遗传-神经网络算法在水下隧道涌水量预测中的应用

水下隧道涌水问题受到多种因素的综合影响,具有非线性和高度复杂性。本文应用遗传算法和BP神经网络,结合工程实例,选用隧道围岩裂隙发育情况、上覆含水体富水性、上覆水压、隧道埋深、隧道围岩上覆相对隔水层强度和上覆基岩破碎带与隧道顶板的距离作为影响水下隧道涌水的主要因素,以调查的数据作为训练样本,使用遗传算法优化BP神经网络的初始权值和阈值,建立了水下隧道涌水量的遗传-神经网络预测模型,并进行了计算分析。结果表明:该模型收敛性能好,预测精度高,简单可行。该方法为水下隧道涌水量的预测提供了一条新思路。     

紧急工程项目管理模式及方法探讨——以引水隧洞缺陷处理工程为例

以深圳东部供水水源工程停水检修期间的引水隧洞缺陷处理工程为例,探讨了紧急工程的项目管理模式及方法。通过对比分析不同管理模式的优劣和研究引水隧洞缺陷处理工程的管理实践,提出紧急工程应结合传统工程与应急工程的特点,在项目管理上采取弹性管理、减小不确定性和强化业主作用的方法。     

The experimental research of a digital sampling signal converter

The sensitivity and rise time of a transient characteristic of a digital sampling signal converter are experimentally researched. The sampling converter is built at a clocked tunnel-diode comparator.     

下一代跨校区数字化校园网实现策略与研究

我国高等教育的快速发展促使大校合并、校区扩建,VPN技术建设校园网使教学资源得到充分优化利用的一种虚拟教育环境。随中国互联网客户逐年增多,IPv4地址问题日趋严重,互联网向IPv6过渡已是大势所趋。本文从VPN和IPV6升级技术、实施方案等方面讨论下一代跨校区数字化校园网实现策略与研究。     

基于多核的IPv4/IPv6过渡技术的研究与实现

针对目前IPv4/IPv6过渡时期存在的问题,结合目前主要过渡技术的不足,提出基于多核的IPv6隧道技术,设计了多核下IPv6隧道模型。利用多核流技术、锁技术和多核负载分担方法,加速转发处理、保护多核临界资源和提高系统整体性能。仿真实验结果表明,IPv6隧道技术基于多核比基于单核的实现,在转发与处理性能上成倍提高,能更好地解决过渡时期存在的问题与适应未来的发展。     

煤矿井下电磁干扰对通信和监控系统的影响分析

文章从电磁波在矩形巷道中的传输特性出发,分析了电磁波低于和高于截止频率的衰减特性,总结了井下各种电磁辐射干扰源产生干扰的情况。通过对井下电磁干扰实测数据的分析,得出井下电磁干扰的频段主要为500 MHz以下;当干扰源与通信和监控系统的距离为几米远时,其对系统基本不会产生影响。     

Design of Scale Model Mechanism in Low Speed Wind Tunnel

Motion control can be considered as the synergistic collaboration of mechanical and electrical engineering, computer science and information technology to apply a controlled force to achieve useful motion in fluid or soiled electromechanical systems. With the development of computer, electronics, and automatic control theory, motion control comes to a new stage. Great applications are based on the characteristics, stemming from advantages of electronics and modern control technology achievements, having a v...     

边坡稳定分析的相对失稳加速度方法

边坡稳定分析是土力学的经典课题,通过稳定分析能够得到边坡的安全系数。但最小安全系数对应的临界滑动面不一定是最先发生滑动的破坏面,失稳加速度临界滑动面则可能最先滑动。失稳加速度方法可以用统一的原理计算边坡和隧洞等所有具有临空面的结构的稳定性;可以利用常规极限平衡法的假定条件进行计算,也可以利用有限元等数值方法的应力计算成果;可以计算失稳加速度,也可以以失稳加速度的模最小来计算安全系数,此时也相当于一种特殊的模式识别方法。本文对失稳加速度方法的提出过程及主要原理进行系统总结,并将其推广到相对失稳加速度和相对安全系数,放松了约束条件,使得它可以具有更加广阔的应用前景。     

隧道围岩沉降自适应实时识别及测量

针对传统隧道围岩沉降监测方法鲁棒性较差、难以及时有效的监测隧道围岩沉降值的问题,提出将坐标注意力结合目标检测算法的隧道围岩沉降自适应识别测量算法。利用工业相机拍摄不同环境靶标图像建立数据集,训练具有坐标注意力的目标检测模型,在测试集中验证模型预测精度为97.9%。使用图像中靶标的数字以及LED灯点的像素坐标标定测量算法模型并计算沉降值。结果表明在25 m范围内测量误差小于1 cm,在10 m范围内的靶标测量误差小于5 mm。     

Physics-informed few-shot learning for wind pressure prediction of low-rise buildings

This research proposes a physics-informed few-shot learning model to predict the wind pressures on full-scale specimens based on scaled wind tunnel experiments. Existing machine learning approaches in the wind engineering domain are incapable of accurately extrapolating the prediction from scaled data to full-scale data. The model presented in this research, on the other hand, is capable of extrapolating prediction from large-scale or small-scale models to full-scale measurements. The proposed ML model combines a few-shot learning model with the existing physical knowledges in the design standards related to the zonal information. This physical information helps in clustering the few-shot learning model and improves prediction performance. Using the proposed techniques, the scaling issue observed in wind tunnel tests can be partially resolved. A low mean-squared error, mean absolute error, and a high coefficient of determination were observed when predicting the mean and standard deviation wind pressure coefficients of the full-scale dataset. In addition, the benefit of incorporating physical knowledge is verified by comparing the results with a baseline few-shot learning model. This method is the first of its type as it is the first time to extrapolate in wind performance prediction by combining prior physical knowledge with a few-shot learning model in the field of wind engineering. With the benefit of the few-shot learning model, only a low-resolution of the measuring tap configuration is required, and the reliance on physical wind tunnel experiments can be reduced. The physics-informed few-shot learning model is an efficient, robust, and accurate alternate solution to predicting wind pressures on full-scale structures based on various modeled scale experiments.