基于支持向量机模型的地表下沉系数计算参数寻优

为准确快速地对地表下沉系数进行预测分析,提出基于支持向量机(SVM)的地表下沉系数的SVM预测模型,结合我国典型的地表移动观测站实际案例,分别采用网格参数寻优算法、遗产算法(GA)、粒子群算法(PSO)对SVM模型进行参数寻优,并采用考虑交叉概率的改进GA算法对地表下沉系数进行预测.结果表明,改进的GA算法预测回归系数可达到0.95271,适合地表下沉系数预测分析,预测准确率最高.     

LTE与NR价值网格识别与评估方法研究

在降本增效的发展背景下，如何寻找新的方法，将网络收入与网络质量相关联？在投资有限的情况下，网络资源应该怎样用，网络保障如何开展，才能更好的支撑市场发展？本文提出一种面向LTE/NR网络的价值网格评估体系，构建了以网络收益、网络能力双维度的“九格四域”网格价值地图，并将其应用的实际网络识别价值网格网络问题根因，解决因资源不足、性能原因导致的网络问题，取得了良好的效果，肯定了该方法对无线网络质量提升的借鉴作用。     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.     

基于离散单元法的群楼拆除爆破仿真模拟

基于青岛开发区一商场爆破工程,利用颗粒离散元法建立离散元网格实体模型,用多面体离散元法模拟分析拆除爆破中建筑物的倒塌过程。研发的网格实体模型详细模拟了建筑物从结构局部失稳到整体完全倒塌的整个过程,确定了爆堆轮廓线和爆堆尺寸,直观给出了爆破设计的效果。触地振动、冲击力变化分析结果表明:所给出的爆破方案可达到预期拆除爆破的效果,触地振动符合《爆破安全规程》要求;该模型可优化爆破设计,指导爆破施工,为建筑物拆除爆破的灾害预测与安全评估提供理论依据。     

基于语义分割算法的水库漂浮物智能识别

为了满足水库漂浮物强监管的需求，发挥水库水雨情视频监控的优势，将语义分割算法DeepLabV3+应用于水库漂浮物智能识别任务中。首先，通过多要素数据采集方法以及多种图像数据增强方式系统性整合得到水库漂浮物语义分割数据集。然后，提出改进的加权代价函数，以适应数据集中各类的总像素数量不平衡的问题。实验结果表明，通过规范采集与多种数据增强建立的数据集，以及改进的加权代价函数，提高了水库漂浮物语义分割模型的准确率。所提模型像素准确率平均值、平均交叉比分别可达到95.21%、91.03%，通过微信企业号实现的移动监管，可为水利相关部门对水库漂浮物进行及时的智能监控预警提供新的技术方案。