基于P-K滤波的X波段雷达雨衰补偿研究

X波段天气雷达的回波信号经过降雨区时会存在偏振参量的衰减现象,偏正参量的衰减对雨量估计和气象天气分类等会产生影响,针对这一问题,本文提出一种结合了粒子滤波和卡尔曼滤波的方法（P-K滤波）。该方法首先对天气雷达回波数据中的差分传播相移参量利用P-K滤波算法进行滤波处理,进而利用订正的结果采用自适应算法对水平反射率因子进行衰减订正。利用P-K滤波算法和其他算法对相同的雷达数据进行处理对比,其结果表明,P-K滤波算法能够更有效地对差分传播相移进行衰减订正,其处理结果的平滑性和准确性更高,进而对水平反射率因子的衰减订正效果更好。     

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.     

Supply chain coordination with two-part tariffs under information asymmetry

Supply chain coordination literature indicates that two-part tariff contracts cannot coordinate a supply chain with a supplier and a retailer under information asymmetry, but can coordinate the channel under full information, while leaving the retailer zero profit. Motivated by the practice of Costco Business Centres, we incorporate customer heterogeneity, near-saturated retail market and asymmetric information into a stylised model. The retailer has the knowledge of customer heterogeneity while the supplier does not. The supplier, on the other hand, designs a menu of two-part tariffs for the retailer to choose from. We have found that two-part tariffs can coordinate the supply chain under asymmetric information, while leaving the retailer a positive profit. In addition, a one-size-fits-all two-part tariff can coordinate the supply chain at equilibrium, i.e. there is no need for the supplier to design different two-part tariffs for the retailer who may possess different types of information.     

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.     

基于时域卷积网络的多尺度双线性天气预测模型

针对时域卷积网络(TCN)提取能力受卷积层感受野限制,难以对天气数据中的季节 性信息和长时信息进行有效提取与分析的问题,提出了一个新的基于 TCN 的多尺度双线性天气 预测模型。该模型由 TCN 层和双线性汇合层 2 部分组成,时域卷积层包含双路 TCN,每个 TCN 利用历史观测数据独立提取特征,除卷积核尺度之外,其他网络参数均保持一致。多尺度的 网络组合可以更深入挖掘数据中潜在关联信息;时域卷积层的输出作为双线性汇合层的输入 进行双线性融合,规范化后得到最终输出,即对未来天气的预测值,进一步提升模型的特征 表示能力。在公开的天气预测数据集上与 5 个基准方法进行对比,实验结果表明所提方法的预 测结果准确率更高;此外,对比 TCN,多尺度双线性天气预测模型面对长时数据信息时表现更 加稳定。     

基于垂直载荷因子的增强型湍流检测方法仿真

大气湍流的存在严重影响航空安全。美国航空无线电技术委员会新修订的机载气象雷达最低性能标准DO-220A 增加了湍流检测的规范,要求检测量需考虑飞机特性的影响。基于该标准,研究了一种基于垂直载荷因子的湍流检测方法,并进行了仿真分析。首先,量化了飞机特性的影响,并同时考虑湍流谱宽,估计得出垂直载荷因子;其次,针对湍流检测门限,利用贝叶斯准则推导得出了基于统计特性的阈值确定准则。仿真分析验证表明:设计的增强型湍流检测方法能够满足DO-220A的要求,且能更准确地评估湍流对飞行的危害,对DO-220A 提出的增强型湍流检测规范具有重要的研究意义。     

新一代天气雷达开关及触发器组件快速检修系统设计

针对新一代天气雷达发射机开关和触发器组件故障频发,维修时效长,难度大等特点,提出了一种能够快速、便捷、高效、安全的新一代天气雷达开关和触发器组件关键芯片自动快速检测系统设计方案。该自动检测系统采用了机外脱机测试方法从而保证了雷达设备和人身的安全。该自动检测系统采用了发光二极管指示灯输出测试结果的自动快速测试方式,保证了雷达修复的较短时效。该自动检测系统直接测试雷达开关和触发器组件内部电路板常用关键芯片,而不是更换整个笨重的分机组件备件,从而使得维修新一代天气雷达故障变得便捷高效。新一代天气雷达开关和触发器快速检修系统研制完成后在多个台站得到了试用,试用实例结果表明其效果良好,可以给全国其他新一代天气雷达站快速维修新一代天气雷达提供借鉴和参考。     

基于语音识别的人影37高炮用弹量计数的研究

长期以来人工影响天气作业用37高炮用弹量计数采用的是人工纪录上报的方式,这种统计方式较为繁琐和落后。特别是近年来河南省人影作业期逐年增长、用弹量逐渐增大,这对高炮用弹量信息采集准确性和时效性的要求越来越高。随着声学技术的发展,语音识别算法不断改进,识别准确度越来越高,应用的行业也越来越多。同时,气象行业信息化水平也不断提高,能够自动采集高炮作业信息的声级采集仪研发成功,使得自动采集并传输作业信息成为可能。将传统的声级采集仪结合先进的语音识别算法,能够克服环境噪音,提高声级采集仪的准确性,这为实现自动采集人影作业高炮信息提供了新的方案。     

基于卫星遥感数据的恶劣气象监测预警系统设计

针对传统恶劣气象监测预警系统的检测准确率低、预警时效性较差的问题,提出基于卫星遥感数据的恶劣气象监测预警系统。利用GDAL软件自带的读写功能,处理已读取的遥感影像基本信息,再根据辐射定标与亮温计算数值,完成卫星遥感影像的预处理模块设计。连接插件模块的逻辑结构,利用待集成的平台插件,控制下级遥感信息显示与预警模块,实现恶劣气象监测预警系统的设计与应用。按照系统多线程的同步与通信关系,完成遥感数据的云识别处理,实现卫星遥感程序与恶劣气象监测预警模块之间的实时交互。选取风向指标作为实验对象,分析对比实验数据可知,在不同风向条件下,卫星遥感型监测系统所检测出的风向指标均呈现显著性状态,能够及时响应系统主机的预警指令。     

特殊天气条件下的目标检测方法综述

在深度学习的推动下,目标检测方法在近些年取得了很大的进展。但在特殊天气条件下,如在常见的雾霾、沙尘、雨天、雪天等天气条件下拍摄到的图像会退化模糊,难以提取有效特征,从而对后期的目标检测带来巨大的困难。对近年来国内外学者在特殊天气下目标检测的研究进行了总结归纳,从相关数据集的建立和用途、面向特殊天气条件下的图像恢复算法研究及其对目标检测任务的影响、迁移学习中的领域自适应方法在目标检测的应用三个方面进行分析。对相关方法的实验结果进行综合比较,并提出今后的研究重点。