjected to earthquake excitation. The non-linear characteristics of the structural behaviour and the effects of time delay in both the measurements and control action are included throughout the entire analysis (design and validation). This is very important since, in current design practice, structures are assumed to behave non-linearly, and time delays induced by sensors and actuator devices are not avoidable. The proposed algorithm focuses on the instantaneous optimal control approach for the development of a control methodology where the non-linearities are brought into the analysis through a non-linear state vector and a non-linear open-loop term. An autoregressive (AR) model is used to predict the earthquake excitation to be considered in the prediction of the structural response. A performance index that is quadratic in the control force and in the predicted non-linear states, with two additional energy related terms, and that is subjected to a non-linear constraint equation, is minimized at every time step. The effectiveness of the proposed closed-open loop non-linear instantaneous optimal prediction control (CONIOPC) strategy is presented by the results of numerical simulations. Since non-linearity and time-delay effects are incorporated in the mathematical model throughout the derivation of the control methodology, good performance and stability of the controlled structural system are guaranteed. Copyright © 1999 John Wiley & Sons, Ltd.     

Semi-active model predictive control for 3rd generation benchmark problem using smart dampers

A semi-active strategy for model predictive control (MPC), in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.     

甘肃省金矿资源预测模型及潜力评价

为了更加科学有效地部署甘肃省金矿勘查找矿工作,必须对全省金资源总量作出定量预测,对省内各成矿带的金资源潜力作出定量评价。文章对甘肃省已发现的429处金矿床(点)的资料及全省1∶20万区域化探资料进行了统计和研究,建立了甘肃省金资源总量丰度估算模型、品位-储量回归预测模型及全省各成矿带金资源潜力分布模型。通过定量预测得知,甘肃省金资源总量的上限值为3166t,金资源总量为2420.6t;西秦岭成矿带的金资源潜力最大,占全省金资源潜力的37%,祁连成矿带次之,占24%,扬子成矿带占15%,北山成矿带占13%。据甘肃省岩金资源回归预测模型,岩金矿床在甘肃省具有极大的资源潜力。     

面向海上目标搜索任务的多无人机协同航路优化

提出了一种面向多无人机(unmanned aerial vehicle,UAV)协同搜索海上目标任务的航路优化方法。首先,分析多无人机协同航路优化问题的基本要素模型。然后,在对各UAV独立维护的目标概率图信息进行探测更新的基础上,采用状态预测一致性算法实现目标概率图信息的快速融合。最后,同时考虑局部搜索收益与未来搜索收益,采用分布式模型预测控制(distributed model predictive control,DMPC)方法优化各UAV的搜索航路。仿真结果表明,本研究提出的方法具有较高的搜索效率,可有效应用于海上目标的快速搜索任务,具有重要的应用价值。