基于特征建模的TDD系统大气波导干扰控制体系研究

大气波导干扰是特定气象条件下发生的时分双工（time-division duplex，TDD）系统内干扰，是TDD移动通信系统大规模组网面临的顽疾。在总结分析大气波导干扰成因和分类等的基础上，对大气波导干扰进行建模和表征，验证了海量干扰源在时域和频域的功率集总特征，并结合大量4G/5G现网实测数据给出了典型条件下内陆波导和海面波导的量化干扰信号传播模型，对于干扰的预测和预防具有重要意义。基于干扰特征，给出了TDD系统预防大气波导干扰的帧结构与组网的4项设计原则，5G现网数据表明干扰控制方案有效，上行干扰下降10 dB以上，相关原则对于6G系统的设计也具有指导意义。     

Effect of heat treatment on the synthesis of hydroxyapatite from Indian clam seashell by hydrothermal method

Hydroxyapatite (HA) powder has been successfully synthesized from low-cost Indian clam seashells by using hydrothermal method. The mixture of tri-calcium phosphate [Ca₃(PO₄)₂], heat-treated ball-milled clam seashell, and demineralized water are heat-treated at several temperatures (700 °C, 800 °C, 900 °C, 1000 °C, and 1100 °C) for various time periods (1 h, 2 h, and 3 h) to perform the hydrothermal reactions. The phases and microstructure of the solid-state reaction products are analyzed through X-ray diffraction (XRD) method and field emission scanning electron microscopy (FESEM) respectively. The crystallite size of all the synthesized powders is calculated by using Scherrer's model. Mainly HA phase is obtained in all the different reaction products. However, these HAs are found to be non-stoichiometric in nature. As per the literature, non-stoichiometric HA is a more biologically active material compared to the stoichiometric one. Almost pure HA is formed with any selected reaction temperature applied for 2 h time duration. The crystallinity and Ca/P ratio of the synthesized pure HA are estimated by using standard model and energy-dispersive X-ray spectroscopy (EDS) analysis, respectively. The highest amount of near stoichiometric crystalline HA has been obtained at 900 °C of reaction temperature applied for 2 h time duration. With raising reaction temperature, the grain size of pure HA is found to be increased. Needle/rod shaped nano grains are noticed to form at lower reaction temperature whereas; beyond 1000 ^oC of temperature globular/spherical shaped grains are also observed to form. At 3 h reaction time agglomeration of grains is found to occur in all the synthesized powders.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

堆本体抗震试验动力相似关系推导与验证

为确保堆本体抗震试验中流体对流效应、脉冲效应和堆本体结构响应的准确性，需保证重力、流体与固体惯性力、结构弹性力和结构应变的相似性。本文从固体结构的振动方程、不可压牛顿流体的动力学方程、流固交界面的边界条件和环形柱体域内液体线性晃动的动力学公式出发，基于控制方程的量纲分析法，推导了考虑液体晃动效应的堆本体地震响应动力相似关系。基于上述相似关系建立了堆容器堆内构件和堆容器内自由液面流体域的缩尺模型，通过有限体积法分析堆容器堆内构件原型和缩尺模型中液体的晃动固有频率、晃动波高、压力以及液体晃动对堆容器支承裙的倾覆力矩。结果表明本文动力相似关系具有合理性和准确性，可用于堆本体缩尺模型的抗震试验研究。     

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.     

基于工程状态标定的诊断载荷试验识别方法及应用

针对在役公路梁式桥梁由于材料性能退化及外部环境等因素共同作用而导致承载力不足、耐久性降低等问题,开展旧桥力学性能的快速诊断试验研究。通过诊断性载荷试验的理论分析和有限元模型标定流程研究,快速、高效地完成在役公路桥梁的承载力性能测量和评估,从而为在役公路桥梁结构的养护管理提供可靠的技术支持。     

Seismic stability of embankments with different densities and upstream conditions related to the water level

Modeling an embankment by subjecting it to different upstream conditions in a centrifuge is challenging. However, the response of an embankment to shaking under different upstream conditions needs to be studied to ensure that the necessary precautions are taken during its construction and maintenance. Herein, the influence of different upstream conditions and embankment densities are investigated. The pore water pressure values at different locations in an embankment were recorded during seepage and under induced shaking. Accelerometers recorded the response of the embankment to shaking, and a linear variable differential transformer (LVDT) measured the settlement at the top of the embankment as a result of the shaking. An image analysis was used to trace the resulting deformation of the embankment due to the shaking. The results indicate that the upstream conditions of the embankment determine the failure mechanism of the embankment in cases of lower density. In addition, the distribution of the water content within the embankment was found to have a greater influence on its response to shaking than the water level height on its upstream side.     

Full scale investigation of GCL damage mechanisms in small earth dam retrofit applications under earthquake loading

This paper reports results of full scale testing to further explore potential GCL damage mechanisms in earth dam retrofit applications in seismically active areas; in particular, to a) investigate whether shear displacements could reduce the magnitude of GCL panel overlap during earthquake shaking; b) explore the influence of gravel particles on GCL thickness at localised point of contact; and c) observe the consequences of an accidental exposure of an uncovered GCL to short duration rainfall in terms of moisture content and effects during subsequent compaction. The results of these experiments indicate that even under severe shaking no movements were detected at the GCL panel overlap. Whereas gravel particles were observed to locally reduce the thickness of the GCL to 2.2 mm, no plowing of the particle into the GCL occurred due to a lack of shear displacement at the interface, resulting in no localised internal erosion through the barrier. Furthermore, hydration of GCL panels during construction due to surface wetting was observed to result in a state of hydration less than its post-construction state. These results indicate that although each of the three GCL damage mechanisms cannot be ruled out to ever be relevant in practice, the performance of the GCL retrofitted earth dam tested was satisfactory under even severe Level 2 earthquake shaking, and suggests that the retrofitting of small earth dams with GCLs is a promising strategy to improve their static and seismic resistance.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.